title: On-Device Learning: A Neural Network Based Field-Trainable Edge AI

abstract:
  In real-world edge AI applications, their accuracy is often affected by
various environmental factors, such as noises, location/calibration of sensors,
and time-related changes. This article introduces a neural network based
on-device learning approach to address this issue without going deep. Our
approach is quite different from de facto backpropagation based training but
tailored for low-end edge devices. This article introduces its algorithm and
implementation on a wireless sensor node consisting of Raspberry Pi Pico and
low-power wireless module. Experiments using vibration patterns of rotating
machines demonstrate that retraining by the on-device learning significantly
improves an anomaly detection accuracy at a noisy environment while saving
computation and communication costs for low power.


title: Pairwise quantum correlations in four-level quantum dot systems

abstract:
  In this paper we assume quantum dots can be assimilated to Fermi Hubbard
sites when the Coulomb interaction between electrons is higher compared to
their tunneling. The study of pairwise entanglement in a small size array of
quantum dots allows to model each pair as a quadrit-quadrit system (4 $\times$
4 mixed state) instead of the more common and simplistic approach of describing
it in quantum information as a qubit-qubit system. We study the effect of
Coulomb interaction and temperature on pairwise entanglement as well as on
quantum coherence and total correlations. The crucial results of this study are
that entanglement resists better the increase in temperature when the Coulomb
interaction is stronger. Moreover, we successfully explain the behavior of
these correlations in terms of the energy spectrum, namely the ground state
degeneracy and the state energy difference.


title: Redefining The Query Optimization Process

abstract:
  Traditionally, query optimizers have been designed for computer systems that
share a common architecture, consisting of a CPU, main memory and disk
subsystem. The efficiency of query optimizers and their successful employment
relied on the fact that these architectures basically stayed the same over the
last decades. However, recently the performance increase of serial instruction
execution has stagnated. As a consequence, computer architectures have started
to diversify. Combined with the fact that the size of main memories has
significantly increased it becomes more important to exploit intrinsic internal
features of computer systems (data coherence mechanisms, TLB and instruction
cache performance, among others) rather than mainly focusing on minimizing disk
I/O. Time has come for a re-evaluation of how (traditional) query optimization
is implemented. Query optimizers must be able to cope with disruptive advances
in computer architecture and employ methods to optimize for
architecture-specific features. In this paper, the query optimization process
is redefined so that compiler optimizations are taken into the game much
earlier than traditionally is done and employ compiler optimizations as the
driving force in query optimization. This new generation of query optimizers
will be capable of optimizing queries to perform significantly better than
contemporary state-of-the-art query optimizers.


title:
A Multi-Scale Time-Frequency Spectrogram Discriminator for GAN-based
  Non-Autoregressive TTS

abstract:
  The generative adversarial network (GAN) has shown its outstanding capability
in improving Non-Autoregressive TTS (NAR-TTS) by adversarially training it with
an extra model that discriminates between the real and the generated speech. To
maximize the benefits of GAN, it is crucial to find a powerful discriminator
that can capture rich distinguishable information. In this paper, we propose a
multi-scale time-frequency spectrogram discriminator to help NAR-TTS generate
high-fidelity Mel-spectrograms. It treats the spectrogram as a 2D image to
exploit the correlation among different components in the time-frequency
domain. And a U-Net-based model structure is employed to discriminate at
different scales to capture both coarse-grained and fine-grained information.
We conduct subjective tests to evaluate the proposed approach. Both multi-scale
and time-frequency discriminating bring significant improvement in the
naturalness and fidelity. When combining the neural vocoder, it is shown more
effective and concise than fine-tuning the vocoder. Finally, we visualize the
discriminating maps to compare their difference to verify the effectiveness of
multi-scale discriminating.


title:
A New Framework for Expressing, Parallelizing and Optimizing Big Data
  Applications

abstract:
  The Forelem framework was first introduced as a means to optimize database
queries using optimization techniques developed for compilers. Since its
introduction, Forelem has proven to be more versatile and to be applicable
beyond database applications. In this paper we show that the original Forelem
framework can be used to express and optimize Big Data applications, more
specifically: k-Means clustering and PageRank, resulting in automatically
generated implementations of these applications. These implementations are more
efficient than state-of-the-art, hand-written MPI C/C++ implementations of
k-Means and PageRank, as well as significantly outperform state-of-the-art
Hadoop implementations.


title: CNF Encodings of Parity

abstract:
  The minimum number of clauses in a CNF representation of the parity function
$x_1 \oplus x_2 \oplus \dotsb \oplus x_n$ is $2^{n-1}$. One can obtain a more
compact CNF encoding by using non-deterministic variables (also known as guess
or auxiliary variables). In this paper, we prove the following lower bounds,
that almost match known upper bounds, on the number $m$ of clauses and the
maximum width $k$ of clauses: 1) if there are at most $s$ auxiliary variables,
then $m \ge \Omega\left(2^{n/(s+1)}/n\right)$ and $k \ge n/(s+1)$; 2) the
minimum number of clauses is at least $3n$. We derive the first two bounds from
the Satisfiability Coding Lemma due to Paturi, Pudlak, and Zane.


title:
The variance of the graph distance in the infinite cluster of
  percolation is sublinear

abstract:
  We consider the standard model of i.i.d. bond percolation on $\mathbb Z^d$ of
parameter $p$. When $p>p_c$, there exists almost surely a unique infinite
cluster $\mathcal C_p$. Using the recent techniques of Cerf and Dembin, we
prove that the variance of the graph distance in $\mathcal C_p$ between two
points of $\mathcal C_p$ is sublinear. The main result extends the works of
Benjamini, Kalai and Schramm, Benaim and Rossignol and Damron, Hanson and Sosoe
for the study of the variance of passage times in first passage percolation
without moment conditions on the edge-weight distribution.


title: Delegated Online Search

abstract:
  In a delegation problem, a principal P with commitment power tries to pick
one out of $n$ options. Each option is drawn independently from a known
distribution. Instead of inspecting the options herself, P delegates the
information acquisition to a rational and self-interested agent A. After
inspection, A proposes one of the options, and P can accept or reject.
  Delegation is a classic setting in economic information design with many
prominent applications, but the computational problems are only poorly
understood. In this paper, we study a natural online variant of delegation, in
which the agent searches through the options in an online fashion. For each
option, he has to irrevocably decide if he wants to propose the current option
or discard it, before seeing information on the next option(s). How can we
design algorithms for P that approximate the utility of her best option in
hindsight?
  We show that in general P can obtain a $\Theta(1/n)$-approximation and extend
this result to ratios of $\Theta(k/n)$ in case (1) A has a lookahead of $k$
rounds, or (2) A can propose up to $k$ different options. We provide
fine-grained bounds independent of $n$ based on two parameters. If the ratio of
maximum and minimum utility for A is bounded by a factor $\alpha$, we obtain an
$\Omega(\log \log \alpha / \log \alpha)$-approximation algorithm, and we show
that this is best possible. Additionally, if P cannot distinguish options with
the same value for herself, we show that ratios polynomial in $1/\alpha$ cannot
be avoided. If the utilities of P and A for each option are related by a factor
$\beta$, we obtain an $\Omega(1/ \log \beta)$-approximation, where $O(\log \log
\beta/ \log \beta)$ is best possible.


title: On Current-Squared Flows and ModMax Theories

abstract:
  We show that the recently introduced ModMax theory of electrodynamics and its
Born-Infeld-like generalization are related by a flow equation driven by a
quadratic combination of stress-energy tensors. The operator associated to this
flow is a $4d$ analogue of the $T\overline{T}$ deformation in two dimensions.
This result generalizes the observation that the ordinary Born-Infeld
Lagrangian is related to the free Maxwell theory by a current-squared flow. As
in that case, we show that no analogous relationship holds in any other
dimension besides $d=4$. We also demonstrate that the $\mathcal{N}=1$
supersymmetric version of the ModMax-Born-Infeld theory obeys a related
supercurrent-squared flow which is formulated directly in $\mathcal{N}=1$
superspace.


title: $\mathcal{T}$-semiring pairs

abstract:
  We develop a general axiomatic theory of algebraic pairs, which
simultaneously generalizes several algebraic structures, in order to bypass
negation as much as feasible. We investigate several classical theorems and
notions in this setting including fractions, integral extensions, and Hilbert's
Nullstellensatz. Finally, we study a notion of growth in this context.


title:
Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving
  Applications

abstract:
  In this paper, we present a complete pipeline for 3D semantic mapping solely
based on a stereo camera system. The pipeline comprises a direct sparse visual
odometry front-end as well as a back-end for global optimization including GNSS
integration, and semantic 3D point cloud labeling. We propose a simple but
effective temporal voting scheme which improves the quality and consistency of
the 3D point labels. Qualitative and quantitative evaluations of our pipeline
are performed on the KITTI-360 dataset. The results show the effectiveness of
our proposed voting scheme and the capability of our pipeline for efficient
large-scale 3D semantic mapping. The large-scale mapping capabilities of our
pipeline is furthermore demonstrated by presenting a very large-scale semantic
map covering 8000 km of roads generated from data collected by a fleet of
vehicles.


title: Remarks on proper conflict-free colorings of graphs

abstract:
  A vertex coloring of a graph is said to be \textit{conflict-free} with
respect to neighborhoods if for every non-isolated vertex there is a color
appearing exactly once in its (open) neighborhood. As defined in [Fabrici et
al., \textit{Proper Conflict-free and Unique-maximum Colorings of Planar Graphs
with Respect to Neighborhoods}, arXiv preprint], the minimum number of colors
in any such proper coloring of graph $G$ is the PCF chromatic number of $G$,
denoted $\chi_{\mathrm{pcf}}(G)$. In this paper, we determine the value of this
graph parameter for several basic graph classes including trees, cycles,
hypercubes and subdivisions of complete graphs. We also give upper bounds on
$\chi_{\mathrm{pcf}}(G)$ in terms of other graph parameters. In particular, we
show that $\chi_{\mathrm{pcf}}(G) \leq5\Delta(G)/2$ and characterize equality.
Several sufficient conditions for PCF $k$-colorability of graphs are
established for $4\le k\le 6$. The paper concludes with few open problems.


title:
Shape constrained CNN for segmentation guided prediction of myocardial
  shape and pose parameters in cardiac MRI

abstract:
  Semantic segmentation using convolutional neural networks (CNNs) is the
state-of-the-art for many medical image segmentation tasks including myocardial
segmentation in cardiac MR images. However, the predicted segmentation maps
obtained from such standard CNN do not allow direct quantification of regional
shape properties such as regional wall thickness. Furthermore, the CNNs lack
explicit shape constraints, occasionally resulting in unrealistic
segmentations. In this paper, we use a CNN to predict shape parameters of an
underlying statistical shape model of the myocardium learned from a training
set of images. Additionally, the cardiac pose is predicted, which allows to
reconstruct the myocardial contours. The integrated shape model regularizes the
predicted contours and guarantees realistic shapes. We enforce robustness of
shape and pose prediction by simultaneously performing pixel-wise semantic
segmentation during training and define two loss functions to impose
consistency between the two predicted representations: one distance-based loss
and one overlap-based loss. We evaluated the proposed method in a 5-fold cross
validation on an in-house clinical dataset with 75 subjects and on the ACDC and
LVQuan19 public datasets. We show the benefits of simultaneous semantic
segmentation and the two newly defined loss functions for the prediction of
shape parameters. Our method achieved a correlation of 99% for left ventricular
(LV) area on the three datasets, between 91% and 97% for myocardial area,
98-99% for LV dimensions and between 80% and 92% for regional wall thickness.


title:
Liquid-organic time projection chamber for detecting low energy
  antineutrinos

abstract:
  The MeV region of antineutrino energy is of special interest for physics
research and for monitoring nuclear nonproliferation. Whereas liquid
scintillation detectors are typically used to detect the Inverse Beta Decay
(IBD), it has recently been proposed to detect it with a liquid-organic Time
Projection Chamber, which could allow a full reconstruction of the particle
tracks of the IBD final state. We present the first comprehensive
simulation-based study of the expected signatures. Their unequivocal signature
could enable a background-minimized detection of electron antineutrinos using
information on energy, location and direction of all final state particles. We
show that the positron track reflects the antineutrino's vertex. It can also be
used to determine the initial neutrino energy. In addition, we investigate the
possibility to reconstruct the antineutrino direction on an event-by-event
basis by the energy deposition of the neutron-induced proton recoils. Our
simulations indicate that this could be a promising approach which should be
further studied through experiments with a detector prototype.


title: Doubly truncated moment risk measures for elliptical distributions

abstract:
  In this paper, we define doubly truncated moment (DTM), doubly truncated
skewness (DTS) and kurtosis (DTK). We derive DTM formulae for elliptical
family, with emphasis on normal, student-$t$, logistic, Laplace and Pearson
type VII distributions. We also present explicit formulas of the DTE (doubly
truncated expectation), DTV (doubly truncated variance), DTS and DTK for those
distributions. As illustrative example, DTEs, DTVs, DTSs and DTKs of three
industry segments' (Banks, Insurance, Financial and Credit Service) stock
return in London stock exchange are discussed.


title: The number of moduli of minimal surfaces in toric 3-folds

abstract:
  In this article we vary f over all nondegenerate Laurent polynomials with
fixed 3-dimensional Newton polytope. Choosing an appropriate surrounding toric
variety we get a family of minimal models of surfaces in a toric 3-fold. We
compute the number of moduli of such a family. We illustrate our results at
some examples of surfaces of general type.


title:
Information Gain Propagation: a new way to Graph Active Learning with
  Soft Labels

abstract:
  Graph Neural Networks (GNNs) have achieved great success in various tasks,
but their performance highly relies on a large number of labeled nodes, which
typically requires considerable human effort. GNN-based Active Learning (AL)
methods are proposed to improve the labeling efficiency by selecting the most
valuable nodes to label. Existing methods assume an oracle can correctly
categorize all the selected nodes and thus just focus on the node selection.
However, such an exact labeling task is costly, especially when the
categorization is out of the domain of individual expert (oracle). The paper
goes further, presenting a soft-label approach to AL on GNNs. Our key
innovations are: i) relaxed queries where a domain expert (oracle) only judges
the correctness of the predicted labels (a binary question) rather than
identifying the exact class (a multi-class question), and ii) new criteria of
maximizing information gain propagation for active learner with relaxed queries
and soft labels. Empirical studies on public datasets demonstrate that our
method significantly outperforms the state-of-the-art GNN-based AL methods in
terms of both accuracy and labeling cost.


title:
Enhanced thermoelectric efficiency of zigzag bilayer phosphorene
  nanoribbon; edge states engineering

abstract:
  We theoretically investigate the thermoelectric properties of zigzag bilayer
phosphorene nanoribbons (ZBPNR). We first, draw an analogy between the extended
Su-Schrieffer-Heeger (SSH) ladder and ZBPNR edge states and obtain their
corresponding band structure and wave functions analytically. Then, by applying
the energy filtering method, we show that the electric power and thermoelectric
efficiency of the ZBPNRs can be improved remarkably in the presence of mid-gap
edge states. We also argue how to engineer the edge modes to further optimize
thermoelectric power and efficiency of the system by applying periodic point
potentials at the boundaries.


title:
A Generalized Approach for Cancellable Template and Its Realization for
  Minutia Cylinder-Code

abstract:
  Hashing technology gains much attention in protecting the biometric template
lately. For instance, Index-of-Max (IoM), a recent reported hashing technique,
is a ranking-based locality sensitive hashing technique, which illustrates the
feasibility to protect the ordered and fixed-length biometric template.
However, biometric templates are not always in the form of ordered and
fixed-length, rather it may be an unordered and variable size point set e.g.
fingerprint minutiae, which restricts the usage of the traditional hashing
technology. In this paper, we proposed a generalized version of IoM hashing
namely gIoM, and therefore the unordered and variable size biometric template
can be used. We demonstrate a realization using a well-known variable size
feature vector, fingerprint Minutia Cylinder-Code (MCC). The gIoM transforms
MCC into index domain to form indexing-based feature representation.
Consequently, the inversion of MCC from the transformed representation is
computational infeasible, thus to achieve non-invertibility while the
performance is preserved. Public fingerprint databases FVC2002 and FVC2004 are
employed for experiment as benchmark to demonstrate a fair comparison with
other methods. Moreover, the security and privacy analysis suggest that gIoM
meets the criteria of template protection: non-invertibility, revocability, and
non-linkability.


title: On packing time-respecting arborescences

abstract:
  We present a slight generalization of the result of Kamiyama and Kawase
\cite{kamkaw} on packing time-respecting arborescences in acyclic pre-flow
temporal networks. Our main contribution is to provide the first results on
packing time-respecting arborescences in non-acyclic temporal networks. As
negative results, we prove the NP-completeness of the decision problem of the
existence of 2 arc-disjoint spanning time-respecting arborescences and of a
related problem proposed in this paper.


title:
Model-agnostic out-of-distribution detection using combined statistical
  tests

abstract:
  We present simple methods for out-of-distribution detection using a trained
generative model. These techniques, based on classical statistical tests, are
model-agnostic in the sense that they can be applied to any differentiable
generative model. The idea is to combine a classical parametric test (Rao's
score test) with the recently introduced typicality test. These two test
statistics are both theoretically well-founded and exploit different sources of
information based on the likelihood for the typicality test and its gradient
for the score test. We show that combining them using Fisher's method overall
leads to a more accurate out-of-distribution test. We also discuss the benefits
of casting out-of-distribution detection as a statistical testing problem,
noting in particular that false positive rate control can be valuable for
practical out-of-distribution detection. Despite their simplicity and
generality, these methods can be competitive with model-specific
out-of-distribution detection algorithms without any assumptions on the
out-distribution.


title: SFCaaS: Service Function Chains as a Service in NFV Environments

abstract:
  With the emergence of network softwarization trend, traditional networking
services offered by Internet providers are expected to evolve by fully
leveraging new recent technologies like network function virtualization and
software defined networking. In this paper, we investigate offering Service
Function Chains as a Service (SFCaaS) in NFV Environments. We first describe
the potential business model to offer such a service. We then conduct a
detailed study of the costs of virtual machine instances offered by Amazon EC2
with respect to the location, instance size, and performance in order to guide
service chain provisioning and resource allocation. Afterwards, we address the
resource allocation problem for service chain functions from the SFC provider's
perspective while leveraging the performed cost study. We hence formulate the
problem as an Integer Linear Program (ILP) aiming at reducing the SFC
provider's operational costs of virtual machine instances and links as well as
the synchronization costs among the instances. We also propose a new heuristic
algorithm to solve the mapping problem with the same aforementioned goals
taking into account the conducted study of the costs of Amazon EC2 instances.
We show through extensive simulations that the proposed heuristic significantly
reduce operational costs compared to a Baseline algorithm inspired by the
existing literature.


title: Decoding-Energy-Rate-Distortion Optimization for Video Coding

abstract:
  This paper presents a method for generating coded video bit streams requiring
less decoding energy than conventionally coded bit streams. To this end, we
propose extending the standard rate-distortion optimization approach to also
consider the decoding energy. In the encoder, the decoding energy is estimated
during runtime using a feature-based energy model. These energy estimates are
then used to calculate decoding-energy-rate-distortion costs that are minimized
by the encoder. This ultimately leads to optimal trade-offs between these three
parameters. Therefore, we introduce the mathematical theory for describing
decoding-energy-rate-distortion optimization and the proposed encoder algorithm
is explained in detail. For rate-energy control, a new encoder parameter is
introduced. Finally, measurements of the software decoding process for
HEVC-coded bit streams are performed. Results show that this approach can lead
to up to 30% of decoding energy reduction at a constant visual objective
quality when accepting a bitrate increase at the same order of magnitude.


title:
Early-warning indicator based on autoregressive moving-average models:
  Critical Transitions and the Atlantic Meridional Overturning Circulation

abstract:
  A statistical indicator for dynamic stability based on autoregressive
moving-average, ARMA(p,q), models is used to gauge the stability and hence
predict approaching tipping points of simulation data from a reduced 5-box
model of the North-Atlantic Meridional Overturning Circulation (AMOC) exposed
to a time dependent hosing function. The hosing function simulates the influx
of fresh water due to the melting of the Greenland ice sheet and increased
precipitation in the North Atlantic. We study the indicator's ability to assess
the stability of a time series subject to different types of tipping, including
bifurcation-induced and rate-induced tipping, and show that the indicator is
indeed able to identify the different types of induced instability. In the
process we extend the underlying models from an ARMA(p,q) process to an
ARIMA(p,q) (autoregressive integrated moving-average) process, which through
the proper application of differencing converts a formerly non-stationary
process into a stationary one, further extending the regime of validity of the
statistical method. In addition, we apply the indicator to simulation data from
the Earth systems model CESM2, to assess how the indicator responds to more
realistic time series data.


title:
Approaching ideal visibility in singlet-triplet qubit operations using
  energy-selective tunneling-based Hamiltonian estimation

abstract:
  We report energy-selective tunneling readout-based Hamiltonian parameter
estimation of a two-electron spin qubit in a GaAs quantum dot array.
Optimization of readout fidelity enables a single-shot measurement time of 16
us on average, with adaptive initialization and unambiguous qubit frequency
estimation based on real-time Bayesian inference. By triggering the operation
sequence conditional on the frequency detected in the probe step, we observed a
40-fold increase in coherence time without resorting to dynamic nuclear
polarization. We also demonstrate quantum oscillation visibility, single-shot
measurement fidelity, and state initialization fidelity of 97.7%, 99%, and over
99.9%, respectively. By pushing the sensitivity of the energy-selective
tunneling-based spin-to-charge conversion to the limit, the technique is useful
for advanced quantum control protocols such as error mitigation schemes, where
fast qubit parameter calibration with a large signal-to-noise ratio is crucial.


title: Maximum and records of random walks with stochastic resetting

abstract:
  We revisit the statistics of extremes and records of symmetric random walks
with stochastic resetting, extending earlier studies in several directions. We
put forward a diffusive scaling regime (symmetric step length distribution with
finite variance, weak resetting probability) where the maximum of the walk and
the number of its records up to discrete time $n$ become asymptotically
proportional to each other for single typical trajectories. Their distributions
obey scaling laws ruled by a common two-parameter scaling function,
interpolating between a half-Gaussian and a Gumbel law. The exact solution of
the problem for the symmetric exponential step length distribution and for the
simple Polya lattice walk, as well as a heuristic analysis of other
distributions, allow a quantitative study of several facets of the statistics
of extremes and records beyond the diffusive scaling regime.


title:
Practical Recommendations for the Design of Automatic Fault Detection
  Algorithms Based on Experiments with Field Monitoring Data

abstract:
  Automatic fault detection (AFD) is a key technology to optimize the Operation
and Maintenance of photovoltaic (PV) systems portfolios. A very common approach
to detect faults in PV systems is based on the comparison between measured and
simulated performance. Although this approach has been explored by many
authors, due to the lack a common basis for evaluating their performance, it is
still unclear what are the influencing aspects in the design of AFD algorithms.
In this study, a series of AFD algorithms have been tested under real operating
conditions, using monitoring data collected over 58 months on 80 rooftop-type
PV systems installed in Germany. The results shown that this type of AFD
algorithm have the potential to detect up to 82.8% of the energy losses with
specificity above 90%. In general, the higher the simulation accuracy, the
higher the specificity. The use of less accurate simulations can increase
sensitivity at the cost of decreasing specificity. Analyzing the measurements
individually makes the algorithm less sensitive to the simulation accuracy. The
use of machine learning clustering algorithm for the statistical analysis
showed exceptional ability to prevent false alerts, even in cases where the
modeling accuracy is not high. If a slightly higher level of false alerts can
be tolerated, the analysis of daily PR using a Shewhart chart provides the high
sensitivity with an exceptionally simple solution with no need for more complex
algorithms for modeling or clustering.


title:
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained
  Language Models

abstract:
  The state-of-the-art Mixture-of-Experts (short as MoE) architecture has
achieved several remarkable successes in terms of increasing model capacity.
However, MoE has been hindered widespread adoption due to complexity,
communication costs, and training instability. Here we present a novel MoE
architecture based on matrix product operators (MPO) from quantum many-body
physics. It can decompose an original matrix into central tensors (containing
the core information) and auxiliary tensors (with only a small proportion of
parameters). With the decomposed MPO structure, we can reduce the parameters of
the original MoE architecture by sharing a global central tensor across experts
and keeping expert-specific auxiliary tensors. We also design the gradient mask
strategy for the tensor structure of MPO to alleviate the overfitting problem.
Experiments on the three well-known downstream natural language datasets based
on GPT2 show improved performance and efficiency in increasing model capacity
(7.26x fewer parameters with the same amount of experts). We additionally
demonstrate an improvement in the positive transfer effects of our approach for
multi-task learning.


title:
Topological Manin triples and Lie bialgebra structures on current
  algebras

abstract:
  The aim of this paper is to provide a partial classification of Lie bialgebra
structures on the Lie algebra of formal Taylor series $\mathfrak{g}[\![x]\!]$
and the corresponding Manin triples, where $\mathfrak{g}$ is a
finite-dimensional simple Lie algebra over an algebraically closed field $F$ of
characteristic $0$. We review and provide another viewpoint on the
classification of classical doubles of Lie bialgebra structures on $
\mathfrak{g}[\![x]\!] $ as well as close the remaining gap in its proof for Lie
algebras of type $ A_n $. We then introduce the notions of topological and
commensurable twists which, in combination with the above-mentioned
classification, allow us to derive a partial classification of Lie bialgebra
structures on $ \mathfrak{g}[\![x]\!] $. When $F = \mathbb{C}$ a more detailed
description of Manin triples and Lie bialgebras is presented.


title: Modular forms on SU(2,1) with weight $\frac{1}{3}$

abstract:
  In this note, we describe several new examples of holomorphic modular forms
on the group SU(2,1). These forms are distinguished by having weight
$\frac{1}{3}$. We also describe a method for determining the levels at which
one should expect to find such fractional weight forms.


title:
ReStore: In-Memory REplicated STORagE for Rapid Recovery in
  Fault-Tolerant Algorithms

abstract:
  Fault-tolerant distributed applications require mechanisms to recover data
lost via a process failure. On modern cluster systems it is typically
impractical to request replacement resources after such a failure. Therefore,
applications have to continue working with the remaining resources. This
requires redistributing the workload and that the non-failed processes reload
the lost data. We present an algorithmic framework and its C++ library
implementation ReStore for MPI programs that enables recovery of lost data
after (a) process failure(s). By storing all required data in memory via an
appropriate data distribution and replication, recovery is substantially faster
than with standard checkpointing schemes that rely on a parallel file system.
As the application developer can specify which data to load, we also support
shrinking recovery instead of recovery using spare compute nodes. We evaluate
ReStore in both controlled, isolated environments and real applications. Our
experiments show loading times of lost input data in the range of milliseconds
on up to 24576 processors and a substantial speedup of the recovery time for
the fault-tolerant version of a widely used bioinformatics application.


title: First 3D Radiation-Hydrodynamic Simulations of Wolf-Rayet Winds

abstract:
  Classical Wolf Rayet (WR) stars are direct supernova progenitors undergoing
vigorous mass-loss. Understanding the dense and fast outflows of such WR stars
is thus crucial for understanding advanced stages of stellar evolution, the
dynamical feedback of massive stars on their environments, and characterizing
the distribution of black hole masses. In this paper, we develop first
time-dependent, multi-dimensional, radiation-hydrodynamical models of the
extended optically thick atmospheres and wind outflows of hydrogen-free
classical WR stars. A flux limiting radiation hydrodynamics approach is used on
a finite volume mesh to model WR outflows. The opacities are described using a
combination of tabulated Rosseland mean opacities and the enhanced line
opacities expected within a supersonic flow. For high-luminosity models, a
radiation-driven, dense, supersonic wind is launched from deep sub-surface
regions associated with peaks in the Rosseland mean opacity. For a model with
lower luminosity, on the other hand, the Rosseland mean opacity is not
sufficient to sustain a net-radial outflow in the sub-surface regions. Rather,
what develops in this case is a "standard" line-driven wind launched from the
optically thin regions above an extended and highly turbulent atmosphere. We
thus find here a natural transition from optically thick outflows of classical
WR stars to optically thin winds of hot, compact sub-dwarfs; in our simulations
this transition occurs approximately at a luminosity that is about 40% of the
Eddington luminosity. Because of the changing character of the wind-launching
mechanism, this transition is also accompanied by a large drop (on the
low-luminosity end) in average mass-loss rate.


title: A note on uncertainty relations of metric-adjusted skew information

abstract:
  The uncertainty principle is one of the fundamental features of quantum
mechanics and plays a vital role in quantum information processing. We study
uncertainty relations based on metric-adjusted skew information for finite
quantum observables. Motivated by the paper [Physical Review A 104, 052414
(2021)], we establish tighter uncertainty relations in terms of different norm
inequalities. Naturally, we generalize the method to uncertainty relations of
metric-adjusted skew information for quantum channels and unitary operators. As
both the Wigner-Yanase-Dyson skew information and the quantum Fisher
information are the special cases of the metric-adjusted skew information
corresponding to different Morozova-Chentsov functions, our results generalize
some existing uncertainty relations. Detailed examples are given to illustrate
the advantages of our methods.


title: The Optimal Noise in Noise-Contrastive Learning Is Not What You Think

abstract:
  Learning a parametric model of a data distribution is a well-known
statistical problem that has seen renewed interest as it is brought to scale in
deep learning. Framing the problem as a self-supervised task, where data
samples are discriminated from noise samples, is at the core of
state-of-the-art methods, beginning with Noise-Contrastive Estimation (NCE).
Yet, such contrastive learning requires a good noise distribution, which is
hard to specify; domain-specific heuristics are therefore widely used. While a
comprehensive theory is missing, it is widely assumed that the optimal noise
should in practice be made equal to the data, both in distribution and
proportion. This setting underlies Generative Adversarial Networks (GANs) in
particular. Here, we empirically and theoretically challenge this assumption on
the optimal noise. We show that deviating from this assumption can actually
lead to better statistical estimators, in terms of asymptotic variance. In
particular, the optimal noise distribution is different from the data's and
even from a different family.


title: Large-Scale Hate Speech Detection with Cross-Domain Transfer

abstract:
  The performance of hate speech detection models relies on the datasets on
which the models are trained. Existing datasets are mostly prepared with a
limited number of instances or hate domains that define hate topics. This
hinders large-scale analysis and transfer learning with respect to hate
domains. In this study, we construct large-scale tweet datasets for hate speech
detection in English and a low-resource language, Turkish, consisting of
human-labeled 100k tweets per each. Our datasets are designed to have equal
number of tweets distributed over five domains. The experimental results
supported by statistical tests show that Transformer-based language models
outperform conventional bag-of-words and neural models by at least 5% in
English and 10% in Turkish for large-scale hate speech detection. The
performance is also scalable to different training sizes, such that 98% of
performance in English, and 97% in Turkish, are recovered when 20% of training
instances are used. We further examine the generalization ability of
cross-domain transfer among hate domains. We show that 96% of the performance
of a target domain in average is recovered by other domains for English, and
92% for Turkish. Gender and religion are more successful to generalize to other
domains, while sports fail most.


title: Hyperparameter optimization of data-driven AI models on HPC systems

abstract:
  In the European Center of Excellence in Exascale computing "Research on AI-
and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop
novel, scalable AI technologies towards Exascale. This work exercises High
Performance Computing resources to perform large-scale hyperparameter
optimization using distributed training on multiple compute nodes. This is part
of RAISE's work on data-driven use cases which leverages AI- and HPC
cross-methods developed within the project. In response to the demand for
parallelizable and resource efficient hyperparameter optimization methods,
advanced hyperparameter search algorithms are benchmarked and compared. The
evaluated algorithms, including Random Search, Hyperband and ASHA, are tested
and compared in terms of both accuracy and accuracy per compute resources
spent. As an example use case, a graph neural network model known as MLPF,
developed for the task of Machine-Learned Particle-Flow reconstruction in High
Energy Physics, acts as the base model for optimization. Results show that
hyperparameter optimization significantly increased the performance of MLPF and
that this would not have been possible without access to large-scale High
Performance Computing resources. It is also shown that, in the case of MLPF,
the ASHA algorithm in combination with Bayesian optimization gives the largest
performance increase per compute resources spent out of the investigated
algorithms.


title:
The three-loop anomalous dimension and the four-loop $\beta$-function
  for ${\cal N}=1$ SQED regularized by higher derivatives

abstract:
  For ${\cal N}=1$ SQED with $N_f$ flavors regularized by higher derivatives in
the general $\xi$-gauge we calculate the three-loop anomalous dimension of the
matter superfields defined in terms of the bare coupling constant and
demonstrate its gauge independence. After this the four-loop $\beta$-function
defined in terms of the bare coupling constant is obtained with the help of the
NSVZ equation, which is valid for these renormalization group functions in all
loops. Next, we calculate the three-loop anomalous dimension and the four-loop
$\beta$-function defined in terms of the renormalized coupling constant for an
arbitrary subtraction scheme supplementing the higher derivative
regularization. Then we construct a renormalization prescription for which the
results coincide with the ones in the $\overline{\mbox{DR}}$-scheme and
describe all NSVZ schemes in the considered approximation. Also we demonstrate
the existence of a subtraction scheme in which the anomalous dimension does not
depend on $N_f$, while the $\beta$-function contains only terms of the first
order in $N_f$. This scheme is obtained with the help of a finite
renormalization compatible with a structure of quantum corrections and is NSVZ.
The existence of such an NSVZ scheme is also proved in all loops.


title: Pattern Recognition and Event Detection on IoT Data-streams

abstract:
  Big data streams are possibly one of the most essential underlying notions.
However, data streams are often challenging to handle owing to their rapid pace
and limited information lifetime. It is difficult to collect and communicate
stream samples while storing, transmitting and computing a function across the
whole stream or even a large segment of it. In answer to this research issue,
many streaming-specific solutions were developed. Stream techniques imply a
limited capacity of one or more resources such as computing power and memory,
as well as time or accuracy limits. Reservoir sampling algorithms choose and
store results that are probabilistically significant. A weighted random
sampling approach using a generalised sampling algorithmic framework to detect
unique events is the key research goal of this work. Briefly, a gradually
developed estimate of the joint stream distribution across all feasible
components keeps k stream elements judged representative for the full stream.
Once estimate confidence is high, k samples are chosen evenly. The complexity
is O(min(k,n-k)), where n is the number of items inspected. Due to the fact
that events are usually considered outliers, it is sufficient to extract
element patterns and push them to an alternate version of k-means as proposed
here. The suggested technique calculates the sum of squared errors (SSE) for
each cluster, and this is utilised not only as a measure of convergence, but
also as a quantification and an indirect assessment of the element
distribution's approximation accuracy. This clustering enables for the
detection of outliers in the stream based on their distance from the usual
event centroids. The findings reveal that weighted sampling and res-means
outperform typical approaches for stream event identification. Detected events
are shown as knowledge graphs, along with typical clusters of events.


title:
A regularity criterion of 3D incompressible MHD system with mixed
  pressure-velocity-magnetic field

abstract:
  This work focuses on the 3D incompressible magnetohydrodynamic (MHD)
equations with mixed pressure-velocity-magnetic field in view of Lorentz
spaces. Our main result shows the weak solution is regular, provided that
$${\frac{\pi }{\left( e^{-\left\vert x\right\vert ^{2}}+\left\vert u\right\vert
+\left\vert b\right\vert \right) ^{\theta }}\in L}^{p}(0,T;L^{q,\infty
}(\mathbb{R}^{3})),\text{ where }\frac{2}{p}+\frac{3}{% q}=2-\theta \text{ and
}0\leq \theta \leq 1.$$


title:
Superiorized Adaptive Projected Subgradient Method with Application to
  MIMO Detection

abstract:
  In this paper, we show that the adaptive projected subgradient method (APSM)
is bounded perturbation resilient. To illustrate a potential application of
this result, we propose a set-theoretic framework for MIMO detection, and we
devise algorithms based on a superiorized APSM. Various low-complexity MIMO
detection algorithms achieve excellent performance on i.i.d. Gaussian channels,
but they typically incur high performance loss if realistic channel models are
considered. Compared to existing low-complexity iterative detectors such as
approximate message passing (AMP), the proposed algorithms can achieve
considerably lower symbol error ratios over correlated channels. At the same
time, the proposed methods do not require matrix inverses, and their complexity
is similar to AMP.


title:
Machine-learning interatomic potential for molecular dynamics simulation
  of ferroelectric KNbO3 perovskite

abstract:
  Ferroelectric perovskites have been ubiquitously applied in piezoelectric
devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based
materials have been recently demonstrated to be an excellent candidate for
sustainable development. Molecular dynamics is a versatile theoretical
calculation approach for the investigation of the dynamical properties of
ferroelectric perovskites. However, molecular dynamics simulation of
ferroelectric perovskites has been limited to simple systems, since the
conventional construction of interatomic potential is rather difficult and
inefficient. In the present study, we construct a machine-learning interatomic
potential of KNbO3 (as a representative system of (K,Na)NbO3) by using a deep
neural network model. Including first-principles calculation data into the
training dataset ensures the quantum-mechanics accuracy of the interatomic
potential. The molecular dynamics based on machine-learning interatomic
potential shows good agreement with the first-principles calculations, which
can accurately predict multiple fundamental properties, e.g., atomic force,
energy, elastic properties, and phonon dispersion. In addition, the interatomic
potential exhibits satisfactory performance in the simulation of domain wall
and temperature-dependent phase transition. The construction of interatomic
potential based on machine learning could potentially be transferred to other
ferroelectric perovskites and consequently benefits the theoretical study of
ferroelectrics.


title:
A multi-task learning for cavitation detection and cavitation intensity
  recognition of valve acoustic signals

abstract:
  With the rapid development of smart manufacturing, data-driven machinery
health management has received a growing attention. As one of the most popular
methods in machinery health management, deep learning (DL) has achieved
remarkable successes. However, due to the issues of limited samples and poor
separability of different cavitation states of acoustic signals, which greatly
hinder the eventual performance of DL modes for cavitation intensity
recognition and cavitation detection. In this work, a novel multi-task learning
framework for simultaneous cavitation detection and cavitation intensity
recognition framework using 1-D double hierarchical residual networks (1-D
DHRN) is proposed for analyzing valves acoustic signals. Firstly, a data
augmentation method based on sliding window with fast Fourier transform
(Swin-FFT) is developed to alleviate the small-sample issue confronted in this
study. Secondly, a 1-D double hierarchical residual block (1-D DHRB) is
constructed to capture sensitive features from the frequency domain acoustic
signals of valve. Then, a new structure of 1-D DHRN is proposed. Finally, the
devised 1-D DHRN is evaluated on two datasets of valve acoustic signals without
noise (Dataset 1 and Dataset 2) and one dataset of valve acoustic signals with
realistic surrounding noise (Dataset 3) provided by SAMSON AG (Frankfurt). Our
method has achieved state-of-the-art results. The prediction accurcies of 1-D
DHRN for cavitation intensitys recognition are as high as 93.75%, 94.31% and
100%, which indicates that 1-D DHRN outperforms other DL models and
conventional methods. At the same time, the testing accuracies of 1-D DHRN for
cavitation detection are as high as 97.02%, 97.64% and 100%. In addition, 1-D
DHRN has also been tested for different frequencies of samples and shows
excellent results for frequency of samples that mobile phones can accommodate.


title: Un lemme combinatoire de H. B. Neumann

abstract:
  We give a notably simpler and shorter proof of H. B. Neumann's result which
is stated, cursorly, like this. For any well-ordered subset, A, of a totally
ordered semigroup, the set of products of any finite number of elements of A is
itself well-ordered. Moreover, for each t, there are only a finite number of
such products equal to t.


title:
turboMagnon -- A code for the simulation of spin-wave spectra using the
  Liouville-Lanczos approach to time-dependent density-functional perturbation
  theory

abstract:
  We introduce turboMagnon, an implementation of the Liouville-Lanczos approach
to linearized time-dependent density-functional theory, designed to simulate
spin-wave spectra in solid-state materials. The code is based on the
noncollinear spin-polarized framework and the self-consistent inclusion of
spin-orbit coupling that allow to model complex magnetic excitations. The spin
susceptibility matrix is computed using the Lanczos recursion algorithm that is
implemented in two flavors - the non-Hermitian and the pseudo-Hermitian one.
turboMagnon is open-source software distributed under the terms of the GPL as a
component of Quantum ESPRESSO. As with other components, turboMagnon is
optimized to run on a variety of different platforms, from workstations to
massively parallel architectures, using native mathematical libraries (LAPACK
and FFTW) and a hierarchy of custom parallelization layers built on top of MPI.
The effectiveness of the code is showcased by computing magnon dispersions for
the CrI3 monolayer, and the importance of the spin-orbit coupling is discussed.


title: VaiPhy: a Variational Inference Based Algorithm for Phylogeny

abstract:
  Phylogenetics is a classical methodology in computational biology that today
has become highly relevant for medical investigation of single-cell data, e.g.,
in the context of development of cancer. The exponential size of the tree space
is unfortunately a formidable obstacle for current Bayesian phylogenetic
inference using Markov chain Monte Carlo based methods since these rely on
local operations. And although more recent variational inference (VI) based
methods offer speed improvements, they rely on expensive auto-differentiation
operations for learning the variational parameters. We propose VaiPhy, a
remarkably fast VI based algorithm for approximate posterior inference in an
augmented tree space. VaiPhy produces marginal log-likelihood estimates on par
with the state-of-the-art methods on real data, and is considerably faster
since it does not require auto-differentiation. Instead, VaiPhy combines
coordinate ascent update equations with two novel sampling schemes: (i)
SLANTIS, a proposal distribution for tree topologies in the augmented tree
space, and (ii) the JC sampler, the, to the best of our knowledge, first ever
scheme for sampling branch lengths directly from the popular Jukes-Cantor
model. We compare VaiPhy in terms of density estimation and runtime.
Additionally, we evaluate the reproducibility of the baselines. We provide our
code on GitHub: https://github.com/Lagergren-Lab/VaiPhy.


title: Mean dimension of natural extension of algebraic systems

abstract:
  Mean dimension may decrease after taking natural extension. In this paper we
show that mean dimension stays the same after taking natural extension for an
endomorphism on a compact metrizable abelian group. As an application, we
obtain that the mean dimension of algebraic cellular automaton coincides with
its natural extension, which strengthens a result of Burguet and Shi
\cite{BS21} with a different proof.


title:
A Constrained Optimization Approach to Bilevel Optimization with
  Multiple Inner Minima

abstract:
  Bilevel optimization has found extensive applications in modern machine
learning problems such as hyperparameter optimization, neural architecture
search, meta-learning, etc. While bilevel problems with a unique inner minimal
point (e.g., where the inner function is strongly convex) are well understood,
bilevel problems with multiple inner minimal points remains to be a challenging
and open problem. Existing algorithms designed for such a problem were
applicable to restricted situations and do not come with the full guarantee of
convergence. In this paper, we propose a new approach, which convert the
bilevel problem to an equivalent constrained optimization, and then the
primal-dual algorithm can be used to solve the problem. Such an approach enjoys
a few advantages including (a) addresses the multiple inner minima challenge;
(b) features fully first-order efficiency without involving second-order
Hessian and Jacobian computations, as opposed to most existing gradient-based
bilevel algorithms; (c) admits the convergence guarantee via constrained
nonconvex optimization. Our experiments further demonstrate the desired
performance of the proposed approach.


title: Lunar Orbit Measurement of Cosmic Dawn 21 cm Global Spectrum

abstract:
  A redshifted 21 cm line absorption signature is commonly expected from the
cosmic dawn era, when the first stars and galaxies formed. The detailed traits
of this signal can provide important insight on the cosmic history. However,
high precision measurement of this signal is hampered by the ionosphere
refraction and absorption, as well as radio frequency interference (RFI). A
space observation can solve the problem of the ionosphere, and the Moon can
shield the RFI from the Earth. In this paper, we present simulations of the
global spectrum measurement in the 30 -- 120 MHz frequency band on the lunar
orbit, from the proposed Discovering the Sky at the Longest wavelength (DSL)
project. In particular, we consider how the measured signal varies as the
satellite moves along the orbit, take into account the blockage of different
parts of the sky by the Moon and the antenna response. We estimate the
sensitivity for such a 21 cm global spectrum experiment. An RMS noise level of
$\le 0.05$ K is expected at 75 MHz after 10 orbits ($\sim$ 1 day) observation,
for a frequency channel width of 0.4 MHz. We also study the influence of a
frequency-dependent beam, which may generate complex spectral structures in the
spectrum. Estimates of the uncertainties in the foreground and 21 cm model
parameters are obtained.


title: Algebraic fibrings of hyperbolic manifolds

abstract:
  We prove that there are finite-volume, hyperbolic manifolds of dimensions
$6$, $7$, and $8$ that algebraically fibre with kernel of type
$\mathtt{FP}_2(\mathbb{Q})$, $\mathtt{F}_2$ and $\mathtt{FP}(\mathbb{Q})$, and
$\mathtt{F}_2$ and $\mathtt{FP}_3(\mathbb{Q})$, respectively. The manifolds are
finite covers of the manifolds constructed by Italiano, Martelli, and
Migliorini.


title: Timelike and null deceleration parameters in tilted Friedmann universes

abstract:
  Information exchanged between observers in the universe typically travels
along the null rays of the associated light signals. One may therefore
decompose the luminosity distance of a given radiation source along these null
geodesics, instead of the timelike worldlines of the emitting/receiving
observers. In so doing, one obtains (among others) the null counterparts of the
familiar Hubble and deceleration parameters. Although the timelike and the null
sets of these parameters coincide in an exact Friedmann universe, they
generally differ in perturbed cosmological models. The situation becomes
further involved in tilted cosmologies, namely in those equipped with two
families of relatively moving observers. There, the two observer groups
generally disagree on the values of these parameters, simply because of their
relative motion. Assuming a perturbed Friedmann universe with dust, we find
that the relative-motion effects alone can locally change the sign of the null
deceleration parameter from positive to negative, while at the same time the
host universe is globally decelerating. An exactly analogous effect has also
been reported with regard to the timelike deceleration parameter. The
difference here is that, in the null case, the impact of the relative motion is
twice as strong, thus making it easier for the unsuspecting observers to
misinterpret a local change in the sign of the deceleration parameter as recent
global acceleration.


title: GCOS -- The Global Cosmic Ray Observatory

abstract:
  Nature is providing particles with energies exceeding 100 EeV. Their
existence imposes immediate questions: Are they ordinary particles, accelerated
in extreme astrophysical environments, or are they annihilation or decay
products of super-heavy dark matter or other exotic objects? If the particles
are accelerated in extreme astrophysical environments, are their sources
related to those of high-energy neutrinos, gamma rays, and/or gravitational
waves, such as the recently observed mergers of compact objects? The particles
can also be used to study physics processes at extreme energies; is Lorentz
invariance still valid? Are the particles interacting according to the Standard
Model or are there new physics processes? The particles can be used to study
hadronic interactions (QCD) in the kinematic forward direction; what is the
cross section of protons at center-of-mass energies $\sqrt{s} > 100$ TeV?
  These questions are addressed at present by installations like the Telescope
Array and the Pierre Auger Observatory. After the year 2030, a next-generation
observatory will be needed to study the physics and properties of the
highest-energy particles in Nature, building on the knowledge harvested from
the existing observatories. It should have an aperture at least an order of
magnitude bigger than the existing observatories. Recently, more than 200
scientists from around the world came together to discuss the future of the
field of multi-messenger astroparticle physics beyond the year 2030. Ideas have
been discussed towards the physics case and possible scenarios for detection
concepts of the Global Cosmic Ray Observatory - GCOS. A synopsis of the key
results discussed during the brainstorming workshop will be presented.


title: Nanophotonic cavity based synapse for scalable photonic neural networks

abstract:
  The bandwidth and energy demands of neural networks has spurred tremendous
interest in developing novel neuromorphic hardware, including photonic
integrated circuits. Although an optical waveguide can accommodate hundreds of
channels with THz bandwidth, the channel count of photonic systems is always
bottlenecked by the devices within. In WDM-based photonic neural networks, the
synapses, i.e. network interconnections, are typically realized by microring
resonators (MRRs), where the WDM channel count (N) is bounded by the
free-spectral range of the MRRs. For typical Si MRRs, we estimate N <= 30
within the C-band. This not only restrains the aggregate throughput of the
neural network but also makes applications with high input dimensions
unfeasible. We experimentally demonstrate that photonic crystal nanobeam based
synapses can be FSR-free within C-band, eliminating the bound on channel count.
This increases data throughput as well as enables applications with
high-dimensional inputs like natural language processing and high resolution
image processing. In addition, the smaller physical footprint of photonic
crystal nanobeam cavities offers higher tuning energy efficiency and a higher
compute density than MRRs. Nanophotonic cavity based synapse thus offers a path
towards realizing highly scalable photonic neural networks.


title:
Defining a synthetic data generator for realistic electric vehicle
  charging sessions

abstract:
  Electric vehicle (EV) charging stations have become prominent in electricity
grids in the past years. Analysis of EV charging sessions is useful for
flexibility analysis, load balancing, offering incentives to customers, etc.
Yet, the limited availability of such EV sessions data hinders further
development in these fields. Addressing this need for publicly available and
realistic data, we develop a synthetic data generator (SDG) for EV charging
sessions. Our SDG assumes the EV inter-arrival time to follow an exponential
distribution. Departure times are modeled by defining a conditional probability
density function (pdf) for connection times. This pdf for connection time and
required energy is fitted by Gaussian mixture models. Since we train our SDG
using a large real-world dataset, its output is realistic.


title:
The Role of the Instrumental Response in 21 cm EoR Power Spectrum
  Gridding Analyses

abstract:
  Reconstruction of the sky brightness measured by radio interferometers is
typically achieved through gridding techniques, or histograms in spatial
Fourier space. For Epoch of Reionisation (EoR) 21 cm power spectrum
measurements, extreme levels of gridding resolution are required to reduce
spectral contamination, as explored in other works. However, the role of the
shape of the Fourier space spreading function, or kernel, also has consequences
in reconstructed power spectra. We decompose the instrumental Murchison
Widefield Array (MWA) beam into a series of Gaussians and simulate the effects
of finite kernel extents and differing shapes in gridding/degridding for
optimal map making analyses. For the MWA, we find that the kernel must extend
out to 0.001--0.0001% of the maximum value in order to measure the EoR using
foreground avoidance. This requirement changes depending on beam shape, with
compact kernels requiring far smaller extents for similar contamination levels
at the cost of less-optimal errors. However, simple calibration using pixelated
degridding results, regardless of shape of the kernel, cannot recover the EoR
due to catastrophic errors caused by the pixel resolution. Including an opaque
horizon with widefield beams also causes significant spectral contamination via
a beam--horizon interaction that creates an infinitely extended kernel in
Fourier space, which cannot be represented well. Thus, our results indicate
that simple calibration via degridded models and optimal map making for extreme
widefield instrumentation are not feasible.


title: MuMuPy: A Dimuonium-Matter Interaction Calculator

abstract:
  In this article, we present MuMuPy, a computational library and cloud-based
tool for calculating cross sections for the interaction of dimuonium (true
muonium) with matter. MuMuPy calculates corresponding form factors and allows
one to find the probabilities of dimuonium transitions in the electric field of
the nucleus.
  MuMuPy was developed in the context of the $\mu\mu$-tron facility, the
project of a low-energy electron-positron collider for production and
experimental study of dimuonium, proposed in our home institute, Budker
Institute of Nuclear Physics.
  The reliability of MuMuPy was verified by three independent methods, one of
which was developed by the authors earlier.


title: Anisotropic nature of space-time in $f(Q)$ gravity

abstract:
  In this paper, we consider the Bianchi type-I space-time with perfect fluid
as matter content of the Universe in the framework of $f(Q)$ gravity (where $%
Q$ is the non-metricity scalar) recently proposed by Jim\'{e}nez et al. (Phys.
Rev. D 98.4 (2018): 044048). We find exact solutions of the field equations
using the anisotropic property of space-time for volumetric hybrid expansion.
The cosmological parameters for the linear form of non-metricity scalar i.e.
$f\left( Q\right) =\alpha Q+\beta $ (where $\alpha $ and $\beta $ are free
model parameters) are discussed and compared with recent Hubble measurements.
We also examined the stability of the model and test of its validity by energy
conditions.


title: Two-body continuum states in non-integer geometry

abstract:
  Wave functions, phase shifts and corresponding elastic cross sections are
investigated for two short-range interacting particles in a deformed external
oscillator field. For this we use the equivalent $d$-method employing a
non-integer dimension $d$. Using a square-well potential, we derive analytic
expressions for scattering lengths and phase shifts. In particular, we consider
the dimension, $d_E$, for infinite scattering length, where the Efimov effect
occurs by addition of a third particle. We give explicitly the equivalent
continuum wave functions in $d$ and ordinary three dimensional (3D) space, and
show that the phase shifts are the same in both methods. Consequently the
$d$-method can be used to obtain low-energy two-body elastic cross sections in
an external field.


title: On bounds of logarithmic mean and mean inequality chain

abstract:
  An upper bound of the logarithmic mean is given by a convex conbination of
the arithmetic mean and the geometric mean. In addition, a lower bound of the
logarithmic mean is given by a geometric bridge of the arithmetic mean and the
geometric mean. In this paper, we study the bounds of the logarithmic mean. We
give operator inequalities and norm inequalities for the fundamental
inequalities on the logarithmic mean. We give monotonicity of the parameter for
the unitarily invariant norm of the Heron mean, and give its optimality as the
upper bound of the unitarily invariant norm of the logarithmic mean. We study
the ordering of the unitarily invariant norms for the Heron mean, the Heinz
mean, the binomial mean and the Lehmer mean. Finally, we give a new mean
inequality chain as an application of the point-wise inequality.


title: A Scalable Approach to Quantum Simulation via Projection-based Embedding

abstract:
  Owing to the computational complexity of electronic structure algorithms
running on classical digital computers, the range of molecular systems amenable
to simulation remains tightly circumscribed even after many decades of work.
Quantum computers hold the promise of transcending such limitations although in
the current era the size and noise of these devices militates against
significant progress. Here we describe a new and chemically intuitive approach
that permits a subdomain of the electronic structure of a molecule to be
calculated accurately on a quantum device, while the rest of the molecule is
described at a lower level of accuracy using density functional theory running
on a classical computer. We demonstrate that our method produces improved
results for molecules that cannot be simulated fully on quantum computers but
which can be resolved classically at a lower level of approximation. Our
algorithm is tunable, so that the size of the quantum simulation can be
adjusted to run on available quantum resources. Therefore, as quantum devices
become larger, our method will enable increasingly large subdomains to be
studied accurately.


title:
A search for planetary companions around 800 pulsars from the Jodrell
  Bank pulsar timing programme

abstract:
  We have searched for planetary companions around 800 pulsars monitored at the
Jodrell Bank Observatory, with both circular and eccentric orbits of periods
between $20$ days and $17$ years and inclination-dependent planetary masses
from $10^{-4}$ to $100\,\mathrm{M}_{\oplus}$. Using a Bayesian framework, we
simultaneously model pulsar timing parameters and a stationary noise process
with a power-law power spectral density. We put limits on the projected masses
of any planetary companions, which reach as low as 1/100th of the mass of the
Moon ($\sim 10^{-4}\,\mathrm{M}_{\oplus}$). We find that two-thirds of our
pulsars are highly unlikely to host any companions above
$2-8\,\mathrm{M}_{\oplus}$. Our results imply that fewer than $0.5\%$ of
pulsars could host terrestrial planets as large as those known to orbit PSR
B1257$+$12 ($\sim4\,\mathrm{M}_{\oplus}$); however, the smaller planet in this
system ($\sim0.02\,\mathrm{M}_{\oplus}$) would be undetectable in $95\%$ of our
sample, hidden by both instrumental and intrinsic noise processes, although it
is not clear if such tiny planets could exist in isolation. We detect
significant periodicities in 15 pulsars, however we find that intrinsic
quasi-periodic magnetospheric effects can mimic the influence of a planet, and
for the majority of these cases we believe this to be the origin of the
detected periodicity. Notably, we find that the highly periodic oscillations in
PSR B0144$+$59 are correlated with changes in the pulse profile and therefore
can be attributed to magnetospheric effects. We believe the most plausible
candidate for planetary companions in our sample is PSR J2007$+$3120.


title: Self-Supervised Scene Flow Estimation with 4D Automotive Radar

abstract:
  Scene flow allows autonomous vehicles to reason about the arbitrary motion of
multiple independent objects which is the key to long-term mobile autonomy.
While estimating the scene flow from LiDAR has progressed recently, it remains
largely unknown how to estimate the scene flow from a 4D radar - an
increasingly popular automotive sensor for its robustness against adverse
weather and lighting conditions. Compared with the LiDAR point clouds, radar
data are drastically sparser, noisier and in much lower resolution. Annotated
datasets for radar scene flow are also in absence and costly to acquire in the
real world. These factors jointly pose the radar scene flow estimation as a
challenging problem. This work aims to address the above challenges and
estimate scene flow from 4D radar point clouds by leveraging self-supervised
learning. A robust scene flow estimation architecture and three novel losses
are bespoken designed to cope with intractable radar data. Real-world
experimental results validate that our method is able to robustly estimate the
radar scene flow in the wild and effectively supports the downstream task of
motion segmentation.


title: Holographic Confinement of the Solitary Quark

abstract:
  Single quarks moving in the vacuum of confining gauge theories are stopped by
a drag force. The holographic description relates the confining scale in the
bulk geometry with a range of physical values for the drag force in the vacuum.
The vacuum drag force acting on the isolated quark directly manifests quark
confinement since it prevents the quark from walking freely in the vacuum.
However, analytical expressions for the drag force as a function of the quark
velocity were lacking. In the present work, we propose that the vacuum drag
force is given by the regularized zero-temperature limit of the corresponding
thermal drag force. Within this approach, we obtain the desired analytic
expressions in two different holographic models: the quadratic dilaton and the
D-instanton. In both cases, we find well-behaved functions belonging to their
physical range of values.


title: Random Models for Exploring Planet Compositions I: Uranus as an Example

abstract:
  Modeling the interior of a planet is difficult because the small number of
measured parameters is insufficient to constrain the many variables involved in
describing the interior structure and composition. One solution is to invoke
additional constraints based on arguments about how the planet formed. However,
a planet's actual structure and composition may hold clues to its formation
which would be lost if this structure were not allowed by the initial
assumptions. It is therefore interesting to explore the space of allowable
compositions and structures in order to better understand which cosmogonic
constraints are absolutely necessary. To this end, we describe a code for
generating random, monotonic, density distributions, rho(r), that fit a given
mass, radius, and moment of inertia.
  Integrating the equation of hydrostatic equilibrium gives the pressure, P(r),
at each point in the body. We then provide three algorithms for generating a
monotonic temperature distribution, T(r), and an associated composition that is
consistent with the rho-P relation, and realistic equations of state. We apply
this code to Uranus as a proof of concept, and show that the ratio of rock to
water cannot be much larger than 2.


title: Finite quotients of 3-manifold groups

abstract:
  For $G$ and $H_1,\dots, H_n$ finite groups, does there exist a $3$-manifold
group with $G$ as a quotient but no $H_i$ as a quotient? We answer all such
questions in terms of the group cohomology of finite groups. We prove
non-existence with topological results generalizing the theory of
semicharacteristics. To prove existence of 3-manifolds with certain finite
quotients but not others, we use a probabilistic method, by first proving a
formula for the distribution of the (profinite completion of) the fundamental
group of a random 3-manifold in the Dunfield-Thurston model of random Heegaard
splittings as the genus goes to infinity. We believe this is the first
construction of a new distribution of random groups from its moments.


title: Implementing the fanout operation with simple pairwise interactions

abstract:
  It has been shown that, for even $n$, evolving $n$ qubits according to a
Hamiltonian that is the sum of pairwise interactions between the particles, can
be used to exactly implement an $(n+1)$-qubit fanout gate using a particular
constant-depth circuit arXiv:quant-ph/0309163. However, the coupling
coefficients in the Hamiltonian considered in that paper are assumed to be all
equal. In this paper, we generalize these results and show that for all $n$,
including odd $n$, one can exactly implement an $(n+1)$-qubit parity gate and
hence, equivalently in constant depth an $(n+1)$-qubit fanout gate, using a
similar Hamiltonian but with unequal couplings, and we give an exact
characterization of which couplings are adequate to implement fanout via the
same circuit.
  We also investigate pairwise couplings that satisfy an inverse square law and
give planar arrangements of four qubits that (together with a target qubit) are
adequate to implement $5$-qubit fanout.


title:
Comparisons between Fourier and STFT multipliers: the smoothing effect
  of the Short-time Fourier Transform

abstract:
  We study the connection between STFT multipliers $A^{g_1,g_2}_{1\otimes m}$
having windows $g_1,g_2$, symbols $a(x,\omega)=(1\otimes
m)(x,\omega)=m(\omega)$, $(x,\omega)\in\mathbb{R}^{2d}$, and the Fourier
multipliers $T_{m_2}$ with symbol $m_2$ on $\mathbb{R}^d$. We find sufficient
and necessary conditions on symbols $m,m_2$ and windows $g_1,g_2$ for the
equality $T_{m_2}= A^{g_1,g_2}_{1\otimes m}$. For $m=m_2$ the former equality
holds only for particular choices of window functions in modulation spaces,
whereas it never occurs in the realm of Lebesgue spaces. In general, the STFT
multiplier $A^{g_1,g_2}_{1\otimes m}$, also called localization operator,
presents a smoothing effect due to the so-called two-window short-time Fourier
transform which enters in the definition of $A^{g_1,g_2}_{1\otimes m}$. As a
by-product we prove necessary conditions for the continuity of anti-Wick
operators $A^{g,g}_{1\otimes m}: L^p\to L^q$ having multiplier $m$ in weak
$L^r$ spaces. Finally, we exhibit the related results for their discrete
counterpart: in this setting STFT multipliers are called Gabor multipliers
whereas Fourier multiplier are better known as linear time invariant (LTI)
filters.


title:
Decision-Making Under Uncertainty for Multi-stage Pipelines: Simulation
  Studies to Benchmark Screening Strategies

abstract:
  Multi-stage screening pipelines are ubiquitous throughout experimental and
computational science. Much of the effort in developing screening pipelines
focuses on improving generative methods or surrogate models in an attempt to
make each screening step effective for a specific application. Little focus has
been placed on characterizing generic screening pipeline performance with
respect to the problem or problem parameters. Here, we develop models and
algorithms to codify and simulate features and properties of the screening
procedure in general. We outline and model common problem settings and
potential opportunities to perform decision-making under uncertainty to
optimize the execution of screening pipelines. We then illustrate the models
and algorithms through several simulation studies. We finally show how such
studies can provide a quantification of the screening pipeline performance with
respect to problem parameters, specifically identifying the significance of
stage-wise covariance structure. We show how such structure can lead to
qualitatively different screening behaviors, and how screening can even perform
worse than random in some cases.


title:
Electrically pumped polarized exciton-polaritons in a halide perovskite
  microcavity

abstract:
  Exciton polaritons, hybrid quasiparticles with part-light part-matter nature
in semiconductor microcavities, are extensively investigated for striking
phenomena such as polariton condensation and quantum emulation. These phenomena
have recently been discovered in emerging lead halide perovskites at elevated
temperatures up to room temperature. For advancing these discoveries into
practical applications, one critical requirement is the realization of
electrically pumped exciton-polaritons. However, electrically pumped polariton
light-emitting devices with perovskites have not yet been achieved
experimentally. Here, we devise a new method to combine the device with the
microcavity and report the first halide perovskite polariton light-emitting
device. Specifically, the device is based on a CsPbBr3 capacitive structure,
which can inject the electrons and holes from the same electrode, conducive to
the formation of excitons and simultaneously maintaining the high quality of
the microcavity. In addition, highly polarization-selective polariton emissions
have been demonstrated due to the optical birefringence in the CsPbBr3
microplate. This work paves the way for realizing practical polaritonic devices
such as high-speed light-emitting devices for information communications and
inversionless electrically pumped lasers based on perovskites.


title:
On the Riccati dynamics of 2D Euler-Poisson equations with attractive
  forcing

abstract:
  The Euler-Poisson (EP) system describes the dynamic behavior of many
important physical flows. In this work, a Riccati system that governs
two-dimensional EP equations is studied. The evolution of divergence is
governed by the Riccati type equation with several nonlinear/nonlocal terms.
Among these, the vorticity accelerates divergence while others further amplify
the blow-up behavior of a flow. The growth of these blow-up amplifying terms
are related to the Riesz transform of density, which lacks a uniform bound
makes it difficult to study global solutions of the multi-dimensional EP
system. We show that the Riccati system can afford to have global solutions, as
long as the growth rate of blow-up amplifying terms is not higher than
exponential, and admits global smooth solutions for a large set of initial
configurations. To show this, we construct an auxiliary system in 3D space and
find an invariant space of the system, then comparison with the original 2D
system is performed. Some numerical examples are also presented.


title: Controlling the Focus of Pretrained Language Generation Models

abstract:
  The finetuning of pretrained transformer-based language generation models are
typically conducted in an end-to-end manner, where the model learns to attend
to relevant parts of the input by itself. However, there does not exist a
mechanism to directly control the model's focus. This work aims to develop a
control mechanism by which a user can select spans of context as "highlights"
for the model to focus on, and generate relevant output. To achieve this goal,
we augment a pretrained model with trainable "focus vectors" that are directly
applied to the model's embeddings, while the model itself is kept fixed. These
vectors, trained on automatic annotations derived from attribution methods, act
as indicators for context importance. We test our approach on two core
generation tasks: dialogue response generation and abstractive summarization.
We also collect evaluation data where the highlight-generation pairs are
annotated by humans. Our experiments show that the trained focus vectors are
effective in steering the model to generate outputs that are relevant to
user-selected highlights.


title:
Slow-Fluor Scintillator for Low Energy Solar Neutrinos and Neutrinoless
  Double Beta Decay

abstract:
  The potential for using slow-fluor liquid scintillators to study low energy
solar neutrinos and neutrinoless double beta decay (0nbb) is explored through a
series of simulations. The fluorescence model assumed for the primary fluor has
characteristics similar to acenaphthene, recently used to demonstrate Cherenkov
separation at energies around 1 MeV. Results here indicate notably better
directional reconstruction in large-scale detectors than has previously been
suggested by other approaches, allowing better identification of low energy
solar neutrinos. These studies indicate that a detector with as little as ~30%
coverage using currently available photomultiplier tubes could be able to make
a measurement of the CNO solar neutrino flux to a precision of better than 10%
(enough to distinguish metallicity models) with a few kiloton-years of
exposure. In terms of 0nbb, studies here suggest that rejection of solar
neutrino backgrounds with such a technique might potentially approach a factor
of 10 for endpoint energies near 2.5 MeV in the angular hemisphere defined by
the solar direction.


title:
Reactive Stepping for Humanoid Robots using Reinforcement Learning:
  Application to Standing Push Recovery on the Exoskeleton Atalante

abstract:
  State-of-the-art reinforcement learning is now able to learn versatile
locomotion, balancing and push-recovery capabilities for bipedal robots in
simulation. Yet, the reality gap has mostly been overlooked and the simulated
results hardly transfer to real hardware. Either it is unsuccessful in practice
because the physics is over-simplified and hardware limitations are ignored, or
regularity is not guaranteed and unexpected hazardous motions can occur. This
paper presents a reinforcement learning framework capable of learning robust
standing push recovery for bipedal robots with a smooth out-of-the-box transfer
to reality, requiring only instantaneous proprioceptive observations. By
combining original termination conditions and policy smoothness conditioning,
we achieve stable learning, sim-to-real transfer and safety using a policy
without memory nor observation history. Reward shaping is then used to give
insights into how to keep balance. We demonstrate its performance in reality on
the lower-limb medical exoskeleton Atalante.


title:
Perfect cuboid, primitive Pythagorean triples and Eulerian
  parallelepipeds. Dynamics of construction

abstract:
  One unsolved mathematical problem remains the perfect cuboid problem. A
perfect cuboid is a rectangular parallelepiped whose edges, face diagonals and
space diagonal are all expressed as integers. No such cuboid has yet been
discovered and its existence has also not been proven. This paper shows a proof
of the non-existence of a perfect cuboid.


title: Embedding K3,3 and K5 on the Double Torus

abstract:
  The Kuratowski graphs $K_{3,3}$ and $K_5$ characterize planarity. Counting
distinct 2-cell embeddings of these two graphs on orientable surfaces was
previously done by using Burnside's Lemma and their automorphism groups,
without actually constructing the embeddings. We obtain all 2-cell embeddings
of these graphs on the double torus, using a constructive approach. This shows
that there is a unique non-orientable 2-cell embedding of $K_{3,3}$, 14
orientable and 17 non-orientable 2-cell embeddings of $K_5$ on the double
torus, which explicitly confirms the enumerative results. As a consequence,
several new polygonal representations of the double torus are presented.


title:
Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by
  Learning Features in Complementary Representations

abstract:
  In this paper we introduce a novel way to predict semantic information from
sparse, single-shot LiDAR measurements in the context of autonomous driving. In
particular, we fuse learned features from complementary representations. The
approach is aimed specifically at improving the semantic segmentation of
top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected
onto two orthogonal 2D representations. For each representation a tailored deep
learning architecture is developed to effectively extract semantic information
which are fused by a superordinate deep neural network. The contribution of
this work is threefold: (1) We examine different stages within the segmentation
network for fusion. (2) We quantify the impact of embedding different features.
(3) We use the findings of this survey to design a tailored deep neural network
architecture leveraging respective advantages of different representations. Our
method is evaluated using the SemanticKITTI dataset which provides a point-wise
semantic annotation of more than 23.000 LiDAR measurements.


title: DisARM: Displacement Aware Relation Module for 3D Detection

abstract:
  We introduce Displacement Aware Relation Module (DisARM), a novel neural
network module for enhancing the performance of 3D object detection in point
cloud scenes. The core idea of our method is that contextual information is
critical to tell the difference when the instance geometry is incomplete or
featureless. We find that relations between proposals provide a good
representation to describe the context. However, adopting relations between all
the object or patch proposals for detection is inefficient, and an imbalanced
combination of local and global relations brings extra noise that could mislead
the training. Rather than working with all relations, we found that training
with relations only between the most representative ones, or anchors, can
significantly boost the detection performance. A good anchor should be
semantic-aware with no ambiguity and independent with other anchors as well. To
find the anchors, we first perform a preliminary relation anchor module with an
objectness-aware sampling approach and then devise a displacement-based module
for weighing the relation importance for better utilization of contextual
information. This lightweight relation module leads to significantly higher
accuracy of object instance detection when being plugged into the
state-of-the-art detectors. Evaluations on the public benchmarks of real-world
scenes show that our method achieves state-of-the-art performance on both SUN
RGB-D and ScanNet V2.


title:
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal
  Multiview Sensory Input

abstract:
  We address the problem of devising the means for a robot to rapidly and
safely learn insertion skills with just a few human interventions and without
hand-crafted rewards or demonstrations. Our InsertionNet version 2.0 provides
an improved technique to robustly cope with a wide range of use-cases featuring
different shapes, colors, initial poses, etc. In particular, we present a
regression-based method based on multimodal input from stereo perception and
force, augmented with contrastive learning for the efficient learning of
valuable features. In addition, we introduce a one-shot learning technique for
insertion, which relies on a relation network scheme to better exploit the
collected data and to support multi-step insertion tasks. Our method improves
on the results obtained with the original InsertionNet, achieving an almost
perfect score (above 97.5$\%$ on 200 trials) in 16 real-life insertion tasks
while minimizing the execution time and contact during insertion. We further
demonstrate our method's ability to tackle a real-life 3-step insertion task
and perfectly solve an unseen insertion task without learning.


title: B/PS bulges in DESI Legacy edge-on galaxies I: Sample building

abstract:
  We present the biggest up-to-date sample of edge-on galaxies with B/PS bulges
and X-structures. The sample was prepared using images from the DESI Legacy
catalogue and contains about 2000 galaxies. To find suitable candidates in
catalogue, we made the assumption that the residues (original images minus
model) of galaxies with B/PS bulges should exhibit a characteristic X-shape.
Galaxies with such features were selected by eye and then used as input data
for a neural network training, which was applied to a bigger sample of edge-on
galaxies. Using the available data and the photometric models from the
literature, we investigated the observational and statistical properties of the
sample created. Comparing the $B/D$ ratios for galaxies with and without B/PS
bulges, we found that the $B/D$ ratio for galaxies from our sample is
statistically higher, with typical values in the range $\approx 0.2-0.5$
depending on the decomposition procedure. We studied how the opening angles
$\varphi$ of the X-structure and the length of its rays are distributed in the
formed sample and found them to be consistent with previous measurements and
predictions from $N$-body models, e.g. $\varphi \gtrsim 25~\deg$, but measured
here for a much larger number of galaxies. We found a sharp increase in the
B/PS bulge fraction for stellar masses $\log M_{\star} \gtrsim 10.4$, but for
edge-on galaxies, which complements the results of previous works. The sample
can be used in future work to test various bar models and their relationship
with B/PS bulges, as well as to study their stability and evolution.


title: Top-N Recommendation Algorithms: A Quest for the State-of-the-Art

abstract:
  Research on recommender systems algorithms, like other areas of applied
machine learning, is largely dominated by efforts to improve the
state-of-the-art, typically in terms of accuracy measures. Several recent
research works however indicate that the reported improvements over the years
sometimes "don't add up", and that methods that were published several years
ago often outperform the latest models when evaluated independently. Different
factors contribute to this phenomenon, including that some researchers probably
often only fine-tune their own models but not the baselines. In this paper, we
report the outcomes of an in-depth, systematic, and reproducible comparison of
ten collaborative filtering algorithms - covering both traditional and neural
models - on several common performance measures on three datasets which are
frequently used for evaluation in the recent literature. Our results show that
there is no consistent winner across datasets and metrics for the examined
top-n recommendation task. Moreover, we find that for none of the accuracy
measurements any of the considered neural models led to the best performance.
Regarding the performance ranking of algorithms across the measurements, we
found that linear models, nearest-neighbor methods, and traditional matrix
factorization consistently perform well for the evaluated modest-sized, but
commonly-used datasets. Our work shall therefore serve as a guideline for
researchers regarding existing baselines to consider in future performance
comparisons. Moreover, by providing a set of fine-tuned baseline models for
different datasets, we hope that our work helps to establish a common
understanding of the state-of-the-art for top-n recommendation tasks.


title: Engineering the Neural Automatic Passenger Counter

abstract:
  Automatic passenger counting (APC) in public transportation has been
approached with various machine learning and artificial intelligence methods
since its introduction in the 1970s. While equivalence testing is becoming more
popular than difference detection (Student's t-test), the former is much more
difficult to pass to ensure low user risk. On the other hand, recent
developments in artificial intelligence have led to algorithms that promise
much higher counting quality (lower bias). However, gradient-based methods
(including Deep Learning) have one limitation: they typically run into local
optima. In this work, we explore and exploit various aspects of machine
learning to increase reliability, performance, and counting quality. We perform
a grid search with several fundamental parameters: the selection and size of
the training set, which is similar to cross-validation, and the initial network
weights and randomness during the training process. Using this experiment, we
show how aggregation techniques such as ensemble quantiles can reduce bias, and
we give an idea of the overall spread of the results. We utilize the test
success chance, a simulative metric based on the empirical distribution. We
also employ a post-training Monte Carlo quantization approach and introduce
cumulative summation to turn counting into a stationary method and allow
unbounded counts.


title:
Artificial Concepts of Artificial Intelligence: Institutional Compliance
  and Resistance in AI Startups

abstract:
  Scholars and industry practitioners have debated how to best develop
interventions for ethical artificial intelligence (AI). Such interventions
recommend that companies building and using AI tools change their technical
practices, but fail to wrangle with critical questions about the organizational
and institutional context in which AI is developed. In this paper, we
contribute descriptive research around the life of "AI" as a discursive concept
and organizational practice in an understudied sphere--emerging AI
startups--and with a focus on extra-organizational pressures faced by
entrepreneurs. Leveraging a theoretical lens for how organizations change, we
conducted semi-structured interviews with 23 entrepreneurs working at
early-stage AI startups. We find that actors within startups both conform to
and resist institutional pressures. Our analysis identifies a central tension
for AI entrepreneurs: they often valued scientific integrity and methodological
rigor; however, influential external stakeholders either lacked the technical
knowledge to appreciate entrepreneurs' emphasis on rigor or were more focused
on business priorities. As a result, entrepreneurs adopted hyped marketing
messages about AI that diverged from their scientific values, but attempted to
preserve their legitimacy internally. Institutional pressures and
organizational constraints also influenced entrepreneurs' modeling practices
and their response to actual or impending regulation. We conclude with a
discussion for how such pressures could be used as leverage for effective
interventions towards building ethical AI.


title: Rethinking Pretraining as a Bridge from ANNs to SNNs

abstract:
  Spiking neural networks (SNNs) are known as a typical kind of brain-inspired
models with their unique features of rich neuronal dynamics, diverse coding
schemes and low power consumption properties. How to obtain a high-accuracy
model has always been the main challenge in the field of SNN. Currently, there
are two mainstream methods, i.e., obtaining a converted SNN through converting
a well-trained Artificial Neural Network (ANN) to its SNN counterpart or
training an SNN directly. However, the inference time of a converted SNN is too
long, while SNN training is generally very costly and inefficient. In this
work, a new SNN training paradigm is proposed by combining the concepts of the
two different training methods with the help of the pretrain technique and
BP-based deep SNN training mechanism. We believe that the proposed paradigm is
a more efficient pipeline for training SNNs. The pipeline includes pipeS for
static data transfer tasks and pipeD for dynamic data transfer tasks. SOTA
results are obtained in a large-scale event-driven dataset ES-ImageNet. For
training acceleration, we achieve the same (or higher) best accuracy as similar
LIF-SNNs using 1/10 training time on ImageNet-1K and 2/5 training time on
ES-ImageNet and also provide a time-accuracy benchmark for a new dataset
ES-UCF101. These experimental results reveal the similarity of the functions of
parameters between ANNs and SNNs and also demonstrate the various potential
applications of this SNN training pipeline.


title: Chemical evolution history of MaNGA galaxies

abstract:
  We show the results of a study using the spectral synthesis technique study
for the full MaNGA sample showing their Chemical Enrichment History (ChEH) as
well as the evolution of the stellar mass-metallicity relation (MZR) over
cosmic time. We find that the more massive galaxies became enriched first and
the lower mass galaxies did so later, producing a change in the MZR which
becomes shallower in time. Separating the sample into morphology and
star-forming status bins some particularly interesting results appear: The mass
dependency of the MZR becomes less relevant for later morphological types, to
the extent that it inverts for Sd/Irr galaxies, suggesting that morphology is
at least as important a factor as mass in chemical evolution. The MZR for the
full sample shows a flattening at the high-mass end and another at the low-mass
range, but the former only appears for retired galaxies while the latter only
appears for star-forming galaxies. We also find that the average metallicity
gradient is currently negative for all mass bins but for low mass galaxies it
was inverted at some point in the past, before which all galaxies had a
positive gradient. We also compare how diverse the ChEHs are in the different
bins considered as well as what primarily drives the diversity: How much
galaxies become enriched or how quickly they do so.


title:
RKHS regularization of singular local stochastic volatility
  McKean-Vlasov models

abstract:
  Motivated by the challenges related to the calibration of financial models,
we consider the problem of solving numerically a singular McKean-Vlasov
equation $$ d S_t= \sigma(t,S_t) S_t \frac{\sqrt v_t}{\sqrt {E[v_t|S_t]}}dW_t,
$$ where $W$ is a Brownian motion and $v$ is an adapted diffusion process. This
equation can be considered as a singular local stochastic volatility model.
Whilst such models are quite popular among practitioners, unfortunately, its
well-posedness has not been fully understood yet and, in general, is possibly
not guaranteed at all. We develop a novel regularization approach based on the
reproducing kernel Hilbert space (RKHS) technique and show that the regularized
model is well-posed. Furthermore, we prove propagation of chaos. We demonstrate
numerically that a thus regularized model is able to perfectly replicate option
prices due to typical local volatility models. Our results are also applicable
to more general McKean--Vlasov equations.