Abstract:
  The chiral Hamiltonian for twisted graphene bilayers is written as a
$2\times2$ matrix operator by a renormalization of the Hamiltonian that takes
into account the particle-hole symmetry. This results in an effective
Hamiltonian with an average field plus and effective non-Abelian gauge
potential. The action of the proposed renormalization maps the zero-mode region
into the ground state. Modes near zero energy have an antibonding nature in a
triangular lattice. This leads to a phase-frustration effect associated with
massive degeneration, and makes flat-bands modes similar to confined modes
observed in other bipartite lattices. Suprisingly, the proposed Hamiltonian
renormalization suggests that flat-bands at magic angles are akin to
floppy-mode bands in flexible crystals or glasses, making an unexpected
connection between rigidity topological theory and magic angle twisted
two-dimensional heterostructures physics.


Abstract:
  While the original goal for developing robots is replacing humans in
dangerous and tedious tasks, the final target shall be completely mimicking the
human cognitive and motor behaviour. Hence, building detailed computational
models for the human brain is one of the reasonable ways to attain this. The
cerebellum is one of the key players in our neural system to guarantee
dexterous manipulation and coordinated movements as concluded from lesions in
that region. Studies suggest that it acts as a forward model providing
anticipatory corrections for the sensory signals based on observed
discrepancies from the reference values. While most studies consider providing
the teaching signal as error in joint-space, few studies consider the error in
task-space and even fewer consider the spiking nature of the cerebellum on the
cellular-level. In this study, a detailed cellular-level forward cerebellar
model is developed, including modeling of Golgi and Basket cells which are
usually neglected in previous studies. To preserve the biological features of
the cerebellum in the developed model, a hyperparameter optimization method
tunes the network accordingly. The efficiency and biological plausibility of
the proposed cerebellar-based controller is then demonstrated under different
robotic manipulation tasks reproducing motor behaviour observed in human
reaching experiments.


Abstract:
  We compute exactly the statistics of the number of records in a discrete-time
random walk model on a line where the walker stays at a given position with a
nonzero probability $0\leq p \leq 1$, while with the complementary probability
$1-p$, it jumps to a new position with a jump length drawn from a continuous
and symmetric distribution $f_0(\eta)$. We have shown that, for arbitrary $p$,
the statistics of records up to step $N$ is completely universal, i.e.,
independent of $f_0(\eta)$ for any $N$. We also compute the connected two-time
correlation function $C_p(m_1, m_2)$ of the record-breaking events at times
$m_1$ and $m_2$ and show it is also universal for all $p$. Moreover, we
demonstrate that $C_p(m_1, m_2)< C_0(m_1, m_2)$ for all $p>0$, indicating that
a nonzero $p$ induces additional anti-correlations between record events. We
further show that these anti-correlations lead to a drastic reduction in the
fluctuations of the record numbers with increasing $p$. This is manifest in the
Fano factor, i.e. the ratio of the variance and the mean of the record number,
which we compute explicitly. We also show that an interesting scaling limit
emerges when $p \to 1$, $N \to \infty$ with the product $t = (1-p)\, N$ fixed.
We compute exactly the associated universal scaling functions for the mean,
variance and the Fano factor of the number of records in this scaling limit. .


Abstract:
  Large organizations such as social media companies continually release data,
for example user images. At the same time, these organizations leverage their
massive corpora of released data to train proprietary models that give them an
edge over their competitors. These two behaviors can be in conflict as an
organization wants to prevent competitors from using their own data to
replicate the performance of their proprietary models. We solve this problem by
developing a data poisoning method by which publicly released data can be
minimally modified to prevent others from train-ing models on it. Moreover, our
method can be used in an online fashion so that companies can protect their
data in real time as they release it.We demonstrate the success of our approach
onImageNet classification and on facial recognition.


Abstract:
  The Born cross sections are measured for the first time for the processes
$e^+e^-\to D_s^{*+}D_{s0}^*(2317)^- +c.c.$ and $e^+e^-\to
D_s^{*+}D_{s1}(2460)^- +c.c.$ at the center-of-mass energy $\sqrt{s}=$
4.600~GeV, 4.612~GeV, 4.626~GeV, 4.640~GeV, 4.660~GeV, 4.68~GeV, and 4.700~GeV,
and for $e^+e^-\to D_s^{*+}D_{s1}(2536)^- +c.c.$ at $\sqrt{s}=$ 4.660~GeV,
4.680~GeV, and 4.700~GeV, using data samples collected with the BESIII detector
at the BEPCII collider. No structures are observed in cross-section
distributions for any of the processes.


Abstract:
  The autoencoder model uses an encoder to map data samples to a lower
dimensional latent space and then a decoder to map the latent space
representations back to the data space. Implicitly, it relies on the encoder to
approximate the inverse of the decoder network, so that samples can be mapped
to and back from the latent space faithfully. This approximation may lead to
sub-optimal latent space representations. In this work, we investigate a
decoder-only method that uses gradient flow to encode data samples in the
latent space. The gradient flow is defined based on a given decoder and aims to
find the optimal latent space representation for any given sample through
optimisation, eliminating the need of an approximate inversion through an
encoder. Implementing gradient flow through ordinary differential equations
(ODE), we leverage the adjoint method to train a given decoder. We further show
empirically that the costly integrals in the adjoint method may not be entirely
necessary. Additionally, we propose a $2^{nd}$ order ODE variant to the method,
which approximates Nesterov's accelerated gradient descent, with faster
convergence per iteration. Commonly used ODE solvers can be quite sensitive to
the integration step-size depending on the stiffness of the ODE. To overcome
the sensitivity for gradient flow encoding, we use an adaptive solver that
prioritises minimising loss at each integration step. We assess the proposed
method in comparison to the autoencoding model. In our experiments, GFE showed
a much higher data-efficiency than the autoencoding model, which can be crucial
for data scarce applications.


Abstract:
  New computing technologies inspired by the brain promise fundamentally
different ways to process information with extreme energy efficiency and the
ability to handle the avalanche of unstructured and noisy data that we are
generating at an ever-increasing rate. To realise this promise requires a brave
and coordinated plan to bring together disparate research communities and to
provide them with the funding, focus and support needed. We have done this in
the past with digital technologies; we are in the process of doing it with
quantum technologies; can we now do it for brain-inspired computing?


Abstract:
  In this paper we characterize the value set $\Delta$ of the $R$-modules of
the form $R+zR$ for the local ring $R$ associated to a germ $\xi$ of an
irreducible plane curve singularity with one Puiseux pair. In the particular
case of the module of K\"ahler differentials attached to $\xi$, we recover some
results of Delorme. From our characterization of $\Delta$ we introduce a proper
subset of semimodules over the value semigroup of the ring $R$. Moreover, we
provide a geometric algorithm to construct all possible semimodules in this
subset for a given value semigroup.


Abstract:
  Chemotaxis, the directional locomotion of cells towards a source of a
chemical gradient, is an integral part of many biological processes - for
example, bacteria motion, single-cell or multicellular organisms development,
immune response, etc. Chemotaxis directs bacteria's movement to find food
(e.g., glucose) by swimming toward the highest concentration of food molecules.
In multicellular organisms, chemotaxis is critical to early development (e.g.,
movement of sperm towards the egg during fertilization). Chemotaxis also helps
mobilize phagocytic and immune cells at sites of infection, tissue injury, and
thus facilitates immune reactions. In this paper, we study a PDE system that
describes such biological processes in one dimension, which may correspond to a
thin channel, the setting relevant in many applications: for example,
spermatozoa progression to the ovum inside a Fallopian tube or immune response
in a blood vessel.


Abstract:
  This study evaluates a suspension design of a passenger car to obtain maximum
rider's comfort when the vehicle is subjected to different road profile or road
surface condition. The challenge will be on finding a balance between the
rider's comfort and vehicle handling to optimize design parameters. The study
uses a simple passive suspension system and an active suspension model
integrated with a pneumatic actuator controlled by proportional integral
derivative (PID) controller in both quarter car and full car models having a
different degree of freedoms (DOF) and increasing degrees of complexities. The
quarter car considered as a 2-DOF model, while the full car model is a 7-DOF
model. The design process set to optimise the spring stiffnesses, damping
coefficients and actuator PID controller gains. For optimisation, the research
featured genetic algorithm optimisation technique to obtain a balanced response
of the vehicle as evaluated from the displacement, velocity and acceleration of
sprung and unsprung masses along with different human comfort and vehicle
performance criteria. The results revealed that the active suspension system
with optimised spring stiffness, damping coefficients and PID gains
demonstrated the superior riding comfort and road holding compared to a passive
suspension system.


Abstract:
  Prompt isolated leptons are essential in many analyses in high-energy
particle physics but are subject to fake-lepton background, i.e. objects that
mimic the lepton signature. The fake-lepton background is difficult to estimate
from simulation and is often directly determined from data. A popular method is
the matrix method, which however suffers from several limitations. This paper
recapitulates an alternative approach based on a likelihood with Poisson
constraints and reformulates the problem from a different starting point in the
framework of Bayesian statistics. The equality of both approaches is shown and
several cases are studied in which the matrix method is limited. In addition,
the fake lepton background is recalculated and compared to the estimate with
the matrix method in an example top-quark measurement.


Abstract:
  Training vision-based Urban Autonomous driving models is a challenging
problem, which is highly researched in recent times. Training such models is a
data-intensive task requiring the storage and processing of vast volumes of
(possibly redundant) driving video data. In this paper, we study the problem of
developing data-efficient autonomous driving systems. In this context, we study
the problem of multi-criteria online video frame subset selection. We study
convex optimization-based solutions and show that they are unable to provide
solutions with high weightage to the loss of selected video frames. We design a
novel convex optimization-based multi-criteria online subset selection
algorithm that uses a thresholded concave function of selection variables. We
also propose and study a submodular optimization-based algorithm. Extensive
experiments using the driving simulator CARLA show that we are able to drop 80%
of the frames while succeeding to complete 100% of the episodes w.r.t. the
model trained on 100% data, in the most difficult task of taking turns. This
results in a training time of less than 30% compared to training on the whole
dataset. We also perform detailed experiments on prediction performances of
various affordances used by the Conditional Affordance Learning (CAL) model and
show that our subset selection improves performance on the crucial affordance
"Relative Angle" during turns.


Abstract:
  Thinking aloud is an effective meta-cognitive strategy human reasoners apply
to solve difficult problems. We suggest to improve the reasoning ability of
pre-trained neural language models in a similar way, namely by expanding a
task's context with problem elaborations that are dynamically generated by the
language model itself. Our main result is that dynamic problem elaboration
significantly improves the zero-shot performance of GPT-2 in a deductive
reasoning and natural language inference task: While the model uses a syntactic
heuristic for predicting an answer, it is capable (to some degree) of
generating reasoned additional context which facilitates the successful
application of its heuristic. We explore different ways of generating
elaborations, including fewshot learning, and find that their relative
performance varies with the specific problem characteristics (such as problem
difficulty). Moreover, the effectiveness of an elaboration can be explained in
terms of the degree to which the elaboration semantically coheres with the
corresponding problem. In particular, elaborations that are most faithful to
the original problem description may boost accuracy by up to 24%.


Abstract:
  We establish a novel approach to computing $G$-equivariant cohomology for a
finite group $G$, and demonstrate it in the case that $G = C_{p^n}$.
  For any commutative ring spectrum $R$, we prove a symmetric monoidal
reconstruction theorem for genuine $G$-$R$-modules, which records them in terms
of their geometric fixedpoints as well as gluing maps involving their Tate
cohomologies. This reconstruction theorem follows from a symmetric monoidal
stratification (in the sense of \cite{AMR-strat}); here we identify the gluing
functors of this stratification in terms of Tate cohomology.
  Passing from genuine $G$-spectra to genuine $G$-$\mathbb{Z}$-modules (a.k.a.
derived Mackey functors) provides a convenient intermediate category for
calculating equivariant cohomology. Indeed, as $\mathbb{Z}$-linear Tate
cohomology is far simpler than $\mathbb{S}$-linear Tate cohomology, the above
reconstruction theorem gives a particularly simple algebraic description of
genuine $G$-$\mathbb{Z}$-modules. We apply this in the case that $G = C_{p^n}$
for an odd prime $p$, computing the Picard group of genuine
$G$-$\mathbb{Z}$-modules (and therefore that of genuine $G$-spectra) as well as
the $RO(G)$-graded and Picard-graded $G$-equivariant cohomology of a point.


Abstract:
  Spin defects in silicon carbide (SiC) have attracted increasing interest due
to their excellent optical and spin properties, which are useful in quantum
information processing. In this paper, we systematically investigate the
temperature dependence of the spin properties of divacancy defects in implanted
4\emph{H}-SiC. The zero-field splitting parameter $D$, the inhomogeneous
dephasing time $T_2^{*}$, the coherence time $T_2$, and the depolarization time
$T_1$ are extensively explored in a temperature range from 5 to 300 K. Two
samples implanted with different nitrogen molecule ion fluences ($\rm
{N_2}^{+}$, $1\times 10^{14}/\rm cm^{2}$ and $1\times 10^{13}/\rm cm^{2}$) are
investigated, whose spin properties are shown to have similar
temperature-dependent behaviors. Still, the sample implanted with a lower ion
fluence has longer $T_{2}$ and $T_{1}$. We provide possible theoretical
explanations for the observed temperature-dependent dynamics. Our work promotes
the understanding of the temperature dependence of spin properties in
solid-state systems, which can be helpful for constructing wide
temperature-range thermometers based on the mature semiconductor material.


Abstract:
  In addition to regular Schwabe cycles (~ 11 years), solar activity also shows
longer periods of enhanced or reduced activity. Of these, reconstructions of
the Dalton Minimum provide controversial sunspot group numbers and limited
sunspot positions, partially due to limited source record accessibility. We
analysed Stephan Prantner's sunspot observations from 1804--1844, the values of
which had only been known through estimates despite their notable chronological
coverage during the Dalton Minimum. We identified his original manuscript in
Stiftsarchiv Wilten, near Innsbruck, Austria. We reviewed his biography
(1782--1873) and located his observational sites at Wilten and Waidring, which
housed the principal telescopes for his early and late observations: a 3.5-inch
astronomical telescope and a Reichenbach 4-feet achromatic erecting telescope,
respectively. We identified 215 days of datable sunspot observations, which are
twice as much data as his estimated data in the existing database (= 115 days).
Prantner counted up to 7--9 sunspot groups per day and measured sunspot
positions, which show their distributions in both solar hemispheres. These
results strikingly emphasise the difference between the Dalton Minimum and the
Maunder Minimum as well as the similarity between the Dalton Minimum and the
modern solar cycles.


Abstract:
  We present algebraic and geometric classifications of the $4$-dimensional
complex nilpotent right alternative algebras. Specifically, we find that, up to
isomorphism, there are only $9$ non-isomorphic nontrivial nilpotent right
alternative algebras. The corresponding geometric variety has dimension $13$
and it is determined by the Zariski closure of $4$ rigid algebras and one
one-parametric family of algebras.


Abstract:
  This paper investigates algebraic objects equipped with an operator, such as
operated monoids, operated algebras etc. Various free object functors in these
operated contexts are explicitly constructed. For operated algebras whose
operator satisfies a set $\Phi$ of relations (usually called operated
polynomial identities (aka. OPIs)), Guo defined free objects, called free
$\Phi$-algebras, via universal algebra. Free $\Phi$-algebras over algebras are
studied in details. A mild sufficient condition is found such that $\Phi$
together with a Gr\"obner-Shirshov basis of an algebra $A$ form a
Gr\"obner-Shirshov basis of the free $\Phi$-algebra over algebra $A$ in the
sense of Guo et al.. Ample examples for which this condition holds are
provided, such as all Rota-Baxter type OPIs, a class of differential type OPIs,
averaging OPIs and Reynolds OPI.


Abstract:
  Neural networks trained with SGD were recently shown to rely preferentially
on linearly-predictive features and can ignore complex, equally-predictive
ones. This simplicity bias can explain their lack of robustness out of
distribution (OOD). The more complex the task to learn, the more likely it is
that statistical artifacts (i.e. selection biases, spurious correlations) are
simpler than the mechanisms to learn. We demonstrate that the simplicity bias
can be mitigated and OOD generalization improved. We train a set of similar
models to fit the data in different ways using a penalty on the alignment of
their input gradients. We show theoretically and empirically that this induces
the learning of more complex predictive patterns. OOD generalization
fundamentally requires information beyond i.i.d. examples, such as multiple
training environments, counterfactual examples, or other side information. Our
approach shows that we can defer this requirement to an independent model
selection stage. We obtain SOTA results in visual recognition on biased data
and generalization across visual domains. The method - the first to evade the
simplicity bias - highlights the need for a better understanding and control of
inductive biases in deep learning.


Abstract:
  Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge
from multiple source domains to the target domain, which is a more practical
and challenging problem compared to the conventional single-source domain
adaptation. In this problem, it is essential to utilize the labeled source data
and the unlabeled target data to approach the conditional distribution of
semantic label on target domain, which requires the joint modeling across
different domains and also an effective domain combination scheme. The
graphical structure among different domains is useful to tackle these
challenges, in which the interdependency among various instances/categories can
be effectively modeled. In this work, we propose two types of graphical
models,i.e. Conditional Random Field for MSDA (CRF-MSDA) and Markov Random
Field for MSDA (MRF-MSDA), for cross-domain joint modeling and learnable domain
combination. In a nutshell, given an observation set composed of a query sample
and the semantic prototypes i.e. representative category embeddings) on various
domains, the CRF-MSDA model seeks to learn the joint distribution of labels
conditioned on the observations. We attain this goal by constructing a
relational graph over all observations and conducting local message passing on
it. By comparison, MRF-MSDA aims to model the joint distribution of
observations over different Markov networks via an energy-based formulation,
and it can naturally perform label prediction by summing the joint likelihoods
over several specific networks. Compared to the CRF-MSDA counterpart, the
MRF-MSDA model is more expressive and possesses lower computational cost. We
evaluate these two models on four standard benchmark data sets of MSDA with
distinct domain shift and data complexity, and both models achieve superior
performance over existing methods on all benchmarks.


Abstract:
  We prove that a simple knot in the lens space $L(p,q)$ fibers if and only if
its order in homology does not divide any remainder occurring in the Euclidean
algorithm applied to the pair $(p,q)$. One corollary is that if $p=m^2$ is a
perfect square, then any simple knot of order $m$ fibers, answering a question
of Cebanu. More generally, we compute the leading coefficient of the Alexander
polynomial of a simple knot, and we describe how to construct a minimum
complexity Seifert surface for one. The methods are direct, combinatorial, and
geometric.


Abstract:
  In Veltman's original view, the Standard Model with a large Higgs particle
mass of about 1 TeV was the natural completion of non-renormalizable Glashow
model. This mass was thus a second threshold for weak interactions, as the W
mass was for the non-renormalizable 4-fermion V-A theory. Today, after the
observation of the narrow scalar resonance at 125 GeV, Veltman's large-mass
idea seems to be ruled out. Yet, this is not necessarily true. Depending on the
description of SSB in $\Phi^4$ theory, and by combining analytic calculations
and lattice simulations, besides the known particle at 125 GeV, a new resonance
of the Higgs field may also show up around 700 GeV. The peculiarity, though, is
that this heavier state would couple to longitudinal vector bosons with the
same typical strength of the low-mass state and thus represent a relatively
narrow resonance. In this way, such hypothetical new resonance would naturally
fit with some excess of 4-lepton events observed by ATLAS around 680 GeV.
Analogous data from CMS are needed to confirm or disprove this interpretation.
Implications of a two-mass structure for radiative corrections are also
discussed.


Abstract:
  The understanding of an offense is subjective and people may have different
opinions about the offensiveness of a comment. Moreover, offenses and hate
speech may occur through sarcasm, which hides the real intention of the comment
and makes the decision of the annotators more confusing. Therefore, providing a
well-structured annotation process is crucial to a better understanding of hate
speech and offensive language phenomena, as well as supplying better
performance for machine learning classifiers. In this paper, we describe a
corpus annotation process proposed by a linguist, a hate speech specialist, and
machine learning engineers in order to support the identification of hate
speech and offensive language on social media. In addition, we provide the
first robust dataset of this kind for the Brazilian Portuguese language. The
corpus was collected from Instagram posts of political personalities and
manually annotated, being composed by 7,000 annotated documents according to
three different layers: a binary classification (offensive versus non-offensive
language), the level of offense (highly offensive, moderately offensive, and
slightly offensive messages), and the identification regarding the target of
the discriminatory content (xenophobia, racism, homophobia, sexism, religious
intolerance, partyism, apology to the dictatorship, antisemitism, and
fatphobia). Each comment was annotated by three different annotators and
achieved high inter-annotator agreement. The proposed annotation approach is
also language and domain-independent nevertheless it is currently customized
for Brazilian Portuguese.


Abstract:
  We describe the addition of Lyman-alpha resonant line transfer to our dust
continuum radiation transfer code SKIRT, verifying our implementation with
published results for spherical problems and using some self-designed
three-dimensional setups. We specifically test spatial discretization through
various grid types, including hierarchical octree grids and unstructured
Voronoi tessellations. We then use a radiation transfer post-processing model
for one of the spiral galaxies produced by the Auriga cosmological zoom
simulations to investigate the effect of spatial discretization on the
synthetic observations. We find that the calculated Lyman-alpha line profiles
exhibit an extraordinarily strong dependence on the type and resolution of the
spatial grid, rendering the results untrustworthy at best. We attribute this
effect to the large gradients in the hydrogen density distribution over small
distances, which remain significantly under-resolved in the input model. We
therefore argue that further research is needed to determine the required
spatial resolution of a hydrodynamical simulation snapshot to enable meaningful
Lyman-alpha line transfer post-processing.


Abstract:
  In modern data science, it is often not enough to obtain only a data-driven
model with a good prediction quality. On the contrary, it is more interesting
to understand the properties of the model, which parts could be replaced to
obtain better results. Such questions are unified under machine learning
interpretability questions, which could be considered one of the area's raising
topics. In the paper, we use multi-objective evolutionary optimization for
composite data-driven model learning to obtain the algorithm's desired
properties. It means that whereas one of the apparent objectives is precision,
the other could be chosen as the complexity of the model, robustness, and many
others. The method application is shown on examples of multi-objective learning
of composite models, differential equations, and closed-form algebraic
expressions are unified and form approach for model-agnostic learning of the
interpretable models.


Abstract:
  Multi-messenger astrophysics is becoming a major avenue to explore the
Universe, with the potential to span a vast range of redshifts. The growing
synergies between different probes is opening new frontiers, which promise
profound insights into several aspects of fundamental physics and cosmology. In
this context, THESEUS will play a central role during the 2030s in detecting
and localizing the electromagnetic counterparts of gravitational wave and
neutrino sources that the unprecedented sensitivity of next generation
detectors will discover at much higher rates than the present. Here, we review
the most important target signals from multi-messenger sources that THESEUS
will be able to detect and characterize, discussing detection rate expectations
and scientific impact.


Abstract:
  We adopt a geometric perspective on Fock space to provide two complementary
insights into the eigenstates in many-body-localized fermionic systems. On the
one hand, individual many-bodylocalized eigenstates are well approximated by a
Slater determinant of single-particle orbitals. On the other hand, the orbitals
of different eigenstates in a given system display a varying, and generally
imperfect, degree of compatibility, as we quantify by a measure based on the
projectors onto the corresponding single-particle subspaces. We study this
incompatibility between states of fixed and differing particle number, as well
as inside and outside the many-body-localized regime. This gives detailed
insights into the emergence and strongly correlated nature of
quasiparticle-like excitations in many-body localized systems, revealing
intricate correlations between states of different particle number down to the
level of individual realizations.


Abstract:
  In applications such as participatory sensing and crowd sensing,
self-interested agents exert costly effort towards achieving an objective for
the system operator. We study such a setup where a principal incentivizes
multiple agents of different types who can collude with each other to derive
rent. The principal cannot observe the efforts exerted directly, but only the
outcome of the task, which is a noisy function of the effort. The type of each
agent influences the effort cost and task output. For a duopoly in which agents
are coupled in their payments, we show that if the principal and the agents
interact finitely many times, the agents can derive rent by colluding even if
the principal knows the types of the agents. However, if the principal and the
agents interact infinitely often, the principal can disincentivize agent
collusion through a suitable data-driven contract.


Abstract:
  We establish an exact formulation for wave scattering of a massless field
with spin and charge by a Kerr-Newman-de Sitter black hole. Our formulation is
based on the exact solution of the Teukolsky equation in terms of the local
Heun function, and does not require any approximation. It serves as simple
exact formulae with arbitrary high precision, which realize fast calculation
without restrictions on model parameters. We highlight several applications
including quasinormal modes, cross section, reflection/absorption rate, and
Green function.


Abstract:
  The Sloan Digital Sky Survey provides colors for more than 100 000 moving
objects, among which around 10 000 have albedos determined. Here we combined
colors and albedo in order to perform a cluster analysis on the small bodies
population, and identify a C-cluster, a group of asteroid related to C-type as
defined in earlier work. Members of this C-cluster are in fair agreement with
the color boundaries of B and C-type defined in DeMeo and Carry (2013). We then
compare colors of C-cluster asteroids to those of carbonaceous chondrites
powders, while taking into account the effect of phase angle. We show that only
CM chondrites have colors in the range of C-cluster asteroids, CO, CR and CV
chondrites being significantly redder. Also, CM chondrites powders are on
average slightly redder than the average C-cluster. The colors of C-cluster
members are further investigated by looking at color variations as a function
of asteroid diameter. We observe that the visible slope becomes bluer with
decreasing asteroids diameter, and a transition seems to be present around 20
km. We discuss the origin of this variation and, if not related to a bias in
the dataset - analysis, we conclude that it is related to the surface texture
of the objects, smaller objects being covered by rocks, while larger objects
are covered by a particulate surface. The blueing is interpreted by an
increased contribution of the first reflection in the case of rock-dominated
surfaces, which can scatter light in a Rayleigh-like manner. We do not have
unambiguous evidence of space weathering within the C-cluster based on this
analysis, however the generally bluer nature of C-cluster objects compared to
CM chondrites could be to some extent related to space weathering.


Abstract:
  An intersecting $r$-uniform straight line system is an intersecting linear
system whose lines consist of $r$ points on straight line segment of
$\mathbb{R}^2$ and any two lines share a point. Recently, the author [A.
V\'azquez-\'Avila, \emph{On intersecting straight line systems}, J. Discret.
Math. Sci. Cryptogr. Accepted] proved that any intersecting $r$-uniform
straight line system $(P,\mathcal{L})$ has transversal number at most
$\nu_2-1$, with $r\geq\nu_2$, where $\nu_2$ is the maximum cardinality of a
subset of lines $R\subseteq\mathcal{L}$ such that every triplet of different
elements of $R$ does not have a common point.
  In this paper, we improve such upper bound if the intersecting $r$-uniform
straight line system satisfies $r=\nu_2$. This result has immediate
consequences for some questions given by Oliveros et al. [D. Oliveros, C.
O'Neill and S. Zerbib, \emph{The geometry and combinatorics of discrete line
segment hypergraphs}, Discrete Math. {\bf 343} (2020), no. 6, 111825].


Abstract:
  The effect of polyvalent molecular cations, such as spermine, on the
condensation of DNA into very well-defined toroidal shapes have been well
studied and understood. However, a great effort has been made trying to obtain
similar condensed structures from either ssRNA or dsRNA, which the latter
carries similar negative charge density as dsDNA, although it adopts a
different helical form. But the analogous condensation of RNA molecules into
well-defined structures has so far been elusive. In this work, we show that
ssRNA molecules can easily be condensed into nanoring structures on a mice
surface, where each nanoring structure is formed mostly by a single RNA
molecule. The condensation occurs in a concentration range of different atomic
cations, from monovalent to trivalent. The structures of the RNA nanorings on
mica surfaces were oberved by atomic force microscopy (AFM). The samples were
observed in tapping mode and were prepared by drop evaporation of a solution of
RNA in the presence of one type of the different cations used. As far as we
know, this is the first time that nanorings or any other well-defined condensed
RNA structures have been reported. The RNA nanorings formation can be
understood by an energy competition between the hydrogen bonding forming
hairpin stems, weakened by the salts, and hairpin loops. This results may have
an important biological relevance, since it has been proposed that RNA is the
oldest genome coding molecule and the formation of these structures could have
given it stability against degradation in primeval times. Even more, the
nanoring structures could have the potential to be used as biosensors and
functionalized nanodevices.


Abstract:
  We explore the feasibility of combining Graph Neural Network-based policy
architectures with Deep Reinforcement Learning as an approach to problems in
systems. This fits particularly well with operations on networks, which
naturally take the form of graphs. As a case study, we take the idea of
data-driven routing in intradomain traffic engineering, whereby the routing of
data in a network can be managed taking into account the data itself. The
particular subproblem which we examine is minimising link congestion in
networks using knowledge of historic traffic flows. We show through experiments
that an approach using Graph Neural Networks (GNNs) performs at least as well
as previous work using Multilayer Perceptron architectures. GNNs have the added
benefit that they allow for the generalisation of trained agents to different
network topologies with no extra work. Furthermore, we believe that this
technique is applicable to a far wider selection of problems in systems
research.


Abstract:
  We seek to design experimentally feasible broadband, temporally multiplexed
optical quantum memory with near-term applications to telecom bands.
Specifically, we devise dispersion compensation for an impedance-matched
narrow-band quantum memory by exploiting Raman processes over two three-level
atomic subensembles, one for memory and the other for dispersion compensation.
Dispersion compensation provides impedance matching over more than a full
cavity linewidth. Combined with one second spin-coherence lifetime the memory
could be capable of power efficiency exceeding 90% leading to 106 modes for
temporal multiplexing. Our design could lead to significant multiplexing
enhancement for quantum repeaters to be used for telecom quantum networks.


Abstract:
  The flux of cosmic rays (CRs) in the heliosphere is subjected to remarkable
time variations caused by the 11-year cycle of solar activity. To help the
study of this effect, we have developed a web application (Heliophysics Virtual
Observatory) that collects real-time data on solar activity, interplanetary
plasma, and charged radiation from several space missions or observatories. As
we will show, our application can be used to visualize, manipulate, and
download updated data on sunspots, heliospheric magnetic fields, solar wind,
and neutron monitors counting rates. Data and calculations are automatically
updated on daily basis. A nowcasting for the energy spectrum of CR protons
near-Earth is also provided using calculations and real-time neutron monitor
data as input.


Abstract:
  Flickering is a universal phenomenon in accreting astronomical systems which
still defies detailed physical understanding. It is particularly evident in
cataclysmic variables (CVs). Attempting to define boundary conditions for
models, the strength of the flickering is measured in several thousand light
curves of more than 100 CVs. The flickering amplitude is parameterized by the
FWHM of a Gaussian fit to the magnitude distribution of data points in a light
curve. This quantity requires several corrections before a comparison between
different sources can be made. While no correlations of the flickering strength
with simple parameters such as component masses, orbital inclination or period
were detected, a dependence on the absolute magnitude of the primary component
and on the CV subtype is found. In particular, flickering in VY Scl tpye
novalike variables is systematically stronger than in UX UMa type novalikes.
The broadband spectrum of the flickering light source can be fit by simple
models but shows excess in the $U$ band. When the data permitted to investigate
the flickering strength as a function of orbital phase in eclipsing CVs, such a
dependence was found, but it is different for different systems. Surprisingly,
there are also indications for variations of the flickering strength with the
superhump phase in novalike variables with permanent superhumps. In dwarf
novae, the flickering amplitude is high during quiescence, drops quickly at an
intermediate magnitude when the system enters into (or returns from) an
outburst and, on average, remains constant above a given brightness threshold.


Abstract:
  Training a multi-speaker Text-to-Speech (TTS) model from scratch is
computationally expensive and adding new speakers to the dataset requires the
model to be re-trained. The naive solution of sequential fine-tuning of a model
for new speakers can cause the model to have poor performance on older
speakers. This phenomenon is known as catastrophic forgetting. In this paper,
we look at TTS modeling from a continual learning perspective where the goal is
to add new speakers without forgetting previous speakers. Therefore, we first
propose an experimental setup and show that serial fine-tuning for new speakers
can result in the forgetting of the previous speakers. Then we exploit two
well-known techniques for continual learning namely experience replay and
weight regularization and we reveal how one can mitigate the effect of
degradation in speech synthesis diversity in sequential training of new
speakers using these methods. Finally, we present a simple extension to improve
the results in extreme setups.


Abstract:
  The difficulty of optimal control problems has classically been characterized
in terms of system properties such as minimum eigenvalues of
controllability/observability gramians. We revisit these characterizations in
the context of the increasing popularity of data-driven techniques like
reinforcement learning (RL), and in control settings where input observations
are high-dimensional images and transition dynamics are unknown. Specifically,
we ask: to what extent are quantifiable control and perceptual difficulty
metrics of a task predictive of the performance and sample complexity of
data-driven controllers? We modulate two different types of partial
observability in a cartpole "stick-balancing" problem -- (i) the height of one
visible fixation point on the cartpole, which can be used to tune fundamental
limits of performance achievable by any controller, and by (ii) the level of
perception noise in the fixation point position inferred from depth or RGB
images of the cartpole. In these settings, we empirically study two popular
families of controllers: RL and system identification-based $H_\infty$ control,
using visually estimated system state. Our results show that the fundamental
limits of robust control have corresponding implications for the
sample-efficiency and performance of learned perception-based controllers.
Visit our project website https://jxu.ai/rl-vs-control-web for more
information.


Abstract:
  We introduce and apply a new approach to probe the response of galactic
stellar haloes to the interplay between cosmological merger histories and
galaxy formation physics. We perform dark-matter-only, zoomed simulations of
two Milky Way-mass hosts and make targeted, controlled changes to their
cosmological histories using the genetic modification technique. Populating
each history's stellar halo with a semi-empirical, particle-tagging approach
then enables a controlled study, with all instances converging to the same
large-scale structure, dynamical and stellar mass at $z=0$ as their reference.
These related merger scenarios alone generate an extended spread in stellar
halo mass fractions (1.5 dex) comparable to the observed population. Largest
scatter is achieved by growing late ($z\leq1$) major mergers that spread out
existing stars to create massive, in-situ dominated stellar haloes. Increasing
a last major merger at $z\sim2$ brings more accreted stars into the inner
regions, resulting in smaller scatter in the outskirts which are predominantly
built by subsequent minor events. Exploiting the flexibility of our
semi-empirical approach, we show that the diversity of stellar halo masses
across scenarios is reduced by allowing shallower slopes in the stellar
mass--halo mass relation for dwarf galaxies, while it remains conserved when
central stars are born with hotter kinematics across cosmic time. The
merger-dependent diversity of stellar haloes thus responds distinctly to
assumptions in modelling the central and dwarf galaxies respectively, opening
exciting prospects to constrain star formation and feedback at different
galactic mass-scales with the coming generation of deep, photometric
observatories.


Abstract:
  This paper presents a method for riggable 3D face reconstruction from
monocular images, which jointly estimates a personalized face rig and per-image
parameters including expressions, poses, and illuminations. To achieve this
goal, we design an end-to-end trainable network embedded with a differentiable
in-network optimization. The network first parameterizes the face rig as a
compact latent code with a neural decoder, and then estimates the latent code
as well as per-image parameters via a learnable optimization. By estimating a
personalized face rig, our method goes beyond static reconstructions and
enables downstream applications such as video retargeting. In-network
optimization explicitly enforces constraints derived from the first principles,
thus introduces additional priors than regression-based methods. Finally,
data-driven priors from deep learning are utilized to constrain the ill-posed
monocular setting and ease the optimization difficulty. Experiments demonstrate
that our method achieves SOTA reconstruction accuracy, reasonable robustness
and generalization ability, and supports standard face rig applications.


Abstract:
  Object recognition has made great advances in the last decade, but
predominately still relies on many high-quality training examples per object
category. In contrast, learning new objects from only a few examples could
enable many impactful applications from robotics to user personalization. Most
few-shot learning research, however, has been driven by benchmark datasets that
lack the high variation that these applications will face when deployed in the
real-world. To close this gap, we present the ORBIT dataset and benchmark,
grounded in the real-world application of teachable object recognizers for
people who are blind/low-vision. The dataset contains 3,822 videos of 486
objects recorded by people who are blind/low-vision on their mobile phones. The
benchmark reflects a realistic, highly challenging recognition problem,
providing a rich playground to drive research in robustness to few-shot,
high-variation conditions. We set the benchmark's first state-of-the-art and
show there is massive scope for further innovation, holding the potential to
impact a broad range of real-world vision applications including tools for the
blind/low-vision community. We release the dataset at
https://doi.org/10.25383/city.14294597 and benchmark code at
https://github.com/microsoft/ORBIT-Dataset.


Abstract:
  The recent inflow of empirical data about the collective behaviour of
strongly correlated biological systems has brought field theory and the
renormalization group into the biophysical arena. Experiments on bird flocks
and insect swarms show that social forces act on the particles' velocity
through the generator of its rotations, namely the spin, indicating that
mode-coupling field theories are necessary to reproduce the correct dynamical
behaviour. Unfortunately, a theory for three coupled fields - density, velocity
and spin - has a prohibitive degree of intricacy. A simplifying path consists
in getting rid of density fluctuations by studying incompressible systems. This
requires imposing a solenoidal constraint on the primary field, an unsolved
problem even for equilibrium mode-coupling theories. Here, we perform an
equilibrium dynamic renormalization group analysis of a mode-coupling field
theory subject to a solenoidal constraint; using the classification of Halperin
and Hohenberg, we can dub this case as a solenoidal Model G. We demonstrate
that the constraint produces a new vertex that mixes static and dynamical
coupling constants, and that this vertex is essential to grant the closure of
the renormalization group structure and the consistency of dynamics with
statics. Interestingly, although the solenoidal constraint leads to a
modification of the static universality class, we find that it does not change
the dynamical universality class, a result that seems to represent an exception
to the general rule that dynamical universality classes are narrower than
static ones. Our results constitute a solid stepping stone in the admittedly
large chasm towards developing an off-equilibrium mode-coupling theory of
biological groups.


Abstract:
  Besides magnetic and charge order, regular arrangements of orbital occupation
constitute a fundamental order parameter of condensed matter physics. Even
though orbital order is difficult to identify directly in experiments, its
presence was firmly established in a number of strongly correlated,
three-dimensional Mott insulators. Here, reporting resonant X-ray scattering
experiments on the layered Van der Waals compound $1T$-TiSe$_2$, we establish
the emergence of orbital order in a weakly correlated, quasi-two-dimensional
material. Our experimental scattering results are consistent with
first-principles calculations that bring to the fore a generic mechanism of
close interplay between charge redistribution, lattice displacements, and
orbital order. It demonstrates the essential role that orbital degrees of
freedom play in TiSe$_2$, and their importance throughout the family of
correlated Van der Waals materials.


Abstract:
  Existing radar sensors can be classified into automotive and scanning radars.
While most radar odometry (RO) methods are only designed for a specific type of
radar, our RO method adapts to both scanning and automotive radars. Our RO is
simple yet effective, where the pipeline consists of thresholding,
probabilistic submap building, and an NDT-based radar scan matching. The
proposed RO has been tested on two public radar datasets: the Oxford Radar
RobotCar dataset and the nuScenes dataset, which provide scanning and
automotive radar data respectively. The results show that our approach
surpasses state-of-the-art RO using either automotive or scanning radar by
reducing translational error by 51% and 30%, respectively, and rotational error
by 17% and 29%, respectively. Besides, we show that our RO achieves
centimeter-level accuracy as lidar odometry, and automotive and scanning RO
have similar accuracy.


Abstract:
  Periodically-driven systems are ubiquitous in science and technology. In
quantum dynamics, even a small number of periodically-driven spins leads to
complicated dynamics. Hence, it is of interest to understand what constraints
such dynamics must satisfy. We derive a set of constraints for each number of
cycles. For pure initial states, the observable being constrained is the
recurrence probability. We use our constraints for detecting undesired coupling
to unaccounted environments and drifts in the driving parameters. To illustrate
the relevance of these results for modern quantum systems we demonstrate our
findings experimentally on a trapped-ion quantum computer, and on various IBM
quantum computers. Specifically, we provide two experimental examples where
these constraints surpass fundamental bounds associated with known one-cycle
constraints. This scheme can potentially be used to detect the effect of the
environment in quantum circuits that cannot be classically simulated. Finally,
we show that, in practice, testing an $n$-cycle constraint requires executing
only $O(\sqrt{n})$ cycles, which makes the evaluation of constraints associated
with hundreds of cycles realistic.


Abstract:
  We study one-loop bulk entanglement entropy in even spacetime dimensions
using the heat kernel method, which captures the universal piece of
entanglement entropy, a logarithmically divergent term in even dimensions. In
four dimensions, we perform explicit calculations for various shapes of
boundary subregions. In particular, for a cusp subregion with an arbitrary
opening angle, we find that the bulk entanglement entropy always encodes the
same universal information about the boundary theories as the leading
entanglement entropy in the large N limit, up to a fixed proportional constant.
By smoothly deforming a circle in the boundary, we find that to leading order
of the deformations, the bulk entanglement entropy shares the same shape
dependence as the leading entanglement entropy and hence the same physical
information can be extracted from both cases. This establishes an interesting
local/nonlocal duality for holographic $\mm{CFT}_3$. However, the result does
not hold for higher dimensional holographic theories.


Abstract:
  Using the nonabilinization procedure, we find an integrable matrix version of
the Euler top on $\mathfrak{so}_3$


Abstract:
  Linear-response time-dependent density functional theory (LR-TDDFT) for core
level spectroscopy using standard local functionals suffers from
self-interaction error and a lack of orbital relaxation upon creation of the
core hole. As a result, LR-TDDFT calculated X-ray absorption near edge
structure (XANES) spectra need to be shifted along the energy axis to match
experimental data. We propose a correction scheme based on many body
perturbation theory to calculate the shift from first principles. The
ionization potential of the core donor state is first computed and then
substituted for the corresponding Kohn--Sham orbital energy, thus emulating
Koopmans' condition. Both self-interaction error and orbital relaxation are
taken into account. The method exploits the localized nature of core states for
efficiency and integrates seamlessly in our previous implementation of core
level LR-TDDFT, yielding corrected spectra in a single calculation. We
benchmark the correction scheme on molecules at the K- and L-edges as well as
for core binding energies and report accuracies comparable to higher order
methods. We also demonstrate applicability in large and extended systems and
discuss efficient approximations.


Abstract:
  A giant impact origin for the Moon is generally accepted, but many aspects of
lunar formation remain poorly understood and debated. \'Cuk et al. (2016)
proposed that an impact that left the Earth-Moon system with high obliquity and
angular momentum could explain the Moon's orbital inclination and isotopic
similarity to Earth. In this scenario, instability during the Laplace Plane
transition, when the Moon's orbit transitions from the gravitational influence
of Earth's figure to that of the Sun, would both lower the system's angular
momentum to its present-day value and generate the Moon's orbital inclination.
Recently, Tian and Wisdom (2020) discovered new dynamical constraints on the
Laplace Plane transition and concluded that the Earth-Moon system could not
have evolved from an initial state with high obliquity. Here we demonstrate
that the Earth-Moon system with an initially high obliquity can evolve into the
present state, and we identify a spin-orbit secular resonance as a key
dynamical mechanism in the later stages of the Laplace Plane transition. Some
of the simulations by Tian and Wisdom (2020) did not encounter this late
secular resonance, as their model suppressed obliquity tides and the resulting
inclination damping. Our results demonstrate that a giant impact that left
Earth with high angular momentum and high obliquity ($\theta > 61^{\circ}$) is
a promising scenario for explaining many properties of the Earth-Moon system,
including its angular momentum and obliquity, the geochemistry of Earth and the
Moon, and the lunar inclination.


Abstract:
  Transmission electron microscopes use electrons with wavelengths of a few
picometers, potentially capable of imaging individual atoms in solids at a
resolution ultimately set by the intrinsic size of an atom. Unfortunately, due
to imperfections in the imaging lenses and multiple scattering of electrons in
the sample, the image resolution reached is 3 to 10 times worse. Here, by
inversely solving the multiple scattering problem and overcoming the
aberrations of the electron probe using electron ptychography to recover a
linear phase response in thick samples, we demonstrate an instrumental blurring
of under 20 picometers. The widths of atomic columns in the measured
electrostatic potential are now no longer limited by the imaging system, but
instead by the thermal fluctuations of the atoms. We also demonstrate that
electron ptychography can potentially reach a sub-nanometer depth resolution
and locate embedded atomic dopants in all three dimensions with only a single
projection measurement.


Abstract:
  The question of whether to use one classifier or a combination of classifiers
is a central topic in Machine Learning. We propose here a method for finding an
optimal linear combination of classifiers derived from a bias-variance
framework for the classification task.


Abstract:
  By fitting stellar populations to SDSS-IV MaNGA survey observations of ~7000
suitably-weighted individual galaxies, we reconstruct the star-formation
history of the Universe, which we find to be in reasonable agreement with
previous studies. Dividing the galaxies by their present-day stellar mass, we
demonstrate the downsizing phenomenon, whereby the more massive galaxies hosted
the most star-formation at earlier times. Further dividing the galaxy sample by
colour and morphology, we find that a galaxy's present-day colour tells us more
about its historical contribution to the cosmic star formation history than its
current morphology. We show that downsizing effects are greatest among galaxies
currently in the blue cloud, but that the level of downsizing in galaxies of
different morphologies depends quite sensitively on the morphological
classification used, due largely to the difficulty in classifying the smaller
low-mass galaxies from their ground-based images. Nevertheless, we find
agreement that among galaxies with stellar masses
$M_{\star}>6\times10^{9}\,M_{\odot}$, downsizing is most significant in
spirals. However, there are complicating factors. For example, for more massive
galaxies, we find that colour and morphology are predictors of the past star
formation over a longer timescale than in less massive systems. Presumably this
effect is reflecting the longer period of evolution required to alter these
larger galaxies' physical properties, but shows that conclusions based on any
single property don't tell the full story.


Abstract:
  Model predictive control (MPC) schemes are commonly designed with fixed,
i.e., time-invariant, horizon length and cost functions. If no stabilizing
terminal ingredients are used, stability can be guaranteed via a sufficiently
long horizon. A suboptimality index can be derived that gives bounds on the
performance of the MPC law over an infinite-horizon (IH). While for
time-invariant schemes such index can be computed offline, less attention has
been paid to time-varying strategies with adapting cost function which can be
found, e.g., in learning-based optimal control. This work addresses the
performance bounds of nonlinear MPC with stabilizing horizon and time-varying
terminal cost. A scheme is proposed that uses the decay of the optimal
finite-horizon cost and convolutes a history stack to predict the bounds on the
IH performance. Based on online information on the decay rate, the performance
bound estimate is improved while the terminal cost is adapted using methods
from adaptive dynamic programming. The adaptation of the terminal cost leads to
performance improvement over a time-invariant scheme with the same horizon
length. The approach is demonstrated in a case study.


Abstract:
  Microlensing is a powerful tool for discovering cold exoplanets, and the The
Roman Space Telescope microlensing survey will discover over 1000 such planets.
Rapid, automated classification of Roman's microlensing events can be used to
prioritize follow-up observations of the most interesting events. Machine
learning is now often used for classification problems in astronomy, but the
success of such algorithms can rely on the definition of appropriate features
that capture essential elements of the observations that can map to parameters
of interest. In this paper, we introduce tools that we have developed to
capture features in simulated Roman light curves of different types of
microlensing events, and evaluate their effectiveness in classifying
microlensing light curves. These features are quantified as parameters that can
be used to decide the likelihood that a given light curve is due to a specific
type of microlensing event. This method leaves us with a list of parameters
that describe features like the smoothness of the peak, symmetry, the number of
peaks, and width and height of small deviations from the main peak. This will
allow us to quickly analyze a set of microlensing light curves and later use
the resulting parameters as input to machine learning algorithms to classify
the events.


Abstract:
  An effective email search engine can facilitate users' search tasks and
improve their communication efficiency. Users could have varied preferences on
various ranking signals of an email, such as relevance and recency based on
their tasks at hand and even their jobs. Thus a uniform matching pattern is not
optimal for all users. Instead, an effective email ranker should conduct
personalized ranking by taking users' characteristics into account. Existing
studies have explored user characteristics from various angles to make email
search results personalized. However, little attention has been given to users'
search history for characterizing users. Although users' historical behaviors
have been shown to be beneficial as context in Web search, their effect in
email search has not been studied and remains unknown. Given these
observations, we propose to leverage user search history as query context to
characterize users and build a context-aware ranking model for email search. In
contrast to previous context-dependent ranking techniques that are based on raw
texts, we use ranking features in the search history. This frees us from
potential privacy leakage while giving a better generalization power to unseen
users. Accordingly, we propose a context-dependent neural ranking model (CNRM)
that encodes the ranking features in users' search history as query context and
show that it can significantly outperform the baseline neural model without
using the context. We also investigate the benefit of the query context vectors
obtained from CNRM on the state-of-the-art learning-to-rank model LambdaMart by
clustering the vectors and incorporating the cluster information. Experimental
results show that significantly better results can be achieved on LambdaMart as
well, indicating that the query clusters can characterize different users and
effectively turn the ranking model personalized.


Abstract:
  Can a regulated, legal market for wildlife products protect species
threatened by poaching? It is one of the most controversial ideas in
biodiversity conservation. Perhaps the most convincing reason for legalizing
wildlife trade is that trade revenue could fund the protection and conservation
of poached species. In this paper, we examine the possible poacher-population
dynamic consequences of legal trade funding conservation. The model consists of
a manager scavenging carcasses for wildlife products, who then sells the
products, and directs a portion of the revenue towards funding anti-poaching
law enforcement. Through a global analysis of the model, we derive the critical
proportion of product the manager must scavenge, and the critical proportion of
trade revenue the manager must allocate towards increased enforcement, in order
for legal trade to lead to abundant long-term wildlife populations. We
illustrate how the model could inform management with parameter values derived
from the African elephant literature, under a hypothetical scenario where a
manager scavenges elephant carcasses to sell ivory. We find that there is a
large region of parameter space where populations go extinct under legal trade,
unless a significant portion of trade revenue is directed towards protecting
populations from poaching. The model is general and therefore can be used as a
starting point for exploring the consequences of funding many conservation
programs using wildlife trade revenue.


Abstract:
  Brain-inspired computing and neuromorphic hardware are promising approaches
that offer great potential to overcome limitations faced by current computing
paradigms based on traditional von-Neumann architecture. In this regard,
interest in developing memristor crossbar arrays has increased due to their
ability to natively perform in-memory computing and fundamental synaptic
operations required for neural network implementation. For optimal efficiency,
crossbar-based circuits need to be compatible with fabrication processes and
materials of industrial CMOS technologies. Herein, we report a complete
CMOS-compatible fabrication process of TiO2-based passive memristor crossbars
with 700 nm wide electrodes. We show successful bottom electrode fabrication by
a damascene process, resulting in an optimised topography and a surface
roughness as low as 1.1 nm. DC sweeps and voltage pulse programming yield
statistical results related to synaptic-like multilevel switching. Both
cycle-to-cycle and device-to-device variability are investigated. Analogue
programming of the conductance using sequences of 200 ns voltage pulses suggest
that the fabricated memories have a multilevel capacity of at least 3 bits due
to the cycle-to-cycle reproducibility.


Abstract:
  We study the effects of bond and site disorder in the classical
$J_{1}$-$J_{2}$ Heisenberg model on a square lattice in the order-by-disorder
frustrated regime $2J_{2}>\left|J_{1}\right|$. Combining symmetry arguments,
numerical energy minimization and large scale Monte Carlo simulations, we
establish that the finite temperature Ising-like transition of the clean system
is destroyed in the presence of any finite concentration of impurities. We
explain this finding via a random-field mechanism which generically emerges in
systems where disorder locally breaks the same real-space symmetry
spontaneously globally broken by the associated order parameter. We also
determine that the phase replacing the clean one is a paramagnet polarized in
the nematic glass order with non-trivial magnetic response. This is because
disorder also induces non-collinear spin-vortex-crystal order and produces a
conjugated transverse dipolar random field. As a result of these many competing
effects, the associated magnetic susceptibilities are non-monotonic functions
of the temperature. As a further application of our methods, we show the
generation of random axes in other frustrated magnets with broken SU(2)
symmetry. We also discuss the generality of our findings and their relevance to
experiments.


Abstract:
  Motivated by a recent first principles prediction of an anisotropic cubic
Dirac semi-metal in a real material Tl(TeMo)$_3$, we study the behavior of
electrons tunneling through a potential barrier in such systems. To clearly
investigate effects from different contributions to the Hamiltonian we study
the model in various limits. First, in the limit of a very thin material where
the linearly dispersive $z$-direction is frozen out at zero momentum and the
dispersion in the $x$-$y$ plane is rotationally symmetric. In this limit we
find a Klein tunneling reminiscent of what is observed in single layer graphene
and linearly dispersive Dirac semi-metals. Second, an increase in thickness of
the material leads to the possibility of a non-zero momentum eigenvalue $k_z$
that acts as an effective mass term in the Hamiltonian. We find that these lead
to a suppression of Klein tunneling. Third, the inclusion of an anisotropy
parameter $\lambda\neq 1$ leads to a breaking of rotational invariance.
Furthermore, we observed that for different values of incident angle $\theta$
and anisotropy parameter $\lambda$ the Hamiltonian supports different numbers
of modes propagating to infinity. We display this effect in form of a diagram
that is similar to a phase diagram of a distant detector. Fourth, we consider
coexistence of both anisotropy and non-zero $k_z$ but do not find any effect
that is unique to the interplay between non-zero momentum $k_z$ and anisotropy
parameter $\lambda$. Last, we studied the case of a barrier that was placed in
the linearly dispersive direction and found Klein tunneling $T-1\propto
\theta^6+\mathcal{O}(\theta^8)$ that is enhanced when compared to the Klein
tunneling in linear Dirac semi-metals or graphene where $T-1\propto
\theta^2+\mathcal{O}(\theta^4)$.


Abstract:
  Let $G$ be an irreducible imprimitive subgroup of
$\operatorname{GL}_n(\mathbb{F})$, where $\mathbb{F}$ is a field. Any system of
imprimitivity for $G$ can be refined to a nonrefinable system of imprimitivity,
and we consider the question of when such a refinement is unique. Examples show
that $G$ can have many nonrefinable systems of imprimitivity, and even the
number of components is not uniquely determined. We consider the case where $G$
is the wreath product of an irreducible primitive $H \leq
\operatorname{GL}_d(\mathbb{F})$ and transitive $K \leq S_k$, where $n = dk$.
We show that $G$ has a unique nonrefinable system of imprimitivity, except in
the following special case: $d = 1$, $n = k$ is even, $|H| = 2$, and $K$ is a
transitive subgroup of $C_2 \wr S_{n/2}$. As a simple application, we prove
results about inclusions between wreath product subgroups.


Abstract:
  We report a configuration strategy for improving the thermoelectric (TE)
performance of two-dimensional (2D) transition metal dichalcogenide (TMDC) WS2
based on the experimentally prepared WS2/WSe2 lateral superlattice (LS)
crystal. On the basis of density function theory combined with Boltzmann
transport equation, we show that the TE figure of merit zT of monolayer WS2 is
remarkably enhanced when forming into a WS2/WSe2 LS crystal. This is primarily
ascribed to the almost halved lattice thermal conductivity due to the enhanced
anharmonic processes. Electronic transport properties parallel (xx) and
perpendicular (yy) to the superlattice period are highly symmetric for both p-
and n-doped LS owing to the nearly isotropic lifetime of charger carriers. The
spin-orbital effect causes a significant split of conduction band and leads to
three-fold degenerate sub-bands and high density of states (DOS), which offers
opportunity to obtain the high n-type Seebeck coefficient (S). Interestingly,
the separated degenerate sub-bands and upper conduction band in monolayer WS2
form a remarkable stairlike DOS, yielding a higher S. The hole carriers with
much higher mobility than electrons reveal the high p-type power factor and the
potential to be good p-type TE materials with optimal zT exceeds 1 at 400K in
WS2/WSe2 LS.


Abstract:
  The flux ratios of high-ionization lines are commonly assumed to indicate the
metallicity of the broad emission line region in luminous quasars. When
accounting for the variation in their kinematic profiles, we show that the
NV/CIV, (SiIV+OIV])/CIV and NV/Lya line ratios do not vary as a function of the
quasar continuum luminosity, black hole mass, or accretion rate. Using
photoionization models from CLOUDY , we further show that the observed changes
in these line ratios can be explained by emission from gas with solar
abundances, if the physical conditions of the emitting gas are allowed to vary
over a broad range of densities and ionizing fluxes. The diversity of broad
line emission in quasar spectra can be explained by a model with emission from
two kinematically distinct regions, where the line ratios suggest that these
regions have either very different metallicity or density. Both simplicity and
current galaxy evolution models suggest that near-solar abundances, with parts
of the spectrum forming in high-density clouds, are more likely. Within this
paradigm, objects with stronger outflow signatures show stronger emission from
gas which is denser and located closer to the ionizing source, at radii
consistent with simulations of line-driven disc-winds. Studies using broad-line
ratios to infer chemical enrichment histories should consider changes in
density and ionizing flux before estimating metallicities.


Abstract:
  Given the coordinates of the terminals $ \{(x_j,y_j)\}_{j=1}^n $ of the full
Euclidean Steiner tree, its length equals $$ \left| \sum_{j=1}^n z_j U_j
\right| \, , $$ where $ \{z_j:=x_j+ \mathbf i y_j\}_{j=1}^n $ and $
\{U_j\}_{j=1}^n $ are suitably chosen $ 6 $th roots of unity. We also extend
this result for the cost of the optimal Weber networks which are topologically
equivalent to some full Steiner trees.


Abstract:
  The Inverse First Ionization Potential (FIP) Effect, the depletion in coronal
abundance of elements like Fe, Mg, and Si that are ionized in the solar
chromosphere relative to those that are neutral, has been identified in several
solar flares. We give a more detailed discussion of the mechanism of
fractionation by the ponderomotive force associated with magnetohydrodynamic
waves, paying special attention to the conditions in which Inverse FIP
fractionation arises in order to better understand its relation to the usual
FIP Effect, i.e. the enhancement of coronal abundance of Fe, Mg, Si, etc. The
FIP Effect is generated by parallel propagating Alfv\'en waves, with either
photospheric, or more likely coronal, origins. The Inverse FIP Effect arises as
upward propagating fast mode waves with an origin in the photosphere or below,
refract back downwards in the chromosphere where the Alfv\'en speed is
increasing with altitude. We give a more physically motivated picture of the
FIP fractionation, based on the wave refraction around inhomogeneities in the
solar atmosphere, and inspired by previous discussions of analogous phenomena
in the optical trapping of particles by laser beams. We apply these insights to
modeling the fractionation and find good agreement with the observations of
Katsuda et al. (2020; arXiv:2001.10643) and Dennis et al. (2015;
arXiv:1503.01602).


Abstract:
  We work out axioms for the duals $G\subset U_N^+$ of the finite quantum
permutation groups, $F\subset S_N^+$ with $|F|<\infty$, and we discuss how the
basic theory of such quantum permutation groups partly simplifies in the dual
setting. We discuss as well some potential extensions to the infinite case, in
connection with the well-known question of axiomatizing the discrete quantum
group actions on the infinite graphs.


Abstract:
  The standard electrocardiogram (ECG) is a point-wise evaluation of the body
potential at certain given locations. These locations are subject to
uncertainty and may vary from patient to patient or even for a single patient.
In this work, we estimate the uncertainty in the ECG induced by uncertain
electrode positions when the ECG is derived from the forward bidomain model. In
order to avoid the high computational cost associated to the solution of the
bidomain model in the entire torso, we propose a low-rank approach to solve the
uncertainty quantification (UQ) problem. More precisely, we exploit the
sparsity of the ECG and the lead field theory to translate it into a set of
deterministic, time-independent problems, whose solution is eventually used to
evaluate expectation and covariance of the ECG. We assess the approach with
numerical experiments in a simple geometry.


Abstract:
  Quantum materials with non-trivial band topology and bulk superconductivity
are considered superior materials to realize topological superconductivity. In
this regard, we report detailed Density Functional Theory (DFT) calculations
and Z2 invaraints for the NbC superconductor, exhibiting its band structure to
be topologically non-trivial. Bulk superconductivity at 8.9K is confirmed
through DC magnetization measurements under Field Cooled (FC) and Zero Field
Cooled (ZFC) protocols. This superconductivity is found to be of type-II nature
as revealed by isothermal M-H measurements and thus calculated the
Ginzberg-Landau parameter. A large intermediate state is evident from the phase
diagram, showing NbC to be a strong type-II superconductor. Comparing with
earlier reports on superconducting NbC, a non-monotonic relationship of
critical temperature with lattice parameters is seen. In conclusion, NbC is a
type-II around 10K superconductor with topological non-trivial surface states.


Abstract:
  Graph transaction processing raises many unique challenges such as random
data access due to the irregularity of graph structures, low throughput and
high abort rate due to the relatively large read/write sets in graph
transactions. To address these challenges, we present G-Tran -- an RDMA-enabled
distributed in-memory graph database with serializable and snapshot isolation
support. First, we propose a graph-native data store to achieve good data
locality and fast data access for transactional updates and queries. Second,
G-Tran adopts a fully decentralized architecture that leverages RDMA to process
distributed transactions with the MPP model, which can achieve high performance
by utilizing all computing resources. In addition, we propose a new MV-OCC
implementation with two optimizations to address the issue of large read/write
sets in graph transactions. Extensive experiments show that G-Tran achieves
competitive performance compared with other popular graph databases on
benchmark workloads.


Abstract:
  We present new observations of the odd $z=0.96$ weak-line quasar PG1407+265,
and report the discovery of CXOU J140927.9+261813, a $z=0.68$ X-ray cluster.
Archival X-ray photometry spanning nearly four decades reveals that PG1407+265
is variable at the 1 dex level on a timescale of years. V-band variability is
present with an amplitude less than 0.1 mag. The emission-line properties of
PG1407+265 also reveal clear evidence for a powerful inflow or outflow due to
near- or super-Eddington accretion, having a mechanical luminosity of order
$10^{48}$ erg s$^{-1}$. Our follow-up {\sl Chandra} exposure centered on this
object reveal a foreground $z=0.68$ cluster roughly 1' x 1'.5 in extent, offset
to the east of PG1407+265, roughly coincident with the $z=0.68$ radio galaxy
FIRST J140927.8+261818. This non-cool-core cluster contributes about 10\% of
the X-ray flux of PG1407+265, has a mass of $(0.6- 5.5)\times10^{14} M_\odot$,
and an X-ray gas temperature of ($2.2-4.3$) keV. Because the projected position
of the quasar lies at about twice that of the cluster's inferred Einstein
radius, lensing by the cluster is unlikely to explain the quasar's unusual
properties. We also discuss the evidence for a second cluster centered on and
at the redshift of the quasar.


Abstract:
  Determining habitable zones in binary star systems can be a challenging task
due to the combination of perturbed planetary orbits and varying stellar
irradiation conditions. The concept of "dynamically informed habitable zones"
allows us, nevertheless, to make predictions on where to look for habitable
worlds in such complex environments. Dynamically informed habitable zones have
been used in the past to investigate the habitability of circumstellar planets
in binary systems and Earth-like analogs in systems with giant planets. Here,
we extend the concept to potentially habitable worlds on circumbinary orbits.
We show that habitable zone borders can be found analytically even when another
giant planet is present in the system. By applying this methodology to
Kepler-16, Kepler-34, Kepler-35, Kepler-38, Kepler-64, Kepler-413, Kepler-453,
Kepler-1647 and Kepler-1661 we demonstrate that the presence of the known giant
planets in the majority of those systems does not preclude the existence of
potentially habitable worlds. Among the investigated systems Kepler-35,
Kepler-38 and Kepler-64 currently seem to offer the most benign environment. In
contrast, Kepler-16 and Kepler-1647 are unlikely to host habitable worlds.


Abstract:
  Ferroelectric materials are spontaneous symmetry breaking systems
characterized by ordered electric polarizations. Similar to its ferromagnetic
counterpart, a ferroelectric domain wall can be regarded as a soft interface
separating two different ferroelectric domains. Here we show that two bound
state excitations of electric polarization (polar wave), or the vibration and
breathing modes, can be hosted and propagate within the ferroelectric domain
wall. Specially, the vibration polar wave has zero frequency gap, thus is
constricted deeply inside ferroelectric domain wall, and can propagate even in
the presence of local pinnings. The ferroelectric domain wall waveguide as
demonstrated here, offers new paradigm in developing ferroelectric information
processing units.


Abstract:
  We present a tight RMR complexity lower bound for the recoverable mutual
exclusion (RME) problem, defined by Golab and Ramaraju \cite{GR2019a}. In
particular, we show that any $n$-process RME algorithm using only atomic read,
write, fetch-and-store, fetch-and-increment, and compare-and-swap operations,
has an RMR complexity of $\Omega(\log n/\log\log n)$ on the CC and DSM model.
This lower bound covers all realistic synchronization primitives that have been
used in RME algorithms and matches the best upper bounds of algorithms
employing swap objects (e.g., [5,6,10]).
  Algorithms with better RMR complexity than that have only been obtained by
either (i) assuming that all failures are system-wide [7], (ii) employing
fetch-and-add objects of size $(\log n)^{\omega(1)}$ [12], or (iii) using
artificially defined synchronization primitives that are not available in
actual systems [6,9].


Abstract:
  By using a sharp isoperimetric inequality and an anisotropic symmetrization
argument, we establish Morrey-Sobolev and Hardy-Sobolev inequalities on
$n$-dimensional Finsler manifolds having nonnegative $n$-Ricci curvature; in
some cases we also discuss the sharpness of these functional inequalities. As
applications, by using variational arguments, we guarantee the
existence/multiplicity of solutions for certain eigenvalue problems and
elliptic PDEs involving the Finsler-Laplace operator. Our results are also new
in the Riemannian setting.


Abstract:
  The topological Hall effect is used extensively to study chiral spin textures
in various materials. However, the factors controlling its magnitude in
technologically-relevant thin films remain uncertain. Using variable
temperature magnetotransport and real-space magnetic imaging in a series of
Ir/Fe/Co/Pt heterostructures, here we report that the chiral spin fluctuations
at the phase boundary between isolated skyrmions and a disordered skyrmion
lattice result in a power-law enhancement of the topological Hall resistivity
by up to three orders of magnitude. Our work reveals the dominant role of
skyrmion stability and configuration in determining the magnitude of the
topological Hall effect.


Abstract:
  We exhibit a Finsler metric on the 2-sphere whose systolic (Holmes-Thompson)
ratio is $\frac{4{\pi}}{3}$. This is bigger than the conjectured maximal
Riemannian systolic ratio of $2\sqrt{3}$ achieved by the Calabi-Croke metric.
The construction of the Finsler metric is heavily inspired by a paper of
Cossarini-Sabourau.


Abstract:
  Let $d \ge 1$. We study a subspace of the space of automorphic forms of
$\mathrm{GL}_d$ over a global field of positive characteristic (or, a function
field of a curve over a finite field). We fix a place $\infty$ of $F$, and we
consider the subspace $\mathcal{A}_{\mathrm{St}}$ consisting of automorphic
forms such that the local component at $\infty$ of the associated automorphic
representation is the Steinberg representation (to be made precise in the
text).
  We have two results.
  One theorem (Theorem 16) describes the constituents of
$\mathcal{A}_{\mathrm{St}}$ as automorphic representation and gives a
multiplicity one type statement.
  For the other theorem (Theorem 12), we construct, using the geometry of the
Bruhat-Tits building, an analogue of modular symbols in
$\mathcal{A}_{\mathrm{St}}$ integrally (that is, in the space of
$\mathbb{Z}$-valued automorphic forms). We show that the quotient is finite and
give a bound on the exponent of this quotient.


Abstract:
  Coronavirus disease 2019 (COVID-19) has caused global disruption and a
significant loss of life. Existing treatments that can be repurposed as
prophylactic and therapeutic agents could reduce the pandemic's devastation.
Emerging evidence of potential applications in other therapeutic contexts has
led to the investigation of dietary supplements and nutraceuticals for
COVID-19. Such products include vitamin C, vitamin D, omega 3 polyunsaturated
fatty acids, probiotics, and zinc, all of which are currently under clinical
investigation. In this review, we critically appraise the evidence surrounding
dietary supplements and nutraceuticals for the prophylaxis and treatment of
COVID-19. Overall, further study is required before evidence-based
recommendations can be formulated, but nutritional status plays a significant
role in patient outcomes, and these products could help alleviate deficiencies.
For example, evidence indicates that vitamin D deficiency may be associated
with greater incidence of infection and severity of COVID-19, suggesting that
vitamin D supplementation may hold prophylactic or therapeutic value. A growing
number of scientific organizations are now considering recommending vitamin D
supplementation to those at high risk of COVID-19. Because research in vitamin
D and other nutraceuticals and supplements is preliminary, here we evaluate the
extent to which these nutraceutical and dietary supplements hold potential in
the COVID-19 crisis.


Abstract:
  We show that the baryon asymmetry of the universe can be explained in models
where the Higgs couples to the Chern-Simons term of the hypercharge group and
is away from the late-time minimum of its potential during inflation. The Higgs
then relaxes toward this minimum once inflation ends which leads to the
production of (hyper)magnetic helicity. We discuss the conditions under which
this helicity can be approximately conserved during its joint evolution with
the thermal plasma. At the electroweak phase transition the helicity is then
converted into a baryon asymmetry by virtue of the chiral anomaly in the
standard model. We propose a simple model which realizes this mechanism and
show that the observed baryon asymmetry of the universe can be reproduced.


Abstract:
  We present a novel expression for an integrated correlation function of four
superconformal primaries in $SU(N)$ $\mathcal{N}=4$ SYM. This integrated
correlator, which is based on supersymmetric localisation, has been the subject
of several recent developments. The correlator is re-expressed as a sum over a
two dimensional lattice that is valid for all $N$ and all values of the complex
Yang-Mills coupling $\tau$. In this form it is manifestly invariant under
$SL(2,\mathbb{Z})$ Montonen-Olive duality. Furthermore, it satisfies a
remarkable Laplace-difference equation that relates the $SU(N)$ to the
$SU(N+1)$ and $SU(N-1)$ correlators. For any fixed value of $N$ the correlator
is an infinite series of non-holomorphic Eisenstein series,
$E(s;\tau,\bar\tau)$ with $s\in \mathbb{Z}$, and rational coefficients. The
perturbative expansion of the integrated correlator is asymptotic and the
$n$-loop coefficient is a rational multiple of $\zeta(2n+1)$. The $n=1$ and
$n=2$ terms agree precisely with results determined directly by integrating the
expressions in one- and two-loop perturbative SYM. Likewise, the charge-$k$
instanton contributions have an asymptotic, but Borel summable, series of
perturbative corrections. The large-$N$ expansion of the correlator with fixed
$\tau$ is a series in powers of $N^{1/2-\ell}$ ($\ell\in \mathbb{Z}$) with
coefficients that are rational sums of $E_s$ with $s\in \mathbb{Z}+1/2$. This
gives an all orders derivation of the form of the recently conjectured
expansion. We further consider 't Hooft large-$N$ Yang-Mills theory. The
coefficient of each order can be expanded as a convergent series in $\lambda$.
For large $\lambda$ this becomes an asymptotic series with coefficients that
are again rational multiples of odd zeta values. The large-$\lambda$ series is
not Borel summable, and its resurgent non-perturbative completion is
$O(\exp(-2\sqrt{\lambda}))$.


Abstract:
  In some conditions, bacteria self-organise into biofilms, supracellular
structures made of a self-produced embedding matrix, mainly composed on
polysaccharides, DNA, proteins and lipids. It is known that bacteria change
their colony/matrix ratio in the presence of external stimuli such as
hydrodynamic stress. However, little is still known about the molecular
mechanisms driving this self-adaptation. In this work, we monitor structural
features of Pseudomonas fluorescens biofilms grown with and without
hydrodynamic stress. Our measurements show that the hydrodynamic stress
concomitantly increases the cell density population and the matrix production.
At short growth timescales, the matrix mediates a weak cell-cell attractive
interaction due to the depletion forces originated by the polymer constituents.
Using a population dynamics model, we conclude that hydrodynamic stress causes
a faster diffusion of nutrients and a higher incorporation of planktonic
bacteria to the already formed microcolonies. This results in the formation of
more mechanically stable biofilms due to an increase of the number of
crosslinks, as shown by computer simulations. The mechanical stability also
lies on a change in the chemical compositions of the matrix, which becomes
enriched in carbohydrates, known to display adhering properties. Overall, we
demonstrate that bacteria are capable of self-adapting to hostile hydrodynamic
stress by tailoring the biofilm chemical composition, thus affecting both the
mesoscale structure of the matrix and its viscoelastic properties that
ultimately regulate the bacteria-polymer interactions.


Abstract:
  Table Structure Recognition is an essential part of end-to-end tabular data
extraction in document images. The recent success of deep learning model
architectures in computer vision remains to be non-reflective in table
structure recognition, largely because extensive datasets for this domain are
still unavailable while labeling new data is expensive and time-consuming.
Traditionally, in computer vision, these challenges are addressed by standard
augmentation techniques that are based on image transformations like color
jittering and random cropping. As demonstrated by our experiments, these
techniques are not effective for the task of table structure recognition. In
this paper, we propose TabAug, a re-imagined Data Augmentation technique that
produces structural changes in table images through replication and deletion of
rows and columns. It also consists of a data-driven probabilistic model that
allows control over the augmentation process. To demonstrate the efficacy of
our approach, we perform experimentation on ICDAR 2013 dataset where our
approach shows consistent improvements in all aspects of the evaluation
metrics, with cell-level correct detections improving from 92.16% to 96.11%
over the baseline.


Abstract:
  Recommender systems have achieved great success in modeling user's
preferences on items and predicting the next item the user would consume.
Recently, there have been many efforts to utilize time information of users'
interactions with items to capture inherent temporal patterns of user behaviors
and offer timely recommendations at a given time. Existing studies regard the
time information as a single type of feature and focus on how to associate it
with user preferences on items. However, we argue they are insufficient for
fully learning the time information because the temporal patterns of user
preference are usually heterogeneous. A user's preference for a particular item
may 1) increase periodically or 2) evolve over time under the influence of
significant recent events, and each of these two kinds of temporal pattern
appears with some unique characteristics. In this paper, we first define the
unique characteristics of the two kinds of temporal pattern of user preference
that should be considered in time-aware recommender systems. Then we propose a
novel recommender system for timely recommendations, called TimelyRec, which
jointly learns the heterogeneous temporal patterns of user preference
considering all of the defined characteristics. In TimelyRec, a cascade of two
encoders captures the temporal patterns of user preference using a proposed
attention module for each encoder. Moreover, we introduce an evaluation
scenario that evaluates the performance on predicting an interesting item and
when to recommend the item simultaneously in top-K recommendation (i.e.,
item-timing recommendation). Our extensive experiments on a scenario for item
recommendation and the proposed scenario for item-timing recommendation on
real-world datasets demonstrate the superiority of TimelyRec and the proposed
attention modules.


Abstract:
  It was shown recently that the f-diagonal tensor in the T-SVD factorization
must satisfy some special properties. Such f-diagonal tensors are called
s-diagonal tensors. In this paper, we show that such a discussion can be
extended to any real invertible linear transformation. We show that two
Eckart-Young like theorems hold for a third order real tensor, under any doubly
real-preserving unitary transformation. The normalized Discrete Fourier
Transformation (DFT) matrix, an arbitrary orthogonal matrix, the product of the
normalized DFT matrix and an arbitrary orthogonal matrix are examples of doubly
real-preserving unitary transformations. We use tubal matrices as a tool for
our study. We feel that the tubal matrix language makes this approach more
natural.


Abstract:
  Observational astronomers survey the sky in great detail to gain a better
understanding of many types of astronomical phenomena. In particular, the
formation and evolution of galaxies, including our own, is a wide field of
research. Three dimensional (spatial 3D) scientific visualisation is typically
limited to simulated galaxies, due to the inherently two dimensional spatial
resolution of Earth-based observations. However, with appropriate means of
reconstruction, such visualisation can also be used to bring out the inherent
3D structure that exists in 2D observations of known galaxies, providing new
views of these galaxies and visually illustrating the spatial relationships
within galaxy groups that are not obvious in 2D. We present a novel approach to
reconstruct and visualise 3D representations of nearby galaxies based on
observational data using the scientific visualisation software Splotch. We
apply our approach to a case study of the nearby barred spiral galaxy known as
M83, presenting a new perspective of the M83 local group and highlighting the
similarities between our reconstructed views of M83 and other known galaxies of
similar inclinations.


Abstract:
  In the univariate setting, using the kernel spectral representation is an
appealing approach for generating stationary covariance functions. However,
performing the same task for multiple-output Gaussian processes is
substantially more challenging. We demonstrate that current approaches to
modelling cross-covariances with a spectral mixture kernel possess a critical
blind spot. For a given pair of processes, the cross-covariance is not
reproducible across the full range of permitted correlations, aside from the
special case where their spectral densities are of identical shape. We present
a solution to this issue by replacing the conventional Gaussian components of a
spectral mixture with block components of finite bandwidth (i.e. rectangular
step functions). The proposed family of kernel represents the first
multi-output generalisation of the spectral mixture kernel that can approximate
any stationary multi-output kernel to arbitrary precision.


Abstract:
  Post-hoc explanation methods are an important class of approaches that help
understand the rationale underlying a trained model's decision. But how useful
are they for an end-user towards accomplishing a given task? In this vision
paper, we argue the need for a benchmark to facilitate evaluations of the
utility of post-hoc explanation methods. As a first step to this end, we
enumerate desirable properties that such a benchmark should possess for the
task of debugging text classifiers. Additionally, we highlight that such a
benchmark facilitates not only assessing the effectiveness of explanations but
also their efficiency.


Abstract:
  Underwater image restoration is of significant importance in unveiling the
underwater world. Numerous techniques and algorithms have been developed in the
past decades. However, due to fundamental difficulties associated with
imaging/sensing, lighting, and refractive geometric distortions, in capturing
clear underwater images, no comprehensive evaluations have been conducted of
underwater image restoration. To address this gap, we have constructed a
large-scale real underwater image dataset, dubbed `HICRD' (Heron Island Coral
Reef Dataset), for the purpose of benchmarking existing methods and supporting
the development of new deep-learning based methods. We employ accurate water
parameter (diffuse attenuation coefficient) in generating reference images.
There are 2000 reference restored images and 6003 original underwater images in
the unpaired training set. Further, we present a novel method for underwater
image restoration based on unsupervised image-to-image translation framework.
Our proposed method leveraged contrastive learning and generative adversarial
networks to maximize the mutual information between raw and restored images.
Extensive experiments with comparisons to recent approaches further demonstrate
the superiority of our proposed method. Our code and dataset are publicly
available at GitHub.


Abstract:
  A classical observation of Deligne shows that, for any prime $p \geq 5$, the
divisor polynomial of the Eisenstein series $E_{p-1}(z)$ mod $p$ is closely
related to the supersingular polynomial at $p$, $$S_p(x) :=
\prod_{E/\overline{\mathbb{F}}_p \text{ supersingular}}(x-j(E)) \in
\mathbb{F}_p[x].$$ Deuring, Hasse, and Kaneko and Zagier found other families
of modular forms which also give the supersingular polynomial at $p$. In a new
approach, we prove an analogue of Deligne's result for the Hecke trace forms
$T_k(z)$ defined by the Hecke action on the space of cusp forms $S_k$. We use
the Eichler-Selberg trace formula to identify congruences between trace forms
of different weights mod $p$, and then relate their divisor polynomials to
$S_p(x)$ using Deligne's observation.


Abstract:
  One of the most important barriers toward a widespread use of mobile robots
in unstructured and human populated work environments is the ability to plan a
safe path. In this paper, we propose to delegate this activity to a human
operator that walks in front of the robot marking with her/his footsteps the
path to be followed. The implementation of this approach requires a high degree
of robustness in locating the specific person to be followed (the leader). We
propose a three phase approach to fulfil this goal: 1. identification and
tracking of the person in the image space, 2. sensor fusion between camera data
and laser sensors, 3. point interpolation with continuous curvature curves. The
approach is described in the paper and extensively validated with experimental
results.