title:
Adorned in Memes: Exploring the Adoption of Social Wearables in Nordic
  Student Culture

abstract:
  Social wearables promise to augment and enhance social interactions. However,
despite two decades of HCI research on wearables, we are yet to see widespread
adoption of social wearables into everyday life. More in-situ investigations
into the social dynamics and cultural practices afforded by wearing interactive
technology are needed to understand the drivers and barriers to adoption. To
this end, we study social wearables in the context of Nordic student culture
and the students' practice of adorning boiler suits. Through a co-creation
process, we designed Digi Merkki, a personalised interactive clothing patch. In
a two-week elicitation diary study, we captured how 16 students adopted Digi
Merkki into their social practices. We found that Digi Merkki afforded a
variety of social interaction strategies, including sharing, spamming, and
stealing pictures, which supported meaning-making and community-building. Based
on our findings, we articulate Memetic Expression as a strong concept for
designing social wearables.


title:
Characterizing the organizational diversity of protein interaction
  networks across three domains of life

abstract:
  Networks exist everywhere in nature from the physical, chemical, biological
or social worlds to the designed spheres. To explore, if there exists some
higher-order organization that can be exploited to distinguish different types
of networks, we study 4,738 protein interaction networks (PINs) belonging to 16
phyla encompassing all the three domains of life. Our method utilizes
positional information of a network's nodes by appropriately normalizing the
frequency of automorphic orbits appearing in the induced graphlets of sizes
2-5. There are some evolutionary constraints imposed on the network's topology
which shape its local architecture as well as its behavior. According to these
rules (features), each type of network occupies its respective position within
a common network space. A deep neural network was trained on differentially
expressed orbits resulting in a prediction accuracy of 85%. Our results
indicate that nature has, probably, allocated a specific band of design space
to various superfamilies of PINs.


title:
Zvonkin's transform and the regularity of solutions to double divergence
  form elliptic equations

abstract:
  We study qualitative properties of solutions to double divergence form
elliptic equations (or stationary Kolmogorov equations) on~$\mathbb{R}^d$. It
is shown that the Harnack inequality holds for nonnegative solutions if the
diffusion matrix $A$ is nondegenerate and satisfies the Dini mean oscillation
condition and the drift coefficient $b$ is locally integrable to a power $p>d$.
We establish new estimates for the $L^p$-norms of solutions and obtain a
generalization of the known theorem of Hasminskii on the existence of a
probability solution to the stationary Kolmogorov equation to the case where
the matrix $A$ satisfies Dini's condition or belongs to the class VMO. These
results are based on a new analytic version of Zvonkin's transform of the drift
coefficient.


title: A characterization of $\dot W^{1,p}(\mathbb R^d)$

abstract:
  For $1<p<\infty$ we give a characterization of the Sobolev space $\dot
W^{1,p}(\mathbb R^d)$ in terms of the oscillations of a function on balls of
varying centers and radii. Our work is motivated both by the study of trace
ideal properties of commutators with singular integral operators and by work of
Nguyen and by Brezis, Van Schaftingen and Yung on derivative-free
characterizations of Sobolev spaces.


title:
Accurate and efficient band-gap predictions for metal halide perovskites
  at finite temperature

abstract:
  We develop a computationally efficient scheme to accurately determine
finite-temperature band gaps. We here focus on materials belonging to the class
ABX3 (A = Rb, Cs; B = Ge, Sn, Pb; and X = F, Cl, Br, I), which includes halide
perovskites. First, an initial estimate of the band gap is provided for the
ideal crystalline structure through the use of a range-separated hybrid
functional, in which the parameters are determined nonempirically from the
electron density and the high-frequency dielectric constant. Next, we consider
two kinds of band-gap corrections to account for spin-orbit coupling and
thermal vibrations including zero-point motions. In particular, the latter
effect is accounted for through the special displacement method, which consists
in using a single distorted configuration obtained from the vibrational
frequencies and eigenmodes, thereby avoiding lengthy molecular dynamics. The
sequential consideration of both corrections systematically improves the band
gaps, reaching a mean absolute error of 0.17 eV with respect to experimental
values. The computational efficiency of our scheme stems from the fact that
only a single calculation at the hybrid-functional level is required and that
it is sufficient to evaluate the corrections at the semilocal level of theory.
Our scheme is particularly convenient for large-size systems and for the
screening of large databases of materials.


title: Framework for Network-Constrained Target Tracking

abstract:
  The increase in perception capabilities of connected mobile sensor platforms
(e.g., self-driving vehicles, drones, and robots) leads to an extensive surge
of sensed features at various temporal and spatial scales. Beyond their
traditional use for safe operation, available observations could enable to see
how and where people move on sidewalks and cycle paths, to eventually obtain a
complete microscopic and macroscopic picture of the traffic flows in a larger
area. This paper proposes a new method for advanced traffic applications,
tracking an unknown and varying number of moving targets (e.g., pedestrians or
cyclists) constrained by a road network, using mobile (e.g., vehicles)
spatially distributed sensor platforms. The key contribution in this paper is
to introduce the concept of network bound targets into the multi-target
tracking problem, and hence to derive a network-constrained multi-hypotheses
tracker (NC-MHT) to fully utilize the available road information. This is done
by introducing a target representation, comprising a traditional target
tracking representation and a discrete component placing the target on a given
segment in the network. A simulation study shows that the method performs well
in comparison to the standard MHT filter in free space. Results particularly
highlight network-constraint effects for more efficient target predictions over
extended periods of time, and in the simplification of the measurement
association process, as compared to not utilizing a network structure. This
theoretical work also directs attention to latent privacy concerns for
potential applications.


title: Improving the Diversity of Bootstrapped DQN via Noisy Priors

abstract:
  Q-learning is one of the most well-known Reinforcement Learning algorithms.
There have been tremendous efforts to develop this algorithm using neural
networks. Bootstrapped Deep Q-Learning Network is amongst one of them. It
utilizes multiple neural network heads to introduce diversity into Q-learning.
Diversity can sometimes be viewed as the amount of reasonable moves an agent
can take at a given state, analogous to the definition of the exploration ratio
in RL. Thus, the performance of Bootstrapped Deep Q-Learning Network is deeply
connected with the level of diversity within the algorithm. In the original
research, it was pointed out that a random prior could improve the performance
of the model. In this article, we further explore the possibility of treating
priors as a special type of noise and sample priors from a Gaussian
distribution to introduce more diversity into this algorithm. We conduct our
experiment on the Atari benchmark and compare our algorithm to both the
original and other related algorithms. The results show that our modification
of the Bootstrapped Deep Q-Learning algorithm achieves significantly higher
evaluation scores across different types of Atari games. Thus, we conclude that
noisy priors can improve Bootstrapped Deep Q-Learning's performance by ensuring
the integrity of diversities.


title:
Sequential Offloading for Distributed DNN Computation in Multiuser MEC
  Systems

abstract:
  This paper studies a sequential task offloading problem for a multiuser
mobile edge computing (MEC) system. We consider a dynamic optimization
approach, which embraces wireless channel fluctuations and random deep neural
network (DNN) task arrivals over an infinite horizon. Specifically, we
introduce a local CPU workload queue (WD-QSI) and an MEC server workload queue
(MEC-QSI) to model the dynamic workload of DNN tasks at each WD and the MEC
server, respectively. The transmit power and the partitioning of the local DNN
task at each WD are dynamically determined based on the instantaneous channel
conditions (to capture the transmission opportunities) and the instantaneous
WD-QSI and MEC-QSI (to capture the dynamic urgency of the tasks) to minimize
the average latency of the DNN tasks. The joint optimization can be formulated
as an ergodic Markov decision process (MDP), in which the optimality condition
is characterized by a centralized Bellman equation. However, the brute force
solution of the MDP is not viable due to the curse of dimensionality as well as
the requirement for knowledge of the global state information. To overcome
these issues, we first decompose the MDP into multiple lower dimensional
sub-MDPs, each of which can be associated with a WD or the MEC server. Next, we
further develop a parametric online Q-learning algorithm, so that each sub-MDP
is solved locally at its associated WD or the MEC server. The proposed solution
is completely decentralized in the sense that the transmit power for sequential
offloading and the DNN task partitioning can be determined based on the local
channel state information (CSI) and the local WD-QSI at the WD only.
Additionally, no prior knowledge of the distribution of the DNN task arrivals
or the channel statistics will be needed for the MEC server.


title:
Stability estimate for an inverse problem for the time harmonic magnetic
  Schr\"odinger operator from the near and far field pattern

abstract:
  We derive conditional stability estimates for inverse scattering problems
related to time harmonic magnetic Schr\"odinger equation. We prove logarithmic
type estimates for retrieving the magnetic (up to a gradient) and electric
potentials from near field or far field maps. Our approach combines techniques
from similar results obtained in the literature for inhomogeneous inverse
scattering problems based on the use of geometrical optics solutions.


title:
Impact of quantum noise on the training of quantum Generative
  Adversarial Networks

abstract:
  Current noisy intermediate-scale quantum devices suffer from various sources
of intrinsic quantum noise. Overcoming the effects of noise is a major
challenge, for which different error mitigation and error correction techniques
have been proposed. In this paper, we conduct a first study of the performance
of quantum Generative Adversarial Networks (qGANs) in the presence of different
types of quantum noise, focusing on a simplified use case in high-energy
physics. In particular, we explore the effects of readout and two-qubit gate
errors on the qGAN training process. Simulating a noisy quantum device
classically with IBM's Qiskit framework, we examine the threshold of error
rates up to which a reliable training is possible. In addition, we investigate
the importance of various hyperparameters for the training process in the
presence of different error rates, and we explore the impact of readout error
mitigation on the results.


title: UAV-Aided Decentralized Learning over Mesh Networks

abstract:
  Decentralized learning empowers wireless network devices to collaboratively
train a machine learning (ML) model relying solely on device-to-device (D2D)
communication. It is known that the convergence speed of decentralized
optimization algorithms severely depends on the degree of the network
connectivity, with denser network topologies leading to shorter convergence
time. Consequently, local connectivity of real world mesh networks, due to the
limited communication range of its wireless nodes, undermines the efficiency of
decentralized learning protocols, rendering them potentially impracticable. In
this work we investigate the role of an unmanned aerial vehicle (UAV), used as
flying relay, in facilitating decentralized learning procedures in such
challenging conditions. We propose an optimized UAV trajectory, that is defined
as a sequence of waypoints that the UAV visits sequentially in order to
transfer intelligence across sparsely connected group of users. We then provide
a series of experiments highlighting the essential role of UAVs in the context
of decentralized learning over mesh networks.


title:
Search for neutral long-lived particles in $pp$ collisions at
  $\sqrt{s}=13$ TeV that decay into displaced hadronic jets in the ATLAS
  calorimeter

abstract:
  A search for decays of pair-produced neutral long-lived particles (LLPs) is
presented using 139 fb$^{-1}$ of proton-proton collision data collected by the
ATLAS detector at the LHC in 2015-2018 at a centre-of-mass energy of 13 TeV.
Dedicated techniques were developed for the reconstruction of displaced jets
produced by LLPs decaying hadronically in the ATLAS hadronic calorimeter. Two
search regions are defined for different LLP kinematic regimes. The observed
numbers of events are consistent with the expected background, and limits for
several benchmark signals are determined. For a SM Higgs boson with a mass of
125 GeV, branching ratios above 10% are excluded at 95% confidence level for
values of $c$ times LLP mean proper lifetime in the range between 20 mm and 10
m depending on the model. Upper limits are also set on the cross-section times
branching ratio for scalars with a mass of 60 GeV and for masses between 200
GeV and 1 TeV.


title:
Pomeranchuk Effect and Tunable Quantum Phase Transitions in
  3L-MoTe2/WSe2

abstract:
  Many sought-after exotic states of matter are known to emerge close to
quantum phase transitions, such as quantum spin liquids (QSL) and
unconventional superconductivity. It is thus desirable to experimentally
explore systems that can be continuously tuned across these transitions. Here,
we demonstrate such tunability and the electronic correlation effects in
triangular moir\'e superlattices formed between trilayer MoTe$_2$ and monolayer
WSe$_2$ (3L-MoTe$_2$/WSe$_2$). Through electric transport measurements, we
firmly establish the Pomeranchuk effect observed at half filling of the first
moir\'e subband, where increasing temperature paradoxically enhances charge
localization. The system simultaneously exhibits the characteristic of a Fermi
liquid with strongly renormalized effective mass, suggesting a correlated metal
state. The state is highly susceptible to out-of-plane electric and magnetic
fields, which induce a Lifshitz transition and a metal-insulator transition
(MIT), respectively. It enables identification of a tricritical point in the
quantum phase diagram at the base temperature. We explain the Lifshitz
transition in terms of interlayer charge transfer by applying the vertical
electric field, which leads to the emergence of a new Fermi surface and
immediate suppression of the Pomeranchuk effect. The existence of quantum
criticality in the magnetic filed induced MIT is supported by scaling behaviors
of the resistance. Our work shows the 3L-MoTe$_2$/WSe$_2$ lies in the vicinity
to the MIT point of the triangular lattice Hubbard model, rendering it a unique
system to manifest the rich correlation effects at an intermediate interaction
strength.


title:
Chemical composition and ages of four globular clusters in M31 from the
  analysis of their integrated-light spectra

abstract:
  We compare the results on the chemical composition of four globular clusters
(GCs) in M31 (Bol 6, Bol 20, Bol 45, and Bol 50) (Maricheva M. 2021). Study of
integrated spectra of four globular clusters in M31. Astrophys. Bull.
76:389-404. doi: https://doi.org/10.1134/S199034132104009X) to the available
literature data on integrated-light spectroscopy of GCs with similar ages and
chemical abundances in our Galaxy and M31 and on the chemical abundances of
stars in two galaxies. The clusters and their literature analogues are of
moderate metallicity -1.1<[Fe/H]<-0.75 dex and old (10 Gyr). Mg, Ca, and C
abundances of four GCs are higher than literature estimates for the GCs in M31
with [Fe/H]~-1 dex obtained using high-resolution integrated-light spectroscopy
methods. Using literature data, we did not find complete analogues for the
studied clusters in our Galaxy and M31 in terms of age, helium mass fraction
(Y), and chemical composition. The alpha-element abundances in four clusters
are about 0.2 dex higher than the average for stars in the Galactic field at
[Fe/H]~-1 dex. We suggest that these and M. Maricheva's (Maricheva M. 2021)
findings about lower metallicities of the studied objects than the average
metallicity of red giants in the M31 halo and about the abundances of
alpha-process elements in them corresponding to the average value for stars in
the M31 inner halo likely indicate that the star formation process in the
vicinity of M31 at the time of our sample cluster formation was complex with
the inflow of fresh gas from the intergalactic medium and violent star forming
events associated with SNe II bursts.


title: Continual Feature Selection: Spurious Features in Continual Learning

abstract:
  Continual Learning (CL) is the research field addressing learning settings
where the data distribution is not static. This paper studies spurious
features' influence on continual learning algorithms. Indeed, we show that
learning algorithms solve tasks by overfitting features that are not
generalizable. To better understand these phenomena and their impact, we
propose a domain incremental scenario that we study through various
out-of-distribution generalizations and continual learning algorithms. The
experiments of this paper show that continual learning algorithms face two
related challenges: (1) the spurious features challenge: some features are well
correlated with labels in train data but not in test data due to a covariate
shift between train and test. (2) the local spurious features challenge: some
features correlate well with labels within a task but not within the whole task
sequence. The challenge is to learn general features that are neither spurious
(in general) nor locally spurious. We prove that the latter is a major cause of
performance decrease in continual learning along with catastrophic forgetting.
Our results indicate that the best solution to overcome the feature selection
problems varies depending on the correlation between spurious features (SFs)
and labels. The vanilla replay approach seems to be a powerful approach to deal
with SFs, which could explain its good performance in the continual learning
literature. This paper presents a different way of understanding performance
decrease in continual learning by describing the influence of spurious/local
spurious features.


title: Shell-model calculation of $^{100}$Mo double-$\beta$ decay

abstract:
  For the first time, the calculation of the nuclear matrix element of the
double-$\beta$ decay of $^{100}$Mo, with and without the emission of two
neutrinos, is performed in the framework of the nuclear shell model. This task
is accomplished starting from a realistic nucleon-nucleon potential, then the
effective shell-model Hamiltonian and decay operators are derived within the
many-body perturbation theory. The exotic features which characterize the
structure of Mo isotopes -- such as shape coexistence and triaxiality softness
-- push the shell-model computational problem beyond its present limits, making
it necessary to truncate the model space. This has been done with the goal to
preserve as much as possible the role of the rejected degrees of freedom in an
effective approach that has been introduced and tested in previous studies.
This procedure is grounded on the analysis of the effective single-particle
energies of a large-scale shell-model Hamiltonian, that leads to a truncation
of the number of the orbitals belonging to the model space. Then, the original
Hamiltonian generates a new one by way of a unitary transformation onto the
reduced model space, to retain effectively the role of the excluded
single-particle orbitals. The predictivity of our calculation of the nuclear
matrix element for the neutrinoless double-$\beta$ decay of $^{100}$Mo is
supported by the comparison with experiment of the calculated spectra,
electromagnetic transition strengths, Gamow-Teller transition strengths and the
two-neutrino double-beta nuclear matrix elements.


title:
Cumulative Merging Percolation: A long-range percolation process in
  networks

abstract:
  Percolation on networks is a common framework to model a wide range of
processes, from cascading failures to epidemic spreading. Standard percolation
assumes short-range interactions, implying that nodes can merge into clusters
only if they are nearest-neighbors. Cumulative Merging Percolation (CMP) is an
new percolation process that assumes long-range interactions, such that nodes
can merge into clusters even if they are topologically distant. Hence in CMP
percolation clusters do not coincide with the topological connected components
of the network. Previous work has shown that a specific formulation of CMP
features peculiar mechanisms for the formation of the giant cluster, and allows
to model different network dynamics such as recurrent epidemic processes. Here
we develop a more general formulation of CMP in terms of the functional form of
the cluster interaction range, showing an even richer phase transition scenario
with competition of different mechanisms resulting in crossover phenomena. Our
analytic predictions are confirmed by numerical simulations.


title: On birational Torelli theorems

abstract:
  Let $G$ be a simple simply-connected connected linear algebraic group over
$\mathbb{C}$. We proved a $2$-birational Torelli theorem for the moduli space
of semistable principal $G$-bundles over a smooth curve of genus $\geq 3$,
which says that if two such moduli spaces are $2$-birational then the curves
are isomorphic. We also proved a $3$-birational Torelli theorem for the moduli
space of stable symplectic parabolic bundles over a smooth curve of genus $\geq
4$.


title: The Theoretical Expressiveness of Maxpooling

abstract:
  Over the decade since deep neural networks became state of the art image
classifiers there has been a tendency towards less use of max pooling: the
function that takes the largest of nearby pixels in an image. Since max pooling
featured prominently in earlier generations of image classifiers, we wish to
understand this trend, and whether it is justified. We develop a theoretical
framework analyzing ReLU based approximations to max pooling, and prove a sense
in which max pooling cannot be efficiently replicated using ReLU activations.
We analyze the error of a class of optimal approximations, and find that whilst
the error can be made exponentially small in the kernel size, doing so requires
an exponentially complex approximation.
  Our work gives a theoretical basis for understanding the trend away from max
pooling in newer architectures. We conclude that the main cause of a difference
between max pooling and an optimal approximation, a prevalent large difference
between the max and other values within pools, can be overcome with other
architectural decisions, or is not prevalent in natural images.


title: TableFormer: Table Structure Understanding with Transformers

abstract:
  Tables organize valuable content in a concise and compact representation.
This content is extremely valuable for systems such as search engines,
Knowledge Graph's, etc, since they enhance their predictive capabilities.
Unfortunately, tables come in a large variety of shapes and sizes. Furthermore,
they can have complex column/row-header configurations, multiline rows,
different variety of separation lines, missing entries, etc. As such, the
correct identification of the table-structure from an image is a non-trivial
task. In this paper, we present a new table-structure identification model. The
latter improves the latest end-to-end deep learning model (i.e.
encoder-dual-decoder from PubTabNet) in two significant ways. First, we
introduce a new object detection decoder for table-cells. In this way, we can
obtain the content of the table-cells from programmatic PDF's directly from the
PDF source and avoid the training of the custom OCR decoders. This
architectural change leads to more accurate table-content extraction and allows
us to tackle non-english tables. Second, we replace the LSTM decoders with
transformer based decoders. This upgrade improves significantly the previous
state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple
tables and from 88.7% to 95% on complex tables.


title:
A Radial Velocity Study of the Planetary System of Pi Mensae: Improved
  Planet Parameters for PI Mensae c and a Third Planet on a 125-d Orbit

abstract:
  Pi Men hosts a transiting planet detected by the TESS space mission and an
outer planet in a 5.7-yr orbit discovered by RV surveys. We studied this system
using new radial velocity (RV) measurements taken with the HARPS spectrograph
on ESO's 3.6-m telescope as well as archival data. We constrain the stellar RV
semi-amplitude due to the transiting planet, Pi Men c, as K_c = 1.21 +/- 0.12
m/s resulting in a planet mass of M_c = 3.63 +/- 0.38 M_Earth. A planet radius
of R_c= 2.145 +/- 0.015 R_Earth yields a bulk density of rho = 2.03 +/- 0.22
g/cm^{-3}. The precisely determined density of this planet and the brightness
of the host star make Pi Men c an excellent laboratory for internal structure
and atmospheric characterization studies. Our HARPS RV measurements also reveal
compelling evidence for a third body, PI Men d, with a minimum mass M sin i =
13.38 +/- 1.35 M_Earth orbiting with a period of P_d = 125 d on an eccentric
orbit (e = 0.22). A simple dynamical analysis indicates that the orbit of Pi
Men d is stable on timescales of at least 20 Myrs. Given the mutual inclination
between the outer gaseous giant and the inner rocky planet and the presence of
a third body at 125 d, Pi Men is an important planetary system for dynamical
and formation studies.


title:
Topological equivalence of submersion functions and topological
  equivalence of their foliations on the plane: the linear-like case

abstract:
  Let $f, g: \mathbb{R}^2 \to \mathbb{R}$ be two submersion functions and
$\mathscr{F}(f)$ and $\mathscr{F}(g)$ be the regular foliations of
$\mathbb{R}^2$ whose leaves are the connected components of the levels sets of
$f$ and $g$, respectively. The topological equivalence of $f$ and $g$ implies
the topological equivalence of $\mathscr{F}(f)$ and $\mathscr{F}(g)$, but the
converse is not true, in general. In this paper, we introduce the class of
linear-like submersion functions, which is wide enough in order to contain
non-trivial behaviors, and provide conditions for the validity of the converse
implication for functions inside this class. Our results lead us to a complete
topological invariant for topological equivalence in a certain subclass of
linear-like submersion functions.


title:
Existence and uniqueness of limits at infinity for homogeneous Sobolev
  functions

abstract:
  We establish the existence and uniqueness of limits at infinity along
infinite curves outside a zero modulus family for functions in a homogeneous
Sobolev space under the assumption that the underlying space is equipped with a
doubling measure which supports a Poincar\'e inequality. We also characterize
the settings where this conclusion is nontrivial. Secondly, we introduce
notions of weak polar coordinate systems and radial curves on metric measure
spaces. Then sufficient and necessary conditions for existence of radial limits
are given. As a consequence, we characterize the existence of radial limits in
certain concrete settings.


title: From Short-Range to Mean-Field Models in Quantum Lattices

abstract:
  Realistic effective interparticle interactions of quantum many-body systems
are widely seen as being short-range. However, the rigorous mathematical
analysis of this type of model turns out to be extremely difficult, in general,
with many important fundamental questions remaining open still nowadays. By
contrast, mean-field models come from different approximations or Ans\"{a}tze,
and are thus less realistic, in a sense, but are technically advantageous, by
allowing explicit computations while capturing surprisingly well many real
physical phenomena. Here, we establish a precise mathematical relation between
mean-field and short-range models, by using the long-range limit that is known
in the literature as the Kac limit. If both attractive and repulsive long-range
forces are present then it turns out that the limit mean-field model is not
necessarily what one traditionally guesses. One important innovation of our
study, in contrast with previous works on the subject, is the fact that we are
able to show the convergence of equilibrium states, i.e., of all correlation
functions. This paves the way for studying phase transitions, or at least
important fingerprints of them like strong correlations at long distances, for
models having interactions whose ranges are finite, but very large. It also
sheds a new light on mean-field models. Even on the level of pressures, our
results go considerably further than previous ones, by allowing, for instance,
a continuum of long-range interaction components, as well as very general
short-range Hamiltonians for the "free" part of the model.


title:
Suppression of the TeV pair-beam plasma instability by a tangled weak
  intergalactic magnetic field

abstract:
  We study the effect of a tangled sub-fG level intergalactic magnetic field
(IGMF) on the electrostatic instability of a blazar-induced pair beam.
Sufficiently strong IGMF may significantly deflect the TeV pair beams, which
would reduce the flux of secondary cascade emission below the observational
limits. A similar flux reduction may result from the electrostatic beam-plasma
instability, which operates the best in the absence of IGMF. Considering IGMF
with correlation lengths smaller than a kpc, we find that weak magnetic fields
increase the transverse momentum of the pair beam particles, which dramatically
reduces the linear growth rate of the electrostatic instability and hence the
energy-loss rate of the pair beam. We show that the beam-plasma instability is
eliminated as an effective energy-loss agent at a field strength three orders
of magnitude below that needed to suppress the secondary cascade emission by
magnetic deflection. For intermediate-strength IGMF, we do not know a viable
process to explain the observed absence of GeV-scale cascade emission.


title:
Nuclear Structure with Discrete Non-Orthogonal Shell-Model : new
  frontiers

abstract:
  We present developments and applications for the diagonalization of
shell-model hamiltonians in a discrete non-orthogonal basis (DNO-SM). The
method, and its actual numerical implementation CARINA, based on mean-field and
beyond-mean field techniques has already been applied in previous studies and
is focused on basis states selection optimization. The method is benchmarked
against a full set of $sd$ shell exact diagonalizations, and is applied for the
first time to the heavy deformed $^{254}$No nucleus.


title: On the Configuration of More and Less Expressive Logic Programs

abstract:
  The decoupling between the representation of a certain problem, i.e., its
knowledge model, and the reasoning side is one of main strong points of
model-based Artificial Intelligence (AI). This allows, e.g. to focus on
improving the reasoning side by having advantages on the whole solving process.
Further, it is also well-known that many solvers are very sensitive to even
syntactic changes in the input. In this paper, we focus on improving the
reasoning side by taking advantages of such sensitivity. We consider two
well-known model-based AI methodologies, SAT and ASP, define a number of
syntactic features that may characterise their inputs, and use automated
configuration tools to reformulate the input formula or program. Results of a
wide experimental analysis involving SAT and ASP domains, taken from respective
competitions, show the different advantages that can be obtained by using input
reformulation and configuration. Under consideration in Theory and Practice of
Logic Programming (TPLP).


title: ReZone: Disarming TrustZone with TEE Privilege Reduction

abstract:
  In TrustZone-assisted TEEs, the trusted OS has unrestricted access to both
secure and normal world memory. Unfortunately, this architectural limitation
has opened an aisle of exploration for attackers, which have demonstrated how
to leverage a chain of exploits to hijack the trusted OS and gain full control
of the system, targeting (i) the rich execution environment (REE), (ii) all
trusted applications (TAs), and (iii) the secure monitor. In this paper, we
propose ReZone. The main novelty behind ReZone design relies on leveraging
TrustZone-agnostic hardware primitives available on commercially off-the-shelf
(COTS) platforms to restrict the privileges of the trusted OS. With ReZone, a
monolithic TEE is restructured and partitioned into multiple sandboxed domains
named zones, which have only access to private resources. We have fully
implemented ReZone for the i.MX 8MQuad EVK and integrated it with Android OS
and OP-TEE. We extensively evaluated ReZone using microbenchmarks and
real-world applications. ReZone can sustain popular applications like
DRM-protected video encoding with acceptable performance overheads. We have
surveyed 80 CVE vulnerability reports and estimate that ReZone could mitigate
86.84% of them.


title:
Cryogenic bath-type heat exchangers for ultra-pure noble gas
  applications

abstract:
  Two cryogenic bath-type heat exchangers for ultra-pure noble gas applications
were developed with particular emphasis on noble gas liquefaction in cryogenic
distillation systems. The main objective was to construct heat exchangers for
xenon from materials that do not emanate radon and that fulfill ultra-high
vacuum standards. Therefore, only high-quality copper and stainless steel
materials were used. Especially, large-area oxygen-free copper fins with high
conductivity in a new design ensure efficient heat transfer. One bath-type
Xe-Xe heat exchanger was designed with a diameter of 50 cm to achieve a xenon
condensing capacity of at least 100 kg/h. In order to guarantee the necessary
heat transfer between the two xenon reservoirs, this heat exchanger features a
specially manufactured stainless steel flange with a copper plate welded
inside. We first tested our concept on a dedicated bath-type heat exchanger
with a reduced diameter of 30 cm using liquid nitrogen to liquefy the xenon. A
model based on conservative assumptions such as film boiling on the nitrogen
side and film condensation on the xenon side was developed and applied to
caluclate the expected heat transfer for our design. We were able to
demonstrate an adjustable xenon liquefaction rate of up to 113 kg/h limited
only by our measurement procedure at a cooling efficiency of $(0.98\pm 0.03)$
for the LN$_2$-Xe heat exchanger.


title: Learning in Sparse Rewards settings through Quality-Diversity algorithms

abstract:
  In the Reinforcement Learning (RL) framework, the learning is guided through
a reward signal. This means that in situations of sparse rewards the agent has
to focus on exploration, in order to discover which action, or set of actions
leads to the reward. RL agents usually struggle with this. Exploration is the
focus of Quality-Diversity (QD) methods. In this thesis, we approach the
problem of sparse rewards with these algorithms, and in particular with Novelty
Search (NS). This is a method that only focuses on the diversity of the
possible policies behaviors. The first part of the thesis focuses on learning a
representation of the space in which the diversity of the policies is
evaluated. In this regard, we propose the TAXONS algorithm, a method that
learns a low-dimensional representation of the search space through an
AutoEncoder. While effective, TAXONS still requires information on when to
capture the observation used to learn said space. For this, we study multiple
ways, and in particular the signature transform, to encode information about
the whole trajectory of observations. The thesis continues with the
introduction of the SERENE algorithm, a method that can efficiently focus on
the interesting parts of the search space. This method separates the
exploration of the search space from the exploitation of the reward through a
two-alternating-steps approach. The exploration is performed through NS. Any
discovered reward is then locally exploited through emitters. The third and
final contribution combines TAXONS and SERENE into a single approach: STAX.
Throughout this thesis, we introduce methods that lower the amount of prior
information needed in sparse rewards settings. These contributions are a
promising step towards the development of methods that can autonomously explore
and find high-performance policies in a variety of sparse rewards settings.


title:
Adaptive guaranteed lower eigenvalue bounds with optimal convergence
  rates

abstract:
  Guaranteed lower Dirichlet eigenvalue bounds (GLB) can be computed for the
$m$-th Laplace operator with a recently introduced extra-stabilized
nonconforming Crouzeix-Raviart ($m=1$) or Morley ($m=2$) finite element
eigensolver. Striking numerical evidence for the superiority of a new adaptive
eigensolver motivates the convergence analysis in this paper with a proof of
optimal convergence rates of the GLB towards a simple eigenvalue. The proof is
based on (a generalization of) known abstract arguments entitled as the axioms
of adaptivity. Beyond the known a priori convergence rates, a medius analysis
is enfolded in this paper for the proof of best-approximation results. This and
subordinated $L^2$ error estimates for locally refined triangulations appear of
independent interest. The analysis of optimal convergence rates of an adaptive
mesh-refining algorithm is performed in $3$D and highlights a new version of
discrete reliability.


title:
Anomalous temperature dependence of high-harmonic generation in Mott
  insulators

abstract:
  High-harmonic generation (HHG) is a fundamental nonlinear optical phenomenon,
whose scope has recently been extended from gases to solids. While HHG in
semiconductors can be understood within a single-electron picture, the
mechanisms underlying HHG in correlated materials remain to be clarified. Here,
we theoretically reveal the crucial effect of spin-charge coupling on HHG in
Mott insulators. In a system with antiferromagnetic correlations, the HHG
signal is drastically enhanced with decreasing temperature, even though the gap
increases and the production of charge carriers is suppressed. This anomalous
behavior, which has also been observed in recent HHG experiments on
Ca$_2$RuO$_4$, originates from a cooperative effect between the spin-charge
coupling and the thermal ensemble, and the resulting strongly
temperature-dependent coherence between charge carriers. Our results
demonstrate that correlations between different degrees of freedom, which are a
characteristic feature of strongly correlated solids, have significant and
nontrivial effects on nonlinear optical responses.


title:
Structural Gaussian Priors for Bayesian CT reconstruction of Subsea
  Pipes

abstract:
  A non-destructive testing (NDT) application of X-ray computed tomography (CT)
is inspection of subsea pipes in operation via 2D cross-sectional scans. Data
acquisition is time-consuming and costly due to the challenging subsea
environment. Reducing the number of projections in a scan can yield time and
cost savings, but compromises the reconstruction quality, if conventional
reconstruction methods are used. In this work we take a Bayesian approach to CT
reconstruction and focus on designing an effective prior to make use of
available structural information about the pipe geometry. We propose a new
class of structural Gaussian priors to enforce expected material properties in
different regions of the reconstructed image based on independent Gaussian
priors in combination with global regularity through a Gaussian Markov Random
Field (GMRF) prior. Numerical experiments with synthetic and real data show
that the proposed structural Gaussian prior can reduce artifacts and enhance
contrast in the reconstruction compared to using only a global GMRF prior or no
prior at all. We show how the resulting posterior distribution can be
efficiently sampled even for large-scale images, which is essential for
practical NDT applications.


title: Residual categories of quadric surface bundles

abstract:
  We show the residual categories of quadric surface bundles are equivalent to
the (twisted) derived categories of some scheme in two situations: (1) the
quadric surface bundle has a smooth section; (2) the total space and the base
of the quadric surface bundle are smooth and the base is a surface. We provide
two proofs in situation (1) describing the scheme as the hyperbolic reduction
and as a subscheme of the relative Hilbert scheme of lines, respectively. In
situation (2) the twisted scheme is obtained by performing birational
transformations to the relative Hilbert scheme of lines. As an application we
apply the results to certain complete intersections of quadrics.


title:
Graph Rewriting and Relabeling with PBPO+: A Unifying Theory for
  Quasitoposes

abstract:
  We extend the powerful Pullback-Pushout (PBPO) approach for graph rewriting
with strong matching. Our approach, called PBPO+, allows more control over the
embedding of the pattern in the host graph, which is important for a large
class of rewrite systems. We argue that PBPO+ can be considered a unifying
theory in the general setting of quasitoposes, by demonstrating that PBPO+ can
define all rewrite relations definable by PBPO, AGREE and DPO, as well as
additional ones. Additionally, we show that PBPO+ is well suited for rewriting
labeled graphs and some classes of attributed graphs, by introducing a lattice
structure on the label set and requiring graph morphisms to be
order-preserving.


title:
STV+AGR: Towards Practical Verification of Strategic Ability Using
  Assume-Guarantee Reasoning

abstract:
  We present a substantially expanded version of our tool STV for strategy
synthesis and verification of strategic abilities. The new version provides a
web interface and support for assume-guarantee verification of multi-agent
systems.


title: Enhanced colloidal transport in twisted magnetic patterns

abstract:
  Bilayers of two-dimensional materials twisted at specific angles can exhibit
exceptional properties such as the occurrence of unconventional
superconductivity in twisted graphene. We demonstrate here that novel phenomena
in twisted materials emerges also in particle-based classical systems. We study
the transport of magnetic colloidal particles driven by a drift force and
located between two twisted periodic magnetic patterns with either hexagonal or
square symmetry. The magnetic potential generated by patterns twisted at
specific magic angles develops flat channels, which increase the mobility of
the colloidal particles compared to that in single patterns. We characterize
the effect of the temperature and that of the magnitude of the drift force on
the colloidal mobility. The transport is more enhanced in square than in
hexagonal twisted patterns. Our work extends twistronics to classical soft
matter systems with potential applications to lab-on-a-chip devices.


title:
Discriminating Against Unrealistic Interpolations in Generative
  Adversarial Networks

abstract:
  Interpolations in the latent space of deep generative models is one of the
standard tools to synthesize semantically meaningful mixtures of generated
samples. As the generator function is non-linear, commonly used linear
interpolations in the latent space do not yield the shortest paths in the
sample space, resulting in non-smooth interpolations. Recent work has therefore
equipped the latent space with a suitable metric to enforce shortest paths on
the manifold of generated samples. These are often, however, susceptible of
veering away from the manifold of real samples, resulting in smooth but
unrealistic generation that requires an additional method to assess the sample
quality along paths. Generative Adversarial Networks (GANs), by construction,
measure the sample quality using its discriminator network. In this paper, we
establish that the discriminator can be used effectively to avoid regions of
low sample quality along shortest paths. By reusing the discriminator network
to modify the metric on the latent space, we propose a lightweight solution for
improved interpolations in pre-trained GANs.


title: The Silicon Electron Multiplier Sensor

abstract:
  The Silicon Electron Multiplier (SiEM) is a novel sensor concept for minimum
ionizing particle (MIP) detection which uses internal gain and fine pitch to
achieve excellent temporal and spatial resolution. In contrast to sensors where
the gain region is induced by doping (LGADs, APDs), amplification in the SiEM
is achieved by applying an electric potential difference in a composite
electrode structure embedded within the silicon bulk using MEMS fabrication
techniques. Since no gain-layer deactivation is expected with radiation damage,
such a structure is expected to withstand fluences of up to $10^{16} n_{eq}$.
Various geometries and biasing configurations are studied, within the
boundaries imposed by the fabrication process being considered. The effective
gain, the field in the sensor, the leakage current and breakdown conditions are
studied for cell sizes in the range of $6 - 15 \mu m$. Simulations show that
gains in excess of 10 can be achieved, and studies of the time structure of the
induced signals from a charge cloud deposited in the middle of the sensor show
that time resolutions similar to other sensors with internal gain can be
expected. Plans for the manufacture of a proof-of-concept sensor and for its
subsequent characterisation are discussed.


title: Asynchronous Optimisation for Event-based Visual Odometry

abstract:
  Event cameras open up new possibilities for robotic perception due to their
low latency and high dynamic range. On the other hand, developing effective
event-based vision algorithms that fully exploit the beneficial properties of
event cameras remains work in progress. In this paper, we focus on event-based
visual odometry (VO). While existing event-driven VO pipelines have adopted
continuous-time representations to asynchronously process event data, they
either assume a known map, restrict the camera to planar trajectories, or
integrate other sensors into the system. Towards map-free event-only monocular
VO in SE(3), we propose an asynchronous structure-from-motion optimisation
back-end. Our formulation is underpinned by a principled joint optimisation
problem involving non-parametric Gaussian Process motion modelling and
incremental maximum a posteriori inference. A high-performance incremental
computation engine is employed to reason about the camera trajectory with every
incoming event. We demonstrate the robustness of our asynchronous back-end in
comparison to frame-based methods which depend on accurate temporal
accumulation of measurements.


title:
Macroscopic behaviour in a two-species exclusion process via the method
  of matched asymptotics

abstract:
  We consider a two-species simple exclusion process on a periodic lattice. We
use the method of matched asymptotics to derive evolution equations for the two
population densities in the dilute regime, namely a cross-diffusion system of
partial differential equations for the two species densities. First, our result
captures non-trivial interaction terms neglected in the mean-field approach,
including a non-diagonal mobility matrix with explicit density dependence.
Second, it generalises the rigorous hydrodynamic limit of Quastel [Commun. Pure
Appl. Math. 45(6), 623--679 (1992)], valid for species with equal jump rates
and given in terms of a non-explicit self-diffusion coefficient, to the case of
unequal rates in the dilute regime. In the equal-rates case, by combining
matched asymptotic approximations in the low- and high-density limits, we
obtain a cubic polynomial approximation of the self-diffusion coefficient that
is numerically accurate for all densities. This cubic approximation agrees
extremely well with numerical simulations. It also coincides with the Taylor
expansion up to the second-order in the density of the self-diffusion
coefficient obtained using a rigorous recursive method.

categories: math-ph cond-mat.stat-mech math.AP math.MP


title: Two concepts of noncontextuality in quantum mechanics

abstract:
  There are two different and logically independent concepts of
noncontextuality in quantum mechanics. First, an ontological (hidden variable)
model for quantum mechanics is called noncontextual if every ontic (hidden)
state determines the probability of the outcomes of every measurement
independently of what other measurements are simultaneously performed. Second,
an ontological model is noncontextual if any two measurements which are
represented by the same self-adjoint operator or, equivalently, which have the
same probability distribution of outcomes in every quantum state also have the
same probability distribution of outcomes in every ontic state. I will call the
first concept simultaneous noncontextuality, the second measurement
noncontextuality. In the paper I will overview and critically analyze some of
the most significant accounts of contextuality in the literature and subsume
them under these two categories.


title:
XMM-Newton discovery of very high obscuration in the candidate
  Supergiant Fast X-ray Transient AX J1714.1-3912

abstract:
  We have analysed an archival XMM-Newton EPIC observation that serendipitously
covered the sky position of a variable X-ray source AX J1714.1-3912, previously
suggested to be a Supergiant Fast X-ray Transient (SFXT). During the XMM-Newton
observation the source is variable on a timescale of hundred seconds and shows
two luminosity states, with a flaring activity followed by unflared emission,
with a variability amplitude of a factor of about 50. We have discovered an
intense iron emission line with a centroid energy of 6.4 keV in the power
law-like spectrum, modified by a large absorption (NH around 1e24 cm-2), never
observed before from this source. This X-ray spectrum is unusual for an SFXT,
but resembles the so-called "highly obscured sources", high mass X-ray binaries
(HMXBs) hosting an evolved B[e] supergiant companion (sgB[e]). This might
suggest that AX J1714.1-3912 is a new member of this rare type of HMXBs, which
includes IGR J16318-4848 and CI Camelopardalis. Increasing this small
population of sources would be remarkable, as they represent an interesting
short transition evolutionary stage in the evolution of massive binaries.
Nevertheless, AX J1714.1-3912 appears to share X-ray properties of both kinds
of HMXBs (SFXT vs sgB[e] HMXB). Therefore, further investigations of the
companion star are needed to disentangle the two hypothesis.


title: Sensitive Pictures: Emotional Interpretation in the Museum

abstract:
  Museums are interested in designing emotional visitor experiences to
complement traditional interpretations. HCI is interested in the relationship
between Affective Computing and Affective Interaction. We describe Sensitive
Pictures, an emotional visitor experience co-created with the Munch art museum.
Visitors choose emotions, locate associated paintings in the museum, experience
an emotional story while viewing them, and self-report their response. A
subsequent interview with a portrayal of the artist employs computer vision to
estimate emotional responses from facial expressions. Visitors are given a
souvenir postcard visualizing their emotional data. A study of 132 members of
the public (39 interviewed) illuminates key themes: designing emotional
provocations; capturing emotional responses; engaging visitors with their data;
a tendency for them to align their views with the system's interpretation; and
integrating these elements into emotional trajectories. We consider how
Affective Computing can hold up a mirror to our emotions during Affective
Interaction.


title: Image-based material analysis of ancient historical documents

abstract:
  Researchers continually perform corroborative tests to classify ancient
historical documents based on the physical materials of their writing surfaces.
However, these tests, often performed on-site, requires actual access to the
manuscript objects. The procedures involve a considerable amount of time and
cost, and can damage the manuscripts. Developing a technique to classify such
documents using only digital images can be very useful and efficient. In order
to tackle this problem, this study uses images of a famous historical
collection, the Dead Sea Scrolls, to propose a novel method to classify the
materials of the manuscripts. The proposed classifier uses the two-dimensional
Fourier Transform to identify patterns within the manuscript surfaces.
Combining a binary classification system employing the transform with a
majority voting process is shown to be effective for this classification task.
This pilot study shows a successful classification percentage of up to 97% for
a confined amount of manuscripts produced from either parchment or papyrus
material. Feature vectors based on Fourier-space grid representation
outperformed a concentric Fourier-space format.


title: First detection of metric emission from a solar surge

abstract:
  We report the first detection of metric radio emission from a surge, observed
with the Nan\c{c}ay Radioheliograph (NRH), STEREO and other instruments. The
emission was observed during the late phase of the M9 complex event
SOL2010-02-012T11:25:00, described in a previous publication and was associated
with a secondary energy release, also observed in STEREO 304 {\AA} images:
there was no detectable soft X-ray emission. Triangulation of the STEREO images
allowed the identification of the surge with NRH sources near the central
meridian. The radio emission of the surge occurred in two phases and consisted
of two sources, one located near the base of the surge, apparently at or near
the site of energy release, and another in the upper part of the surge; these
were best visible in the frequency range of 445.0 to about 300MHz, whereas a
spectral component of different nature was observed at lower frequencies.
Sub-second time variations were detected in both sources during both phases,
with 0.2-0.3s a delay of the upper source with respect to the lower, suggesting
superluminal velocities. This effect can be explained if the emission of the
upper source was due to scattering of radiation from the source at the base of
the surge. In addition, the radio emission showed signs of pulsations and
spikes. We discuss possible emission mechanisms for the slow time variability
component of the lower radio source. Gyrosynchrotron emission reproduced fairly
well the characteristics of the observed total intensity spectrum at the start
of the second phase of the event, but failed to reproduce the high degree of
the observed circular polarization as well the spectra at other instances. On
the other hand, type IV-like plasma emission from the fundamental could explain
the high polarization and the fine structure in the dynamic spectrum; moreover,
it gives projected radio source positions on the plane of the sky, as seen from
STEREO-A, near the base of the surge. Taking everything into consideration, we
suggest type IV-like plasma emission with a low intensity gyrosynchrotron
component as the most plausible mechanism.


title: SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs

abstract:
  Entity alignment, aiming to identify equivalent entities across different
knowledge graphs (KGs), is a fundamental problem for constructing Web-scale
KGs. Over the course of its development, the label supervision has been
considered necessary for accurate alignments. Inspired by the recent progress
of self-supervised learning, we explore the extent to which we can get rid of
supervision for entity alignment. Commonly, the label information (positive
entity pairs) is used to supervise the process of pulling the aligned entities
in each positive pair closer. However, our theoretical analysis suggests that
the learning of entity alignment can actually benefit more from pushing
unlabeled negative pairs far away from each other than pulling labeled positive
pairs close. By leveraging this discovery, we develop the self-supervised
learning objective for entity alignment. We present SelfKG with efficient
strategies to optimize this objective for aligning entities without label
supervision. Extensive experiments on benchmark datasets demonstrate that
SelfKG without supervision can match or achieve comparable results with
state-of-the-art supervised baselines. The performance of SelfKG suggests that
self-supervised learning offers great potential for entity alignment in KGs.
The code and data are available at https://github.com/THUDM/SelfKG.


title: A Bayesian Approach to CT Reconstruction with Uncertain Geometry

abstract:
  Computed tomography is a method for synthesizing volumetric or
cross-sectional images of an object from a collection of projections. Popular
reconstruction methods for computed tomography are based on idealized models
and assumptions that may not be valid in practice. One such assumption is that
the exact projection geometry is known. The projection geometry describes the
relative location of the radiation source, object, and detector for each
projection. However, in practice, the geometric parameters used to describe the
position and orientation of the radiation source, object, and detector are
estimated quantities with uncertainty. A failure to accurately estimate the
geometry may lead to reconstructions with severe misalignment artifacts that
significantly decrease their scientific or diagnostic value. We propose a novel
reconstruction method that jointly estimates the reconstruction and the
projection geometry. The reconstruction method is based on a Bayesian approach
that yields a point estimate for the reconstruction and geometric parameters
and, in addition, provides valuable information regarding their uncertainty.
This is achieved by approximately sampling from the joint posterior
distribution of the reconstruction and projection geometry using a hierarchical
Gibbs sampler. Using real tomographic data, we demonstrate that the proposed
reconstruction method significantly reduces misalignment artifacts. Compared
with two commonly used alignment methods, our proposed method achieves
comparable or better results under challenging conditions.


title:
Droplet beam generated with the round-tip axicon: Exact solutions for
  different axicon shapes

abstract:
  We theoretically investigate droplet quasi-Bessel beams for different shapes
of the round-tip axicon. Exact solutions for the Fresnel diffraction integrals
describing the axial distribution of the electric field amplitude behind the
axicon are demonstrated. The analysis of the exact solutions shows that the
period of {\guillemotleft}light droplets{\guillemotright} is not a constant
value, but depends on the axial distance and on the deviation of the axicon
surface from the conical shape far from the rounded region. The predicted
effect can be applied for the reconstruction of the exact shape of the axicon
surface without 3D scanning.


title:
Stratified Mobility, Segregated Boundary, and Socioeconomic Mixing in
  New York

abstract:
  Mobility cross spatial units represents the embodiment of how people manage
activities between locations along temporal sequences. Spatiotemporal pattern
nevertheless interacts with the socioeconomic characteristics of respected
origin (push factors) and destination (pull factors) which widely discussed in
spatial interaction literature. Observing this dynamics at higher spatial
resolution allows us to entangle multifaceted nature of city, its complexity as
a system or network, and the way it shapes movement of people. This study
explore the extent interconnected elements of urban system or urban networks,
in parallel with the appearance of external shock namely COVID outbreak, may
affect estimation of mobility flows. To improve predictive power, gravity model
is extended to urban system model by augmenting the complexities of urban
network based on micro-analytical approach (intra-city networks). Our findings
reveals better performance of a more complex urban system model as to compared
with gravity model. Here, we leverage stratification in mobility by specifying
mobility flows with respect to income status of respected areas. The occurrence
of COVID outbreak followed by lockdown measure increases intra-class mobility,
indicating the coupling between socioeconomic distance and geographical
distance. Flows between areas with similar economic ranges are more predictable
than the one of different level. Furthermore, the presence of pull factors is
more affluent than push factors in determining mobility regardless the severity
of external shock.


title:
Developing solution algorithm for LR-type fully interval-valued
  intuitionistic fuzzy linear programming problems using lexicographic-ranking
  method

abstract:
  In this article, a new concept of LR-type interval-valued intuitionistic
fuzzy numbers (LR-type IVIFN) has been introduced. The theory has also been
enriched by demonstrating diagrammatic representations of LR-type IVIFNs and
establishing arithmetic operations among these fuzzy numbers. The total order
properties of lexicographic criteria have been used for ranking LR-type IVIFNs.
Further, a linear programming problem having both equality as well as
inequality type constraints with all the parameters as LR-type IVIFNs and
unrestricted decision variables has been formulated. An algorithm to find a
unique optimal solution to the problem using the lexicographic ranking method
has been developed. In the proposed methodology, the given linear programming
problem is converted to an equivalent mixed 0-1 lexicographic non-linear
programming problem. Various theorems have been proved to show the equivalence
of the proposed problem and its different constructions. The model formulation,
algorithm and discussed results have not only developed a new idea but also
generalized various well-known related works existing in the literature. A
numerical problem has also been exemplified to show the steps involved in the
approach. Finally, a practical application in production planning is framed,
solved and analyzed to establish the applicability of the study.


title:
The Nature of 500 Micron Risers II: Sub-mm Faint Dusty Star Forming
  Galaxies

abstract:
  We present SCUBA-2 and SMA follow-up observations of four candidate high
redshift Dusty Star-Forming Galaxies, selected as sources with rising SEDs in
the 250, 350 and 500$\mu$m Herschel SPIRE bands. Previous SMA observations
showed no counterparts to these sources, but in our deeper sub-mm observations
we detect counterparts to all four 500$\mu$m risers, with three resolving into
multiple systems. For these three multiple systems, the SMA 345GHz ($\approx
870\mu$m) observations recover $123 \pm 73\%$, $60 \pm 15\%$ and $19 \pm 4\%$
respectively of the integrated 850$\mu$m flux density from SCUBA-2, indicating
that there may be additional sources below our SMA detection limit making up a
dense, protocluster core. The fourth 500$\mu$m riser was observed at a lower
frequency and so we cannot make a similar comparison. We estimate photometric
redshifts based on FIR/sub-mm colours, finding that 3/4 likely lie at $z \geq
2$. This fits with the interpretation that the 500$\mu$m riser selection
criterion selects both intrinsically red, individual galaxies at $z > 4$, and
multiple systems at more moderate redshifts, artificially reddened by the
effects of blending. We use the SCUBA-2 850$\mu$m maps to investigate the
environments of these 500$\mu$m risers. By constructing cumulative number
counts and estimating photometric redshifts for surrounding SCUBA-2 detections,
we find that one of our 500$\mu$m risers could plausibly reside in a $z \geq 2$
protocluster. We infer that bright 500$\mu$m risers with faint 850$\mu$m flux
densities are typically multiple systems at $z \geq 2$ that may reside in
overdensities of bright sub-mm galaxies.


title:
Local cascade and dissipation in incompressible Hall magnetohydrodynamic
  turbulence: the Coarse-Graining approach

abstract:
  We derive the coarse-graining (CG) equations of incompressible Hall
Magnetohydrodynamics (HMHD) turbulence to investigate the {\it local (in
space)} energy cascade rate as a function of the filtering scale $\ell$. First,
the CG equations are space averaged to obtain the analytical expression of the
mean cascade rate. Its application to 3 dimensional (3D) simulations of (weakly
compressible) HMHD shows a cascade rate consistent with the value of the mean
dissipation rate in the simulations and with the classical estimates based on
the "third-order" law. Furthermore, we developed an anisotropic version of CG
that allows us to study the magnitude of the cascade rate along different
directions with respect to the mean magnetic field. Its implementation on the
numerical data with moderate background magnetic field shows a weaker cascade
along the magnetic field than in the perpendicular plane, while an isotropic
cascade is recovered in the absence of a background field. The strength of the
CG approach is further revealed when considering the {\it local-in-space}
energy transfer, which is shown theoretically and numerically to match at a
given position $\bm{x}$, when locally averaged over a neighboring region, the
(quasi-)local dissipation. Prospects of exploiting this new model to
investigate local dissipation in spacecraft data are discussed.


title:
3D object reconstruction and 6D-pose estimation from 2D shape for
  robotic grasping of objects

abstract:
  We propose a method for 3D object reconstruction and 6D-pose estimation from
2D images that uses knowledge about object shape as the primary key. In the
proposed pipeline, recognition and labeling of objects in 2D images deliver 2D
segment silhouettes that are compared with the 2D silhouettes of projections
obtained from various views of a 3D model representing the recognized object
class. By computing transformation parameters directly from the 2D images, the
number of free parameters required during the registration process is reduced,
making the approach feasible. Furthermore, 3D transformations and projective
geometry are employed to arrive at a full 3D reconstruction of the object in
camera space using a calibrated set up. Inclusion of a second camera allows
resolving remaining ambiguities. The method is quantitatively evaluated using
synthetic data and tested with real data, and additional results for the
well-known Linemod data set are shown. In robot experiments, successful
grasping of objects demonstrates its usability in real-world environments, and,
where possible, a comparison with other methods is provided. The method is
applicable to scenarios where 3D object models, e.g., CAD-models or point
clouds, are available and precise pixel-wise segmentation maps of 2D images can
be obtained. Different from other methods, the method does not use 3D depth for
training, widening the domain of application.


title: Unsupervised Anomaly Detection from Time-of-Flight Depth Images

abstract:
  Video anomaly detection (VAD) addresses the problem of automatically finding
anomalous events in video data. The primary data modalities on which current
VAD systems work on are monochrome or RGB images. Using depth data in this
context instead is still hardly explored in spite of depth images being a
popular choice in many other computer vision research areas and the increasing
availability of inexpensive depth camera hardware. We evaluate the application
of existing autoencoder-based methods on depth video and propose how the
advantages of using depth data can be leveraged by integration into the loss
function. Training is done unsupervised using normal sequences without need for
any additional annotations. We show that depth allows easy extraction of
auxiliary information for scene analysis in the form of a foreground mask and
demonstrate its beneficial effect on the anomaly detection performance through
evaluation on a large public dataset, for which we are also the first ones to
present results on.


title:
Unfreezing Social Navigation: Dynamical Systems based Compliance for
  Contact Control in Robot Navigation

abstract:
  Large efforts have focused on ensuring that the controllers for mobile
service robots follow proxemics and other social rules to ensure both safe and
socially acceptable distance to pedestrians. Nonetheless, involuntary contact
may be unavoidable when the robot travels in crowded areas or when encountering
adversarial pedestrians. Freezing the robot in response to contact might be
detrimental to bystanders' safety and prevents it from achieving its task.
Unavoidable contacts must hence be controlled to ensure the safe and smooth
travelling of robots in pedestrian alleys. We present a force-limited and
obstacle avoidance controller integrated into a time-invariant dynamical system
(DS) in a closed-loop force controller that let the robot react instantaneously
to contact or to the sudden appearance of pedestrians. Mitigating the risk of
collision is done by modulating the velocity commands upon detecting a contact
and by absorbing part of the contact force through active compliant control
when the robot bumps inadvertently against a pedestrian. We evaluated our
method with a personal mobility robot -- Qolo -- showing contact mitigation
with passive and active compliance. We showed the robot able to overcome an
adversarial pedestrian within 9 N of the set limit contact force for speeds
under 1 m/s. Moreover, we evaluated integrated obstacle avoidance proving the
ability to advance without incurring any other collision.


title:
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and
  Challenges

abstract:
  As an important fine-grained sentiment analysis problem, aspect-based
sentiment analysis (ABSA), aiming to analyze and understand people's opinions
at the aspect level, has been attracting considerable interest in the last
decade. To handle ABSA in different scenarios, various tasks have been
introduced for analyzing different sentiment elements and their relations,
including the aspect term, aspect category, opinion term, and sentiment
polarity. Unlike early ABSA works focusing on a single sentiment element, many
compound ABSA tasks involving multiple elements have been studied in recent
years for capturing more complete aspect-level sentiment information. However,
a systematic review of various ABSA tasks and their corresponding solutions is
still lacking, which we aim to fill in this survey. More specifically, we
provide a new taxonomy for ABSA which organizes existing studies from the axes
of concerned sentiment elements, with an emphasis on recent advances of
compound ABSA tasks. From the perspective of solutions, we summarize the
utilization of pre-trained language models for ABSA, which improved the
performance of ABSA to a new stage. Besides, techniques for building more
practical ABSA systems in cross-domain/lingual scenarios are discussed.
Finally, we review some emerging topics and discuss some open challenges to
outlook potential future directions of ABSA.


title:
Estimation of the invariant density for discretely observed diffusion
  processes: impact of the sampling and of the asynchronicity

abstract:
  We aim at estimating in a non-parametric way the density $\pi$ of the
stationary distribution of a $d$-dimensional stochastic differential equation
$(X_t)_{t \in [0, T]}$, for $d \ge 2$, from the discrete observations of a
finite sample $X_{t_0}$, ... , $X_{t_n}$ with $0= t_0 < t_1 < ... < t_n =:
T_n$. We propose a kernel density estimation and we study its convergence rates
for the pointwise estimation of the invariant density under anisotropic Holder
smoothness constraints. First of all, we find some conditions on the
discretization step that ensures it is possible to recover the same rates as
when the continuous trajectory of the process was available. As proven in the
recent work \cite{Companion}, such rates are optimal and new in the context of
density estimator. Then we deal with the case where such a condition on the
discretization step is not satisfied, which we refer to as intermediate regime.
In this new regime we identify the convergence rate for the estimation of the
invariant density over anisotropic Holder classes, which is the same
convergence rate as for the estimation of a probability density belonging to an
anisotropic Holder class, associated to $n$ iid random variables $X_1, ...,
X_n$. After that we focus on the asynchronous case, in which each component can
be observed in different moments. Even if the asynchrony of the observations
implies some difficulties, we are able to overcome them by considering some
combinatorics and by proving some sharper bounds on the variance which allow us
to lighten the condition needed on the discretization step.


title: Multi-topological Floquet metals in a photonic lattice

abstract:
  Topological materials are usually classified according to a single
topological invariant. The engineering of synthetic structures characterised by
more than one class of topological invariants would open the way to the
combination of different topological properties, enlarging the richness of
topological phase diagrams. Using a synthetic photonic lattice implemented in a
two-coupled ring system we engineer an anomalous Floquet metal that is gapless
in the bulk and shows simultaneously two different topological properties. On
the one hand, this synthetic lattice presents bands characterised by a winding
number that directly relates to the period of Bloch oscillations within its
bulk. On the other, the Floquet nature of our implementation results in
well-known anomalous insulating phases with topological edge states. Our
experiments open the way to study unconventional multi-topological phases in
synthetic lattices.


title:
Colar: Effective and Efficient Online Action Detection by Consulting
  Exemplars

abstract:
  Online action detection has attracted increasing research interests in recent
years. Current works model historical dependencies and anticipate the future to
perceive the action evolution within a video segment and improve the detection
accuracy. However, the existing paradigm ignores category-level modeling and
does not pay sufficient attention to efficiency. Considering a category, its
representative frames exhibit various characteristics. Thus, the category-level
modeling can provide complimentary guidance to the temporal dependencies
modeling. This paper develops an effective exemplar-consultation mechanism that
first measures the similarity between a frame and exemplary frames, and then
aggregates exemplary features based on the similarity weights. This is also an
efficient mechanism, as both similarity measurement and feature aggregation
require limited computations. Based on the exemplar-consultation mechanism, the
long-term dependencies can be captured by regarding historical frames as
exemplars, while the category-level modeling can be achieved by regarding
representative frames from a category as exemplars. Due to the complementarity
from the category-level modeling, our method employs a lightweight architecture
but achieves new high performance on three benchmarks. In addition, using a
spatio-temporal network to tackle video frames, our method makes a good
trade-off between effectiveness and efficiency. Code is available at
https://github.com/VividLe/Online-Action-Detection.


title:
Universal suppression of superfluid weight by disorder independent of
  quantum geometry and band dispersion

abstract:
  Motivated by the experimental progress in controlling the properties of the
energy bands in superconductors, significant theoretical efforts have been
devoted to study the effect of the quantum geometry and the flatness of the
dispersion on the superfluid weight. In conventional superconductors, where the
energy bands are wide and the Fermi energy is large, the contribution due to
the quantum geometry is negligible, but in the opposite limit of flat-band
superconductors the superfluid weight originates purely from the quantum
geometry of Bloch wave functions. Here, we study how the energy band dispersion
and the quantum geometry affect the disorder-induced suppression of the
superfluid weight. Surprisingly, we find that the disorder-dependence of the
superfluid weight is universal across a variety of models, and independent of
the quantum geometry and the flatness of the dispersion. Our results suggest
that a flat-band superconductor is as resilient to disorder as a conventional
superconductor.


title:
Principal Eigenvalue and Landscape Function of the Anderson Model on a
  Large Box

abstract:
  We state a precise formulation of a conjecture concerning the product of the
principal eigenvalue and the sup-norm of the landscape function of the Anderson
model restricted to a large box. We first provide the asymptotic of the
principal eigenvalue as the size of the box grows and then use it to give a
partial proof of the conjecture. We give a complete proof for the one
dimensional case.


title: Reasoning with belief functions over Belnap--Dunn logic

abstract:
  We design an expansion of Belnap--Dunn logic with belief and plausibility
functions that allow non-trivial reasoning with inconsistent and incomplete
probabilistic information. We also formalise reasoning with non-standard
probabilities and belief functions in two ways. First, using a calculus of
linear inequalities, akin to the one presented
in~\cite{FaginHalpernMegiddo1990}. Second, as a two-layered modal logic wherein
reasoning with evidence (the outer layer) utilises paraconsistent expansions of
\L{}ukasiewicz logic. The second approach is inspired
by~\cite{BaldiCintulaNoguera2020}. We prove completeness for both kinds of
calculi and show their equivalence by establishing faithful translations in
both directions.


title: Real-Time Trajectory Planning for Aerial Perching

abstract:
  This paper presents a novel trajectory planning method for aerial perching.
Compared with the existing work, the terminal states and the trajectory
durations can be adjusted adaptively, instead of being determined in advance.
Furthermore, our planner is able to minimize the tangential relative speed on
the premise of safety and dynamic feasibility. This feature is especially
notable on micro aerial robots with low maneuverability or scenarios where the
space is not enough. Moreover, we design a flexible transformation strategy to
eliminate terminal constraints along with reducing optimization variables.
Besides, we take precise SE(3) motion planning into account to ensure that the
drone would not touch the landing platform until the last moment. The proposed
method is validated onboard by a palm-sized micro aerial robot with quite
limited thrust and moment (thrust-to-weight ratio 1.7) perching on a mobile
inclined surface. Sufficient experimental results show that our planner
generates an optimal trajectory within 20ms, and replans with warm start in
2ms.


title: Is Bianchi I a Bouncing Cosmology in the Wheeler-DeWitt picture?

abstract:
  We provide a quantum picture for the emergence of a bouncing cosmology,
according to the idea that a semiclassical behavior of the Universe towards the
singularity is not available in many relevant Minisuperspace models. In
particular, we study the Bianchi I model in vacuum adopting the isotropic
Misner variable as an internal clock for the quantum evolution. The isomorphism
between the Wheeler-DeWitt equation in this Minisuperspace representation and
the Klein-Gordon one for a relativistic scalar field allows to identify the
positive and negative frequency solutions as associated to the collapsing and
expanding Universe respectively. We clarify how any Bianchi I localized wave
packet unavoidably spreads when the singularity is approached and therefore the
semiclassical description of the model evolution in the Planckian region loses
its predictability. Then, we calculate the transition amplitude that a
collapsing Universe is turned into an expanding one, according to the standard
techniques of relativistic quantum mechanics, thanks to the introduction of an
ekpyrotic-like matter component which mimics a time-dependent potential term
and breaks the frequency separation. In particular, the transition probability
of this "Quantum Big Bounce" acquires a maximum value when the mean values of
the momenta conjugate to the anisotropies in the collapsing Universe are close
enough to the corresponding mean values in the expanding one, depending on the
variances of the in-going and out-going Universe wave packets. This symmetry
between the pre-Bounce and post-Bounce mean values reflects what happens in the
semiclassical bouncing cosmology, with the difference that here the connection
of the two branches takes place on a pure probabilistic level.


title: Discontinuous Constituency and BERT: A Case Study of Dutch

abstract:
  In this paper, we set out to quantify the syntactic capacity of BERT in the
evaluation regime of non-context free patterns, as occurring in Dutch. We
devise a test suite based on a mildly context-sensitive formalism, from which
we derive grammars that capture the linguistic phenomena of control verb
nesting and verb raising. The grammars, paired with a small lexicon, provide us
with a large collection of naturalistic utterances, annotated with verb-subject
pairings, that serve as the evaluation test bed for an attention-based span
selection probe. Our results, backed by extensive analysis, suggest that the
models investigated fail in the implicit acquisition of the dependencies
examined.


title:
Hidden power of near-infrared data for the study of young clusters:
  Illustrative case of RCW 38

abstract:
  Studies of star formation rely heavily on observations in the near-infrared,
but they typically need information from other wavelengths for interpretation.
We show that we can infer distances and estimate the membership of young
stellar objects for young clusters independently using (ground-based)
near-infrared, $J$, $H,$ and $K_S$ broadband data alone. We also show that we
can estimate a lower limit for the fraction of sources with $2.2~\mu$m excess
emission with a sensitivity comparable to that of mid-infrared space data, but
with better resolution and fewer biases. Finally, we show that the typical
methods for inferring masses from these data may produce substantially
unreliable results. This method is applied to the young, massive cluster RCW
38, for which we estimate a distance of 1.5 kpc and a $K_S$-band excess
fraction larger than 60\%.


title:
Internal Structure and Magnetic Moment of Rocky Planets. Application to
  the first exoplanets discovered by TESS

abstract:
  For a planet to be considered habitable on its surface, it is an important
advantage for it to have a magnetic field that protects its atmosphere from
stellar winds as well as cosmic rays. Magnetic protection of potentially
habitable planets plays a key role in determining the chances of detecting
atmospheric biosignatures. This paper proposes to use the Preliminary Reference
Earth Model (PREM) internal structure as the base of a numerical model. With
this model, we estimate the magnetic properties of dry and water-rich
Earth-like and Super-Earth-like planets. We apply it to those of this kind at
the first 176 planets confirmed by TESS. Using PREM as a reference, we estimate
the internal structure of dry and water-rich rocky planets. This model provides
an estimation of the average density and core size of the planet and, with
them, its magnetic moment depending on whether it is tidally locked or not. Our
model estimates the thermodynamic variables as a function of pressure and
includes saltwater as a component of water-rich exoplanets. We have not used
the perfect layer differentiation approximation. We have validated our model
with those planets and satellites in the Solar system with similar
characteristics. The differences with the observed values in the internal
structure characteristics, mass, average density, moment of inertia factor, and
local Rossby number are remarkably low or even negligible. The estimated
magnetic moments are also very similar to the observed ones. We have applied
the model to the first 176 planets confirmed by the TESS, finding that, from an
astrobiological perspective TOI-700 d and TOI-2257 b are the most interesting
ones as being located in the habitable zone (HZ), although their magnetic
moments are only about 0.01 of the Earth's magnetic moment.


title: On subwavelength resolution granted by dielectric microparticles

abstract:
  In this work we report a theoretical study of the lateral resolution granted
by a simple glass microcylinder. In this 2D study, we had in mind the 3D
analogue -- a microsphere whose ability to form a deeply subwavelength and
strongly magnified image of submicron objects has been known since 2011.
Conventionally, the microscope in which such the image is observed is tuned so
that to see the areas behind the microsphere. This corresponds to the location
of the virtual source formed by the microsphere at a distance longer than the
distance of the real source to the miscroscope. Recently, we theoretically
found a new scenario of superresolution, when the virtual source is formed in
the wave beam transmitted through the microsphere. However, in this work we
concentrated on the case when the superresolution is achieved in the
impractical imaging system, in which the microscope objective lens is replaced
by a microlens located at a distance smaller than the Rayleigh range. The
present paper theoretically answers an important question: which scenario of
far-field nanoimaging by a microsphere grants the finest spatial at very large
distances. We found that the novel scenario (corresponding to higher refractive
indices) promises further enhancement of the resolution.


title: A new approach to projectivity in the categories of complexes, II

abstract:
  It is now very known how the subprojectivity of modules provides a fruitful
new unified framework of the classical projectivity and flatness. In this
paper, we extend this fact to the category of complexes by generalizing and
unifying several known classical results. We further provide various examples
to illustrate the scopes and limits of the established results. This paper is a
continuation of a recent work in which it was shown among other several things
that the subprojectivity of complexes can be characterized in terms of
morphisms in the homotopy category.


title:
Satellite Image and Machine Learning based Knowledge Extraction in the
  Poverty and Welfare Domain

abstract:
  Recent advances in artificial intelligence and machine learning have created
a step change in how to measure human development indicators, in particular
asset based poverty. The combination of satellite imagery and machine learning
has the capability to estimate poverty at a level similar to what is achieved
with workhorse methods such as face-to-face interviews and household surveys.
An increasingly important issue beyond static estimations is whether this
technology can contribute to scientific discovery and consequently new
knowledge in the poverty and welfare domain. A foundation for achieving
scientific insights is domain knowledge, which in turn translates into
explainability and scientific consistency. We review the literature focusing on
three core elements relevant in this context: transparency, interpretability,
and explainability and investigate how they relates to the poverty, machine
learning and satellite imagery nexus. Our review of the field shows that the
status of the three core elements of explainable machine learning
(transparency, interpretability and domain knowledge) is varied and does not
completely fulfill the requirements set up for scientific insights and
discoveries. We argue that explainability is essential to support wider
dissemination and acceptance of this research, and explainability means more
than just interpretability.


title: Enhanced Decentralized Autonomous Aerial Swarm with Group Planning

abstract:
  Autonomous aerial swarm remains a grand challenge in robotics. Existing works
in this field can be categorized as centralized and decentralized. Centralized
methods suffer from scale dilemmas, while decentralized ones often lead to poor
planning quality. In this paper, we propose an enhanced decentralized
autonomous aerial swarm system with group planning. According to the spatial
distribution of agents, the system dynamically divides the swarm into several
groups and isolated agents. For conflicts within each group, we propose a novel
coordination mechanism named group planning. The group planning consists of
efficient multi-agent pathfinding and trajectory joint optimization, which can
significantly improve the planning quality and success rate. We demonstrate
simulations and real-world experiments that our method not only has
applicability for a large-scale swarm, but also has top-level planning quality.


title: First-order logic axiomatization of metric graph theory

abstract:
  The main goal of this note is to provide a First-Order Logic with Betweenness
(FOLB) axiomatization of the main classes of graphs occurring in Metric Graph
Theory, in analogy to Tarski's axiomatization of Euclidean geometry. We provide
such an axiomatization for weakly modular graphs and their principal subclasses
(median and modular graphs, bridged graphs, Helly graphs, dual polar graphs,
etc), basis graphs of matroids and even $\Delta$-matroids, partial cubes and
their subclasses (ample partial cubes, tope graphs of oriented matroids and
complexes of oriented matroids, bipartite Pasch and Peano graphs, cellular and
hypercellular partial cubes, almost-median graphs, netlike partial cubes), and
Gromov hyperbolic graphs. On the other hand, we show that some classes of
graphs (including chordal, planar, Eulerian, and dismantlable graphs), closely
related with Metric Graph Theory, but defined in a combinatorial or topological
way, do not allow such an axiomatization.


title: Algebraic Representations of Unique Bipartite Perfect Matching

abstract:
  We obtain complete characterizations of the Unique Bipartite Perfect Matching
function, and of its Boolean dual, using multilinear polynomials over the
reals. Building on previous results, we show that, surprisingly, the dual
description is sparse and has low $\ell_1$-norm -- only exponential in
$\Theta(n \log n)$, and this result extends even to other families of
matching-related functions. Our approach relies on the M\"obius numbers in the
matching-covered lattice, and a key ingredient in our proof is M\"obius'
inversion formula.
  These polynomial representations yield complexity-theoretic results. For
instance, we show that unique bipartite matching is evasive for classical
decision trees, and nearly evasive even for generalized query models. We also
obtain a tight $\Theta(n \log n)$ bound on the log-rank of the associated
two-party communication task.


title:
OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose
  Estimation

abstract:
  This paper proposes a universal framework, called OVE6D, for model-based 6D
object pose estimation from a single depth image and a target object mask. Our
model is trained using purely synthetic data rendered from ShapeNet, and,
unlike most of the existing methods, it generalizes well on new real-world
objects without any fine-tuning. We achieve this by decomposing the 6D pose
into viewpoint, in-plane rotation around the camera optical axis and
translation, and introducing novel lightweight modules for estimating each
component in a cascaded manner. The resulting network contains less than 4M
parameters while demonstrating excellent performance on the challenging T-LESS
and Occluded LINEMOD datasets without any dataset-specific training. We show
that OVE6D outperforms some contemporary deep learning-based pose estimation
methods specifically trained for individual objects or datasets with real-world
training data.
  The implementation and the pre-trained model will be made publicly available.


title:
Recursively feasible stochastic predictive control using an
  interpolating initial state constraint -- extended version

abstract:
  We present a stochastic model predictive control (SMPC) framework for linear
systems subject to i.i.d. additive disturbances. State of the art SMPC
approaches with closed-loop chance constraint satisfaction recursively
initialize the nominal state based on the previously predicted nominal state or
possibly the measured state under some case distinction. We improve these
initialization strategies by allowing for a continuous optimization over the
nominal initial state in an interpolation of these two extremes. The resulting
SMPC scheme can be implemented as one standard quadratic program and is more
flexible compared to state-of-the-art initialization strategies, resulting in
improved performance. As the main technical contribution, we show that the
proposed SMPC framework also ensures closed-loop satisfaction of chance
constraints and suitable performance bounds.


title: Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation

abstract:
  Environment perception in autonomous driving vehicles often heavily relies on
deep neural networks (DNNs), which are subject to domain shifts, leading to a
significantly decreased performance during DNN deployment. Usually, this
problem is addressed by unsupervised domain adaptation (UDA) approaches trained
either simultaneously on source and target domain datasets or even source-free
only on target data in an offline fashion. In this work, we further expand a
source-free UDA approach to a continual and therefore online-capable UDA on a
single-image basis for semantic segmentation. Accordingly, our method only
requires the pre-trained model from the supplier (trained in the source domain)
and the current (unlabeled target domain) camera image. Our method Continual
BatchNorm Adaptation (CBNA) modifies the source domain statistics in the batch
normalization layers, using target domain images in an unsupervised fashion,
which yields consistent performance improvements during inference. Thereby, in
contrast to existing works, our approach can be applied to improve a DNN
continuously on a single-image basis during deployment without access to source
data, without algorithmic delay, and nearly without computational overhead. We
show the consistent effectiveness of our method across a wide variety of
source/target domain settings for semantic segmentation. As part of this work,
our code will be made publicly available.


title: Reliable validation of Reinforcement Learning Benchmarks

abstract:
  Reinforcement Learning (RL) is one of the most dynamic research areas in Game
AI and AI as a whole, and a wide variety of games are used as its prominent
test problems. However, it is subject to the replicability crisis that
currently affects most algorithmic AI research. Benchmarking in Reinforcement
Learning could be improved through verifiable results. There are numerous
benchmark environments whose scores are used to compare different algorithms,
such as Atari. Nevertheless, reviewers must trust that figures represent
truthful values, as it is difficult to reproduce an exact training curve. We
propose improving this situation by providing access to the original
experimental data to validate study results. To that end, we rely on the
concept of minimal traces. These allow re-simulation of action sequences in
deterministic RL environments and, in turn, enable reviewers to verify, re-use,
and manually inspect experimental results without needing large compute
clusters. It also permits validation of presented reward graphs, an inspection
of individual episodes, and re-use of result data (baselines) for proper
comparison in follow-up papers. We offer plug-and-play code that works with Gym
so that our measures fit well in the existing RL and reproducibility
eco-system. Our approach is freely available, easy to use, and adds minimal
overhead, as minimal traces allow a data compression ratio of up to $\approx
10^4:1$ (94GB to 8MB for Atari Pong) compared to a regular MDP trace used in
offline RL datasets. The paper presents proof-of-concept results for a variety
of games.


title:
Time-Domain Analysis for Resonant Beam Charging and Communications With
  Delay-Divide Demodulation

abstract:
  Laser has unique advantages such as abundant spectrum resources and low
propagation divergence in wireless charging and wireless communications,
compared with radio frequency. Resonant beams, as a kind of intra-cavity laser
beams, have been proposed as the carrier of wireless charging and
communication, as it has unique features including high power, intrinsic
safety, and self-aligned mobility. However, this system has problems such as
intra-cavity echo interference and power fluctuation. To study the time-domain
behavior of the resonant beam system, we create a simulation algorithm by
discretizing the laser rate equations which model the dynamics of the excited
atom density in the gain medium and the photon density in the cavity. The
simulation results are in good agreement with theoretical calculation. We also
propose a delay-divide demodulation method to address the echo interference
issue, and use the simulation algorithm to verify its feasibility. The results
show that the resonant beam charging and communication system with the proposed
demodulator is feasible and performs well. The analysis in this work also helps
researchers to deeply understand the behavior of the resonant beam system.