abstract:
  Variational autoencoders (VAEs) have witnessed great success in performing
the compression of image datasets. This success, made possible by the bits-back
coding framework, has produced competitive compression performance across many
benchmarks. However, despite this, VAE architectures are currently limited by a
combination of coding practicalities and compression ratios. That is, not only
do state-of-the-art methods, such as normalizing flows, often demonstrate
out-performance, but the initial bits required in coding makes single and
parallel image compression challenging. To remedy this, we introduce Split
Hierarchical Variational Compression (SHVC). SHVC introduces two novelties.
Firstly, we propose an efficient autoregressive prior, the autoregressive
sub-pixel convolution, that allows a generalisation between per-pixel
autoregressions and fully factorised probability models. Secondly, we define
our coding framework, the autoregressive initial bits, that flexibly supports
parallel coding and avoids -- for the first time -- many of the practicalities
commonly associated with bits-back coding. In our experiments, we demonstrate
SHVC is able to achieve state-of-the-art compression performance across
full-resolution lossless image compression tasks, with up to 100x fewer model
parameters than competing VAE approaches.


title:
Trajectories and Radiation of Charged Particles in the Pulsar
  Magnetosphere

abstract:
  Trajectories and radiation of the accelerating electrons are studied in the
pulsar magnetosphere approximated as the electromagnetic field of the Deutsch's
solutions. Because the electrons are accelerated rapidly to ultra-relativistic
velocity near the neutron star surface, the electron velocity vector (and then
its trajectory) is derived from the balance between Lorentz force and radiation
reaction force, which makes the pitch angle between electron trajectories and
magnetic field lines nonzero in most part of the magnetosphere. In such a case,
the spectral energy distributions (SEDs) of synchro-curvature radiation for the
accelerating electrons with a mono-energetic form are calculated. Our results
indicate that: (i) the pitch angle is the function of electron position ($r,
\theta, \phi$) in the open field line regions, and increases with increasing
$r$ and $\theta$ as well as increasing the inclination angle; (ii) the radius
of curvature becomes large along the particle trajectory, and (iii) the SED
appears a double peak structure depending on the emission position, where the
synchrotron radiation plays an important role in X-ray band and curvature
radiation mainly works in GeV band, which is only determined by parameters
$\alpha$ and $\zeta$


title:
Asymptotic Theory for Moderate Deviations from the Unit Boundary in
  Quantile Autoregressive Time Series

abstract:
  We establish the asymptotic theory in quantile autoregression when the model
parameter is specified with respect to moderate deviations from the unit
boundary of the form (1 + c / k) with a convergence sequence that diverges at a
rate slower than the sample size n. Then, extending the framework proposed by
Phillips and Magdalinos (2007), we consider the limit theory for the
near-stationary and the near-explosive cases when the model is estimated with a
conditional quantile specification function and model parameters are
quantile-dependent. Additionally, a Bahadur-type representation and limiting
distributions based on the M-estimators of the model parameters are derived.
Specifically, we show that the serial correlation coefficient converges in
distribution to a ratio of two independent random variables. Monte Carlo
simulations illustrate the finite-sample performance of the estimation
procedure under investigation.


title:
Acute sensitivity of global ocean circulation and heat content to eddy
  energy dissipation time-scale

abstract:
  The global ocean overturning circulation, critically dependent on the global
density stratification, plays a central role in regulating climate evolution.
While it is well-known that the global stratification profile exhibits a strong
dependence to Southern Ocean dynamics and in particular to wind and buoyancy
forcing, we demonstrate here that the stratification is also acutely sensitive
to the mesoscale eddy energy dissipation time-scale. Within the context of a
global ocean circulation model with an energy constrained mesoscale eddy
parameterization, it is shown that modest variations in the eddy energy
dissipation time-scale lead to significant variations in key metrics relating
to ocean circulation, namely the Antarctic Circumpolar Current transport,
Atlantic Meridional Overturning Circulation strength, and global ocean heat
content, over long time-scales. The results highlight a need to constrain
uncertainties associated with eddy energy dissipation for climate model
projections over centennial time-scales, but also for paleoclimate simulations
over millennial time-scales.


title: Complex-Valued Autoencoders for Object Discovery

abstract:
  Object-centric representations form the basis of human perception and enable
us to reason about the world and to systematically generalize to new settings.
Currently, most machine learning work on unsupervised object discovery focuses
on slot-based approaches, which explicitly separate the latent representations
of individual objects. While the result is easily interpretable, it usually
requires the design of involved architectures. In contrast to this, we propose
a distributed approach to object-centric representations: the Complex
AutoEncoder. Following a coding scheme theorized to underlie object
representations in biological neurons, its complex-valued activations represent
two messages: their magnitudes express the presence of a feature, while the
relative phase differences between neurons express which features should be
bound together to create joint object representations. We show that this simple
and efficient approach achieves better reconstruction performance than an
equivalent real-valued autoencoder on simple multi-object datasets.
Additionally, we show that it achieves competitive unsupervised object
discovery performance to a SlotAttention model on two datasets, and manages to
disentangle objects in a third dataset where SlotAttention fails - all while
being 7-70 times faster to train.


title: Separability and harmony in ecumenical systems

abstract:
  The quest of smoothly combining logics so that connectives from classical and
intuitionistic logics can co-exist in peace has been a fascinating topic of
research for decades now. In 2015, Dag Prawitz proposed a natural deduction
system for an ecumenical first-order logic. We start this work by proposing a
{\em pure} sequent calculus version for it, in the sense that connectives are
introduced without the use of other connectives. For doing this, we extend
sequents with an extra context, the stoup, and define the ecumenical notion of
polarities. Finally, we smoothly extend these ideas for handling modalities,
presenting pure labeled and nested systems for ecumenical modal logics.


title:
A note on quadratic Poisson brackets on ${\mathrm{gl}}(n,{\mathbb{R}})$
  related to Toda lattices

abstract:
  It is well known that the compatible linear and quadratic Poisson brackets of
the full symmetric and of the standard open Toda lattices are restrictions of
linear and quadratic $r$-matrix Poisson brackets on the associative algebra
${\mathrm{gl}}(n,{\mathbb{R}})$. We here show that the quadratic bracket on
${\mathrm{gl}}(n,{\mathbb{R}})$, corresponding to the $r$-matrix defined by the
splitting of ${\mathrm{gl}}(n,{\mathbb{R}})$ into the direct sum of the upper
triangular and orthogonal Lie subalgebras, descends by Poisson reduction from a
quadratic Poisson structure on the cotangent bundle
$T^*{\mathrm{GL}}(n,{\mathbb{R}})$. This complements the interpretation of the
linear $r$-matrix bracket as a reduction of the canonical Poisson bracket of
the cotangent bundle..


title: Semi-supervised Semantic Segmentation with Error Localization Network

abstract:
  This paper studies semi-supervised learning of semantic segmentation, which
assumes that only a small portion of training images are labeled and the others
remain unlabeled. The unlabeled images are usually assigned pseudo labels to be
used in training, which however often causes the risk of performance
degradation due to the confirmation bias towards errors on the pseudo labels.
We present a novel method that resolves this chronic issue of pseudo labeling.
At the heart of our method lies error localization network (ELN), an auxiliary
module that takes an image and its segmentation prediction as input and
identifies pixels whose pseudo labels are likely to be wrong. ELN enables
semi-supervised learning to be robust against inaccurate pseudo labels by
disregarding label noises during training and can be naturally integrated with
self-training and contrastive learning. Moreover, we introduce a new learning
strategy for ELN that simulates plausible and diverse segmentation errors
during training of ELN to enhance its generalization. Our method is evaluated
on PASCAL VOC 2012 and Cityscapes, where it outperforms all existing methods in
every evaluation setting.


title: Quotient spaces with strong subgyrogroups

abstract:
  In this paper, we mainly investigate the quotient spaces G/H when G is a
strongly topological gyrogroup and H is a strong subgyrogroup of G. It is shown
that if G is a strongly topological gyrogroup, H is a closed strong
subgyrogroup of G and H is inner neutral, then the quotient space G/H is
first-countable if and only if G/H is a bisequential space if and only if G/H
is a weakly first-countable space if and only if G/H is a csf-countable and
sequential a7-space. Moreover, it is shown that if H is a locally compact
metrizable strong subgyrogroup of G and the quotient space G/H is sequential,
then G is also sequential; if H is a closed first-countable and separable
strong subgyrogroup of G, the quotient space G/H is a cosmic space, then G is
also a cosmic space; if the quotient space G/H has a star-countable cs-network
or star-countable wcs*-network, then G also has a star-countable cs-network or
star-countable wcs*-network, respectively.


title: Photometric redshift-aided classification using ensemble learning

abstract:
  We present SHEEP, a new machine learning approach to the classic problem of
astronomical source classification, which combines the outputs from the
XGBoost, LightGBM, and CatBoost learning algorithms to create stronger
classifiers. A novel step in our pipeline is that prior to performing the
classification, SHEEP first estimates photometric redshifts, which are then
placed into the dataset as an additional feature for classification model
training; this results in significant improvement in the subsequent
classification performance. SHEEP contains two distinct classification
methodologies: (i) Multi-class; (ii) one vs all with correction by a
meta-learner. We demonstrate the performance of SHEEP for the classification of
stars, galaxies and quasars using a dataset composed of SDSS and WISE
photometry of 3.5 million astronomical sources. The resulting F1-scores are as
follows: (i) 0.992 for galaxies; (ii) 0.967 for quasars; (iii) and 0.985 for
stars. In terms of the F1-scores for the three classes, SHEEP is found to
outperform the recent RandomForest-based classification approach of Clarke et
al. (2020) using an essentially identical dataset. Our methodology also
facilitates model and dataset explainability via feature importances; it also
allows the selection of sources whose uncertain classifications may make them
interesting sources for follow-up observations.


title: Real-time Online Multi-Object Tracking in Compressed Domain

abstract:
  Recent online Multi-Object Tracking (MOT) methods have achieved desirable
tracking performance. However, the tracking speed of most existing methods is
rather slow. Inspired from the fact that the adjacent frames are highly
relevant and redundant, we divide the frames into key and non-key frames
respectively and track objects in the compressed domain. For the key frames,
the RGB images are restored for detection and data association. To make data
association more reliable, an appearance Convolutional Neural Network (CNN)
which can be jointly trained with the detector is proposed. For the non-key
frames, the objects are directly propagated by a tracking CNN based on the
motion information provided in the compressed domain. Compared with the
state-of-the-art online MOT methods,our tracker is about 6x faster while
maintaining a comparable tracking performance.


title:
The evolution of the radio luminosity function of group galaxies in
  COSMOS

abstract:
  To understand the role of the galaxy group environment on galaxy evolution,
we present a study of radio luminosity functions (RLFs) of group galaxies based
on the Karl G. Jansky Very Large Array-COSMOS 3 GHz Large Project. The
radio-selected sample of 7826 COSMOS galaxies with robust optical/near-infrared
counterparts, excellent photometric coverage, and the COSMOS X-ray galaxy
groups (M_200c > 10^13.3 M_0) enables us to construct the RLF of group galaxies
(GGs) and their contribution to the total RLF since z ~ 2.3. Using the Markov
chain Monte Carlo algorithm, we fit a redshift-dependent pure luminosity
evolution model and a linear and power-law model to the luminosity functions.
We compare it with past RLF studies from VLA-COSMOS on individual populations
of radio-selected star-forming galaxies (SFGs) and galaxies hosting active
galactic nuclei (AGN). These populations are classified based on the presence
or absence of a radio excess concerning the star-formation rates derived from
the infrared emission. We find that the fraction of radio group galaxies
evolves by a factor of ~ 3 from z ~ 2 to the present day. The increase in the
galaxy group contribution is due to the radio activity in groups being nearly
constant at z < 1, while it is declining in the field. We show that massive
galaxies inside galaxy groups remain radio active below redshift 1, contrary to
the ones in the field. This evolution in the GG RLF is driven mainly by
satellite galaxies in groups. Group galaxies associated with SFGs dominate the
GG RLF at z_med = 0.3, while at z_med = 0.8, the peak in the RLF, coinciding
with a known overdensity in COSMOS, is mainly driven by AGN. The study provides
an observational probe for the accuracy of the numerical predictions of the
radio emission in galaxies in a group environment.


title: The number of extended irreducible binary Goppa codes

abstract:
  Goppa, in the 1970s, discovered the relation between algebraic geometry and
codes, which led to the family of Goppa codes. As one of the most interesting
subclasses of linear codes, the family of Goppa codes is often chosen as a key
in the McEliece cryptosystem. Knowledge of the number of inequivalent binary
Goppa codes for fixed parameters may facilitate in the evaluation of the
security of such a cryptosystem. Let $n\geq5$ be an odd prime number, let
$q=2^n$ and let $r\geq3$ be a positive integer satisfying $\gcd(r,n)=1$. The
purpose of this paper is to establish an upper bound on the number of
inequivalent extended irreducible binary Goppa codes of length $q+1$ and degree
$r$.A potential mathematical object for this purpose is to count the number of
orbits of the projective semi-linear group ${\rm
PGL}_2(\mathbb{F}_q)\rtimes{\rm Gal}(\mathbb{F}_{q^r}/\mathbb{F}_2)$ on the set
$\mathcal{I}_r$ of all monic irreducible polynomials of degree $r$ over the
finite field $\mathbb{F}_q$. An explicit formula for the number of orbits of
${\rm PGL}_2(\mathbb{F}_q)\rtimes{\rm Gal}(\mathbb{F}_{q^r}/\mathbb{F}_2)$ on
$\mathcal{I}_r$ is given, and consequently, an upper bound for the number of
inequivalent extended irreducible binary Goppa codes of length $q+1$ and degree
$r$ is derived. Our main result naturally contains the main results of Ryan
(IEEE-TIT 2015), Huang and Yue (IEEE-TIT, 2022) and, Chen and Zhang (IEEE-TIT,
2022), which considered the cases $r=4$, $r=6$ and $\gcd(r,q^3-q)=1$
respectively.


title:
Real-time Hyperspectral Imaging in Hardware via Trained Metasurface
  Encoders

abstract:
  Hyperspectral imaging has attracted significant attention to identify
spectral signatures for image classification and automated pattern recognition
in computer vision. State-of-the-art implementations of snapshot hyperspectral
imaging rely on bulky, non-integrated, and expensive optical elements,
including lenses, spectrometers, and filters. These macroscopic components do
not allow fast data processing for, e.g real-time and high-resolution videos.
This work introduces Hyplex, a new integrated architecture addressing the
limitations discussed above. Hyplex is a CMOS-compatible, fast hyperspectral
camera that replaces bulk optics with nanoscale metasurfaces inversely designed
through artificial intelligence. Hyplex does not require spectrometers but
makes use of conventional monochrome cameras, opening up the possibility for
real-time and high-resolution hyperspectral imaging at inexpensive costs.
Hyplex exploits a model-driven optimization, which connects the physical
metasurfaces layer with modern visual computing approaches based on end-to-end
training. We design and implement a prototype version of Hyplex and compare its
performance against the state-of-the-art for typical imaging tasks such as
spectral reconstruction and semantic segmentation. In all benchmarks, Hyplex
reports the smallest reconstruction error. We additionally present what is, to
the best of our knowledge, the largest publicly available labeled hyperspectral
dataset for semantic segmentation.


title:
High-throughput Pairwise Alignment with the Wavefront Algorithm using
  Processing-in-Memory

abstract:
  We show that the wavefront algorithm can achieve higher pairwise read
alignment throughput on a UPMEM PIM system than on a server-grade
multi-threaded CPU system.


title: Gravity at the Tip of the Throat

abstract:
  We study the gravitational signatures that arise from compactifying Type IIB
supergravity on a compact space containing a Klebanov-Strassler warped throat.
After reviewing the dimensional reduction of the 10d graviton and explicitly
obtaining the equations of motion for the 4d tensor $h_{\mu\nu}$, vector
$h_{\mu n}$ and scalar $h_{mn}$ modes, we find the masses and wavefunctions of
the Kaluza-Klein tower of spin-2 states. We explore how the masses and
wavefunctions depend on the balance between the strength of the warping and the
size of the bulk, and how these relate to the range and strength of the
interactions which correct the Newtonian gravitational potential. By computing
the modified Newtonian potential for sources on a brane somewhere along the
throat, and applying consistency constraints on the Klebanov-Strassler
parameters, we obtain predictions for the phenomenological parameter space. In
the case of a fully warped throat, and depending on where the brane is along
the throat, these predictions are narrow in range and consistent with current
observational and experimental constraints. We also begin an exploration of
gravitational wave signatures of KK gravitons in warped throats, finding that
strong warping can bring the corresponding frequencies down to the windows of
current and proposed experiments.


title: Projection onto quadratic hypersurfaces

abstract:
  We address the problem of projecting a point onto a quadratic hypersurface,
more specifically a central quadric. We show how this problem reduces to
finding a given root of a scalar-valued nonlinear function. We completely
characterize one of the optimal solutions of the projection as either the
unique root of this nonlinear function on a given interval, or as a point that
belongs to a finite set of computable solutions. We then leverage this
projection and the recent advancements in splitting methods to compute the
projection onto the intersection of a box and a quadratic hypersurface with
alternating projections and Douglas-Rachford splitting methods. We test these
methods on a practical problem from the power systems literature, and show that
they outperform IPOPT and Gurobi in terms of objective, execution time and
feasibility of the solution.


title:
A Two-student Learning Framework for Mixed Supervised Target Sound
  Detection

abstract:
  Target sound detection (TSD) aims to detect the target sound from mixture
audio given the reference information. Previous work shows that a good
detection performance relies on fully-annotated data. However, collecting
fully-annotated data is labor-extensive. Therefore, we consider TSD with mixed
supervision, which learns novel categories (target domain) using weak
annotations with the help of full annotations of existing base categories
(source domain). We propose a novel two-student learning framework, which
contains two mutual helping student models ($\mathit{s\_student}$ and
$\mathit{w\_student}$) that learn from fully- and weakly-annotated datasets,
respectively. Specifically, we first propose a frame-level knowledge
distillation strategy to transfer the class-agnostic knowledge from
$\mathit{s\_student}$ to $\mathit{w\_student}$. After that, a pseudo supervised
(PS) training is designed to transfer the knowledge from $\mathit{w\_student}$
to $\mathit{s\_student}$. Lastly, an adversarial training strategy is proposed,
which aims to align the data distribution between source and target domains. To
evaluate our method, we build three TSD datasets based on UrbanSound and
Audioset. Experimental results show that our methods offer about 8\%
improvement in event-based F score.


title:
$\mathbb{Z}_4$-symmetric perturbations to the XY model from functional
  renormalization

abstract:
  We employ the second order of the derivative expansion of the nonperturbative
renormalization group to study cubic ($\mathbb{Z}_4$-symmetric) perturbations
to the classical $XY$ model in dimensionality $d\in [2,4]$. In $d=3$ we provide
accurate estimates of the eigenvalue $y_4$ corresponding to the leading
irrelevant perturbation and follow the evolution of the physical picture upon
reducing spatial dimensionality from $d=3$ towards $d=2$, where we
approximately recover the onset of the Kosterlitz-Thouless physics. We analyze
the interplay between the leading irrelevant eigenvalues related to
$O(2)$-symmetric and $\mathbb{Z}_4$-symmetric perturbations and their
approximate collapse for $d\to 2$. We compare and discuss different
implementations of the derivative expansion in cases involving one and two
invariants of the corresponding symmetry group.


title: VocaLiST: An Audio-Visual Synchronisation Model for Lips and Voices

abstract:
  In this paper, we address the problem of lip-voice synchronisation in videos
containing human face and voice. Our approach is based on determining if the
lips motion and the voice in a video are synchronised or not, depending on
their audio-visual correspondence score. We propose an audio-visual cross-modal
transformer-based model that outperforms several baseline models in the
audio-visual synchronisation task on the standard lip-reading speech benchmark
dataset LRS2. While the existing methods focus mainly on the lip
synchronisation in speech videos, we also consider the special case of singing
voice. Singing voice is a more challenging use case for synchronisation due to
sustained vowel sounds. We also investigate the relevance of lip
synchronisation models trained on speech datasets in the context of singing
voice. Finally, we use the frozen visual features learned by our lip
synchronisation model in the singing voice separation task to outperform a
baseline audio-visual model which was trained end-to-end. The demos, source
code, and the pre-trained model will be made available on
https://ipcv.github.io/VocaLiST/


title: P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior

abstract:
  Monocular depth estimation is vital for scene understanding and downstream
tasks. We focus on the supervised setup, in which ground-truth depth is
available only at training time. Based on knowledge about the high regularity
of real 3D scenes, we propose a method that learns to selectively leverage
information from coplanar pixels to improve the predicted depth. In particular,
we introduce a piecewise planarity prior which states that for each pixel,
there is a seed pixel which shares the same planar 3D surface with the former.
Motivated by this prior, we design a network with two heads. The first head
outputs pixel-level plane coefficients, while the second one outputs a dense
offset vector field that identifies the positions of seed pixels. The plane
coefficients of seed pixels are then used to predict depth at each position.
The resulting prediction is adaptively fused with the initial prediction from
the first head via a learned confidence to account for potential deviations
from precise local planarity. The entire architecture is trained end-to-end
thanks to the differentiability of the proposed modules and it learns to
predict regular depth maps, with sharp edges at occlusion boundaries. An
extensive evaluation of our method shows that we set the new state of the art
in supervised monocular depth estimation, surpassing prior methods on NYU
Depth-v2 and on the Garg split of KITTI. Our method delivers depth maps that
yield plausible 3D reconstructions of the input scenes. Code is available at:
https://github.com/SysCV/P3Depth


title: Universality of SIS epidemics starting from small initial conditions

abstract:
  We are investigating deterministic SIS dynamics on large networks starting
from only a few infected individuals. Under mild assumptions we show that any
two epidemic curves { on the same network and with the same parameters { are
almost identical up to time translation when initial conditions are small
enough regardless of how infections are distributed at the beginning. The limit
of an epidemic starting from the infinite past with infinitesimal prevalence is
identified as the nontrivial eternal solution connecting the disease free state
with the endemic equilibrium. Our framework covers several benchmark models
including the N-Intertwined Mean Field Approximation (NIMFA) and the
Inhomogeneous Mean Field Approximation (IMFA).


title:
A machine learning-based framework for high resolution mapping of PM2.5
  in Tehran, Iran, using MAIAC AOD data

abstract:
  This paper investigates the possibility of high resolution mapping of PM2.5
concentration over Tehran city using high resolution satellite AOD (MAIAC)
retrievals. For this purpose, a framework including three main stages, data
preprocessing; regression modeling; and model deployment was proposed. The
output of the framework was a machine learning model trained to predict PM2.5
from MAIAC AOD retrievals and meteorological data. The results of model testing
revealed the efficiency and capability of the developed framework for high
resolution mapping of PM2.5, which was not realized in former investigations
performed over the city. Thus, this study, for the first time, realized daily,
1 km resolution mapping of PM2.5 in Tehran with R2 around 0.74 and RMSE better
than 9.0 mg/m3.
  Keywords: MAIAC; MODIS; AOD; Machine learning; Deep learning; PM2.5;
Regression


title:
The Nicolai map for super Yang-Mills theory and application to the
  supermembrane

abstract:
  The nonlocal bosonic theory obtained from integrating out all anticommuting
and auxiliary variables in a globally supersymmetric theory is characterized by
the Nicolai map. We present a universal formula for the latter in terms of an
ordered exponential of the integrated coupling flow operator, which can be
canonically constructed. Also for supersymmetric gauge theories, this allows us
to perturbatively construct the Nicolai map explicitly in terms of tree
diagrams. For off-shell supersymmetry this works in any gauge, in the on-shell
case the Landau gauge is required. The dimensional reduction of $D{=}10$ super
Yang-Mills to maximally supersymmetric SU($N$) matrix mechanics (the BFSS
model) is known to provide a regularization of the $D{=}11$ supermembrane in
light-cone gauge via its incarnation as a one-dimensional gauge theory of
area-preserving membrane diffeomorphisms. We show how a well-defined
corresponding Nicolai map perturbatively linearizes the supermembrane in the
small-tension regime and points the way for a computation of vertex-operator
correlators.


title: Streaming Facility Location in High Dimension via New Geometric Hashing

abstract:
  In Euclidean Uniform Facility Location, the input is a set of clients in
$\mathbb{R}^d$ and the goal is to place facilities to serve them, so as to
minimize the total cost of opening facilities plus connecting the clients. We
study the classical setting of dynamic geometric streams, where the clients are
presented as a sequence of insertions and deletions of points in the grid
$\{1,\ldots,\Delta\}^d$, and we focus on the high-dimensional regime, where the
algorithm's space complexity must be polynomial (and certainly not exponential)
in $d\cdot\log\Delta$.
  We present a new algorithmic framework, based on importance sampling from the
stream, for $O(1)$-approximation of the optimal cost using only
$\mathrm{poly}(d\cdot\log\Delta)$ space. This framework is easy to implement in
two passes, one for sampling points and the other for estimating their
contribution. Over random-order streams, we can extend this to a one-pass
algorithm by using the two halves of the stream separately. Our main result,
for arbitrary-order streams, computes $O(d^{1.5})$-approximation in one pass by
using the new framework but combining the two passes differently. This improves
upon previous algorithms that either need space exponential in $d$ or only
guarantee $O(d\cdot\log^2\Delta)$-approximation, and therefore our algorithms
for high-dimensional streams are the first to avoid the $O(\log\Delta)$-factor
in approximation that is inherent to the widely-used quadtree decomposition.
Our improvement is achieved by introducing a novel geometric hashing scheme
that maps points in $\mathbb{R}^d$ into buckets of bounded diameter, with the
key property that every point set of small-enough diameter is hashed into at
most $\mathrm{poly}(d)$ distinct buckets.
  Finally, we complement our results by showing $1.085$-approximation requires
space exponential in $\mathrm{poly}(d\cdot\log\Delta)$, even for insertion-only
streams.


title: On $k$-universal quadratic lattices over unramified dyadic local fields

abstract:
  Let $k$ be a positive integer and let $F$ be a finite unramified extension of
$\mathbb{Q}_2$ with ring of integers $\mathcal{O}_F$. An integral (resp.
classic) quadratic form over $\mathcal{O}_F$ is called $k$-universal (resp.
classically $k$-universal) if it represents all integral (resp. classic)
quadratic forms of dimension $k$. In this paper, we provide a complete
classification of $k$-universal and classically $k$-universal quadratic forms
over $\mathcal{O}_F$. The results are stated in terms of the fundamental
invariants associated to Jordan splittings of quadratic lattices.


title:
Simulated Annealing is a Polynomial-Time Approximation Scheme for the
  Minimum Spanning Tree Problem

abstract:
  We prove that Simulated Annealing with an appropriate cooling schedule
computes arbitrarily tight constant-factor approximations to the minimum
spanning tree problem in polynomial time. This result was conjectured by
Wegener (2005). More precisely, denoting by $n, m, w_{\max}$, and $w_{\min}$
the number of vertices and edges as well as the maximum and minimum edge weight
of the MST instance, we prove that simulated annealing with initial temperature
$T_0 \ge w_{\max}$ and multiplicative cooling schedule with factor $1-1/\ell$,
where $\ell = \omega (mn\ln(m))$, with probability at least $1-1/m$ computes in
time $O(\ell (\ln\ln (\ell) + \ln(T_0/w_{\min}) ))$ a spanning tree with weight
at most $1+\kappa$ times the optimum weight, where $1+\kappa =
\frac{(1+o(1))\ln(\ell m)}{\ln(\ell) -\ln (mn\ln (m))}$. Consequently, for any
$\epsilon>0$, we can choose $\ell$ in such a way that a
$(1+\epsilon)$-approximation is found in time
$O((mn\ln(n))^{1+1/\epsilon+o(1)}(\ln\ln n + \ln(T_0/w_{\min})))$ with
probability at least $1-1/m$. In the special case of so-called
$(1+\epsilon)$-separated weights, this algorithm computes an optimal solution
(again in time $O( (mn\ln(n))^{1+1/\epsilon+o(1)}(\ln\ln n +
\ln(T_0/w_{\min})))$), which is a significant speed-up over Wegener's runtime
guarantee of $O(m^{8 + 8/\epsilon})$.


title:
Constraining primordial black holes as fraction of dark matter through
  accretion disk luminosity

abstract:
  In this paper, we consider the hypothesis that fractions of dark matter could
be constituted by primordial black holes (PBHs). To test this possibility, we
work out the observational properties of a static black hole embedded in the
dark matter envelope made of a PBH source. The corresponding modifications of
geometry due to such a physical system are investigated, with particular focus
on the accretion disk luminosity in spiral galaxies. The impact of the PBH
presence is analyzed through modification of the disk luminosity and kinematic
quantities. Thus, we discuss possible constraints on the PBH abundance in view
of the most recent theoretical bounds. The results of our study indicate that
suitable PBH masses are $M_\text{PBH}\in[10^6,10^{12}]M_\odot$ for PBH
fractions $f_\text{PBH}\in[10^{-3},1]$. In particular, a comparison with the
predictions of the exponential sphere density profile for dark matter suggests
that the best-matching configuration is achieved for $f_\text{PBH}=1$ and
$M_\text{PBH}=10^6 M_\odot$. Consequences with respect to the current knowledge
on primordial black hole physics are discussed.


title:
Collective control of modular soft robots via embodied Spiking Neural
  Cellular Automata

abstract:
  Voxel-based Soft Robots (VSRs) are a form of modular soft robots, composed of
several deformable cubes, i.e., voxels. Each VSR is thus an ensemble of simple
agents, namely the voxels, which must cooperate to give rise to the overall VSR
behavior. Within this paradigm, collective intelligence plays a key role in
enabling the emerge of coordination, as each voxel is independently controlled,
exploiting only the local sensory information together with some knowledge
passed from its direct neighbors (distributed or collective control). In this
work, we propose a novel form of collective control, influenced by Neural
Cellular Automata (NCA) and based on the bio-inspired Spiking Neural Networks:
the embodied Spiking NCA (SNCA). We experiment with different variants of SNCA,
and find them to be competitive with the state-of-the-art distributed
controllers for the task of locomotion. In addition, our findings show
significant improvement with respect to the baseline in terms of adaptability
to unforeseen environmental changes, which could be a determining factor for
physical practicability of VSRs.


title:
Self-supervised learning -- A way to minimize time and effort for
  precision agriculture?

abstract:
  Machine learning, satellites or local sensors are key factors for a
sustainable and resource-saving optimisation of agriculture and proved its
values for the management of agricultural land. Up to now, the main focus was
on the enlargement of data which were evaluated by means of supervised learning
methods. Nevertheless, the need for labels is also a limiting and
time-consuming factor, while in contrast, ongoing technological development is
already providing an ever-increasing amount of unlabeled data. Self-supervised
learning (SSL) could overcome this limitation and incorporate existing
unlabeled data. Therefore, a crop type data set was utilized to conduct
experiments with SSL and compare it to supervised methods. A unique feature of
our data set from 2016 to 2018 was a divergent climatological condition in 2018
that reduced yields and affected the spectral fingerprint of the plants. Our
experiments focused on predicting 2018 using SLL without or a few labels to
clarify whether new labels should be collected for an unknown year. Despite
these challenging conditions, the results showed that SSL contributed to higher
accuracies. We believe that the results will encourage further improvements in
the field of precision farming, why the SSL framework and data will be
published (Marszalek, 2021).


title: Non-Linear Speech coding with MLP, RBF and Elman based prediction

abstract:
  In this paper we propose a nonlinear scalar predictor based on a combination
of Multi Layer Perceptron, Radial Basis Functions and Elman networks. This
system is applied to speech coding in an ADPCM backward scheme. The combination
of this predictors improves the results of one predictor alone. A comparative
study of this three neural networks for speech prediction is also presented.


title:
Dynamic Federations for 6G Cell-Free Networking: Concepts and
  Terminology

abstract:
  Cell-Free networking is one of the prime candidates for 6G networks. Despite
being capable of providing the 6G needs, practical limitations and
considerations are often neglected in current research. In this work, we
introduce the concept of federations to dynamically scale and select the best
set of resources, e.g., antennas, computing and data resources, to serve a
given application. Next to communication, 6G systems are expected to provide
also wireless powering, positioning and sensing, further increasing the
complexity of such systems. Therefore, each federation is self-managing and is
distributed over the area in a cell-free manner. Next to the dynamic
federations, new accompanying terminology is proposed to design cell-free
systems taking into account practical limitations such as time synchronization
and distributed processing. We conclude with an illustration with four
federations, serving distinct applications, and introduce two new testbeds to
study these architectures and concepts.


title:
Quantum noise limited microwave amplification using a graphene Josephson
  junction

abstract:
  Josephson junctions (JJ) and their tunable properties, including their
nonlinearities, form the core of superconducting circuit quantum
electrodynamics (cQED). In quantum circuits, low-noise amplification of feeble
microwave signals is essential and the Josephson parametric amplifiers (JPA)
are the widely used devices. The existing JPAs are based on Al-AlOx-Al tunnel
junctions realized in a superconducting quantum interference device geometry,
where magnetic flux is the knob for tuning the frequency. Recent experimental
realizations of 2D van der Waals JJs provide an opportunity to implement
various cQED devices with the added advantage of tuning the junction properties
and the operating point using a gate potential. While other components of a
possible 2D van der Waals cQED architecture have been demonstrated -- quantum
noise limited amplifier, an essential component, has not been realized. Here we
implement a quantum noise limited JPA, using a graphene JJ, that has linear
resonance gate tunability of 3.5 GHz. We report 24 dB amplification with 10 MHz
bandwidth and -130 dBm saturation power; performance on par with the best
single-junction JPAs. Importantly, our gate tunable JPA works in the
quantum-limited noise regime which makes it an attractive option for highly
sensitive signal processing. Our work has implications for novel bolometers --
the low heat capacity of graphene together with JJ nonlinearity can result in
an extremely sensitive microwave bolometer embedded inside a quantum
noise-limited amplifier. In general, our work will open up exploration of
scalable device architecture of 2D van der Waals materials by integrating a
sensor with the quantum amplifier.


title: Watermarked ReRAM: A Technique to Prevent Counterfeit Memory Chips

abstract:
  Electronic counterfeiting is a longstanding problem with adverse long-term
effects for many sectors, remaining on the rise. This article presents a novel
low-cost technique to embed watermarking in devices with resistive-RAM (ReRAM)
by manipulating its analog physical characteristics through switching
(set/reset) operation to prevent counterfeiting. We develop a system-level
framework to control memory cells' physical properties for imprinting
irreversible watermarks into commercial ReRAMs that will be retrieved by
sensing the changes in cells' physical properties. Experimental results show
that our proposed ReRAM watermarking is robust against temperature variation
and acceptably fast with ~0.6bit/min of imprinting and ~15.625bits/s of
retrieval rates.


title:
GROMACS Stochastic Dynamics and BAOAB are equivalent configurational
  sampling algorithms

abstract:
  Two of the most widely used Langevin integrators for molecular dynamics
simulations are the GROMACS Stochastic Dynamics (GSD) integrator and the
splitting method BAOAB. We show that the GROMACS Stochastic Dynamics integrator
is equal to the less frequently used splitting method BAOA. It immediately
follows that GSD and BAOAB sample the same configurations and have the same
high configurational accuracy. Our numerical results indicate that GSD/BAOA has
higher kinetic accuracy than BAOAB.


title:
How do media talk about the Covid-19 pandemic? Metaphorical thematic
  clustering in Italian online newspapers

abstract:
  The contribution presents a study on figurative language of the first months
of the COVID-19 crisis in Italian online newspapers. Particularly, we contrast
topics and metaphorical language used by journalists in the first and second
phase of the government response to the pandemic in Spring 2020. The analysis
is conducted on a journalistic corpus collected between February 24th and June
3rd, 2020. The analysis is performed using both quantitative and qualitative
approaches, combining Structural Topic Modelling (Roberts et al. 2016),
Conceptual Metaphor Theory (Lakoff & Johnson, 1980), and qualitative-corpus
based metaphor analysis (Charteris-Black, 2004). We find a significant shift in
topics discussed across Phase 1 and Phase 2, and interesting overlaps in
topic-specific metaphors. Using qualitative corpus analysis, we present a more
in-depth case study discussing metaphorical collocations of the topics of
Economy and Society


title:
A GPU-accelerated computational fluid dynamics solver for assessing
  shear-driven indoor airflow and virus transmission by scale-resolved
  simulations

abstract:
  We explore the applicability of MATLAB for 3D computational fluid dynamics
(CFD) of shear-driven indoor airflows. A new scale-resolving, large-eddy
simulation (LES) solver titled DNSLABIB is proposed for MATLAB utilizing
graphics processing units (GPUs). The solver is first validated against another
CFD software (OpenFOAM). Next, we demonstrate the solver performance in three
isothermal indoor ventilation configurations and the results are discussed in
the context of airborne transmission of COVID-19. Ventilation in these cases is
studied at both low (0.1 m/s) and high (1 m/s) airflow rates corresponding to
$Re=5000$ and $Re=50000$. An analysis of the indoor CO$_2$ concentration is
carried out as the room is emptied from stale, high CO$_2$ content air. We
estimate the air changes per hour (ACH) values for three different room
geometries and show that the numerical estimates from 3D CFD simulations may
differ by 80-150 % ($Re=50000$) and 75-140 % ($Re=5000$) from the theoretical
ACH value based on the perfect mixing assumption. Additionally, the analysis of
the CO$_2$ probability distributions (PDFs) indicates a relatively non-uniform
distribution of fresh air indoors. Finally, utilizing a time-dependent
Wells-Riley analysis, an example is provided on the growth of the cumulative
infection risk being reduced rapidly after the ventilation is started. The
average infection risk is shown to reduce by a factor of 2 for lower
ventilation rates (ACH=3.4-6.3) and 10 for the higher ventilation rates
(ACH=37-64). The results indicate a high potential for DNSLABIB in various
future developments on airflow prediction.


title: When Locality Meets Preservation

abstract:
  This paper investigates the expressiveness of a fragment of first-order
sentences in Gaifman normal form, namely the positive Boolean combinations of
basic local sentences. We show that they match exactly the first-order
sentences preserved under local elementary embeddings, thus providing a new
general preservation theorem and extending the L\'os-Tarski Theorem.
  This full preservation result fails as usual in the finite, and we show
furthermore that the naturally related decision problems are undecidable. In
the more restricted case of preservation under extensions, it nevertheless
yields new well-behaved classes of finite structures: we show that preservation
under extensions holds if and only if it holds locally.


title: Distance to the Brick cloud using stellar kinematics

abstract:
  Context. The central molecular zone at the Galactic center is currently being
studied intensively to understand how star formation proceeds under the extreme
conditions of a galactic nucleus. Knowing the position of molecular clouds
along the line of sight toward the Galactic center has had important
implications in our understanding of the physics of the gas and star formation
in the central molecular zone.It was recently claimed that the dense molecular
cloud G0.253 + 0.016 (the Brick) has a distance of $\sim$7.20 kpc from the Sun.
That would place it outside of the central molecular zone, and therefore of the
nuclear stellar disk, but still inside the Bulge.
  Aims. Theoretical considerations as well as observational studies show that
stars that belong to the nuclear stellar disk have different kinematics from
those that belong to the inner Bulge. Therefore, we aim to constrain the
distance to the Brick by studying the proper motions of the stars in the area.
  Results. The stellar population seen toward the nuclear stellar disk shows
the following three kinematic components: 1) Bulge stars with an isotropic
velocity dispersion of $\sim$3.5 micro-arc second per year; 2) eastward moving
stars on the near side of the nuclear stellar disk; and 3) westward moving
stars on the far side of the nuclear stellar disk. We clearly see all three
components toward the comparison field. However, toward the Brick, which blocks
the light from stars behind it, we can only see kinematic components 1) and 2).
  Conclusions. While the Brick blocks the light from stars on the far side of
the nuclear stellar disk, the detection of a significant component of eastward
streaming stars implies that the Brick must be located inside the nuclear
stellar disk and, therefore, that it forms part of the central molecular zone.


title: Study of relativistic accretion flow around KTN black hole with shocks

abstract:
  We present the global solutions of low angular momentum, inviscid, advective
accretion flow around Kerr-Taub-NUT (KTN) black hole in presence and absence of
shock waves. These solutions are obtained by solving the governing equations
that describe the relativistic accretion flow in KTN spacetime which is
characterized by the Kerr parameter ($a_{\rm k}$) and NUT parameter ($n$).
During accretion, rotating flow experiences centrifugal barrier that eventually
triggers the discontinuous shock transition provided the relativistic shock
conditions are satisfied. In reality, the viability of shocked accretion
solution appears more generic over the shock free solution as the former
possesses high entropy content at the inner edge of the disc. Due to shock
compression, the post-shock flow (equivalently post-shock corona, hereafter
PSC) becomes hot and dense, and therefore, can produce high energy radiations
after reprocessing the soft photons from the pre-shock flow via inverse
Comptonization. In general, PSC is characterized by the shock properties,
namely shock location ($r_s$), compression ratio ($R$) and shock strength
($S$), and we examine their dependencies on the energy (${\cal E}$) and angular
momentum ($\lambda$) of the flow as well as black hole parameters. We identify
the effective domain of the parameter space in $\lambda-{\cal E}$ plane for
shock and observe that shock continues to form for wide range of flow
parameters. We also find that $a_{\rm k}$ and $n$ act oppositely in determining
the shock properties and shock parameter space. Finally, we calculate the disc
luminosity ($L$) considering free-free emissions and observe that accretion
flows containing shocks are more luminous compared to the shock free solutions.


title:
Birds of A Feather Flock Together: Category-Divergence Guidance for
  Domain Adaptive Segmentation

abstract:
  Unsupervised domain adaptation (UDA) aims to enhance the generalization
capability of a certain model from a source domain to a target domain. Present
UDA models focus on alleviating the domain shift by minimizing the feature
discrepancy between the source domain and the target domain but usually ignore
the class confusion problem. In this work, we propose an Inter-class Separation
and Intra-class Aggregation (ISIA) mechanism. It encourages the cross-domain
representative consistency between the same categories and differentiation
among diverse categories. In this way, the features belonging to the same
categories are aligned together and the confusable categories are separated. By
measuring the align complexity of each category, we design an Adaptive-weighted
Instance Matching (AIM) strategy to further optimize the instance-level
adaptation. Based on our proposed methods, we also raise a hierarchical
unsupervised domain adaptation framework for cross-domain semantic segmentation
task. Through performing the image-level, feature-level, category-level and
instance-level alignment, our method achieves a stronger generalization
performance of the model from the source domain to the target domain. In two
typical cross-domain semantic segmentation tasks, i.e., GTA5 to Cityscapes and
SYNTHIA to Cityscapes, our method achieves the state-of-the-art segmentation
accuracy. We also build two cross-domain semantic segmentation datasets based
on the publicly available data, i.e., remote sensing building segmentation and
road segmentation, for domain adaptive segmentation.


title:
GP-BART: a novel Bayesian additive regression trees approach using
  Gaussian processes

abstract:
  The Bayesian additive regression trees (BART) model is an ensemble method
extensively and successfully used in regression tasks due to its consistently
strong predictive performance and its ability to quantify uncertainty. BART
combines "weak" tree models through a set of shrinkage priors, whereby each
tree explains a small portion of the variability in the data. However, the lack
of smoothness and the absence of a covariance structure over the observations
in standard BART can yield poor performance in cases where such assumptions
would be necessary. We propose Gaussian processes Bayesian additive regression
trees (GP-BART) as an extension of BART which assumes Gaussian process (GP)
priors for the predictions of each terminal node among all trees. We illustrate
our model on simulated and real data and compare its performance to traditional
modelling approaches, outperforming them in many scenarios. An implementation
of our method is available in the R package rGPBART available at:
https://github.com/MateusMaiaDS/gpbart


title: RSVD-CUR Decomposition for Matrix Triplets

abstract:
  We propose a restricted SVD based CUR (RSVD-CUR) decomposition for matrix
triplets $(A, B, G)$. Given matrices $A$, $B$, and $G$ of compatible
dimensions, such a decomposition provides a coordinated low-rank approximation
of the three matrices using a subset of their rows and columns. We pick the
subset of rows and columns of the original matrices by applying either the
discrete empirical interpolation method (DEIM) or the L-DEIM scheme on the
orthogonal and nonsingular matrices from the restricted singular value
decomposition of the matrix triplet. We investigate the connections between a
DEIM type RSVD-CUR approximation and a DEIM type CUR factorization, and a DEIM
type generalized CUR decomposition. We provide an error analysis that shows
that the accuracy of the proposed RSVD-CUR decomposition is within a factor of
the approximation error of the restricted singular value decomposition of given
matrices. An RSVD-CUR factorization may be suitable for applications where we
are interested in approximating one data matrix relative to two other given
matrices. Two applications that we discuss include multi-view/label dimension
reduction, and data perturbation problems of the form $A_E=A + BFG$, where
$BFG$ is a nonwhite noise matrix. In numerical experiments, we show the
advantages of the new method over the standard CUR approximation for these
applications.


title: Radio-loud Quasars above Redshift 4: VLBI Imaging of an Extended Sample

abstract:
  High-redshift radio sources provide plentiful opportunities for studying the
formation and evolution of early galaxies and supermassive black holes.
However, the number of known radio-loud active galactic nuclei (AGN) above
redshift 4 is rather limited. At high redshifts, it appears that blazars, with
relativistically beamed jets pointing towards the observer, are in majority
compared to radio-loud sources with jets misaligned with respect to the line of
sight. To find more of these misaligned AGN, milliarcsec-scale imaging studies
carried out with very long baseline interferometry (VLBI) are needed, as they
allow us to distinguish between compact core--jet radio sources and those with
more extended emission. Previous high-resolution VLBI studies revealed that
some of the radio sources among blazar candidates in fact show unbeamed radio
emission on milliarcsecond scales. The most accurate optical coordinates
determined with the Gaia astrometric space mission are also useful in the
classification process. Here, we report on dual-frequency imaging observations
of 13 high-redshift (4 < z < 4.5) quasars at 1.7 and 5 GHz with the European
VLBI Network. This sample increases the number of z>4 radio sources for which
VLBI observations are available by about a quarter. Using structural and
physical properties, such as radio morphology, spectral index, variability,
brightness temperature, as well as optical coordinates, we identified six
blazars and six misaligned radio AGNs, with the remaining one tentatively
identified as blazar.


title: Leaderless Population Protocols Decide Double-exponential Thresholds

abstract:
  Population protocols are a model of distributed computation in which
finite-state agents interact randomly in pairs. A protocol decides for any
initial configuration whether it satisfies a fixed property, specified as a
predicate on the set of configurations. The state complexity of a predicate is
smallest number of states of any protocol deciding that predicate. For
threshold predicates of the form $x\ge k$, with $k$ constant, prior work has
shown that they have state complexity $\Theta(\log\log k)$ if the protocol is
extended with leaders. For ordinary protocols it is only known to be in
$\Omega(\log\log k)\cap \mathcal{O}(\log k)$. We close this remaining gap by
showing that it is $\Theta(\log\log k)$ as well, i.e. we construct protocols
with $\mathcal{O}(n)$ states deciding $x\ge k$ with $k\ge2^{2^n}$.


title:
Extended regime of coexisting metallic and insulating phases in a
  two-orbital electronic system

abstract:
  We investigate the metal-to-insulator phase transition driven by electronic
interactions in the quarter-filled Hubbard-Kanamori model on a cubic lattice
with two orbitals split by a crystal field. We show that a systematic
consideration of the non-local collective electronic fluctuations strongly
affects the state-of-the-art picture of the phase transition provided by the
dynamical mean field theory. Our calculations reveal a region of phase
coexistence between the metallic and the Mott insulating states, which is
missing in the local approximation to electronic correlations. This coexistence
region is remarkably broad in terms of the interaction strength. It starts at a
critical value of the interaction slightly larger than the bandwidth and
extends to more than twice the bandwidth, where the two solutions merge into a
Mott insulating phase. Our results illustrate that non-local correlations can
have crucial consequences on the electronic properties in the strongly
correlated regime, even in the simplest multi-orbital systems.


title:
Boundary Control of the Kuramoto-Sivashinsky Equation Under Intermittent
  Data Availability: With Proofs

abstract:
  In this paper, two boundary controllers are proposed to stabilize the origin
of the nonlinear Kuramoto-Sivashinsky equation under intermittent measurements.
More precisely, the spatial domain is divided into two sub-domains. The state
of the system on the first sub-domain is measured along a given interval of
time, and the state on the remaining sub-domain is measured along another
interval of time. Under the proposed sensing scenario, we control the
considered equation by designing the value of the state at three isolated
spatial points, the two extremities of the spatial domain plus one inside
point. Furthermore, we impose a null value for the spatial gradient of the
state at these three locations. Under such a control loop, we propose two types
of controllers and we analyze the stability of the resulting closed-loop system
in each case. The paper is concluded with some discussions and future works.


title:
Spectroscopic investigations of detachment on the MAST Upgrade Super-X
  divertor

abstract:
  We present the first analysis of the atomic and molecular processes at play
during detachment in the MAST-U Super-X divertor using divertor spectroscopy
data. Our analysis indicates detachment in the MAST-U Super-X divertor can be
separated into four sequential phases: First, the ionisation region detaches
from the target at detachment onset leaving a region of increased molecular
densities downstream. The plasma interacts with these molecules, resulting in
molecular ions ($D_2^+$ and/or $D_2^- \rightarrow D + D^-$) that further react
with the plasma leading to Molecular Activated Recombination and Dissociation
(MAR and MAD), which results in excited atoms and significant Balmer line
emission. Second, the MAR region detaches from the target leaving a sub-eV
temperature region downstream. Third, an onset of strong emission from
electron-ion recombination (EIR) ensues. Finally, the electron density decays
near the target, resulting in a density front moving upstream.
  The analysis in this paper indicates that plasma-molecule interactions have a
larger impact than previously reported and play a critical role in the
intensity and interpretation of hydrogen atomic line emission characteristics
on MAST-U. Furthermore, we find that the Fulcher band emission profile in the
divertor can be used as a proxy for the ionisation region and may also be
employed as a plasma temperature diagnostic for improving the separation of
hydrogenic emission arising from electron-impact excitation and that from
plasma-molecular interactions.
  We provide evidences for the presence of low electron temperatures ($<0.5$
eV) during detachment phases III-IV based on quantitative spectroscopy
analysis, a Boltzmann relation of the high-n Balmer line transitions together
with an analysis of the brightness of high-n Balmer lines.


title:
Transition Information Enhanced Disentangled Graph Neural Networks for
  Session-based Recommendation

abstract:
  Session-based recommendation is a practical recommendation task that predicts
the next item based on an anonymous behavior sequence, and its performance
relies heavily on the transition information between items in the sequence. The
SOTA methods in SBR employ GNN to model neighboring item transitions from
global (i.e, other sessions) and local (i.e, current session) contexts.
However, most existing methods treat neighbors from different sessions equally
without considering that the neighbor items from different sessions may share
similar features with the target item on different aspects and may have
different contributions. In other words, they have not explored
finer-granularity transition information between items in the global context,
leading to sub-optimal performance. In this paper, we fill this gap by
proposing a novel Transition Information Enhanced Disentangled Graph Neural
Network (TIE-DGNN) model to capture finer-granular transition information
between items and try to interpret the reason of the transition by modeling the
various factors of the item. Specifically, we propose a position-aware global
graph, which utilizes the relative position information to model the
neighboring item transition. Then, we slice item embeddings into blocks, each
of which represents a factor, and use disentangling module to separately learn
the factor embeddings over the global graph. For local context, we train item
embeddings by using attention mechanisms to capture transition information from
the current session. To this end, our model considers two levels of transition
information. Especially in global text, we not only consider finer-granularity
transition information between items but also take user intents at factor-level
into account to interpret the key reason for the transition. Extensive
experiments on three datasets demonstrate the superiority of our method over
the SOTA methods.


title:
Event-based Navigation for Autonomous Drone Racing with Sparse Gated
  Recurrent Network

abstract:
  Event-based vision has already revolutionized the perception task for robots
by promising faster response, lower energy consumption, and lower bandwidth
without introducing motion blur. In this work, a novel deep learning method
based on gated recurrent units utilizing sparse convolutions for detecting
gates in a race track is proposed using event-based vision for the autonomous
drone racing problem. We demonstrate the efficiency and efficacy of the
perception pipeline on a real robot platform that can safely navigate a typical
autonomous drone racing track in real-time. Throughout the experiments, we show
that the event-based vision with the proposed gated recurrent unit and
pretrained models on simulated event data significantly improve the gate
detection precision. Furthermore, an event-based drone racing dataset
consisting of both simulated and real data sequences is publicly released.


title: MetaAudio: A Few-Shot Audio Classification Benchmark

abstract:
  Currently available benchmarks for few-shot learning (machine learning with
few training examples) are limited in the domains they cover, primarily
focusing on image classification. This work aims to alleviate this reliance on
image-based benchmarks by offering the first comprehensive, public and fully
reproducible audio based alternative, covering a variety of sound domains and
experimental settings. We compare the few-shot classification performance of a
variety of techniques on seven audio datasets (spanning environmental sounds to
human-speech). Extending this, we carry out in-depth analyses of joint training
(where all datasets are used during training) and cross-dataset adaptation
protocols, establishing the possibility of a generalised audio few-shot
classification algorithm. Our experimentation shows gradient-based
meta-learning methods such as MAML and Meta-Curvature consistently outperform
both metric and baseline methods. We also demonstrate that the joint training
routine helps overall generalisation for the environmental sound databases
included, as well as being a somewhat-effective method of tackling the
cross-dataset/domain setting.


title:
Twisted differentials and Lee classes of locally conformally symplectic
  complex surfaces

abstract:
  We study the set of deRham classes of Lee $1$-forms of the locally
conformally symplectic (LCS) structures taming the complex structure of a
compact complex surface in the Kodaira class VII, and show that the existence
of non-trivial upper/lower bounds with respect to the degree function
correspond respectively to the existence of certain negative/non-negative PSH
functions on the universal cover. We use this to prove that the set of Lee
deRham classes of taming LCS is connected, as well as to obtain an explicit
negative upper bound for this set on the hyperbolic Kato surfaces. This leads
to a complete description of the sets of Lee classes on the known examples of
class VII complex surfaces, and to a new obstruction to the existence of
bi-hermitian structures on the hyperbolic Kato surfaces of the intermediate
type. Our results also reveal a link between bounds of the set of Lee classes
and non-trivial logarithmic holomorphic $1$-forms with values in a flat
holomorphic line bundle.


title:
Improved and Efficient Conversational Slot Labeling through Question
  Answering

abstract:
  Transformer-based pretrained language models (PLMs) offer unmatched
performance across the majority of natural language understanding (NLU) tasks,
including a body of question answering (QA) tasks. We hypothesize that
improvements in QA methodology can also be directly exploited in dialog NLU;
however, dialog tasks must be \textit{reformatted} into QA tasks. In
particular, we focus on modeling and studying \textit{slot labeling} (SL), a
crucial component of NLU for dialog, through the QA optics, aiming to improve
both its performance and efficiency, and make it more effective and resilient
to working with limited task data. To this end, we make a series of
contributions: 1) We demonstrate how QA-tuned PLMs can be applied to the SL
task, reaching new state-of-the-art performance, with large gains especially
pronounced in such low-data regimes. 2) We propose to leverage contextual
information, required to tackle ambiguous values, simply through natural
language. 3) Efficiency and compactness of QA-oriented fine-tuning are boosted
through the use of lightweight yet effective adapter modules. 4) Trading-off
some of the quality of QA datasets for their size, we experiment with larger
automatically generated QA datasets for QA-tuning, arriving at even higher
performance. Finally, our analysis suggests that our novel QA-based slot
labeling models, supported by the PLMs, reach a performance ceiling in
high-data regimes, calling for more challenging and more nuanced benchmarks in
future work.


title:
Selectively biased tri-terminal vertically-integrated memristor
  configuration

abstract:
  Memristors, when utilized as electronic components in circuits, can offer
opportunities for the implementation of novel reconfigurable electronics. While
they have been used in large arrays, studies in ensembles of devices are
comparatively limited. Here we propose a vertically stacked memristor
configuration with a shared middle electrode. We study the compound resistive
states presented by the combined in-series devices and we alter them either by
controlling each device separately, or by altering the full configuration,
which depends on selective usage of the middle floating electrode. The shared
middle electrode enables a rare look into the combined system, which is not
normally available in vertically stacked devices. In the course of this study
it was found that separate switching of individual devices carries over its
effects to the complete device (albeit non-linearly), enabling increased
resistive state range, which leads to a larger number of distinguishable states
(above SNR variance limits) and hence enhanced device memory. Additionally, by
applying a switching stimulus to the external electrodes it is possible to
switch both devices simultaneously, making the entire configuration a voltage
divider with individual memristive components. Through usage of this type of
configuration and by taking advantage of the voltage division, it is possible
to surge-protect fragile devices, while it was also found that simultaneous
reset of stacked devices is possible, significantly reducing the required reset
time in larger arrays.


title: Dynamic screening of quasiparticles in WS$_2$ monolayers

abstract:
  We unravel the influence of quasiparticle screening in the non-equilibrium
exciton dynamics of monolayer WS$_2$. We report pump photon energy-dependent
exciton blue/red-shifts from time-resolved-reflectance contrast measurements.
Based on a phenomenological model, we isolate the effective impact of excitons
and free carriers on the renormalization of the quasi-free particle band gap,
exciton binding energy and linewidth broadening. This work provides a
comprehensive picture of the competing phenomena governing the exciton dynamics
in WS$_2$ upon photoexcitation.


title:
Impossibility of obtaining a CP-violating Euler-Heisenberg effective
  theory from a viable modification of QED

abstract:
  In this paper, we examine the CP-violating term of the Euler-Heisenberg
action. We focus in the aspects related with the generation of such a term from
a QED-like model in terms of the effective action approach. In particular, we
show that the generation of the CP-violating term is closely related with both
of vector and axial fermionic bilinears. Although, these anomalous models are
not a "viable" extension of QED, we argue that the CP-violating term in the
photon sector is obtained only from this class of models, and not from any
fundamental field theory.


title:
New evidences in IRDC G333.73+0.37: colliding filamentary clouds,
  hub-filament system, and embedded cores

abstract:
  To unravel the star formation process, we present a multi-scale and
multi-wavelength study of the filamentary infrared dark cloud (IRDC)
G333.73+0.37, which hosts previously known two HII regions located at its
center. Each HII region is associated with a mid-infrared source, and is
excited by a massive OB star. Two filamentary structures and a hub-filament
system (HFS) associated with one HII region are investigated in absorption
using the Spitzer 8.0 $\mu$m image. The $^{13}$CO(J = 2-1) and C$^{18}$O(J =
2-1) line data reveal two velocity components (around $-$35.5 and $-$33.5 km
s$^{-1}$) toward the IRDC, favouring the presence of two filamentary clouds at
different velocities. Nonthermal (or turbulent) motions are depicted in the
IRDC using the C$^{18}$O line data. The spatial distribution of young stellar
objects (YSOs) identified using the VVV near-infrared data traces star
formation activities in the IRDC. Low-mass cores are identified toward both the
HII regions using the ALMA 1.38 mm continuum map. The VLT/NACO adaptive-optics
L$^{\prime}$-band images show the presence of at least three point-like sources
and the absence of small-scale features in the inner 4000 AU around YSOs NIR31
and MIR 16 located toward the HII regions. The HII regions and groups of YSO
are observed toward the central part of the IRDC, where the two filamentary
clouds intersect. A scenario of cloud-cloud collision or converging flows in
the IRDC seems to be applicable, which may explain star formation activities
including HFS and massive stars.


title: Computing in Anonymous Dynamic Networks Is Linear

abstract:
  We give the first linear-time counting algorithm for processes in anonymous
1-interval-connected dynamic networks with a leader. As a byproduct, we are
able to compute in $3n$ rounds every function that is computable
deterministically in such networks. If explicit termination is not required,
the running time improves to $2n$ rounds, which we show to be optimal up to a
small additive constant (this is also the first non-trivial lower bound for
counting). As our main tool of investigation, we introduce a combinatorial
structure called "history tree", which is of independent interest. This makes
our paper completely self-contained, our proofs elegant and transparent, and
our algorithms straightforward to implement.
  In recent years, considerable effort has been devoted to the design and
analysis of counting algorithms for anonymous 1-interval-connected networks
with a leader. A series of increasingly sophisticated works, mostly based on
classical mass-distribution techniques, have recently led to a celebrated
counting algorithm in $O({n^{4+ \epsilon}} \log^{3} (n))$ rounds (for
$\epsilon>0$), which was the state of the art prior to this paper. Our
contribution not only opens a promising line of research on applications of
history trees, but also demonstrates that computation in anonymous dynamic
networks is practically feasible, and far less demanding than previously
conjectured.


title:
Scalable global state synchronization of discrete-time double integrator
  multi-agent systems with input saturation via linear protocol (Completed
  Version)

abstract:
  This paper studies scalable global state synchronization of discrete-time
double integrator multi-agent systems in presence of input saturation based on
localized information exchange. A scale-free collaborative linear dynamic
protocols design methodology is developed for discrete-time multi-agent systems
with both full and partial-state couplings. And the protocol design methodology
does not need any knowledge of the directed network topology and the spectrum
of the associated Laplacian matrix. Meanwhile, the protocols are parametric
based on a parameter set in which the designed protocols can guarantee the
global synchronization result. Furthermore, the proposed protocol is scalable
and achieves synchronization for any arbitrary number of agents.


title:
SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features
  Learning from a Language Model

abstract:
  A large number of inorganic and organic compounds are able to bind DNA and
form complexes, among which drug-related molecules are important. Chromatin
accessibility changes not only directly affects drug-DNA interactions, but also
promote or inhibit the expression of critical genes associated with drug
resistance by affecting the DNA binding capacity of TFs and transcriptional
regulators. However, Biological experimental techniques for measuring it are
expensive and time consuming. In recent years, several kinds of computational
methods have been proposed to identify accessible regions of the genome.
Existing computational models mostly ignore the contextual information of bases
in gene sequences. To address these issues, we proposed a new solution named
SemanticCAP. It introduces a gene language model which models the context of
gene sequences, thus being able to provide an effective representation of a
certain site in gene sequences. Basically, we merge the features provided by
the gene language model into our chromatin accessibility model. During the
process, we designed some methods to make feature fusion smoother. Compared
with other systems under public benchmarks, our model proved to have better
performance.


title:
Tate cohomology of Whittaker lattices and Base change of cuspidal
  representations of $\rm GL_3$

abstract:
  Let $p$ and $l$ be distinct primes and let $n$ be a positive integer. Let $E$
be a finite Galois extension of degree $l$ of a $p$-adic field $F$. Let $\pi$
and $\rho$ be two $l$-adic integral smooth cuspidal representations of ${\rm
GL}_n(E)$ and ${\rm GL}_n(F)$ respectively such that $\pi$ is obtained as base
change of $\rho$. Then the Tate cohomology $\widehat{H}^0(\pi)$, as an
$l$-modular representation of ${\rm GL}_n(F)$, is well defined. In this
article, when $n=2,3$ we show that $\widehat{H}^0(\pi)$ is isomorphic to the
Frobenius twist of the reduction mod-$l$ of the representation $\rho$. When
$n=2$ we assume that $l$ and $p$ are both odd primes and when $n=3$ we assume
that $l, p\neq 3$.


title:
Flux and spectral variability of Mrk 421 during its moderate activity
  state using NuSTAR: Possible accretion disc contribution?

abstract:
  The X-ray emission in BL Lac objects is believed to be dominated by
synchrotron emission from their relativistic jets. However, when the jet
emission is not strong, one could expect signatures of X-ray emission from
inverse Compton scattering of accretion disc photons by the corona. Moreover,
the observed X-ray variability can also originate in the disc, and gets
propagated and amplified by the jet. Here, we present results on the BL Lac
object Mrk 421 using the NuSTAR data acquired during 2017 when the source was
in a moderate X-ray brightness state. For comparison with high jet activity, we
also considered one epoch data in April 2013 during a very high X-ray
brightness state. Our aim is to explore the possibility of the signature of
accretion disc emission in the overall X-ray emission from Mrk 421. The
spectral fitting of the data using the two component advective flow model shows
gives (a) the size of the dynamic corona at the base of the jet from ~28 to 10
r$_s$, (b) the disc mass accretion rate from 0.021 to 0.051 $\dot M_{\rm Edd}$,
(c) the halo mass accretion rate from 0.22 to 0.35 $\dot M_{\rm Edd}$, and (d)
the viscosity parameter of the Keplerian accretion disc from 0.18 $-$ 0.25. In
the assumed model, the total flux, disc and jet flux correlate with the radio
flux observed during these epochs. We conclude that the spectra of all the
epochs of Mrk 421 in 2017 are well described by the accretion disc based two
component advective flow model. The estimated disc and jet flux relations with
radio flux show that accretion disc can contribute to the observed X-ray
emission, when X-ray data (that covers a small portion of the broad band
spectral energy distribution of Mrk 421) is considered in isolation. However,
the present disc based models are disfavoured with respect to the relativistic
jet models when considering the X-ray data in conjunction with data at other
wavelengths.


title:
Variable mass accretion and failed wind explain changing look phenomena
  in NGC 1365

abstract:
  Changing look active galactic nuclei (CLAGNs) show complex nature in their
X-ray spectral shape and line of sight column density variation. The physical
mechanisms responsible for these variations are unclear. Here, we study the
spectral properties of a CLAGN, NGC\,1365 using combined {\it XMM-Newton} and
{\it NuSTAR} observations to understand the CL behavior. The model fitted mass
accretion rate varied between $0.003\pm 0.001$ and $0.009\pm0.002$ $\dot M_{\rm
Edd}$ and the dynamic corona changed from $28\pm 3$ to $10\pm1$ $r_g$. We found
that the variable absorption column density correlates with the mass accretion
rate and the geometry of the corona. The derived wind velocity was sufficiently
low compared to the escape velocity to drive the wind away from the disc for
the epochs when column densities were high. This suggests that the high and
variable absorption can be due to failed winds from the disc. Our estimated
ratio of mass outflow to inflow rate from the inner region of the disc lies
between $0.019\pm0.006$ and $0.12\pm0.04$. From spectral fitting of the
combined data, we found the mass of the central black hole to be constant
$4.38\pm0.34 - 4.51\pm0.29 \times10^{6} M_\odot$, consistent with earlier
findings. The confidence contours of $N_H$ with other model parameters show
that the model fitted parameters are robust and non-degenerate. Our study
construed that the changing accretion rate, which is a fundamental physical
quantity and the geometry of the corona driving the CL phenomena in NGC\,1365.
The physical picture considered in this work connects both variable continuum
and variable absorbing medium scenarios.


title:
Scalable tube model predictive control of uncertain linear systems using
  ellipsoidal sets

abstract:
  This work proposes a novel robust model predictive control (MPC) algorithm
for linear systems affected by dynamic model uncertainty and exogenous
disturbances. The uncertainty is modeled using a linear fractional perturbation
structure with a time-varying perturbation matrix, enabling the algorithm to be
applied to a large model class. The MPC controller constructs a state tube as a
sequence of parameterized ellipsoidal sets to bound the state trajectories of
the system. The proposed approach results in a semidefinite program to be
solved online, whose size scales linearly with the order of the system. The
design of the state tube is formulated as an offline optimization problem,
which offers flexibility to impose desirable features such as robust invariance
on the terminal set. This contrasts with most existing tube MPC strategies
using polytopic sets in the state tube, which are difficult to design and whose
complexity grows combinatorially with the system order. The algorithm
guarantees constraint satisfaction, recursive feasibility, and stability of the
closed loop. The advantages of the algorithm are demonstrated using two
simulation studies.


title:
Exploring the influence of fine-tuning data on wav2vec 2.0 model for
  blind speech quality prediction

abstract:
  Recent studies have shown how self-supervised models can produce accurate
speech quality predictions. Speech representations generated by the pre-trained
wav2vec 2.0 model allows constructing robust predicting models using small
amounts of annotated data. This opens the possibility of developing strong
models in scenarios where labelled data is scarce. It is known that fine-tuning
improves the model's performance; however, it is unclear how the data (e.g.,
language, amount of samples) used for fine-tuning is influencing that
performance. In this paper, we explore how using different speech corpus to
fine-tune the wav2vec 2.0 can influence its performance. We took four speech
datasets containing degradations found in common conferencing applications and
fine-tuned wav2vec 2.0 targeting different languages and data size scenarios.
The fine-tuned models were tested across all four conferencing datasets plus an
additional dataset containing synthetic speech and they were compared against
three external baseline models. Results showed that fine-tuned models were able
to compete with baseline models. Larger fine-tune data guarantee better
performance; meanwhile, diversity in language helped the models deal with
specific languages. Further research is needed to evaluate other wav2vec 2.0
models pre-trained with multi-lingual datasets and to develop prediction models
that are more resilient to language diversity.


title:
Overcoming Catastrophic Forgetting in Incremental Object Detection via
  Elastic Response Distillation

abstract:
  Traditional object detectors are ill-equipped for incremental learning.
However, fine-tuning directly on a well-trained detection model with only new
data will lead to catastrophic forgetting. Knowledge distillation is a flexible
way to mitigate catastrophic forgetting. In Incremental Object Detection (IOD),
previous work mainly focuses on distilling for the combination of features and
responses. However, they under-explore the information that contains in
responses. In this paper, we propose a response-based incremental distillation
method, dubbed Elastic Response Distillation (ERD), which focuses on
elastically learning responses from the classification head and the regression
head. Firstly, our method transfers category knowledge while equipping student
detector with the ability to retain localization information during incremental
learning. In addition, we further evaluate the quality of all locations and
provide valuable responses by the Elastic Response Selection (ERS) strategy.
Finally, we elucidate that the knowledge from different responses should be
assigned with different importance during incremental distillation. Extensive
experiments conducted on MS COCO demonstrate our method achieves
state-of-the-art result, which substantially narrows the performance gap
towards full training.


title: Properties of low-lying charmonia and bottomonia from lattice QCD + QED

abstract:
  The precision of lattice QCD calculations has been steadily improving for
some time and is now approaching, or has surpassed, the 1% level for multiple
quantities. At this level QED effects, i.e. the fact that quarks carry electric
as well as color charge, come into play. In this report we will summarise
results from the first lattice QCD+QED computations of the properties of
ground-state charmonium and bottomonium mesons by the HPQCD Collaboration.


title:
Constraining scalar-tensor theories using neutron star mass and radius
  measurements

abstract:
  We use neutron star mass and radius measurements to constrain the spontaneous
scalarization phenomenon in scalar-tensor theories using Bayesian analysis.
Neutron star structures in this scenario can be significantly different from
the case of general relativity, which can be used to constrain the theory
parameters. We utilize this idea to obtain lower bounds on the coupling
parameter $\beta$ for the case of massless scalars. These constraints are
currently weaker than the ones coming from binary observations, and they have
relatively low precision due to the approximations in our method. Nevertheless,
our results clearly demonstrate the power of the mass-radius data in testing
gravity, and can be further improved with future observations. The picture is
different for massive scalars, for which the same data is not able to
effectively constrain the theory parameters in an unexpected manner. We
identify the main reason for this to be a large high-likelihood region in the
parameter space where deviations from general relativity are relatively small.
We hope this initial study to be an invitation to use neutron star structure
measurements more effectively to test alternative theories in general.


title: Detector-Free Weakly Supervised Group Activity Recognition

abstract:
  Group activity recognition is the task of understanding the activity
conducted by a group of people as a whole in a multi-person video. Existing
models for this task are often impractical in that they demand ground-truth
bounding box labels of actors even in testing or rely on off-the-shelf object
detectors. Motivated by this, we propose a novel model for group activity
recognition that depends neither on bounding box labels nor on object detector.
Our model based on Transformer localizes and encodes partial contexts of a
group activity by leveraging the attention mechanism, and represents a video
clip as a set of partial context embeddings. The embedding vectors are then
aggregated to form a single group representation that reflects the entire
context of an activity while capturing temporal evolution of each partial
context. Our method achieves outstanding performance on two benchmarks,
Volleyball and NBA datasets, surpassing not only the state of the art trained
with the same level of supervision, but also some of existing models relying on
stronger supervision.


title: Off-forward anomalous dimensions of non-singlet transversity operators

abstract:
  We determine the anomalous dimension matrix for the transversity operator
mixing into total derivative operators in the limit of a large number of quark
flavors $n_f$ to fourth order in the strong coupling $\alpha_s$ in the
$\overline{\text{MS}}$-scheme. This is achieved by using a consistency relation
between the operator anomalous dimensions, which follows from the
renormalization structure of the operators in the chiral limit.


title:
Tunable Energy Level Alignment in the Multilayers of Carboxylic Acids on
  Silver

abstract:
  The precise energy level alignment between a metal electrode and an organic
semiconductor is required to reduce contact resistance and enhance the
efficiency of organic-semiconductor-based devices. One of the ways is to
include interlayers that mediate the energy level alignment, i.e., charge
injection layers (CILs). Here we introduce the monolayer thick CILs based on
the aromatic carboxylic acids that can induce the energy level shift in the
subsequent layers by up to 0.8 eV. By gradual chemical transformation of the
as-deposited molecules, we achieve a highly tunable energy level shift in the
range of 0.5 eV. We reveal that the position of both the work function and
energy-level position in the CIL increases linearly with the density of induced
dipoles. The energy level position of subsequent layers changes in the same way
as the CIL. Our results thus connect the energy alignment quantities, i.e.,
energy level positions of both CIL and subsequent layers and sample work
function. The high tunability would allow precise tuning of the active layers
deposited on the CIL, which marks the path towards efficient charge injection
layers on metal electrodes.


title:
Computationally efficient robust MPC using optimized constraint
  tightening

abstract:
  A robust model predictive control (MPC) method is presented for linear,
time-invariant systems affected by bounded additive disturbances. The main
contribution is the offline design of a disturbance-affine feedback gain
whereby the resulting constraint tightening is minimized. This is achieved by
formulating the constraint tightening problem as a convex optimization problem
with the feedback term as a variable. The resulting MPC controller has the
computational complexity of nominal MPC, and guarantees recursive feasibility,
stability and constraint satisfaction. The advantages of the proposed approach
compared to existing robust MPC methods are demonstrated using numerical
examples.


title:
RaDur: A Reference-aware and Duration-robust Network for Target Sound
  Detection

abstract:
  Target sound detection (TSD) aims to detect the target sound from a mixture
audio given the reference information. Previous methods use a conditional
network to extract a sound-discriminative embedding from the reference audio,
and then use it to detect the target sound from the mixture audio. However, the
network performs much differently when using different reference audios (e.g.
performs poorly for noisy and short-duration reference audios), and tends to
make wrong decisions for transient events (i.e. shorter than $1$ second). To
overcome these problems, in this paper, we present a reference-aware and
duration-robust network (RaDur) for TSD. More specifically, in order to make
the network more aware of the reference information, we propose an embedding
enhancement module to take into account the mixture audio while generating the
embedding, and apply the attention pooling to enhance the features of target
sound-related frames and weaken the features of noisy frames. In addition, a
duration-robust focal loss is proposed to help model different-duration events.
To evaluate our method, we build two TSD datasets based on UrbanSound and
Audioset. Extensive experiments show the effectiveness of our methods.


title: On the semi-symmetric Lorentzian spaces

abstract:
  We give some properties of semi-symmetric pseudo-Riemannian manifolds. These
are foliated manifolds and for the Lorentzian metric, the Ricci operator has
only real eigenvalues.


title:
Mean-Field Limit Derivation of a Monokinetic Spray Model with Gyroscopic
  Effects

abstract:
  In this paper we derive a two dimensional spray model with gyroscopic effects
as the mean-field limit of a system modeling the interaction between an
incompressible fluid and a finite number of solid particles. This spray model
has been studied by Moussa and Sueur (Asymptotic Anal., 2013), in particular
the mean-field limit was established in the case of W 1,$\infty$ interactions.
First we prove the local in time existence and uniqueness of strong solutions
of a monokinetic version of the model with a fixed point method. Then we adapt
the proof of Duerinckx and Serfaty (Duke Math. J., 2020) to establish the
mean-field limit to the spray model in the monokinetic regime with Coulomb
interactions.


title: Nonexistence of DEC spin fill-ins

abstract:
  In this note, we show that a closed spin Riemannian manifold does not admit a
spin fill-in satisfying the dominant energy condition (DEC) if a certain
generalized mean curvature function is point-wise large.


title: High-fidelity quantum control by polychromatic pulse trains

abstract:
  We introduce a quantum control technique using polychromatic pulse sequences
(PPS), consisting of pulses with different carrier frequencies, i.e. different
detunings with respect to the qubit transition frequency. We derive numerous
PPS, which generate broadband, narrowband, and passband excitation profiles for
different target transition probabilities. This makes it possible to create
high-fidelity excitation profiles which are either (i) robust to deviations in
the experimental parameters, which is attractive for quantum computing, or (ii)
more sensitive to such variations, which is attractive for cross talk
elimination and quantum sensing. The method is demonstrated experimentally
using one of IBM's superconducting quantum processors, in a very good agreement
between theory and experiment. These results demonstrate both the excellent
coherence properties of the IBM qubits and the accuracy, robustness and
flexibility of the proposed quantum control technique. They also show that the
detuning is as efficient control parameter as the pulse phase that is commonly
used in composite pulses. Hence the method opens a variety of perspectives for
quantum control in areas where phase manipulation is difficult or inaccurate.


title:
Dual-AI: Dual-path Actor Interaction Learning for Group Activity
  Recognition

abstract:
  Learning spatial-temporal relation among multiple actors is crucial for group
activity recognition. Different group activities often show the diversified
interactions between actors in the video. Hence, it is often difficult to model
complex group activities from a single view of spatial-temporal actor
evolution. To tackle this problem, we propose a distinct Dual-path Actor
Interaction (DualAI) framework, which flexibly arranges spatial and temporal
transformers in two complementary orders, enhancing actor relations by
integrating merits from different spatiotemporal paths. Moreover, we introduce
a novel Multi-scale Actor Contrastive Loss (MAC-Loss) between two interactive
paths of Dual-AI. Via self-supervised actor consistency in both frame and video
levels, MAC-Loss can effectively distinguish individual actor representations
to reduce action confusion among different actors. Consequently, our Dual-AI
can boost group activity recognition by fusing such discriminative features of
different actors. To evaluate the proposed approach, we conduct extensive
experiments on the widely used benchmarks, including Volleyball, Collective
Activity, and NBA datasets. The proposed Dual-AI achieves state-of-the-art
performance on all these datasets. It is worth noting the proposed Dual-AI with
50% training data outperforms a number of recent approaches with 100% training
data. This confirms the generalization power of Dual-AI for group activity
recognition, even under the challenging scenarios of limited supervision.


title:
Automatic Image Content Extraction: Operationalizing Machine Learning in
  Humanistic Photographic Studies of Large Visual Archives

abstract:
  Applying machine learning tools to digitized image archives has a potential
to revolutionize quantitative research of visual studies in humanities and
social sciences. The ability to process a hundredfold greater number of photos
than has been traditionally possible and to analyze them with an extensive set
of variables will contribute to deeper insight into the material. Overall,
these changes will help to shift the workflow from simple manual tasks to more
demanding stages.
  In this paper, we introduce Automatic Image Content Extraction (AICE)
framework for machine learning-based search and analysis of large image
archives. We developed the framework in a multidisciplinary research project as
framework for future photographic studies by reformulating and expanding the
traditional visual content analysis methodologies to be compatible with the
current and emerging state-of-the-art machine learning tools and to cover the
novel machine learning opportunities for automatic content analysis. The
proposed framework can be applied in several domains in humanities and social
sciences, and it can be adjusted and scaled into various research settings. We
also provide information on the current state of different machine learning
techniques and show that there are already various publicly available methods
that are suitable to a wide-scale of visual content analysis tasks.


title:
A stabilizer framework for Contextual Subspace VQE and the noncontextual
  projection ansatz

abstract:
  Quantum chemistry is a promising application for noisy intermediate-scale
quantum (NISQ) devices. However, quantum computers have thus far not succeeded
in providing solutions to problems of real scientific significance, with
algorithmic advances being necessary to fully utilise even the modest NISQ
machines available today. We discuss a method of ground state energy estimation
predicated on a partitioning the molecular Hamiltonian into two parts: one that
is noncontextual and can be solved classically, supplemented by a contextual
component that yields quantum corrections obtained via a Variational Quantum
Eigensolver (VQE) routine. This approach has been termed Contextual Subspace
VQE (CS-VQE), but there are obstacles to overcome before it can be deployed on
NISQ devices. The problem we address here is that of the ansatz - a
parametrized quantum state over which we optimize during VQE. It is not
initially clear how a splitting of the Hamiltonian should be reflected in our
CS-VQE ans\"atze. We propose a 'noncontextual projection' approach that is
illuminated by a reformulation of CS-VQE in the stabilizer formalism. This
defines an ansatz restriction from the full electronic structure problem to the
contextual subspace and facilitates an implementation of CS-VQE that may be
deployed on NISQ devices. We validate the noncontextual projection ansatz using
a quantum simulator, with results obtained herein for a suite of trial
molecules.


title: Large time behavior of solutions to nonlinear beam equations

abstract:
  In this note we analyze the large time behavior of solutions to a class of
initial/boundary problems involving a damped nonlinear beam equation. We show
that under mild conditions on the damping term of the equation of motions the
solutions of the dynamical problem converge to the solution of the stationary
problem. We also show that this convergence is exponential.