title:
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable
  Communications

abstract:
  Federated learning (FL) enables edge devices to collaboratively learn a model
in a distributed fashion. Many existing researches have focused on improving
communication efficiency of high-dimensional models and addressing bias caused
by local updates. However, most of FL algorithms are either based on reliable
communications or assume fixed and known unreliability characteristics. In
practice, networks could suffer from dynamic channel conditions and
non-deterministic disruptions, with time-varying and unknown characteristics.
To this end, in this paper we propose a sparsity enabled FL framework with both
communication efficiency and bias reduction, termed as SAFARI. It makes novel
use of a similarity among client models to rectify and compensate for bias that
is resulted from unreliable communications. More precisely, sparse learning is
implemented on local clients to mitigate communication overhead, while to cope
with unreliable communications, a similarity-based compensation method is
proposed to provide surrogates for missing model updates. We analyze SAFARI
under bounded dissimilarity and with respect to sparse models. It is
demonstrated that SAFARI under unreliable communications is guaranteed to
converge at the same rate as the standard FedAvg with perfect communications.
Implementations and evaluations on CIFAR-10 dataset validate the effectiveness
of SAFARI by showing that it can achieve the same convergence speed and
accuracy as FedAvg with perfect communications, with up to 80% of the model
weights being pruned and a high percentage of client updates missing in each
round.


title:
Complexity Bounds of Iterative Linear Quadratic Optimization Algorithms
  for Discrete Time Nonlinear Control

abstract:
  A classical approach for solving discrete time nonlinear control on a finite
horizon consists in repeatedly minimizing linear quadratic approximations of
the original problem around current candidate solutions. While widely popular
in many domains, such an approach has mainly been analyzed locally. We observe
that global convergence guarantees can be ensured provided that the linearized
discrete time dynamics are surjective and costs on the state variables are
strongly convex. We present how the surjectivity of the linearized dynamics can
be ensured by appropriate discretization schemes given the existence of a
feedback linearization scheme. We present complexity bounds of algorithms based
on linear quadratic approximations through the lens of generalized Gauss-Newton
methods. Our analysis uncovers several convergence phases for regularized
generalized Gauss-Newton algorithms.


title:
Nearly minimax robust estimator of the mean vector by iterative spectral
  dimension reduction

abstract:
  We study the problem of robust estimation of the mean vector of a
sub-Gaussian distribution. We introduce an estimator based on spectral
dimension reduction (SDR) and establish a finite sample upper bound on its
error that is minimax-optimal up to a logarithmic factor. Furthermore, we prove
that the breakdown point of the SDR estimator is equal to $1/2$, the highest
possible value of the breakdown point. In addition, the SDR estimator is
equivariant by similarity transforms and has low computational complexity. More
precisely, in the case of $n$ vectors of dimension $p$ -- at most $\varepsilon
n$ out of which are adversarially corrupted -- the SDR estimator has a squared
error of order $\big(\frac{r_\Sigma}{n} +
\varepsilon^2\log(1/\varepsilon)\big){\log p}$ and a running time of order $p^3
+ n p^2$. Here, $r_\Sigma\le p$ is the effective rank of the covariance matrix
of the reference distribution. Another advantage of the SDR estimator is that
it does not require knowledge of the contamination rate and does not involve
sample splitting. We also investigate extensions of the proposed algorithm and
of the obtained results in the case of (partially) unknown covariance matrix.


title:
Prevalence of trivial zero-energy sub-gap states in non-uniform helical
  spin chains on the surface of superconductors

abstract:
  Helical spin chains, consisting of magnetic (ad-)atoms, on the surface of
bulk superconductors are predicted to host Majorana bound states (MBSs) at the
ends of the chain. Here, we investigate the prevalence of trivial zero-energy
bound states in these helical spin chain systems. First, we show that the
Hamiltonian of a helical spin chain on a superconductor can be mapped to an
effective Hamiltonian reminiscent of a semiconductor nanowire with strong
Rashba spin-orbit coupling. In particular, we show that a varying rotation rate
between neighbouring magnetic moments maps to smooth non-uniform potentials in
the effective nanowire Hamiltonian. Previously it has been found that trivial
zero-energy states are abundant in nanowire systems with smooth potentials.
Therefore, we perform an extensive search for zero-energy bound states in
helical spin chain systems with varying rotation rates. Although bound states
with near zero-energy do exist for certain dimensionalities and rotation
profiles, we find that zero-energy bound states are far less prevalent than in
semiconductor nanowire systems with equivalent non-uniformities. In particular,
utilising varying rotation rates, we do not find zero-energy bound states in
the most experimentally relevant setup consisting of a one-dimensional helical
spin chain on the surface of a three-dimensional superconductor, even for
profiles that produce near zero-energy states in equivalent one- and two-
dimensional systems. Although our findings do not rule them out, the much
reduced prevalence of zero-energy bound states in long non-uniform helical spin
chains compared with equivalent semi-conductor nanowires, as well as the
ability to measure states locally via STM, should reduce the experimental
barrier to identifying MBSs in such systems.


title:
A lightweight and accurate YOLO-like network for small target detection
  in Aerial Imagery

abstract:
  Despite the breakthrough deep learning performances achieved for automatic
object detection, small target detection is still a challenging problem,
especially when looking at fast and accurate solutions suitable for mobile or
edge applications. In this work we present YOLO-S, a simple, fast and efficient
network for small target detection. The architecture exploits a small feature
extractor based on Darknet20, as well as skip connection, via both bypass and
concatenation, and reshape-passthrough layer to alleviate the vanishing
gradient problem, promote feature reuse across network and combine low-level
positional information with more meaningful high-level information. To verify
the performances of YOLO-S, we build "AIRES", a novel dataset for cAr detectIon
fRom hElicopter imageS acquired in Europe, and set up experiments on both AIRES
and VEDAI datasets, benchmarking this architecture with four baseline
detectors. Furthermore, in order to handle efficiently the issue of data
insufficiency and domain gap when dealing with a transfer learning strategy, we
introduce a transitional learning task over a combined dataset based on DOTAv2
and VEDAI and demonstrate that can enhance the overall accuracy with respect to
more general features transferred from COCO data. YOLO-S is from 25% to 50%
faster than YOLOv3 and only 15-25% slower than Tiny-YOLOv3, outperforming also
YOLOv3 in terms of accuracy in a wide range of experiments. Further simulations
performed on SARD dataset demonstrate also its applicability to different
scenarios such as for search and rescue operations. Besides, YOLO-S has an 87%
decrease of parameter size and almost one half FLOPs of YOLOv3, making
practical the deployment for low-power industrial applications.


title: Root finding techniques that work

abstract:
  In most introductory numerical analysis textbooks, the treatment of a single
nonlinear equation often consists of a collection of all-purpose methods that
frequently do not work or are inefficient. These textbooks neglect to teach the
importance of adapting a method to the given problem, and consequently also
neglect to provide the tools to accomplish this.
  Several general techniques are described here to incorporate the specific
structure or properties of a nonlinear equation into a method for solving it.
This can mean the construction of a method specifically tailored to the
equation, or the transformation of the equation into an equivalent one for
which an existing method is well-suited. The techniques are illustrated with
the help of several case studies taken from the literature.


title:
Low-temperature thermal expansion of the topological material candidates
  $\beta$-PtBi$_2$ and $\beta$-Bi$_2$Pd

abstract:
  We report on the low-temperature ($T <$ 120 K) thermal expansion of the
bismuth-based topological semimetal $\beta$-PtBi$_2$ and topological
superconductor $\beta$-Bi$_2$Pd candidates. The linear thermal-expansion
coefficient of tetragonal $\beta$-Bi$_2$Pd shows a pronounced anisotropy
between the $a$- and $c$-axis while the volume thermal-expansion coefficient
$\alpha_V$($\beta$-Bi$_2$Pd) is considerable larger than
$\alpha_V$($\beta$-PtBi$_2$). The coefficient $\alpha_V$($\beta$-PtBi$_2$)
nearly matches the experimental specific heat, from which a Debye temperature
$\theta_D =$ 199 K is obtained. On the other hand, $\alpha_V$($\beta$-Bi$_2$Pd)
reasonably fits the Debye model with $\theta_D =$ 138 K, extracted from the
low-temperature specific heat. An almost constant Gr\"uneisen parameter $\Gamma
\approx$ 2 is obtained for both compounds. No magnetostriction is observed in
any of both compounds up to $\mu_0 H =$ 16 T. We compare our results with other
Bi-based topological materials.


title: A gyroscopic polynomial basis in the sphere

abstract:
  Standard spectral codes for full sphere dynamics utilize a combination of
spherical harmonics and a suitableradial basis to represent fluid variables.
These basis functions have a rotational invariance not present ingeophysical
flows. Gyroscopic alignment - alignment of dynamics along the axis of rotation
- is ahallmark of geophysical fluids in the rapidly rotating regime. The
Taylor-Proudman theorem, resultingfrom a dominant balance of the Coriolis force
and the pressure gradient force, yields nearly invariant flows along this axial
direction.In this paper we tailor a coordinate system to the cylindrical
structures found in rotating spherical flows.This "spherindrical" coordinate
system yields a natural hierarchy of basis functions, composed of Jacobi
polynomialsin the radial and vertical direction, regular throughout the ball.We
expand fluid variables using this basis and utilize sparse Jacobi polynomial
algebra to implement all operatorsrelevant for partial differential equations
in the spherical setting. We demonstrate the representation power ofthe basis
in three eigenvalue problems for rotating fluids.


title: Can language models learn from explanations in context?

abstract:
  Large language models can perform new tasks by adapting to a few in-context
examples. For humans, rapid learning from examples can benefit from
explanations that connect examples to task principles. We therefore investigate
whether explanations of few-shot examples can allow language models to adapt
more effectively. We annotate a set of 40 challenging tasks from BIG-Bench with
explanations of answers to a small subset of questions, as well as a variety of
matched control explanations. We evaluate the effects of various zero-shot and
few-shot prompts that include different types of explanations, instructions,
and controls on the performance of a range of large language models. We analyze
these results using statistical multilevel modeling techniques that account for
the nested dependencies among conditions, tasks, prompts, and models. We find
that explanations of examples can improve performance. Adding untuned
explanations to a few-shot prompt offers a modest improvement in performance;
about 1/3 the effect size of adding few-shot examples, but twice the effect
size of task instructions. We then show that explanations tuned for performance
on a small validation set offer substantially larger benefits; building a
prompt by selecting examples and explanations together substantially improves
performance over selecting examples alone. Hand-tuning explanations can
substantially improve performance on challenging tasks. Furthermore, even
untuned explanations outperform carefully matched controls, suggesting that the
benefits are due to the link between an example and its explanation, rather
than lower-level features of the language used. However, only large models can
benefit from explanations. In summary, explanations can support the in-context
learning abilities of large language models on


title:
Fast syndrome-based Chase decoding of binary BCH codes through Wu list
  decoding

abstract:
  We present a new fast Chase decoding algorithm for binary BCH codes. The new
algorithm reduces the complexity in comparison to a recent fast Chase decoding
algorithm for Reed--Solomon (RS) codes by the authors (IEEE Trans. IT, 2022),
by requiring only a single Koetter iteration per edge of the decoding tree. In
comparison to the fast Chase algorithms presented by Kamiya (IEEE Trans. IT,
2001) and Wu (IEEE Trans. IT, 2012) for binary BCH codes, the polynomials
updated throughout the algorithm of the current paper typically have a much
lower degree.
  To achieve the complexity reduction, we build on a new isomorphism between
two solution modules in the binary case, and on a degenerate case of the
soft-decision (SD) version of the Wu list decoding algorithm. Roughly speaking,
we prove that when the maximum list size is $1$ in Wu list decoding of binary
BCH codes, assigning a multiplicity of $1$ to a coordinate has the same effect
as flipping this coordinate in a Chase-decoding trial.
  The solution-module isomorphism also provides a systematic way to benefit
from the binary alphabet for reducing the complexity in bounded-distance
hard-decision (HD) decoding. Along the way, we briefly develop the
Groebner-bases formulation of the Wu list decoding algorithm for binary BCH
codes, which is missing in the literature.


title: Study of the rare decay $J/\psi \to 2\gamma+hadrons$ at the BESIII

abstract:
  Two-photon radiative decay process $J/\psi \to 2\gamma+hadrons$ is studied
and the branch ratio of the leading contribution $J/\psi \to 2\gamma + g g g$
is calculated, which is $(1.39 \pm 0.02)\times10^{-6}.$ With the specific
condition at the BESIII, this rare decay process and the main background
process $e^{+} e^{-} \to \gamma \gamma q \bar{q}$ are investigated. The results
show that the ratio of signal to background can reach 27% with the optimized
selection criteria at the BESIII. In addition, a few distribution of the signal
and background are presented, and the results show that the signal is large
enough to be measured in experiment.


title:
Coalescence of geodesics and the BKS midpoint problem in planar
  first-passage percolation

abstract:
  We consider first-passage percolation on $\mathbb Z^2$ with independent and
identically distributed weights whose common distribution is absolutely
continuous with a finite exponential moment. Under the assumption that the
limit shape has more than 32 extreme points, we prove that geodesics with
nearby starting and ending points have significant overlap, coalescing on all
but small portions near their endpoints. The statement is quantified, with
power-law dependence of the involved quantities on the length of the geodesics.
  The result leads to a quantitative resolution of the
Benjamini--Kalai--Schramm midpoint problem. It is shown that the probability
that the geodesic between two given points passes through a given edge is
smaller than a power of the distance between the points and the edge.
  We further prove that the limit shape assumption is satisfied for a specific
family of distributions.


title: A CR invariant sphere theorem

abstract:
  We prove that every closed, universally embeddable CR three-manifold with
nonnegative Yamabe constant and positive total $Q^\prime$-curvature is contact
diffeomorphic to a quotient of the standard contact three-sphere. We also prove
that every closed, embeddable CR three-manifold with zero Yamabe constant and
nonnegative total $Q^\prime$-curvature is CR equivalent to a compact quotient
of the Heisenberg group with its flat CR structure.


title: Impact of Data Quality on Renewable Energy Potential Estimations

abstract:
  Potential analyses identify possible locations for renewable energy
installations, such as as wind turbines and photovoltaic arrays. The results of
previous potential studies, however, are not consistent due to different
assumptions, methods, and datasets. In this study, we compare commonly used
land use data sources with regard to area and position. Using Corine Land Cover
leads to an overestimation of the potential areas in a typical wind potential
analysis by a factor of 4.6 and 5.2 in comparison to Basis-DLM and Open Street
Map, respectively. Furthermore, we develop scenarios for onshore wind, offshore
wind, and open-field photovoltaic potential estimations based on land
eligibility analyses and calculate rooftop photovoltaic potential using 3D
building data. The potential capacities and possible locations are published
for all administrative levels in Germany in the freely accessible database
trep-db, for example, to be incorporated into energy system models. The
investigations are validated using high-resolution regional potential analyses
and benchmarked against other studies in the literature. Findings from the
literature, which can be used by legislators to design regulation, are rarely
comparable and consistent due to differences in the datasets used.


title:
All-Pairs Shortest Path Distances with Differential Privacy: Improved
  Algorithms for Bounded and Unbounded Weights

abstract:
  We revisit the problem of privately releasing the all-pairs shortest path
distances of a weighted undirected graph up to low additive error, which was
first studied by Sealfon [Sea16]. In this paper, we improve significantly on
Sealfon's results, both for arbitrary weighted graphs and for bounded-weight
graphs on $n$ nodes. Specifically, we provide an approximate-DP algorithm that
outputs all-pairs shortest path distances up to maximum additive error
$\tilde{O}(\sqrt{n})$, and a pure-DP algorithm that outputs all pairs shortest
path distances up to maximum additive error $\tilde{O}(n^{2/3})$ (where we
ignore dependencies on $\varepsilon, \delta$). This improves over the previous
best result of $\tilde{O}(n)$ additive error for both approximate-DP and
pure-DP [Sea16], and partially resolves an open question posed by Sealfon
[Sea16, Sea20]. We also show that if the graph is promised to have reasonably
bounded weights, one can improve the error further to roughly
$n^{\sqrt{2}-1+o(1)}$ in the approximate-DP setting and roughly
$n^{(\sqrt{17}-3)/2 + o(1)}$ in the pure-DP setting. Previously, it was only
known how to obtain $\tilde{O}(n^{1/2})$ additive error in the approximate-DP
setting and $\tilde{O}(n^{2/3})$ additive error in the pure-DP setting for
bounded-weight graphs [Sea16].


title:
Kinship Is a Network Tracking Social Technology, Not an Evolutionary
  Phenomenon

abstract:
  On one hand, kinship is a universal human phenomenon that tends to align with
biological relatedness, which might suggest evolutionary foundations. On the
other hand, kinship has exceptional variation across the human populations,
which points to cultural foundations. Furthermore, even if its foundation was
biological, kinship is often too imprecise to track genetic relatedness
efficiently, while inclusive fitness theory would suggest focusing only on the
closest relatives, which is not the case in most human cultures. It was the
parallel validity of these contradicting arguments that led to decades of
fierce debate about the definition and measurement of the phenomenon. This
paper offers a new approach to kinship. First, the model demonstrates that it
is possible to generate kinship networks (a) derived from the kind of basic kin
connections that our species shares with other apes, but (b) driven by network
rather than biological logic beyond the immediate family. Second the model
demonstrates that kinship as a network heuristic works efficiently only in high
fertility societies, and gives way to similarity-based friendship with
demographic transition. The results explain (i) why kinship labelling is unique
to our species, (ii) why kinship is universal among human cultures, (iii) why
kinship terminology systems are varied across cultures, (iv) why linguistic kin
assignment is imprecise, and (v) why kinship is replaced by homophily when
relatives are scarce. The model offers a unifying framework to the debate
between social and evolutionary anthropology concerning the concept of human
kinship.


title: Multi-Scale Representation Learning on Proteins

abstract:
  Proteins are fundamental biological entities mediating key roles in cellular
function and disease. This paper introduces a multi-scale graph construction of
a protein -- HoloProt -- connecting surface to structure and sequence. The
surface captures coarser details of the protein, while sequence as primary
component and structure -- comprising secondary and tertiary components --
capture finer details. Our graph encoder then learns a multi-scale
representation by allowing each level to integrate the encoding from level(s)
below with the graph at that level. We test the learned representation on
different tasks, (i.) ligand binding affinity (regression), and (ii.) protein
function prediction (classification). On the regression task, contrary to
previous methods, our model performs consistently and reliably across different
dataset splits, outperforming all baselines on most splits. On the
classification task, it achieves a performance close to the top-performing
model while using 10x fewer parameters. To improve the memory efficiency of our
construction, we segment the multiplex protein surface manifold into molecular
superpixels and substitute the surface with these superpixels at little to no
performance loss.


title:
MGDCF: Distance Learning via Markov Graph Diffusion for Neural
  Collaborative Filtering

abstract:
  Collaborative filtering (CF) is widely used by personalized recommendation
systems, which aims to predict the preference of users with historical
user-item interactions. In recent years, Graph Neural Networks (GNNs) have been
utilized to build CF models and have shown promising performance. Recent
state-of-the-art GNN-based CF approaches simply attribute their performance
improvement to the high-order neighbor aggregation ability of GNNs. However, we
observe that some powerful deep GNNs such as JKNet and DropEdge, can
effectively exploit high-order neighbor information on other graph tasks but
perform poorly on CF tasks, which conflicts with the explanation of these
GNN-based CF research. Different from these research, we investigate the
GNN-based CF from the perspective of Markov processes for distance learning
with a unified framework named Markov Graph Diffusion Collaborative Filtering
(MGDCF). We design a Markov Graph Diffusion Network (MGDN) as MGDCF's GNN
encoder, which learns vertex representations by trading off two types of
distances via a Markov process. We show the theoretical equivalence between
MGDN's output and the optimal solution of a distance loss function, which can
boost the optimization of CF models. MGDN can generalize state-of-the-art
models such as LightGCN and APPNP, which are heterogeneous GNNs. In addition,
MGDN can be extended to homogeneous GNNs with our sparsification technique. For
optimizing MGDCF, we propose the InfoBPR loss function, which extends the
widely used BPR loss to exploit multiple negative samples for better
performance. We conduct experiments to perform detailed analysis on MGDCF. The
source code is publicly available at https://github.com/hujunxianligong/MGDCF.


title: Simple parametrisation for coupled dark energy

abstract:
  We propose a phenomenological generalisation of the standard model with only
one extra degree of freedom that parametrises the evolution of a scalar field
responsible for the cosmic acceleration. The model also foresees an additional
parameter in the form of a coupling between dark energy and dark matter. This
model captures a large diversity of dark energy evolutions at low redshift and
could usefully complement common CPL parametrisations widely used. In this
context, we have been constraining the parametrisation with data from Planck
and KiDS, bringing different results between the early and late universe
observations.


title:
Efficient adiabatic connection approach for strongly correlated systems.
  Application to singlet-triplet gaps of biradicals

abstract:
  Strong correlation can be essentially captured with multireference
wavefunction methods such as complete active space self-consistent field
(CASSCF) or density matrix renormalization group (DMRG). Still, an accurate
description of the electronic structure of strongly correlated systems requires
accounting for the dynamic electron correlation, which CASSCF and DMRG largely
miss. In this work a new approach for the correlation energy based on the
adiabatic connection (AC) is proposed. The AC$_{\rm n}$ method accounts for
terms up to the desired order n in the coupling constant, is rigorously
size-consistent, free from instabilities and intruder states. It employs the
particle-hole multireference random phase approximation and the Cholesky
decomposition technique, which leads to a computational cost growing with the
fifth power of the system size. Thanks to AC$_{\rm n}$ depending solely on one-
and two-electron CAS reduced density matrix, the method is much more efficient
than existing ab initio dynamic correlation methods for strong correlation.
AC$_{\rm n}$ affords excellent results for singlet-triplet gaps of challenging
organic biradicals. Development presented in this work opens new perspectives
for accurate calculations of systems with dozens of strongly correlated
electrons.


title:
IFTT-PIN: Demonstrating the Self-Calibration Paradigm on a PIN-Entry
  Task

abstract:
  We demonstrate IFTT-PIN, a self-calibrating version of the PIN-entry method
introduced in Roth et al. (2004) [1]. In [1], digits are split into two sets
and assigned a color respectively. To communicate their digit, users press the
button with the same color that is assigned to their digit, which can be
identified by elimination after a few iterations. IFTT-PIN uses the same
principle but does not pre-assign colors to each button. Instead, users are
free to choose which button to use for each color. IFTT-PIN infers both the
user's PIN and their preferred button-to-color mapping at the same time, a
process called self-calibration. Different versions of IFTT-PIN can be tested
at https://jgrizou.github.io/IFTT-PIN/ and a video introduction at
https://youtu.be/5I1ibPJdLHM.


title:
From Monolith to Microservices: Software Architecture for Autonomous UAV
  Infrastructure Inspection

abstract:
  Linear-infrastructure Mission Control (LiMiC) is an application for
autonomous Unmanned Aerial Vehicle (UAV) infrastructure inspection mission
planning developed in monolithic software architecture. The application
calculates routes along the infrastructure based on the users' inputs, the
number of UAVs participating in the mission, and UAVs' locations. LiMiC1.0 is
the latest application version migrated from monolith to microservices,
continuously integrated, and deployed using DevOps tools to facilitate future
features development, enable better traffic management, and improve the route
calculation processing time. Processing time was improved by refactoring the
route calculation algorithm into services, scaling them in the Kubernetes
cluster, and enabling asynchronous communication in between. In this paper, we
discuss the differences between the monolith and microservice architecture to
justify our decision for migration. We describe the methodology for the
application's migration and implementation processes, technologies we use for
continuous integration and deployment, and we present microservices improved
performance results compared with the monolithic application.


title:
Existence, uniqueness and approximation of solutions of SDEs with
  superlinear coefficients in the presence of discontinuities of the drift
  coefficient

abstract:
  Existence, uniqueness, and $L_p$-approximation results are presented for
scalar stochastic differential equations (SDEs) by considering the case where,
the drift coefficient has finitely many spatial discontinuities while both
coefficients can grow superlinearly (in the space variable). These
discontinuities are described by a piecewise local Lipschitz continuity and a
piecewise monotone-type condition while the diffusion coefficient is assumed to
be locally Lipschitz continuous and non-degenerate at the discontinuity points
of the drift coefficient. Moreover, the superlinear nature of the coefficients
is dictated by a suitable coercivity condition and a polynomial growth of the
(local) Lipschitz constants of the coefficients. Existence and uniqueness of
strong solutions of such SDEs are obtained. Furthermore, the classical
$L_p$-error rate $1/2$, for a suitable range of values of $p$, is recovered for
a tamed Euler scheme which is used for approximating these solutions. To the
best of the authors' knowledge, these are the first existence, uniqueness and
approximation results for this class of SDEs.


title: Bayesian Quantile Regression for Longitudinal Count Data

abstract:
  This work introduces Bayesian quantile regression modeling framework for the
analysis of longitudinal count data. In this model, the response variable is
not continuous and hence an artificial smoothing of counts is incorporated. The
Bayesian implementation utilizes the normal-exponential mixture representation
of the asymmetric Laplace distribution for the response variable. An efficient
Gibbs sampling algorithm is derived for fitting the model to the data. The
model is illustrated through simulation studies and implemented in an
application drawn from neurology. Model comparison demonstrates the practical
utility of the proposed model.


title: Splitting of elliptic flow in a tilted fireball

abstract:
  The splitting of elliptic flow measured in different regions of the momentum
space of produced hadrons has been recently studied in transport models and
proposed as a sensitive probe of the angular momentum carried by the fireball
produced in a relativistic heavy ion collision. The initial state angular
momentum also gives rise to rapidity odd directed flow which has been measured.
We consider a relativistic hydrodynamic framework with the initial matter
distribution suitably calibrated to describe the observed directed flow and
apply it to study the spilt in the elliptic flow. Our study suggests that the
split in the elliptic flow is mostly driven by directed and triangular flows
and may be used to constrain models of initial state rapidity distribution of
matter in the fireball.


title: Finitely Heterogeneous Treatment Effect in Event-study

abstract:
  Treatment effect estimation strategies in the event-study setup, namely a
panel data with variation in treatment timing, often use the parallel trend
assumption that assumes mean independence across different treatment timings.
In this paper, I relax the parallel trend assumption by including a latent type
variable and develop a conditional two-way fixed-effects model. With finite
support assumption on the latent type variable, I show that an extremum
classifier consistently estimates the type assignment. Firstly, I solve the
endogeneity problem of the selection into treatment by conditioning on the
latent type, through which the treatment timing is correlated with the outcome.
Secondly, as the type assignment is explicitly estimated, further heterogeneity
than the usual unit fixed-effects across units can be documented; treatment is
allowed to affect units of different types differently and the variation in
treatment effect is documented jointly with the variation in untreated outcome.


title:
Optical ultracompact directional antenna based on a dimer nanorod
  structure

abstract:
  Controlling directionality of optical emitters is of utmost importance for
their application in communication and biosensing devices. Metallic
nanoantennas have been proven to affect both excitation and emission properties
of nearby emitters, including directionality of their emission. In this regard,
optical directional nanoantennas based on a Yagi-Uda design have been
demonstrated in the visible range. Despite this impressive proof of concept,
their overall size and considerable number of elements represent obstacles for
the exploitation of these antennas in nanophotonic applications and for their
incorporation onto photonic chips. In order to address these challenges, we
investigate an alternative design. In particular, we numerically demonstrate
unidirectionality of an ultracompact optical antenna based on two parallel gold
nanorods (side-by-side dimer). Our results show that exciting the antiphase
mode by an emitter placed in the near-field can lead to unidirectional
emission. Furthermore, in order to verify the feasibility of this design, we
study the effect on the directionality of several parameters such as shape of
the nanorods, possible defects in dimer assembly, and different position and
orientation of the emitter. We conclude that this design is robust to changes,
making it experimentally achievable.


title:
digHolo : High-speed library for off-axis digital holography and
  Hermite-Gaussian decomposition

abstract:
  'digHolo' is a numerical library for processing batches of input off-axis
digital holography interferograms and outputting the corresponding
reconstructed fields. Optionally the library can perform a modal decomposition
of the reconstructed fields and is particularly efficient at Hermite-Gaussian
and Laguerre-Gaussian decomposition.
  The library is written in C++11 for the x86-64 (AVX2) architecture and is
intended for use primarily as a dynamic linked library (Windows) or shared
object (Linux) which the user can integrate into their own software. The
library can be compiled as an executable for processing interferograms from
disk using the command line. The library has dependencies on two external
libraries; FFTW and BLAS/LAPACK (functions cgemv, cgemm, cgesvd, sgels), but a
precompiled statically linked DLL is provided for Windows.
  A video tutorial is provided explaining usage of the software in addition to
this user guide. Coding examples for use in Matlab, Python and C++ are
supplied.


title:
On Bernstein- and Marcinkiewicz-type inequalities on multivariate
  $C^\alpha$-domains

abstract:
  We prove new Bernstein and Markov type inequalities in $L^p$ spaces
associated with the normal and the tangential derivatives on the boundary of a
general compact $C^\alpha$-domain with $1\leq \alpha\leq 2$. These estimates
are also applied to establish Marcinkiewicz type inequalities for
discretization of $L^p$ norms of algebraic polynomials on $C^\alpha$-domains
with asymptotically optimal number of function samples used.


title: Information-Theoretic Policy Extraction from Partial Observations

abstract:
  We investigate the problem of extracting a control policy from a single or
multiple partial observation sequences. Therefore we cast the problem as a
Controlled Hidden Markov Model. We then sketch two information-theoretic
approaches to extract a policy which we refer to as A Posterior Control
Distributions. The performance of both methods is investigated and compared
empirically on a linear tracking problem.


title:
Test Against High-Dimensional Uncertainties: Accelerated Evaluation of
  Autonomous Vehicles with Deep Importance Sampling

abstract:
  Evaluating the performance of autonomous vehicles (AV) and their complex
subsystems to high precision under naturalistic circumstances remains a
challenge, especially when failure or dangerous cases are rare. Rarity does not
only require an enormous sample size for a naive method to achieve high
confidence estimation, but it also causes dangerous underestimation of the true
failure rate and it is extremely hard to detect. Meanwhile, the
state-of-the-art approach that comes with a correctness guarantee can only
compute an upper bound for the failure rate under certain conditions, which
could limit its practical uses. In this work, we present Deep Importance
Sampling (Deep IS) framework that utilizes a deep neural network to obtain an
efficient IS that is on par with the state-of-the-art, capable of reducing the
required sample size 43 times smaller than the naive sampling method to achieve
10% relative error and while producing an estimate that is much less
conservative. Our high-dimensional experiment estimating the misclassification
rate of one of the state-of-the-art traffic sign classifiers further reveals
that this efficiency still holds true even when the target is very small,
achieving over 600 times efficiency boost. This highlights the potential of
Deep IS in providing a precise estimate even against high-dimensional
uncertainties.


title:
Asynchronous Load Balancing and Auto-scaling: Mean-Field Limit and
  Optimal Design

abstract:
  We introduce a Markovian framework for load balancing where classical
algorithms such as Power-of-$d$ are combined with asynchronous auto-scaling
features. These allow the net service capacity to scale up or down in response
to the current load within the same timescale of job dynamics. This is inspired
by serverless frameworks such as Knative, used among others by Google Cloud
Run, where servers are software functions that can be flexibly instantiated in
milliseconds according to user-defined scaling rules. In this context, load
balancing and auto-scaling are employed together to optimize both
user-perceived delay performance and energy consumption. In the literature,
these mechanisms are synchronous or rely on a central queue. The architectural
novelty of our work is to consider an asynchronous and decentralized system, as
in Knative, which takes scalability to the next level.
  Under a general assumption on the auto-scaling process, we prove a mean-field
limit theorem that provides an accurate approximation for the system dynamics
when the mean demand and nominal service capacity grow large in proportion. We
characterize the fixed points of the mean-field limit model and provide a
simple condition telling whether or not all the available servers need to be
turned on to handle the incoming demand. Then, we investigate how to design
optimal auto-scaling rules and find a general condition able to drive the
mean-field dynamics to delay and relative energy optimality, a situation where
the user-perceived delay and the relative energy wastage induced by idle
servers vanish. The proposed optimality condition suggests to scale up capacity
if and only if the mean demand exceeds the overall rate at which servers become
idle and active. This yields the definition of tractable optimization
frameworks to trade off between energy and performance, which we show as an
application of our work.


title: The Cyclic Flats of a $q$-Matroid

abstract:
  In this paper we develop the theory of cyclic flats of $q$-matroids. We show
that the lattice of cyclic flats, together with their ranks, uniquely
determines a $q$-matroid and hence derive a new $q$-cryptomorphism. We
introduce the notion of $\mathbb{F}_{q^m}$-independence of an
$\mathbb{F}_q$-subspace of $\mathbb{F}_q^n$ and we show that $q$-matroids
generalize this concept, in the same way that matroids generalize the notion of
linear independence of vectors over a given field.


title: GeoSPM: Geostatistical parametric mapping for medicine

abstract:
  The characteristics and determinants of health and disease are often
organised in space, reflecting our spatially extended nature. Understanding the
influence of such factors requires models capable of capturing spatial
relations. Though a mature discipline, spatial analysis is comparatively rare
in medicine, arguably a consequence of the complexity of the domain and the
inclemency of the data regimes that govern it. Drawing on statistical
parametric mapping, a framework for topological inference well-established in
the realm of neuroimaging, we propose and validate a novel approach to the
spatial analysis of diverse clinical data - GeoSPM - based on differential
geometry and random field theory. We evaluate GeoSPM across an extensive array
of synthetic simulations encompassing diverse spatial relationships, sampling,
and corruption by noise, and demonstrate its application on large-scale data
from UK Biobank. GeoSPM is transparently interpretable, can be implemented with
ease by non-specialists, enables flexible modelling of complex spatial
relations, exhibits robustness to noise and under-sampling, offers well-founded
criteria of statistical significance, and is through computational efficiency
readily scalable to large datasets. We provide a complete, open-source software
implementation of GeoSPM, and suggest that its adoption could catalyse the
wider use of spatial analysis across the many aspects of medicine that urgently
demand it.


title:
ConfPred: A layered intergrowth structure prediction model based on
  confinement self-assembly in two-dimensional interlayer space

abstract:
  We constructed a simple but effective model to predict the layered
intergrowth structures by combining the self-assembly phenomenon in confined
space and the sandwich configuration of layered materials. In this model, a
two-dimensional confined space is constructed by two known block layers, such
as the Fe$_2$As$_2$ block of iron-based superconductors. Then, the crystal
structure prediction is carried out only inside the confined space to search
for brand-new block layers. We realized this model on the basis of the USPEX9.4
code. In the test, the already existing iron-based superconductors can be
always successfully found, such as Ba$_2$Ti$_2$Fe$_2$As$_4$O,
Sr$_3$Sc$_2$Fe$_2$As$_2$O$_5$, Sr$_4$V$_2$Fe$_2$As$_2$O$_6$, and so on. The
comparison test suggests that our model has remarkable advantages in searching
for intergrowth structures. With this space confinement prediction model, a
structure prediction of layered intergrowth materials even with up to six
elements can be performed with an acceptable machine time consumption. So far,
we have done some multi-composition crystal structure predictions of iron-based
superconductor, and found several stable and metastable structures, such as
Ba$_2$Fe$_2$As$_3$, Eu$_2$Fe$_2$As$_3$, La$_2$O$_2$ClFeAs,
LiOMn$_2$As,Li$_4$OFe$_2$As$_2$.


title:
Magnetic system for cleaning the gamma beam at the LUE-40 electron linac
  output

abstract:
  The bremsstrahlung of accelerated electrons passing through a converter is
used to study multiparticle photo-nuclear reactions. The results of
calculations, numerical modeling, design, and testing of a special magnetic
cleaning system to obtain a "pure" beam of bremsstrahlung quanta when studying
the cross-sections of such reactions at the LUE-40 linac are presented. The
system is based on commercially available permanent magnets of rectangular
cross-sections. The maximum on-axis field is 0.9 T, which provides sufficient
separation of the electron beam and gamma rays at a distance of more than 90 mm
from the magnet.


title:
Ensemble model of turbulence based on states of constant flux in
  wavenumber space

abstract:
  An ensemble model of turbulence is proposed. The ensemble consists of flow
fields in which the flux of an inviscid conserved quantity, such as energy (or
enstrophy in two-dimensional flow fields), across the wavenumber $k$ is a
constant independent of $k$ in an appropriate range. Two-dimensional flow
fields of constant enstrophy flux are sampled randomly by a Monte Carlo method.
The energy spectra $E_k$ of the flow fields are consistent with the scaling
$E_k \propto k^{-3}(\ln(k/k_\mathrm{b}))^{-1/3}$ where $k_\mathrm{b}$ is the
bottom wavenumber of the constant-flux range.


title: Multipartite correlations in quantum collision models

abstract:
  Quantum collision models have proved to be useful for a clear and concise
description of many physical phenomena in the field of open quantum systems:
thermalization, decoherence, homogenization, nonequilibrium steady state,
entanglement generation, simulation of many-body dynamics, quantum thermometry.
A challenge in the standard collision model, where the system and many ancillas
are all initially uncorrelated, is how to describe quantum correlations among
ancillas induced by successive system-ancilla interactions. Another challenge
is how to deal with initially correlated ancillas. Here we develop a tensor
network formalism to address both challenges. We show that the induced
correlations in the standard collision model are well captured by a matrix
product state (a matrix product density operator) if the colliding particles
are in pure (mixed) states. In the case of the initially correlated ancillas,
we construct a general tensor diagram for the system dynamics and derive a
memory-kernel master equation. Analyzing the perturbation series for the memory
kernel, we go beyond the recent results concerning the leading role of
two-point correlations and consider multipoint correlations (Waldenfelds
cumulants) that become relevant in the higher order stroboscopic limits. These
results open an avenue for a further analysis of memory effects in the
collisional quantum dynamics.


title: Gradient Descent Bit-Flipping Decoding with Momentum

abstract:
  In this paper, we propose a Gradient Descent Bit-Flipping (GDBF) decoding
with momentum, which considers past updates to provide inertia to the decoding
process. We show that GDBF or randomized GDBF decoders with momentum may
closely approach the floating-point Belief-Propagation decoding performance,
and even outperform it in the error-floor region, especially for graphs with
high connectivity degree.


title:
Scientometric Review of Artificial Intelligence for Operations &
  Maintenance of Wind Turbines: The Past, Present and Future

abstract:
  Wind energy has emerged as a highly promising source of renewable energy in
recent times. However, wind turbines regularly suffer from operational
inconsistencies, leading to significant costs and challenges in operations and
maintenance (O&M). Condition-based monitoring (CBM) and performance
assessment/analysis of turbines are vital aspects for ensuring efficient O&M
planning and cost minimisation. Data-driven decision making techniques have
witnessed rapid evolution in the wind industry for such O&M tasks during the
last decade, from applying signal processing methods in early 2010 to
artificial intelligence (AI) techniques, especially deep learning in 2020. In
this article, we utilise statistical computing to present a scientometric
review of the conceptual and thematic evolution of AI in the wind energy
sector, providing evidence-based insights into present strengths and
limitations of data-driven decision making in the wind industry. We provide a
perspective into the future and on current key challenges in data availability
and quality, lack of transparency in black box-natured AI models, and
prevailing issues in deploying models for real-time decision support, along
with possible strategies to overcome these problems. We hope that a systematic
analysis of the past, present and future of CBM and performance assessment can
encourage more organisations to adopt data-driven decision making techniques in
O&M towards making wind energy sources more reliable, contributing to the
global efforts of tackling climate change.


title: Origin of supermassive black holes in massive metal-poor protoclusters

abstract:
  While large numbers of supermassive black holes have been detected at z>6,
their origin is still essentially unclear. Numerical simulations have shown
that the conditions for the classical direct collapse scenario are very
restrictive and fragmentation is very difficult to be avoided. We thus consider
here a more general case of a dense massive protostar cluster at low
metallicity (<~ 10^{-3} Z_solar) embedded in gas. We estimate the mass of the
central massive object, formed via collisions and gas accretion, considering
the extreme cases of a logarithmically flat and a Salpeter-type initial mass
function. Objects with masses of at least 10^4 solar could be formed for
inefficient radiative feedback, whereas ~10^3 solar mass objects could be
formed when the accretion time is limited via feedback. These masses will vary
depending on the environment and could be considerably larger, particularly due
to the continuous infall of gas into the cloud. As a result, one may form
intermediate mass black holes of ~ 10^4 solar masses or more. Upcoming
observations with the James Webb Space Telescope (JWST) and other observatories
may help to detect such massive black holes and their environment, thereby
shedding additional light on such a formation channel.


title:
Challenges and Opportunities of Edge AI for Next-Generation Implantable
  BMIs

abstract:
  Neuroscience and neurotechnology are currently being revolutionized by
artificial intelligence (AI) and machine learning. AI is widely used to study
and interpret neural signals (analytical applications), assist people with
disabilities (prosthetic applications), and treat underlying neurological
symptoms (therapeutic applications). In this brief, we will review the emerging
opportunities of on-chip AI for the next-generation implantable brain-machine
interfaces (BMIs), with a focus on state-of-the-art prosthetic BMIs. Major
technological challenges for the effectiveness of AI models will be discussed.
Finally, we will present algorithmic and IC design solutions to enable a new
generation of AI-enhanced and high-channel-count BMIs.


title: Towards Best Practices for Training Multilingual Dense Retrieval Models

abstract:
  Dense retrieval models using a transformer-based bi-encoder design have
emerged as an active area of research. In this work, we focus on the task of
monolingual retrieval in a variety of typologically diverse languages using one
such design. Although recent work with multilingual transformers demonstrates
that they exhibit strong cross-lingual generalization capabilities, there
remain many open research questions, which we tackle here. Our study is
organized as a "best practices" guide for training multilingual dense retrieval
models, broken down into three main scenarios: where a multilingual transformer
is available, but relevance judgments are not available in the language of
interest; where both models and training data are available; and, where
training data are available not but models. In considering these scenarios, we
gain a better understanding of the role of multi-stage fine-tuning, the
strength of cross-lingual transfer under various conditions, the usefulness of
out-of-language data, and the advantages of multilingual vs. monolingual
transformers. Our recommendations offer a guide for practitioners building
search applications, particularly for low-resource languages, and while our
work leaves open a number of research questions, we provide a solid foundation
for future work.


title:
A Unified Complexity Metric for Nonconvex Matrix Completion and Matrix
  Sensing in the Rank-one Case

abstract:
  In this work, we develop a new complexity metric for an important class of
low-rank matrix optimization problems, where the metric aims to quantify the
complexity of the nonconvex optimization landscape of each problem and the
success of local search methods in solving the problem. The existing literature
has focused on two complexity measures. The RIP constant is commonly used to
characterize the complexity of matrix sensing problems. On the other hand, the
sampling rate and the incoherence are used when analyzing matrix completion
problems. The proposed complexity metric has the potential to unify these two
notions and also applies to a much larger class of problems. To mathematically
study the properties of this metric, we focus on the rank-$1$ generalized
matrix completion problem and illustrate the usefulness of the new complexity
metric from three aspects. First, we show that instances with the RIP condition
have a small complexity. Similarly, if the instance obeys the Bernoulli
sampling model, the complexity metric will take a small value with high
probability. Moreover, for a one-parameter class of instances, the complexity
metric shows consistent behavior to the first two scenarios. Furthermore, we
establish theoretical results to provide sufficient conditions and necessary
conditions on the existence of spurious solutions in terms of the proposed
complexity metric. This contrasts with the RIP and incoherence notions that
fail to provide any necessary condition.


title: On Boussinesq's equation for water waves

abstract:
  A century and a half ago, J. Boussinesq derived an equation for the
propagation of shallow water waves in a channel. Despite the fundamental
importance of this equation for a number of physical phenomena, mathematical
results on it remain scarce. One reason for this is that the equation is
ill-posed. In this paper, we establish several results on the Boussinesq
equation. First, by solving the direct and inverse problems for an associated
third-order spectral problem, we develop an Inverse Scattering Transform (IST)
approach to the initial value problem. Using this approach, we establish a
number of existence, uniqueness, and blow-up results. For example, the IST
approach allows us to identify physically meaningful global solutions and to
construct, for each $T > 0$, solutions that blow up exactly at time $T$. Our
approach also yields an expression for the solution of the initial value
problem for the Boussinesq equation in terms of the solution of a
Riemann--Hilbert problem. By analyzing this Riemann--Hilbert problem, we arrive
at asymptotic formulas for the solution. We identify ten main asymptotic
sectors in the $(x,t)$-plane; in each of these sectors, we compute an exact
expression for the leading asymptotic term together with a precise error
estimate. The asymptotic picture that emerges consists, roughly speaking, of
two nonlinearly coupled copies of the corresponding picture for the
(unidirectional) KdV equation, one copy for right-moving and one for
left-moving waves. Of particular interest are the sectors which describe the
interaction of right and left moving waves, which present qualitatively new
phenomena.


title:
Large-scale nonconvex optimization: randomization, gap estimation, and
  numerical resolution

abstract:
  We address a large-scale and nonconvex optimization problem, involving an
aggregative term. This term can be interpreted as the sum of the contributions
of N agents to some common good, with N large. We investigate a relaxation of
this problem, obtained by randomization. The relaxation gap is proved to
converge to zeros as N goes to infinity, independently of the dimension of the
aggregate. We propose a stochastic method to construct an approximate minimizer
of the original problem, given an approximate solution of the randomized
problem. McDiarmid's concentration inequality is used to quantify the
probability of success of the method. We consider the Frank-Wolfe (FW)
algorithm for the resolution of the randomized problem. Each iteration of the
algorithm requires to solve a subproblem which can be decomposed into N
independent optimization problems. A sublinear convergence rate is obtained for
the FW algorithm. In order to handle the memory overflow problem possibly
caused by the FW algorithm, we propose a stochastic Frank-Wolfe (SFW)
algorithm, which ensures the convergence in both expectation and probability
senses. Numerical experiments on a mixed-integer quadratic program illustrate
the efficiency of the method.


title: Embedding of Time-Delayed Quantum Feedback in a Nonreciprocal Array

abstract:
  Time-delayed quantum feedback is a fast and efficient method to control and
stabilize few and many-body quantum systems. However, a proper understanding of
such systems stays opaque due to the non-Markovian nature of the feedback
protocol. Here, we present a method of encoding time-delayed quantum feedback
into a chain of nonreciprocally coupled auxiliary oscillators. Our approach
serves as a novel method of introducing time-delayed quantum feedback and
provides the advantage of large tunability. Importantly, within our approach
the original non-Markovian system is embedded into an enlarged Markovian open
quantum system, which can be treated efficiently with quantum many-body
methods. As an exemplary illustration, we apply our Markovian embedding
approach to the paradigmatic case of a driven-dissipative atom in front of a
mirror.


title: Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis

abstract:
  Generating sub-optimal synthesis transformation sequences ("synthesis
recipe") is an important problem in logic synthesis. Manually crafted synthesis
recipes have poor quality. State-of-the art machine learning (ML) works to
generate synthesis recipes do not scale to large netlists as the models need to
be trained from scratch, for which training data is collected using time
consuming synthesis runs. We propose a new approach, Bulls-Eye, that fine-tunes
a pre-trained model on past synthesis data to accurately predict the quality of
a synthesis recipe for an unseen netlist. This approach on achieves 2x-10x
run-time improvement and better quality-of-result (QoR) than state-of-the-art
machine learning approaches.


title: Recoil experiments determine the eigenmodes of viscoelastic fluids

abstract:
  We experimentally investigate the recoil dynamics of a colloidal probe
particle after shearing it with constant velocity through a viscoelastic fluid.
The recoil displays two distinct timescales which are in excellent agreement
with a microscopic model built on a particle being linked to two bath particles
by harmonic springs. This model yields analytical expressions which reproduce
all experimental protocols, including additional waiting periods before
particle release. Notably, two sets of timescales appear, corresponding to
reciprocal and nonreciprocal eigenmodes of the model.


title:
How much classical information is carried by a quantum state? An
  approach inspired by Kolmogorov complexity

abstract:
  In quantum mechanics, a state is an element of a Hilbert space whose
dimension exponentially grows with the increase of the number of particles (or
qubits, in quantum computing). The vague question "is this huge Hilbert space
really there?" has been rigorously formalized inside the computational
complexity theory; the research suggests a positive answer to the question.
Along this line, I give a definition of the (classical) information content of
a quantum state, taking inspiration from the Kolmogorov complexity. I show
that, for some well-known quantum circuits (having a number of gates polynomial
in the number of qubits), the information content of the output state,
evaluated according to my definition, is polynomial in the number of qubits. On
the other hand, applying known results, it is possible to devise quantum
circuits that generate much more complex states, having an
exponentially-growing information content. A huge amount of classical
information can be really present inside a quantum state, however, I show that
this property is not necessarily exploited by quantum computers, not even by
quantum algorithms showing an exponential speed-up with respect to classical
computation.


title:
Optical Proximity Sensing for Pose Estimation During In-Hand
  Manipulation

abstract:
  During in-hand manipulation, robots must be able to continuously estimate the
pose of the object in order to generate appropriate control actions. The
performance of algorithms for pose estimation hinges on the robot's sensors
being able to detect discriminative geometric object features, but previous
sensing modalities are unable to make such measurements robustly. The robot's
fingers can occlude the view of environment- or robot-mounted image sensors,
and tactile sensors can only measure at the local areas of contact. Motivated
by fingertip-embedded proximity sensors' robustness to occlusion and ability to
measure beyond the local areas of contact, we present the first evaluation of
proximity sensor based pose estimation for in-hand manipulation. We develop a
novel two-fingered hand with fingertip-embedded optical time-of-flight
proximity sensors as a testbed for pose estimation during planar in-hand
manipulation. Here, the in-hand manipulation task consists of the robot moving
a cylindrical object from one end of its workspace to the other. We
demonstrate, with statistical significance, that proximity-sensor based pose
estimation via particle filtering during in-hand manipulation: a) exhibits 50%
lower average pose error than a tactile-sensor based baseline; b) empowers a
model predictive controller to achieve 30% lower final positioning error
compared to when using tactile-sensor based pose estimates.


title: Jump-Start Reinforcement Learning

abstract:
  Reinforcement learning (RL) provides a theoretical framework for continuously
improving an agent's behavior via trial and error. However, efficiently
learning policies from scratch can be very difficult, particularly for tasks
with exploration challenges. In such settings, it might be desirable to
initialize RL with an existing policy, offline data, or demonstrations.
However, naively performing such initialization in RL often works poorly,
especially for value-based methods. In this paper, we present a meta algorithm
that can use offline data, demonstrations, or a pre-existing policy to
initialize an RL policy, and is compatible with any RL approach. In particular,
we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs
two policies to solve tasks: a guide-policy, and an exploration-policy. By
using the guide-policy to form a curriculum of starting states for the
exploration-policy, we are able to efficiently improve performance on a set of
simulated robotic tasks. We show via experiments that JSRL is able to
significantly outperform existing imitation and reinforcement learning
algorithms, particularly in the small-data regime. In addition, we provide an
upper bound on the sample complexity of JSRL and show that with the help of a
guide-policy, one can improve the sample complexity for non-optimism
exploration methods from exponential in horizon to polynomial.


title: Data-driven Influence Based Clustering of Dynamical Systems

abstract:
  Community detection is a challenging and relevant problem in various
disciplines of science and engineering like power systems, gene-regulatory
networks, social networks, financial networks, astronomy etc. Furthermore, in
many of these applications the underlying system is dynamical in nature and
because of the complexity of the systems involved, deriving a mathematical
model which can be used for clustering and community detection, is often
impossible. Moreover, while clustering dynamical systems, it is imperative that
the dynamical nature of the underlying system is taken into account. In this
paper, we propose a novel approach for clustering dynamical systems purely from
time-series data which inherently takes into account the dynamical evolution of
the underlying system. In particular, we define a \emph{distance/similarity}
measure between the states of the system which is a function of the influence
that the states have on each other, and use the proposed measure for clustering
of the dynamical system. For data-driven computation we leverage the Koopman
operator framework which takes into account the nonlinearities (if present) of
the underlying system, thus making the proposed framework applicable to a wide
range of application areas. We illustrate the efficacy of the proposed approach
by clustering three different dynamical systems, namely, a linear system, which
acts like a proof of concept, the highly non-linear IEEE 39 bus transmission
network and dynamic variables obtained from atmospheric data over the Amazon
rain forest.

categories: eess.SY cs.IT cs.LG cs.SY math.DS math.IT


title: Causal Discovery of Macroeconomic State-Space Models

abstract:
  This paper presents a set of tests and an algorithm for agnostic, data-driven
selection among macroeconomic DSGE models inspired by structure learning
methods for DAGs. As the log-linear state-space solution to any DSGE model is
also a DAG it is possible to use associated concepts to identify a unique
ground-truth state-space model which is compatible with an underlying DGP,
based on the conditional independence relationships which are present in that
DGP. In order to operationalise search for this ground-truth model, the
algorithm tests feasible analogues of these conditional independence criteria
against the set of combinatorially possible state-space models over observed
variables. This process is consistent in large samples. In small samples the
result may not be unique, so conditional independence tests can be combined
with likelihood maximisation in order to select a single optimal model. The
efficacy of this algorithm is demonstrated for simulated data, and results for
real data are also provided and discussed.


title:
Boundary null-controllability of 1d linearized compressible
  Navier-Stokes system by one control force

abstract:
  In this article, we study the boundary null-controllability of the
one-dimensional linearized compressible Navier-Stokes equations in the interval
$(0,1)$ with a Dirichlet boundary control only on the density component and
homogeneous Dirichlet boundary conditions on the velocity component. We prove
that the linearized system is null-controllable in $\dot{H}^s_{per}(0,1) \times
L^2(0,1)$ for any $s > 1/2$ provided the time $T > 1$, where
$\dot{H}^s_{per}(0,1)$ denotes the Sobolev space of periodic functions with
zero mean value. As it is well-known, the time restriction is arising due to
the presence of a transport equation in the system. The proof for the
null-controllability is based on solving a mixed parabolic-hyperbolic moments
problem. To apply this, we perform a spectral analysis of the associated
adjoint operator which is the main involved part of this work.


title: Local volatility under rough volatility

abstract:
  Several asymptotic results for the implied volatility generated by a rough
volatility model have been obtained in recent years (notably in the
small-maturity regime), providing a better understanding of the shapes of the
volatility surface induced by rough volatility models, and supporting their
calibration power to S&P500 option data. Rough volatility models also generate
a local volatility surface, via the so-called Markovian projection of the
stochastic volatility. We complement the existing results on the implied
volatility by studying the asymptotic behavior of the local volatility surface
generated by a class of rough stochastic volatility models, encompassing the
rough Bergomi model. Notably, we observe that the celebrated "1/2 skew rule"
linking the short-term at-the-money skew of the implied volatility to the
short-term at-the-money skew of the local volatility, a consequence of the
celebrated "harmonic mean formula" of [Berestycki, Busca, and Florent, QF
2002], is replaced by a new rule: the ratio of the at-the-money implied and
local volatility skews tends to the constant 1/(H + 3/2) (as opposed to the
constant 1/2), where H is the regularity index of the underlying instantaneous
volatility process.


title:
High accuracy PN and NR comparisons involving higher modes for eccentric
  BBHs and a dominant mode eccentric IMR model

abstract:
  Spherical harmonic modes of gravitational waveforms for inspiraling compact
binaries in eccentric orbits from post-Newtonian (PN) theory accurate to third
post-Newtonian order, and those extracted from Numerical Relativity (NR)
simulations for binary black holes (BBHs) are compared. We combine results from
the two approaches (PN and NR) to construct time-domain hybrid waveforms that
describe the complete evolution of BBH mergers through inspiral(I), merger(M)
and the ringdown(R) stages. These hybrids are then used in constructing a fully
analytical dominant mode (l=2, |m|=2) eccentric IMR model. Overlaps with
quasi-circular IMR waveform models including the effect of higher modes,
maximized over a time- and phase-shift, hint at the importance (mismatches >
1%) of including eccentricity in gravitational waveforms when analysing BBHs
lighter than ~ 80 M_sun, irrespective of the binary's eccentricity (as it
enters the LIGO bands), or mass-ratio. Combined impact of eccentricity and
higher modes seems to become more apparent through smaller overlaps with
increasing inclination angles and mass ratios. Finally, we show that the
state-of-the-art quasi-circular models including the effect of higher modes
will not be adequate in extracting source properties for signals with initial
eccentricities e0 >= 0.1.


title: The eikonal operator at arbitrary velocities I: the soft-radiation limit

abstract:
  Observables related to the real part of the gravitational eikonal, such as
the deflection angle and time delay, have been found so far to have a smooth
post-Minkowskian (PM) expansion whose validity extends from the
non-relativistic to the most extreme ultra-relativistic (UR) regime, which
smoothly connects with massless particle collisions. To describe gravitational
radiation, the eikonal phase has to be promoted to a unitary operator for which
we motivate a proposal and start discussing properties in the soft-radiation
limit. A convergent PM expansion is found to only hold below an UR bound
(discussed in the GR literature in the seventies) above which a different
expansion is instead needed implying, in general, some non-analyticity in
Newton's constant. In this extreme UR regime soft radiative observables receive
contributions only from gravitons and are therefore universal. This generalises
the pattern discussed in \cite{DiVecchia:2020ymx} beyond the elastic case.


title: Stratospheric Modulation of the MJO through Cirrus Cloud Feedbacks

abstract:
  Recent observations have indicated significant modulation of the Madden
Julian Oscillation (MJO) by the phase of the stratospheric Quasi-Biennial
Oscillation (QBO) during boreal winter. Composites of the MJO show that upper
tropospheric ice cloud fraction and water vapor anomalies are generally
collocated, and that an eastward tilt with height in cloud fraction exists.
Through radiative transfer calculations, it is shown that ice clouds have a
stronger tropospheric radiative forcing than do water vapor anomalies,
highlighting the importance of incorporating upper tropospheric/lower
stratospheric processes into simple models of the MJO. The coupled
troposphere-stratosphere linear model previously developed by the authors is
extended by including a mean wind in the stratosphere and a prognostic equation
for cirrus clouds, which are forced dynamically and allowed to modulate
tropospheric radiative cooling, similar to the effect of tropospheric water
vapor in previous formulations. Under these modifications, the model still
produces a slow, eastward propagating mode that resembles the MJO. The sign of
zonal mean wind in the stratosphere is shown to control both the upward wave
propagation and tropospheric vertical structure of the mode. Under varying
stratospheric wind and interactive cirrus cloud radiation, the MJO-like mode
has weaker growth rates under stratospheric westerlies than easterlies,
consistent with the observed MJO-QBO relationship. These results are directly
attributable to an enhanced barotropic mode under QBO easterlies. It is also
shown that differential zonal advection of cirrus clouds leads to weaker growth
rates under stratospheric westerlies than easterlies. Implications and
limitations of the linear theory are discussed.


title: CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations

abstract:
  Providing explanations in the context of Visual Question Answering (VQA)
presents a fundamental problem in machine learning. To obtain detailed insights
into the process of generating natural language explanations for VQA, we
introduce the large-scale CLEVR-X dataset that extends the CLEVR dataset with
natural language explanations. For each image-question pair in the CLEVR
dataset, CLEVR-X contains multiple structured textual explanations which are
derived from the original scene graphs. By construction, the CLEVR-X
explanations are correct and describe the reasoning and visual information that
is necessary to answer a given question. We conducted a user study to confirm
that the ground-truth explanations in our proposed dataset are indeed complete
and relevant. We present baseline results for generating natural language
explanations in the context of VQA using two state-of-the-art frameworks on the
CLEVR-X dataset. Furthermore, we provide a detailed analysis of the explanation
generation quality for different question and answer types. Additionally, we
study the influence of using different numbers of ground-truth explanations on
the convergence of natural language generation (NLG) metrics. The CLEVR-X
dataset is publicly available at
\url{https://explainableml.github.io/CLEVR-X/}.


title:
Hear No Evil: Towards Adversarial Robustness of Automatic Speech
  Recognition via Multi-Task Learning

abstract:
  As automatic speech recognition (ASR) systems are now being widely deployed
in the wild, the increasing threat of adversarial attacks raises serious
questions about the security and reliability of using such systems. On the
other hand, multi-task learning (MTL) has shown success in training models that
can resist adversarial attacks in the computer vision domain. In this work, we
investigate the impact of performing such multi-task learning on the
adversarial robustness of ASR models in the speech domain. We conduct extensive
MTL experimentation by combining semantically diverse tasks such as accent
classification and ASR, and evaluate a wide range of adversarial settings. Our
thorough analysis reveals that performing MTL with semantically diverse tasks
consistently makes it harder for an adversarial attack to succeed. We also
discuss in detail the serious pitfalls and their related remedies that have a
significant impact on the robustness of MTL models. Our proposed MTL approach
shows considerable absolute improvements in adversarially targeted WER ranging
from 17.25 up to 59.90 compared to single-task learning baselines (attention
decoder and CTC respectively). Ours is the first in-depth study that uncovers
adversarial robustness gains from multi-task learning for ASR.


title:
Down but not out: properties of the molecular gas in the stripped Virgo
  Cluster early-type galaxy NGC4526

abstract:
  We present ALMA data on the 3mm continuum emission, CO isotopologues (12CO,
13CO, C18O), and high-density molecular tracers (HCN, HCO+, HNC, HNCO, CS, CN,
and CH3OH) in NGC4526. These data enable a detailed study of the physical
properties of the molecular gas in a longtime resident of the Virgo Cluster;
comparisons to more commonly-studied spiral galaxies offer intriguing hints
into the processing of molecular gas in the cluster environment. Many molecular
line ratios in NGC4526, along with our inferred abundances and CO/H2 conversion
factors, are similar to those found in nearby spirals. One striking exception
is the very low observed 12CO/13CO(1-0) line ratio, $3.4\pm0.3$, which is
unusually low for spirals though not for Virgo Cluster early-type galaxies. We
carry out radiative transfer modeling of the CO isotopologues with some
archival (2-1) data, and we use Bayesian analysis with Markov chain Monte Carlo
techniques to infer the physical properties of the CO-emitting gas. We find
surprisingly low [12CO/13CO] abundance ratios of $7.8^{+2.7}_{-1.5}$ and
$6.5^{+3.0}_{-1.3}$ at radii of 0.4 kpc and 1 kpc. The emission from the
high-density tracers HCN, HCO+, HNC, CS and CN is also relatively bright, and
CN is unusually optically thick in the inner parts of NGC4526. These features
hint that processing in the cluster environment may have removed much of the
galaxy's relatively diffuse, optically thinner molecular gas along with its
atomic gas. Angular momentum transfer to the surrounding intracluster medium
may also have caused contraction of the disk, magnifying radial gradients such
as we find in [13CO/C18O]. More detailed chemical evolution modeling would be
interesting in order to explore whether the unusual [12CO/13CO] abundance ratio
is entirely an environmental effect or whether it also reflects the relatively
old stellar population in this early-type galaxy.


title: Microresonator Soliton Frequency Combs in the Zero-Dispersion Regime

abstract:
  Chip-scale optical frequency combs have attracted significant research
interest and can be used in applications ranging from precision spectroscopy to
telecom channel generators and lidar systems. In the time domain,
microresonator based frequency combs correspond to self-stabilized soliton
pulses. In two distinct regimes, microresonators have shown to emit either
bright solitons in the anomalous dispersion regime or dark solitons (a short
time of darkness in a bright background signal) in the normal dispersion
regime. Here, we investigate the dynamics of continuous-wave-laser-driven
soliton generation in the zero-group-velocity-dispersion (GVD) regime, as well
as the generation of solitons that are spectrally crossing different dispersion
regimes. In the measurements we observe zero-dispersion solitons with doublet
structures (soliton molecules) that can be deterministically accessed with a
predictable spectral envelope. Numerical simulations agree well with the
observed soliton structures. These results could be of interest for chip-based
pump-probe schemes, optical telecom systems, gas sensing and precision
metrology.


title: Concavity of solutions to semilinear equations in dimension two

abstract:
  We consider the Dirichlet problem for a class of semilinear equations on two
dimensional convex domains. We give a sufficient condition for the solution to
be concave. Our condition uses comparison with ellipses, and is motivated by an
idea of Kosmodem'yanskii.
  We also prove a result on propagation of concavity of solutions from the
boundary, which holds in all dimensions.


title: Learning Speech Emotion Representations in the Quaternion Domain

abstract:
  The modeling of human emotion expression in speech signals is an important,
yet challenging task. The high resource demand of speech emotion recognition
models, combined with the the general scarcity of emotion-labelled data are
obstacles to the development and application of effective solutions in this
field. In this paper, we present an approach to jointly circumvent these
difficulties. Our method, named RH-emo, is a novel semi-supervised architecture
aimed at extracting quaternion embeddings from real-valued monoaural
spectrograms, enabling the use of quaternion-valued networks for speech emotion
recognition tasks. RH-emo is a hybrid real/quaternion autoencoder network that
consists of a real-valued encoder in parallel to a real-valued emotion
classifier and a quaternion-valued decoder. On the one hand, the classifier
permits to optimize each latent axis of the embeddings for the classification
of a specific emotion-related characteristic: valence, arousal, dominance and
overall emotion. On the other hand, the quaternion reconstruction enables the
latent dimension to develop intra-channel correlations that are required for an
effective representation as a quaternion entity. We test our approach on speech
emotion recognition tasks using four popular datasets: Iemocap, Ravdess, EmoDb
and Tess, comparing the performance of three well-established real-valued CNN
architectures (AlexNet, ResNet-50, VGG) and their quaternion-valued equivalent
fed with the embeddings created with RH-emo. We obtain a consistent improvement
in the test accuracy for all datasets, while drastically reducing the
resources' demand of models. Moreover, we performed additional experiments and
ablation studies that confirm the effectiveness of our approach. The RH-emo
repository is available at: https://github.com/ispamm/rhemo.


title:
Pyramid Frequency Network with Spatial Attention Residual Refinement
  Module for Monocular Depth Estimation

abstract:
  Deep-learning-based approaches to depth estimation are rapidly advancing,
offering superior performance over existing methods. To estimate the depth in
real-world scenarios, depth estimation models require the robustness of various
noise environments. In this work, a Pyramid Frequency Network(PFN) with Spatial
Attention Residual Refinement Module(SARRM) is proposed to deal with the weak
robustness of existing deep-learning methods. To reconstruct depth maps with
accurate details, the SARRM constructs a residual fusion method with an
attention mechanism to refine the blur depth. The frequency division strategy
is designed, and the frequency pyramid network is developed to extract features
from multiple frequency bands. With the frequency strategy, PFN achieves better
visual accuracy than state-of-the-art methods in both indoor and outdoor scenes
on Make3D, KITTI depth, and NYUv2 datasets. Additional experiments on the noisy
NYUv2 dataset demonstrate that PFN is more reliable than existing deep-learning
methods in high-noise scenes.


title:
Probing the nuclear deformation with three-particle asymmetric cumulant
  in RHIC isobar runs

abstract:
  $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr collisions at
$\sqrt{s_{_{\rm NN}}}=200$ GeV provide unique opportunities to study the
geometry and fluctuations raised from the deformation of the colliding nuclei.
Using iEBE-VISHNU hybrid model, we predict ${\rm ac}_{2}\{3\}$ ratios between
these two collision systems and demonstrate that the ratios of ${\rm
ac}_{2}\{3\}$, as well as the ratios of the involving flow harmonics and
event-plane correlations, are sensitive to $\beta_{2}$ quadrupole and
$\beta_{3}$ octupole deformations, which provides strong constrain on the shape
differences between $^{96}$Ru and $^{96}$Zr.


title:
Heavy quark potential and LQCD based quark condensate at finite magnetic
  field

abstract:
  We have studied various properties of heavy quarkonia in hot and magnetized
quark gluon plasma. Inverse magnetic catalysis (IMC) effect is incorporated by
modifying the effective quark masses. Then we obtain the real and imaginary
part of the heavy quark potential. After evaluating binding energy and decay
width we obtain the dissociation temperature of heavy quarkonia in presence of
magnetic field.


title: ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer

abstract:
  Objects play a crucial role in our everyday activities. Though multisensory
object-centric learning has shown great potential lately, the modeling of
objects in prior work is rather unrealistic. ObjectFolder 1.0 is a recent
dataset that introduces 100 virtualized objects with visual, acoustic, and
tactile sensory data. However, the dataset is small in scale and the
multisensory data is of limited quality, hampering generalization to real-world
scenarios. We present ObjectFolder 2.0, a large-scale, multisensory dataset of
common household objects in the form of implicit neural representations that
significantly enhances ObjectFolder 1.0 in three aspects. First, our dataset is
10 times larger in the amount of objects and orders of magnitude faster in
rendering time. Second, we significantly improve the multisensory rendering
quality for all three modalities. Third, we show that models learned from
virtual objects in our dataset successfully transfer to their real-world
counterparts in three challenging tasks: object scale estimation, contact
localization, and shape reconstruction. ObjectFolder 2.0 offers a new path and
testbed for multisensory learning in computer vision and robotics. The dataset
is available at https://github.com/rhgao/ObjectFolder.


title: Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower

abstract:
  We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a
means of efficiently moving scattered objects into a target receptacle. Due to
the chaotic nature of aerodynamic forces, a blowing controller must (i)
continually adapt to unexpected changes from its actions, (ii) maintain
fine-grained control, since the slightest misstep can result in large
unintended consequences (e.g., scatter objects already in a pile), and (iii)
infer long-range plans (e.g., move the robot to strategic blowing locations).
We tackle these challenges in the context of deep reinforcement learning,
introducing a multi-frequency version of the spatial action maps framework.
This allows for efficient learning of vision-based policies that effectively
combine high-level planning and low-level closed-loop control for dynamic
mobile manipulation. Experiments show that our system learns efficient
behaviors for the task, demonstrating in particular that blowing achieves
better downstream performance than pushing, and that our policies improve
performance over baselines. Moreover, we show that our system naturally
encourages emergent specialization between the different subpolicies spanning
low-level fine-grained control and high-level planning. On a real mobile robot
equipped with a miniature air blower, we show that our simulation-trained
policies transfer well to a real environment and can generalize to novel
objects.


title:
Hamiltonicity after reversing the directed edges at a vertex of a
  Cartesian product

abstract:
  Let $\vec C_m$ and $\vec C_n$ be directed cycles of length $m$ and $n$, with
$m,n \ge 3$, and let $P(\vec C_m \mathbin{\Box} \vec C_n)$ be the digraph that
is obtained from the Cartesian product $\vec C_m \mathbin{\Box} \vec C_n$ by
choosing a vertex $v$, and reversing the orientation of all four directed edges
that are incident with $v$. (This operation is called "pushing" at the vertex
$v$.) By applying a special case of unpublished work of S.X.Wu, we find
elementary number-theoretic necessary and sufficient conditions for the
existence of a hamiltonian cycle in $P(\vec C_m \mathbin{\Box} \vec C_n)$.
  A consequence is that if $P(\vec C_m \mathbin{\Box} \vec C_n)$ is
hamiltonian, then $\gcd(m,n) = 1$, which implies that $\vec C_m \mathbin{\Box}
\vec C_n$ is not hamiltonian. This final conclusion verifies a conjecture of
J.B.Klerlein and E.C.Carr.


title:
Deep Interactive Motion Prediction and Planning: Playing Games with
  Motion Prediction Models

abstract:
  In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not
informed by the planned trajectory of the AV. This work presents a module that
tightly couples these layers via a game-theoretic Model Predictive Controller
(MPC) that uses a novel interactive multi-agent neural network policy as part
of its predictive model. In our setting, the MPC planner considers all the
surrounding agents by informing the multi-agent policy with the planned state
sequence. Fundamental to the success of our method is the design of a novel
multi-agent policy network that can steer a vehicle given the state of the
surrounding agents and the map information. The policy network is trained
implicitly with ground-truth observation data using backpropagation through
time and a differentiable dynamics model to roll out the trajectory forward in
time. Finally, we show that our multi-agent policy network learns to drive
while interacting with the environment, and, when combined with the
game-theoretic MPC planner, can successfully generate interactive behaviors.


title: Action-Conditioned Contrastive Policy Pretraining

abstract:
  Deep visuomotor policy learning achieves promising results in control tasks
such as robotic manipulation and autonomous driving, where the action is
generated from the visual input by the neural policy. However, it requires a
huge number of online interactions with the training environment, which limits
its real-world application. Compared to the popular unsupervised feature
learning for visual recognition, feature pretraining for visuomotor control
tasks is much less explored. In this work, we aim to pretrain policy
representations for driving tasks using hours-long uncurated YouTube videos. A
new contrastive policy pretraining method is developed to learn
action-conditioned features from video frames with action pseudo labels.
Experiments show that the resulting action-conditioned features bring
substantial improvements to the downstream reinforcement learning and imitation
learning tasks, outperforming the weights pretrained from previous unsupervised
learning methods. Code and models will be made publicly available.


title:
SE(3)-Equivariant Attention Networks for Shape Reconstruction in
  Function Space

abstract:
  We propose the first SE(3)-equivariant coordinate-based network for learning
occupancy fields from point clouds. In contrast to previous shape
reconstruction methods that align the input to a regular grid, we operate
directly on the irregular, unoriented point cloud. We leverage attention
mechanisms in order to preserve the set structure (permutation equivariance and
variable length) of the input. At the same time, attention layers enable local
shape modelling, a crucial property for scalability to large scenes. In
contrast to architectures that create a global signature for the shape, we
operate on local tokens. Given an unoriented, sparse, noisy point cloud as
input, we produce equivariant features for each point. These serve as keys and
values for the subsequent equivariant cross-attention blocks that parametrize
the occupancy field. By querying an arbitrary point in space, we predict its
occupancy score. We show that our method outperforms previous SO(3)-equivariant
methods, as well as non-equivariant methods trained on SO(3)-augmented
datasets. More importantly, local modelling together with SE(3)-equivariance
create an ideal setting for SE(3) scene reconstruction. We show that by
training only on single objects and without any pre-segmentation, we can
reconstruct a novel scene with single-object performance.


title:
A Piecewise Learning Framework for Control of Unknown Nonlinear Systems
  with Stability Guarantees

abstract:
  We propose a piecewise learning framework for controlling nonlinear systems
with unknown dynamics. While model-based reinforcement learning techniques in
terms of some basis functions are well known in the literature, when it comes
to more complex dynamics, only a local approximation of the model can be
obtained using a limited number of bases. The complexity of the identifier and
the controller can be considerably high if obtaining an approximation over a
larger domain is desired. To overcome this limitation, we propose a general
piecewise nonlinear framework where each piece is responsible for locally
learning and controlling over some region of the domain. We obtain rigorous
uncertainty bounds for the learned piecewise models. The piecewise affine (PWA)
model is then studied as a special case, for which we propose an
optimization-based verification technique for stability analysis of the
closed-loop system. Accordingly, given a time-discretization of the learned
{PWA} system, we iteratively search for a common piecewise Lyapunov function in
a set of positive definite functions, where a non-monotonic convergence is
allowed. This Lyapunov candidate is verified on the uncertain system to either
provide a certificate for stability or find a counter-example when it fails.
This counter-example is added to a set of samples to facilitate the further
learning of a Lyapunov function. We demonstrate the results on two examples and
show that the proposed approach yields a less conservative region of attraction
(ROA) compared with alternative state-of-the-art approaches. Moreover, we
provide the runtime results to demonstrate potentials of the proposed framework
in real-world implementations.


title: KiDS-1000: cosmic shear with enhanced redshift calibration

abstract:
  We present a cosmic shear analysis with an improved redshift calibration for
the fourth data release of the Kilo-Degree Survey (KiDS-1000) using
self-organising maps (SOMs). Compared to the previous analysis of the KiDS-1000
data, we expand the redshift calibration sample to more than twice its size,
now consisting of data of 17 spectroscopic redshift campaigns, and
significantly extending the fraction of KiDS galaxies we are able to calibrate
with our SOM redshift methodology. We then enhance the calibration sample with
precision photometric redshifts from COSMOS2015 and the Physics of the
Accelerated Universe Survey (PAUS), allowing us to fill gaps in the
spectroscopic coverage of the KiDS data. Finally we perform a Complete
Orthogonal Sets of E/B-Integrals (COSEBIs) cosmic shear analysis of the newly
calibrated KiDS sample. We find $S_8 = 0.748_{-0.025}^{+0.021}$, which is in
good agreement with previous KiDS studies and increases the tension with
measurements of the cosmic microwave background to 3.4{\sigma}. We repeat the
redshift calibration with different subsets of the full calibration sample and
obtain, in all cases, agreement within at most 0.5{\sigma} in $S_8$ compared to
our fiducial analysis. Including additional photometric redshifts allows us to
calibrate an additional 6 % of the source galaxy sample. Even though further
systematic testing with simulated data is necessary to quantify the impact of
redshift outliers, precision photometric redshifts can be beneficial at high
redshifts and to mitigate selection effects commonly found in spectroscopically
selected calibration samples.


title:
SALISA: Saliency-based Input Sampling for Efficient Video Object
  Detection

abstract:
  High-resolution images are widely adopted for high-performance object
detection in videos. However, processing high-resolution inputs comes with high
computation costs, and naive down-sampling of the input to reduce the
computation costs quickly degrades the detection performance. In this paper, we
propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for
video object detection that allows for heavy down-sampling of unimportant
background regions while preserving the fine-grained details of a
high-resolution image. The resulting image is spatially smaller, leading to
reduced computational costs while enabling a performance comparable to a
high-resolution input. To achieve this, we propose a differentiable resampling
module based on a thin plate spline spatial transformer network (TPS-STN). This
module is regularized by a novel loss to provide an explicit supervision signal
to learn to "magnify" salient regions. We report state-of-the-art results in
the low compute regime on the ImageNet-VID and UA-DETRAC video object detection
datasets. We demonstrate that on both datasets, the mAP of an EfficientDet-D1
(EfficientDet-D2) gets on par with EfficientDet-D2 (EfficientDet-D3) at a much
lower computational cost. We also show that SALISA significantly improves the
detection of small objects. In particular, SALISA with an EfficientDet-D1
detector improves the detection of small objects by $77\%$, and remarkably also
outperforms EfficientDetD3 baseline.


title: fBLS -- a fast-folding BLS algorithm

abstract:
  We present fBLS -- a novel fast-folding technique to search for transiting
planets, based on the fast-folding algorithm (FFA), which is extensively used
in pulsar astronomy. For a given lightcurve with $N$ data points, fBLS
simultaneously produces all the binned phase-folded lightcurves for an array of
$N_p$ trial periods. For each folded lightcurve produced by fBLS, the algorithm
generates the standard BLS periodogram and statistics. The number of performed
arithmetic operations is $\mathcal{O}\big(N_p\cdot\log N_p \big)$, while
regular BLS requires $\mathcal{O}\big(N_p\cdot N\big)$ operations. fBLS can be
used to detect small rocky transiting planets, with periods shorter than one
day, a period range for which the computation is extensive. We demonstrate the
capabilities of the new algorithm by performing a preliminary fBLS search for
planets with ultra-short periods in the Kepler main-sequence lightcurves. In
addition, we developed a simplistic signal validation scheme for vetting the
planet candidates. This two-stage preliminary search identified all known
ultra-short planet candidates and found three new ones.


title:
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer
  Classification

abstract:
  The automatic early diagnosis of prodromal stages of Alzheimer's disease is
of great relevance for patient treatment to improve quality of life. We address
this problem as a multi-modal classification task. Multi-modal data provides
richer and complementary information. However, existing techniques only
consider either lower order relations between the data and single/multi-modal
imaging data. In this work, we introduce a novel semi-supervised hypergraph
learning framework for Alzheimer's disease diagnosis. Our framework allows for
higher-order relations among multi-modal imaging and non-imaging data whilst
requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy
for constructing a robust hypergraph that preserves the data semantics. We
achieve this by enforcing perturbation invariance at the image and graph levels
using a contrastive based mechanism. Secondly, we present a dynamically
adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the
predictive uncertainty. We demonstrate, through our experiments, that our
framework is able to outperform current techniques for Alzheimer's disease
diagnosis.


title: What can predictive speech coders learn from speaker recognizers?

abstract:
  This paper compares the speech coder and speaker recognizer applications,
showing some parallelism between them. In this paper, some approaches used for
speaker recognition are applied to speech coding in order to improve the
prediction accuracy. Experimental results show an improvement in Segmental SNR
(SEGSNR).