title: Rank One Approximation as a Strategy for Wordle

abstract:
  This paper presents a mathematical method of playing the puzzle game Wordle.
In Wordle, the player has six tries to guess a secret word. After each guess
the player is told how their guess compares to the secret word. With the
available information the player makes their next guess. This paper proposes
combining a rank one approximation and latent semantic indexing to a matrix
representing the list of all possible solutions. Rank one approximation finds
the dominant eigenvector of a matrix of words, and latent semantic indexing
reveals which word is closest to the dominant eigenvector. The word whose
column vector is closest to the dominant eigenvector is chosen as the next
guess. With this method the most representative word of the set of all possible
solutions is selected. This paper describes how a word can be converted to a
vector and the theory behind a rank one approximation and latent semantic
indexing. This paper presents results demonstrating that with an initial guess
of "SLATE" the method solves the puzzle in 4.04 guesses on average, with a
success rate of 98.7%


title:
Lessons learned from replicating a study on information-retrieval based
  test case prioritization

abstract:
  Objective: In this study, we aim to replicate an artefact-based study on
software testing to address the gap. We focus on (a) providing a step by step
guide of the replication, reflecting on challenges when replicating
artefact-based testing research, (b) Evaluating the replicated study concerning
its validity and robustness of the findings.
  Method: We replicate a test case prioritization technique by Kwon et al. We
replicated the original study using four programs, two from the original study
and two new programs. The replication study was implemented using Python to
support future replications. Results: Various general factors facilitating
replications are identified, such as: (1) the importance of documentation; (2)
the need of assistance from the original authors; (3) issues in the maintenance
of open source repositories (e.g., concerning needed software dependencies);
(4) availability of scripts. We also raised several observations specific to
the study and its context, such as insights from using different mutation tools
and strategies for mutant generation. Conclusion: We conclude that the study by
Kwon et al. is replicable for small and medium programs and could be automated
to facilitate software practitioners, given the availability of required
information.


title: Recognition of Freely Selected Keypoints on Human Limbs

abstract:
  Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of
keypoints. Standard HPE models trained on such datasets can only detect these
keypoints. If more points are desired, they have to be manually annotated and
the model needs to be retrained. Our approach leverages the Vision Transformer
architecture to extend the capability of the model to detect arbitrary
keypoints on the limbs of persons. We propose two different approaches to
encode the desired keypoints. (1) Each keypoint is defined by its position
along the line between the two enclosing keypoints from the fixed set and its
relative distance between this line and the edge of the limb. (2) Keypoints are
defined as coordinates on a norm pose. Both approaches are based on the
TokenPose architecture, while the keypoint tokens that correspond to the fixed
keypoints are replaced with our novel module. Experiments show that our
approaches achieve similar results to TokenPose on the fixed keypoints and are
capable of detecting arbitrary keypoints on the limbs.


title: Nonlocal generalization of the SM as an explanation of recent CDF result

abstract:
  In this note I show that nonlocal generalization of the standard model (SM)
with nonlocal scale $\Lambda_{nl} \approx 3$ TeV can explain recent CDF result
on the W-mass measurement


title: HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition

abstract:
  Pre-training with self-supervised models, such as Hidden-unit BERT (HuBERT)
and wav2vec 2.0, has brought significant improvements in automatic speech
recognition (ASR). However, these models usually require an expensive
computational cost to achieve outstanding performance, slowing down the
inference speed. To improve the model efficiency, we propose an early exit
scheme for ASR, namely HuBERT-EE, that allows the model to stop the inference
dynamically. In HuBERT-EE, multiple early exit branches are added at the
intermediate layers, and each branch is used to decide whether a prediction can
be exited early. Experimental results on the LibriSpeech dataset show that
HuBERT-EE can accelerate the inference of a large-scale HuBERT model while
simultaneously balancing the trade-off between the word error rate (WER)
performance and the latency.


title:
On the (Non-)Stationary Density of Fractional-Driven Stochastic
  Differential Equations

abstract:
  We investigate the stationary measure $\pi$ of SDEs driven by additive
fractional noise with any Hurst parameter and establish that $\pi$ admits a
smooth Lebesgue density obeying both Gaussian-type lower and upper bounds. The
proofs are based on a novel representation of the stationary density in terms
of a Wiener-Liouville bridge, which proves to be of independent interest: We
show that it also allows to obtain Gaussian bounds on the non-stationary
density, which extend previously known results in the additive setting. In
addition, we study a parameter-dependent version of the SDE and prove
smoothness of the stationary density, jointly in the parameter and the spatial
coordinate. With this we revisit the fractional averaging principle of Li and
Sieber [Ann. Appl. Probab. (in press)] and remove an ad-hoc assumption on the
limiting coefficients. Avoiding any use of Malliavin calculus in our arguments,
we can prove our results under minimal regularity requirements.


title: Effects of stellar-mass primordial black holes on first star formation

abstract:
  We use cosmological hydrodynamic zoom-in simulations and semi-analytical
models to study the effects of primordial black holes (PBHs) on first star
formation. Our models self-consistently combine two competing effects: initial
(isocurvature) perturbations induced by PBHs and BH accretion feedback.
Focusing on PBHs with masses $\sim 30\ \rm M_{\odot}$, we find that the
standard picture of first star formation in molecular-cooling minihaloes is not
changed by PBHs, as the simulated star-forming gas clouds in the central parsec
are very similar to those in the $\rm \Lambda CDM$ case when PBHs make up
$f_{\rm PBH}\sim 10^{-4}-0.1$ of dark matter. With a dynamical friction
timescale of $\sim 2-10\ \rm Myr$ when the central gas density reaches $10^{5}\
\rm cm^{-3}$, it is also unlikely that PBHs can sink into star-forming discs
and affect the evolution of protostars, although they may interact with the
stars during the main-sequence stage. At larger scales, PBHs tend to shift star
formation to more massive haloes, and accelerate structure formation. The
latter effect is stronger in regions with higher initial overdensities. For
$f_{\rm PBH}\sim 10^{-4}-0.01$ (allowed by observational constraints), the
collapsed mass fraction of haloes hosting Population III stars is similar
(within a factor of $\sim2$ at $z\lesssim 30$) to that in $\rm \Lambda CDM$,
implying that the impact of stellar-mass PBHs on the cosmic star formation
history at $z\gtrsim 10$ is small. We also find that the Lyman-Werner photons
from PBH accretion in atomic-cooling haloes may facilitate the formation of
direct-collapse BHs.


title: Transparent Shape from Single Polarization Images

abstract:
  This paper presents a data-driven approach for transparent shape from
polarization. Due to the inherent high transmittance, the previous shape from
polarization(SfP) methods based on specular reflection model have difficulty in
estimating transparent shape, and the lack of datasets for transparent SfP also
limits the application of the data-driven approach. Hence, we construct the
transparent SfP dataset which consists of both synthetic and real-world
datasets. To determine the reliability of the physics-based reflection model,
we define the physics-based prior confidence by exploiting the inherent fault
of polarization information, then we propose a multi-branch fusion network to
embed the confidence. Experimental results show that our approach outperforms
other SfP methods. Compared with the previous method, the mean and median
angular error of our approach are reduced from $19.00^\circ$ and $14.91^\circ$
to $16.72^\circ$ and $13.36^\circ$, and the accuracy $11.25^\circ, 22.5^\circ,
30^\circ$ are improved from $38.36\%, 77.36\%, 87.48\%$ to $45.51\%, 78.86\%,
89.98\%$, respectively.


title: Axionic surface wave in dynamical axion insulators

abstract:
  The electromagnetic response of three-dimensional topological insulators can
be described by an effective axion action with a quantized axion field. Once
both time-reversal and inversion symmetries are broken, for example, in
antiferromagnetic topological insulators, the axion field becomes dynamical
along with magnetic fluctuations. The dynamical axion field, when coupled to
electromagnetic fields, can lead to rich physical phenomena. Here, based on the
modified Maxwell's equations, we reveal the existence of an exotic type of
polariton excitation, termed surface axion polariton, which propagates in the
interface between a dynamical axion insulator and a dielectric. When doping the
dynamical axion insulator to be metallic, the coexistence of axion-photon
coupling and plasmon-photon coupling will further generate a mixed surface
axion plasmon polariton. We also propose a Kretschmann-Raether configuration to
experimentally detect the surface axion polariton. Our result provides an
alternative way to study the axion electrodynamics in condensed matter physics.


title:
Temperature-dependent broadening of coherent current peaks in InAs
  double quantum dots

abstract:
  Quantum systems as used for quantum computation or quantum sensing are
nowadays often realized in solid state devices as e.g. complex Josephson
circuits or coupled quantum-dot systems. Condensed matter as an environment
influences heavily the quantum coherence of such systems. Here, we investigate
electron transport through asymmetrically coupled InAs double quantum dots and
observe an extremely strong temperature dependence of the coherent current
peaks of single-electron tunneling. We analyze experimentally and theoretically
the broadening of such coherent current peaks up to temperatures of 20 K and we
are able to model it with quantum dissipation being due to two different
bosonic baths. These bosonic baths mainly originate from substrate phonons.
Application of a magnetic field helps us to identify the different quantum dot
states through their temperature dependence.


title:
Modeling crack propagation in heterogeneous materials: Griffith's law,
  intrinsic crack resistance and avalanches

abstract:
  Various kinds of heterogeneity in solids including atomistic discreteness
affect the fracture strength as well as the failure dynamics remarkably. Here
we study the effects of an initial crack in a discrete model for fracture in
heterogeneous materials, known as the fiber bundle model. We find three
distinct regimes for fracture dynamics depending on the initial crack size. If
the initial crack is smaller than a certain value, it does not affect the
rupture dynamics and the critical stress. While for a larger initial crack, the
growth of the crack leads to a breakdown of the entire system, and the critical
stress depends on the crack size in a power-law manner with a nontrivial
exponent. The exponent, as well as the limiting crack size, depend on the
strength of heterogeneity and the range of stress relaxation in the system.


title: Swarm Modelling with Dynamic Mode Decomposition

abstract:
  Modelling biological or engineering swarms is challenging due to the
inherently high dimension of the system, despite the often low-dimensional
emergent dynamics. Most existing swarm modelling approaches are based on first
principles and often result in swarm-specific parameterizations that do not
generalize to a broad range of applications. In this work, we apply a purely
data-driven method to (1) learn local interactions of homogeneous swarms
through observation data and to (2) generate similar swarming behaviour using
the learned model. In particular, a modified version of dynamic mode
decomposition with control, called swarmDMD, is developed and tested on the
canonical Vicsek swarm model. The goal is to use swarmDMD to learn inter-agent
interactions that give rise to the observed swarm behaviour. We show that
swarmDMD can faithfully reconstruct the swarm dynamics, and the model learned
by swarmDMD provides a short prediction window for data extrapolation with a
trade-off between prediction accuracy and prediction horizon. We also provide a
comprehensive analysis on the efficacy of different observation data types on
the modelling, where we find that inter-agent distance yields the most accurate
models. We believe the proposed swarmDMD approach will be useful for studying
multi-agent systems found in biology, physics, and engineering.

categories: cs.NE cs.RO math.DS nlin.AO physics.bio-ph


title: FiN: A Smart Grid and Power Line Communication Dataset

abstract:
  The increasing complexity of low-voltage networks poses a growing challenge
for the reliable and fail-safe operation of electricity grids. The reasons for
this include an increasingly decentralized energy generation (photovoltaic
systems, wind power, etc.) and the emergence of new types of consumers
(e-mobility, domestic electricity storage, etc.). At the same time, the
low-voltage grid is largely unmonitored and local power failures are sometimes
hard to detect. To overcome this, power line communication (PLC) has emerged as
a potential solution for reliable monitoring of the low-voltage grid. In
addition to establishing a communication infrastructure, PLC also offers the
possibility of evaluating the cables themselves, as well as the connection
quality between individual cable distributors based on their Signal-to-Noise
Ratio (SNR). The roll-out of a large-scale PLC infrastructure therefore not
only ensures communication, but also introduces a tool for monitoring the
entire network. To evaluate the potential of this data, we installed 38 PLC
modems in three different areas of a German city with a population of about
150,000 as part of the F\"uhler-im-Netz project. Over a period of 22 months, an
SNR spectrum of each connection between adjacent PLC modems was generated every
quarter of an hour. % and the voltage was measured every minute. The
availability of this real-world PLC data opens up new possibilities to react to
the increasingly complex challenges in future smart grids. This paper provides
a detailed analysis of the data generation and describes how the data was
collected during normal operation of the electricity grid. In addition, we
present common anomalies, effects, and trends that could be observed in the PLC
data at daily, weekly, or seasonal levels. Finally, we discuss potential use
cases and the remote inspection of a cable section is highlighted as an
example.


title:
A Novel Approach to Train Diverse Types of Language Models for Health
  Mention Classification of Tweets

abstract:
  Health mention classification deals with the disease detection in a given
text containing disease words. However, non-health and figurative use of
disease words adds challenges to the task. Recently, adversarial training
acting as a means of regularization has gained popularity in many NLP tasks. In
this paper, we propose a novel approach to train language models for health
mention classification of tweets that involves adversarial training. We
generate adversarial examples by adding perturbation to the representations of
transformer models for tweet examples at various levels using Gaussian noise.
Further, we employ contrastive loss as an additional objective function. We
evaluate the proposed method on the PHM2017 dataset extended version. Results
show that our proposed approach improves the performance of classifier
significantly over the baseline methods. Moreover, our analysis shows that
adding noise at earlier layers improves models' performance whereas adding
noise at intermediate layers deteriorates models' performance. Finally, adding
noise towards the final layers performs better than the middle layers noise
addition.


title:
Infrared study of the star-forming region associated with the UC HII
  regions G45.07+0.13 and G45.12+0.13

abstract:
  UCHII regions are an important phase in the formation and early evolution of
massive stars and a key component of the interstellar medium. The main
objectives of this work are to study the young stellar objects (YSOs)
associated with the G45.07+0.13 (G45.07) and G45.12+0.13 (G45.12) UCHII
regions, as well as the interstellar medium in which they are embedded. We
determined the distribution of the hydrogen column density (N(H2)) and dust
temperature (Td) in the molecular cloud using Herschel images. We used infrared
photometric data to identify and classify the YSOs. We also constructed a
colour-magnitude diagram and K luminosity functions (KLFs) to compare the
parameters of YSOs with the results of the radiative transfer models. We found
that N(H2) varies from about 3.0 to 5.5x10^(23)cm^(-2) within the G45.07 and
G45.12 regions, respectively. The maximum Td value is 35K in G45.12 and 42K in
G45.07. Td reaches about 18-20K at distances of 2.6 and 3.7pc from
IRAS19110+1045 (G45.07) and IRAS19111+1048 (G45.12), respectively. The gas-dust
mass value included in G45.12 is 3.4x10^5Msun and 1.7x10^5Msun in G45.07. The
UCHII regions are connected through a cold (Td=19K) bridge. The density
distribution of 518 YSOs exhibits dense clusters around both IRAS sources. The
parameters of YSOs in the IRAS clusters (124 objects) and 394 non-cluster
objects surrounding them show some differences. About 75% of the YSOs belonging
to the IRAS clusters have an evolutionary age greater than 10^6 years. Their
slope alpha of the KLF agrees well with a Salpeter-type Initial Mass Function
(IMF) (gamma=1.35) for a high mass range (O-F stars, beta=2) at 1Myr. The
non-cluster objects are uniformly distributed in the molecular cloud, 80% of
which are located to the right of the 0.1Myr isochrone. Based on the small age
spread of the stellar objects, we suggest that the clusters originate from a
single triggering shock.


title:
A scaling limit theorem for Galton-Watson processes in varying
  environments

abstract:
  We prove a scaling limit theorem for discrete Galton-Watson processes in
varying environments. A simple sufficient condition for the weak convergence in
the Skorokhod space is given in terms of probability generating functions. The
limit theorem gives rise to the continuous-state branching processes in varying
environments studied recently by several authors.


title: Distributionally Robust Models with Parametric Likelihood Ratios

abstract:
  As machine learning models are deployed ever more broadly, it becomes
increasingly important that they are not only able to perform well on their
training distribution, but also yield accurate predictions when confronted with
distribution shift. The Distributionally Robust Optimization (DRO) framework
proposes to address this issue by training models to minimize their expected
risk under a collection of distributions, to imitate test-time shifts. This is
most commonly achieved by instance-level re-weighting of the training objective
to emulate the likelihood ratio with possible test distributions, which allows
for estimating their empirical risk via importance sampling (assuming that they
are subpopulations of the training distribution). However, re-weighting schemes
in the literature are usually limited due to the difficulty of keeping the
optimization problem tractable and the complexity of enforcing normalization
constraints. In this paper, we show that three simple ideas -- mini-batch level
normalization, a KL penalty and simultaneous gradient updates -- allow us to
train models with DRO using a broader class of parametric likelihood ratios. In
a series of experiments on both image and text classification benchmarks, we
find that models trained with the resulting parametric adversaries are
consistently more robust to subpopulation shifts when compared to other DRO
approaches, and that the method performs reliably well with little
hyper-parameter tuning. Code to reproduce our experiments can be found at
https://github.com/pmichel31415/P-DRO.


title:
Improving Differential-Neural Distinguisher Model For DES, Chaskey, and
  PRESENT

abstract:
  In CRYPTO'19, Gohr proposed a new cryptanalysis strategy using machine
learning algorithms. Combining the differential-neural distinguisher with a
differential path and integrating the advanced key recovery procedure, Gohr
achieved a 12-round key recovery attack on Speck32/64. Chen and Yu improved
prediction accuracy of differential-neural distinguisher considering derived
features from multiple-ciphertext pairs instead of single-ciphertext pairs. By
modifying the kernel size of initial convolutional layer to capture more
dimensional information, the prediction accuracy of differential-neural
distinguisher can be improved for for three reduced symmetric ciphers. For DES,
we improve the prediction accuracy of (5-6)-round differential-neural
distinguisher and train a new 7-round differential-neural distinguisher. For
Chaskey, we improve the prediction accuracy of (3-4)-round differential-neural
distinguisher. For PRESENT, we improve the prediction accuracy of (6-7)-round
differential-neural distinguisher. The source codes are available in
https://drive.google.com/drive/folders/1i0RciZlGZsEpCyW-wQAy7zzJeOLJNWqL?usp=sharing.


title:
On the holographic domain-wall/cosmology correspondence and scalar
  one-point functions

abstract:
  We review the construction of holography for cosmologies with an emphasis on
asymptotically de Sitter cosmologies. We do this by composition of the AdS/CFT
correspondence and domain-wall/cosmology (DW/C) correspondence with the novel
added benefit of keeping track of the DW/C marker $\eta$. This allows us to
build explicitly the holographic dual of the DW/C correspondence. We then test
this framework on a toy model: the GPPZ flow. We find the scalar one-point
function for this flow to be invariant under the domain-wall/cosmology
correspondence. Moreover, our construction seems to agree with previous
literature on holography for cosmologies.


title:
Single-grasp deformable object discrimination: the effect of gripper
  morphology, sensing modalities, and action parameters

abstract:
  We studied the discrimination of deformable objects by grasping them using 4
different robot hands / grippers: Barrett hand (3 fingers with adjustable
configuration, 96 tactile, 8 position, 3 torque sensors), qb SoftHand (5
fingers, 1 motor, position and current feedback), and two industrial type
parallel jaw grippers with position and effort feedback (Robotiq 2F-85 and
OnRobot RG6). A set of 9 ordinary objects differing in size and stiffness and
another highly challenging set of 20 polyurethane foams differing in material
properties only was used. We systematically compare the grippers' performance,
together with the effects of: (1) type of classifier (k-NN, SVM, LSTM)
operating on raw time series or on features, (2) action parameters (grasping
configuration and speed of squeezing), (3) contribution of sensory modalities.
Classification results are complemented by visualization of the data using PCA.
We found: (i) all the grippers but the qb SoftHand could reliably distinguish
the ordinary objects set; (ii) Barrett Hand reached around 95% accuracy on the
foams; OnRobot RG6 around 75% and Robotiq 2F-85 around 70%; (iii) across all
grippers, SVM over features and LSTM on raw time series performed best; (iv)
faster compression speeds degrade classification performance; (v) transfer
learning between compression speeds worked well for the Barrett Hand only;
transfer between grasping configurations is limited; (vi) ablation experiments
provided intriguing insights -- sometimes a single sensory channel suffices for
discrimination. Overall, the Barrett Hand as a complex and expensive device
with rich sensory feedback provided best results, but uncalibrated parallel jaw
grippers without tactile sensors can have sufficient performance for
single-grasp object discrimination based on position and effort data only.
Transfer learning between the different robot hands remains a challenge.


title:
CoDGraD: A Code-based Distributed Gradient Descent Scheme for
  Decentralized Convex Optimization

abstract:
  In this paper, we consider a large network containing many regions such that
each region is equipped with a worker with some data processing and
communication capability. For such a network, some workers may become
stragglers due to the failure or heavy delay on computing or communicating. To
resolve the above straggling problem, a coded scheme that introduces certain
redundancy for every worker was recently proposed, and a gradient coding
paradigm was developed to solve convex optimization problems when the network
has a centralized fusion center. In this paper, we propose an iterative
distributed algorithm, referred as Code-Based Distributed Gradient Descent
algorithm (CoDGraD), to solve convex optimization problems over distributed
networks. In each iteration of the proposed algorithm, an active worker shares
the coded local gradient and approximated solution of the convex optimization
problem with non-straggling workers at the adjacent regions only. In this
paper, we also provide the consensus and convergence analysis for the CoDGraD
algorithm and we demonstrate its performance via numerical simulations.


title:
Interior H\"older regularity for stable solutions to semilinear elliptic
  equations up to dimension 5

abstract:
  Let $2\le n\le 5$. We establish an apriori interior H\"older regularity of
$C^2$-stable solutions to the semilinear equation $-\Delta u=f(u)$ in any
domain of $R^n$ for any nonlinearity $f\in C^{0,1}(R) $.If $f $ is
nondecreasing and convex in addition,we obtain an interior H\"older regularity,
and hence the local boundedness, of $W^{1,2}(\Omega)$-stable solutions by
locally approximating them via $C^2(\Omega)$-stable solutions. In particular,
we do not require any lower bound on $f$.


title:
Atomic bonding and electrical characteristics of metallic and
  semi-metallic elements from the binding energy and bond charge model

abstract:
  In this paper, we use density functional theory to calculate the electronic
structure and properties of 47 metallic and semi-metallic elements. The binding
energy and bond charge model (BBC) model is combined with the tight binding and
density functional tight binding approaches to obtain quantitative information
about atomic bonding at the atomic scale and to understand the contributions
and effects of deformation energy density, energy shifts, and atomic bonding on
the Hamiltonian.


title:
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified
  Contextual Topics and Entity Types

abstract:
  Multimodal Entity Linking (MEL) which aims at linking mentions with
multimodal contexts to the referent entities from a knowledge base (e.g.,
Wikipedia), is an essential task for many multimodal applications. Although
much attention has been paid to MEL, the shortcomings of existing MEL datasets
including limited contextual topics and entity types, simplified mention
ambiguity, and restricted availability, have caused great obstacles to the
research and application of MEL. In this paper, we present WikiDiverse, a
high-quality human-annotated MEL dataset with diversified contextual topics and
entity types from Wikinews, which uses Wikipedia as the corresponding knowledge
base. A well-tailored annotation procedure is adopted to ensure the quality of
the dataset. Based on WikiDiverse, a sequence of well-designed MEL models with
intra-modality and inter-modality attentions are implemented, which utilize the
visual information of images more adequately than existing MEL models do.
Extensive experimental analyses are conducted to investigate the contributions
of different modalities in terms of MEL, facilitating the future research on
this task. The dataset and baseline models are available at
https://github.com/wangxw5/wikiDiverse.


title:
Meaningful machine learning models and machine-learned pharmacophores
  from fragment screening campaigns

abstract:
  Machine learning (ML) is widely used in drug discovery to train models that
predict protein-ligand binding. These models are of great value to medicinal
chemists, in particular if they provide case-specific insight into the physical
interactions that drive the binding process. In this study we derive ML models
from over 50 fragment-screening campaigns to introduce two important elements
that we believe are absent in most -- if not all -- ML studies of this type
reported to date: First, alongside the observed hits we use to train our
models, we incorporate true misses and show that these experimentally validated
negative data are of significant importance to the quality of the derived
models. Second, we provide a physically interpretable and verifiable
representation of what the ML model considers important for successful binding.
This representation is derived from a straightforward attribution procedure
that explains the prediction in terms of the (inter-)action of chemical
environments. Critically, we validate the attribution outcome on a large scale
against prior annotations made independently by expert molecular modellers. We
find good agreement between the key molecular substructures proposed by the ML
model and those assigned manually, even when the model's performance in
discriminating hits from misses is far from perfect. By projecting the
attribution onto predefined interaction prototypes (pharmacophores), we show
that ML allows us to formulate simple rules for what drives fragment binding
against a target automatically from screening data.


title:
Secure Formation Control via Edge Computing Enabled by Fully Homomorphic
  Encryption and Mixed Uniform-Logarithmic Quantization

abstract:
  Recent developments in communication technologies, such as 5G, together with
innovative computing paradigms, such as edge computing, provide further
possibilities for the implementation of real-time networked control systems.
However, privacy and cyber-security concerns arise when sharing private data
between sensors, agents and a third-party computing facility. In this paper, a
secure version of the distributed formation control is presented, analyzed and
simulated, where gradient-based formation control law is implemented in the
edge, with sensor and actuator information being secured by fully homomorphic
encryption method based on learning with error (FHE-LWE) combined with a
proposed mixed uniform-logarithmic quantizer (MULQ). The novel quantizer is
shown to be suitable for realizing secure control systems with FHE-LWE where
the critical real-time information can be quantized into a prescribed bounded
space of plaintext while satisfying a sector bound condition whose lower and
upper-bound can be made sufficiently close to an identity. An absolute
stability analysis is presented, that shows the asymptotic stability of the
closed-loop secure control system.


title: LDPC codes: comparing cluster graphs to factor graphs

abstract:
  We present a comparison study between a cluster and factor graph
representation of LDPC codes. In probabilistic graphical models, cluster graphs
retain useful dependence between random variables during inference, which are
advantageous in terms of computational cost, convergence speed, and accuracy of
marginal probabilities. This study investigates these benefits in the context
of LDPC codes and shows that a cluster graph representation outperforms the
traditional factor graph representation.


title:
IRS-assisted Multi-cell Multi-band Systems: Practical Reflection Model
  and Joint Beamforming Design

abstract:
  Intelligent reflecting surface (IRS) has been regarded as a promising and
revolutionary technology for future wireless communication systems owing to its
capability of tailoring signal propagation environment in an
energy/spectrum/hardware-efficient manner. However, most existing studies on
IRS optimizations are based on a simple and ideal reflection model that is
impractical in hardware implementation, which thus leads to severe performance
loss in realistic wideband/multi-band systems. To deal with this problem, in
this paper we first propose a more practical and more tractable IRS reflection
model that describes the difference of reflection responses for signals at
different frequencies. Then, we investigate the joint transmit beamforming and
IRS reflection beamforming design for an IRS-assisted multi-cell multi-band
system. Both power minimization and sum-rate maximization problems are solved
by exploiting popular second-order cone programming (SOCP), Riemannian
manifold, minimization-majorization (MM), weighted minimum mean square error
(WMMSE), and block coordinate descent (BCD) methods. Simulation results
illustrate the significant performance improvement of our proposed joint
transmit beamforming and reflection design algorithms based on the practical
reflection model in terms of power saving and rate enhancement.


title:
Techtile: a Flexible Testbed for Distributed Acoustic Indoor Positioning
  and Sensing

abstract:
  The proposed infrastructure, named Techtile, provides a unique R&D facility
as features dispersed electronics enables transmission and capturing of a
multitude of signals in 3D. Specific available equipment that enhances the
design process from smooth prototyping to a commercial product is discussed.
The acoustic parameters of the room, particularly the reverberation and ambient
noise, are measured to take these into account for future innovative acoustic
indoor positioning and sensing systems. This can have a positive influence on
the accuracy and precision. The wooden construction represents an acoustically
challenging room for audible sound with a maximum measured RT60 value of 1.17s
at 5kHz, while for ultrasound it is rather challenging due to the present
ambient noise sources. In general, the Techtile room can be compared with a
home or quiet office environment, in terms of sound pressure levels (SPLs). In
addition to the acoustic properties, possible research and development options
are discussed in combination with the associated challenges. Many of the
designs described are available through open source.


title: AHP: Learning to Negative Sample for Hyperedge Prediction

abstract:
  Hypergraphs (i.e., sets of hyperedges) naturally represent group relations
(e.g., researchers co-authoring a paper and ingredients used together in a
recipe), each of which corresponds to a hyperedge (i.e., a subset of nodes).
Predicting future or missing hyperedges bears significant implications for many
applications (e.g., collaboration and recipe recommendation). What makes
hyperedge prediction particularly challenging is the vast number of
non-hyperedge subsets, which grows exponentially with the number of nodes.
Since it is prohibitive to use all of them as negative examples for model
training, it is inevitable to sample a very small portion of them, and to this
end, heuristic sampling schemes have been employed. However, trained models
suffer from poor generalization capability for examples of different natures.
In this paper, we propose AHP, an adversarial training-based
hyperedge-prediction method. It learns to sample negative examples without
relying on any heuristic schemes. Using six real hypergraphs, we show that AHP
generalizes better to negative examples of various natures. It yields up to
28.2% higher AUROC than the best existing methods and often even outperforms
its variants with sampling schemes tailored to test sets.


title: Complexity in the Lipkin-Meshkov-Glick Model

abstract:
  We study complexity in a spin system with infinite range interaction, via the
paradigmatic Lipkin-Meshkov-Glick model, in the thermodynamic limit. Exact
expressions for the Nielsen complexity (NC) and the Fubini-Study complexity
(FSC) are derived, that helps us to establish several distinguishing features
compared to complexity in other known spin models. In a time-independent LMG
model, close to phase transition, the NC diverges logarithmically, much like
the entanglement entropy. Remarkably however, in a time dependent scenario,
this divergence is replaced by a finite discontinuity, as we show by using the
Lewis-Riesenfeld theory of time-dependent invariant operators. The FSC of a
variant of the LMG model shows novel behaviour compared to quasi free spin
models. Namely, it diverges logarithmically when the target (or reference)
state is near the separatrix. Numerical analysis indicates that this is due to
the fact that geodesics starting with arbitrary boundary conditions are
"attracted" towards the separatrix and that near this line, a finite change in
the affine parameter of the geodesic produces an infinitesimal change of the
geodesic length. The same divergence is shared by the NC of this model as well.


title: Local Feature Swapping for Generalization in Reinforcement Learning

abstract:
  Over the past few years, the acceleration of computing resources and research
in deep learning has led to significant practical successes in a range of
tasks, including in particular in computer vision. Building on these advances,
reinforcement learning has also seen a leap forward with the emergence of
agents capable of making decisions directly from visual observations. Despite
these successes, the over-parametrization of neural architectures leads to
memorization of the data used during training and thus to a lack of
generalization. Reinforcement learning agents based on visual inputs also
suffer from this phenomenon by erroneously correlating rewards with unrelated
visual features such as background elements. To alleviate this problem, we
introduce a new regularization technique consisting of channel-consistent local
permutations (CLOP) of the feature maps. The proposed permutations induce
robustness to spatial correlations and help prevent overfitting behaviors in
RL. We demonstrate, on the OpenAI Procgen Benchmark, that RL agents trained
with the CLOP method exhibit robustness to visual changes and better
generalization properties than agents trained using other state-of-the-art
regularization techniques. We also demonstrate the effectiveness of CLOP as a
general regularization technique in supervised learning.


title:
A methane line list with sub-MHz accuracy in the 1250 to 1380 cm-1 range
  from optical frequency comb Fourier transform spectroscopy

abstract:
  We use a Fourier transform spectrometer based on a difference frequency
generation optical frequency comb to measure high-resolution, low-pressure,
room-temperature spectra of methane in the 1250 - 1380 cm$^{-1}$ range. From
these spectra, we retrieve line positions and intensities of 678 lines of two
isotopologues: 157 lines from the $^{12}$CH${_4}$ ${\nu}$${_4}$ fundamental
band, 131 lines from the $^{13}$CH${_4}$ ${\nu}$${_4}$ fundamental band, as
well as 390 lines from two $^{12}$CH${_4}$ hot bands, ${\nu}$${_2}$ +
${\nu}$${_4}$ - ${\nu}$${_2}$ and 2${\nu}$${_4}$ - ${\nu}$${_4}$. For another
165 lines from the $^{12}$CH${_4}$ ${\nu}$${_4}$ fundamental band we retrieve
line positions only. The uncertainties of the line positions range from 0.19 to
2.3 MHz, and their median value is reduced by a factor of 18 and 59 compared to
the previously available data for the $^{12}$CH${_4}$ fundamental and hot
bands, respectively, obtained from conventional FTIR absorption measurements.
The new line positions are included in the global models of the spectrum of
both methane isotopologues, and the fit residuals are reduced by a factor of 8
compared to previous absorption data, and 20 compared to emission data. The
experimental line intensities have relative uncertainties in the range of 1.5 -
7.7%, similar to those in the previously available data; 235 new
$^{12}$CH${_4}$ line intensities are included in the global model.


title:
Linear Programs with Polynomial Coefficients and Applications to 1D
  Cellular Automata

abstract:
  Given a matrix $A$ and vector $b$ with polynomial entries in $d$ real
variables $\delta=(\delta_1,\ldots,\delta_d)$ we consider the following notion
of feasibility: the pair $(A,b)$ is locally feasible if there exists an open
neighborhood $U$ of $0$ such that for every $\delta\in U$ there exists $x$
satisfying $A(\delta)x\ge b(\delta)$ entry-wise. For $d=1$ we construct a
polynomial time algorithm for deciding local feasibility. For $d \ge 2$ we show
local feasibility is NP-hard.
  As an application (which was the primary motivation for this work) we give a
computer-assisted proof of ergodicity of the following elementary 1D cellular
automaton: given the current state $\eta_t \in \{0,1\}^{\mathbb{Z}}$ the next
state $\eta_{t+1}(n)$ at each vertex $n\in \mathbb{Z}$ is obtained by
$\eta_{t+1}(n)= \text{NAND}\big(\text{BSC}_\delta(\eta_t(n-1)),
\text{BSC}_\delta(\eta_t(n))\big)$. Here the binary symmetric channel
$\text{BSC}_\delta$ takes a bit as input and flips it with probability $\delta$
(and leaves it unchanged with probability $1-\delta$).
  We also consider the problem of broadcasting information on the 2D-grid of
noisy binary-symmetric channels $\text{BSC}_\delta$, where each node may apply
an arbitrary processing function to its input bits. We prove that there exists
$\delta_0'>0$ such that for all noise levels $0<\delta<\delta_0'$ it is
impossible to broadcast information for any processing function, as conjectured
in Makur, Mossel, Polyanskiy (ISIT 2021).


title:
Nonclassicality gain/loss through photon-addition/subtraction on
  multi-mode Gaussian states

abstract:
  Photon addition and subtraction are well-known to render Gaussian states non
Gaussian. We evaluate how their optical nonclassicality is affected by the
photon addition/subtraction process. It is known that photon addition always
transforms a Gaussian state into a nonclassical state. We show that photon
subtraction transforms classical states into classical ones and nonclassical
ones into nonclassical ones. For a quantitative analysis of the resulting
nonclassicality, we use a recently introduced nonclassicality witness, the
quadrature coherence scale, and compute the relative nonclassicality gain of
the single-photon added/subtracted Gaussian states with respect to the original
Gaussian states. For arbitrary single-mode Gaussian states, we show that this
relative gain can be substantial in both cases. Whereas the gain is always
higher for photon-added states, it is only slightly so for a large parameter
range and in particular in the limit of high squeezing, where both gains are
identical. We also analyze the Wigner negative volume of the
photon-added/subtracted states. Whereas photon-added Gaussian states are never
Wigner positive, we show that single-mode photon-subtracted states are Wigner
positive if and only if they are weakly nonclassical or classical. Our analysis
relies on explicit and general expressions for the characteristic and Wigner
functions of photon added/subtracted multi-mode Gaussian states that we obtain
simply.


title:
Proceedings of the 10th International Conference on Non-Classical
  Logics. Theory and Applications

abstract:
  Non-Classical Logics. Theory and Applications (NCL) is an international
conference aimed at presenting novel results and survey works in widely
understood non-classical logics and their applications. This year's edition was
also an opportunity to celebrate the 50th anniversary of the logic journal
Bulletin of the Section of Logic (BSL), established in 1971 and since then
published by the University of Lodz. On this occassion a number of renowned
researchers having bonds with BSL were asked to deliver invited talks.
Additionally, during the conference a special session was held commemorating J.
Michael Dunn, outstanding logician and collecting editor of BSL, who passed
away in 2021. Finally, collocated with the conference were three workshops
devoted to, subsequently, non-Fregean logics, relating logics, and formal
ontology, as well as a tutorial on graded logic.


title: Compact extra dimensions as the source of primordial black holes

abstract:
  We discuss the model of the primordial black holes formation at the reheating
stage. These small massive black holes appear due to specific properties of the
compact extra dimensions. The latter gives rise the low energy model containing
the effective scalar field potential capable for the domain walls production.
Formed during inflation, these walls are quite dense so they collapse soon
after inflation ends. The discussion is performed within the scope of
multidimensional $f(R)$-gravity.


title:
Interface to high-performance periodic coupled-cluster theory
  calculations with atom-centered, localized basis functions

abstract:
  Coupled cluster (CC) theory is often considered the gold standard of
quantum-chemistry. For solids, however, the available software is scarce. We
present CC-aims, which can interface ab initio codes with localized atomic
orbitals and the CC for solids (CC4S) code by the group of A. Gr\"uneis. CC4S
features a continuously growing selection of wave function-based methods
including perturbation and CC theory. The CC-aims interface was developed for
the FHI-aims code (https://fhi-aims.org) but is implemented such that other
codes may use it as a starting point for corresponding interfaces. As CC4S
offers treatment of both molecular and periodic systems, the CC-aims interface
is a valuable tool, where DFT is either too inaccurate or too unreliable, in
theoretical chemistry and materials science alike.


title:
A Review of Machine Learning Methods Applied to Structural Dynamics and
  Vibroacoustic

abstract:
  The use of Machine Learning (ML) has rapidly spread across several fields,
having encountered many applications in Structural Dynamics and Vibroacoustic
(SD\&V). The increasing capabilities of ML to unveil insights from data, driven
by unprecedented data availability, algorithms advances and computational
power, enhance decision making, uncertainty handling, patterns recognition and
real-time assessments. Three main applications in SD\&V have taken advantage of
these benefits. In Structural Health Monitoring, ML detection and prognosis
lead to safe operation and optimized maintenance schedules. System
identification and control design are leveraged by ML techniques in Active
Noise Control and Active Vibration Control. Finally, the so-called ML-based
surrogate models provide fast alternatives to costly simulations, enabling
robust and optimized product design. Despite the many works in the area, they
have not been reviewed and analyzed. Therefore, to keep track and understand
this ongoing integration of fields, this paper presents a survey of ML
applications in SD\&V analyses, shedding light on the current state of
implementation and emerging opportunities. The main methodologies, advantages,
limitations, and recommendations based on scientific knowledge were identified
for each of the three applications. Moreover, the paper considers the role of
Digital Twins and Physics Guided ML to overcome current challenges and power
future research progress. As a result, the survey provides a broad overview of
the present landscape of ML applied in SD\&V and guides the reader to an
advanced understanding of progress and prospects in the field.


title:
Cohomologie de de Rham du rev\^etement mod\'er\'e de l'espace de
  Drinfeld

abstract:
  In this article, we study the De Rham cohomology of the first cover in the
Drinfel'd tower. In particular, we get a purely local proof that the
supercuspidal part realizes the local Jacquet-Langlands correspondence for
${\rm GL}_n$ by comparing it to the rigid cohomology of some Deligne-Lusztig
varieties. The representations obtained are analogous to the ones appearing in
the $\ell$-adic cohomology if we forget the action of the Weil group. The proof
relies on the generalization of an excision result of Grosse-Kl\"onne and on
the explicit description of the first cover as a cyclic cover obtained by the
author on a previous work.


title:
Mitigating Bias in Facial Analysis Systems by Incorporating Label
  Diversity

abstract:
  Facial analysis models are increasingly applied in real-world applications
that have significant impact on peoples' lives. However, as previously shown,
models that automatically classify facial attributes might exhibit algorithmic
discrimination behavior with respect to protected groups, potentially posing
negative impacts on individuals and society. It is therefore critical to
develop techniques that can mitigate unintended biases in facial classifiers.
Hence, in this work, we introduce a novel learning method that combines both
subjective human-based labels and objective annotations based on mathematical
definitions of facial traits. Specifically, we generate new objective
annotations from a large-scale human-annotated dataset, each capturing a
different perspective of the analyzed facial trait. We then propose an ensemble
learning method, which combines individual models trained on different types of
annotations. We provide an in-depth analysis of the annotation procedure as
well as the dataset distribution. Moreover, we empirically demonstrate that, by
incorporating label diversity, and without additional synthetic images, our
method successfully mitigates unintended biases, while maintaining significant
accuracy on the downstream task.


title:
LyMAS reloaded: improving the predictions of the large-scale
  Lyman-{\alpha} forest statistics from dark matter density and velocity fields

abstract:
  We present LyMAS2, an improved version of the "Lyman-{\alpha} Mass
Association Scheme" aiming at predicting the large-scale 3d clustering
statistics of the Lyman-{\alpha} forest (Ly-{\alpha}) from moderate resolution
simulations of the dark matter (DM) distribution, with prior calibrations from
high resolution hydrodynamical simulations of smaller volumes. In this study,
calibrations are derived from the Horizon-AGN suite simulations, (100 Mpc/h)^3
comoving volume, using Wiener filtering, combining information from dark matter
density and velocity fields (i.e. velocity dispersion, vorticity, line of sight
1d-divergence and 3d-divergence). All new predictions have been done at z=2.5
in redshift-space, while considering the spectral resolution of the SDSS-III
BOSS Survey and different dark matter smoothing (0.3, 0.5 and 1.0 Mpc/h
comoving). We have tried different combinations of dark matter fields and found
that LyMAS2, applied to the Horizon-noAGN dark matter fields, significantly
improves the predictions of the Ly-{\alpha} 3d clustering statistics,
especially when the DM overdensity is associated with the velocity dispersion
or the vorticity fields. Compared to the hydrodynamical simulation trends, the
2-point correlation functions of pseudo-spectra generated with LyMAS2 can be
recovered with relative differences of ~5% even for high angles, the flux 1d
power spectrum (along the light of sight) with ~2% and the flux 1d probability
distribution function exactly. Finally, we have produced several large mock
BOSS spectra (1.0 and 1.5 Gpc/h) expected to lead to much more reliable and
accurate theoretical predictions.


title: Emergence of Time from Unitary Equivalence

abstract:
  We implement the unitary equivalence of modular Hamiltonian
$\hat{H}_{\mathrm{mod}}$ and Hamiltonian $\hat{H}$,
$\hat{H}\sim\hat{U}^{\dagger}\hat{H}_{\mathrm{mod}}\hat{U}$, to realize the
emergence of time from an unitary operator $\hat{U}$. The unitary equivalence
relation implies a duality between the modular flow and time evolution.
Requiring the unitary equivalence also implies "Modular Chaos Bound = Chaos
Bound". We demonstrate the duality of quantum chaos quantities in the
thermofield double state defined in a fermionic system. We then show the
explicit duality between a specific form of correlators, the spectral form
factor, and the Loschmidt echo with $\hat{H}_{\mathrm{mod}}$. In the end, we
also consider a two-qubit system to demonstrate that a different entanglement
spectrum does not provide the same constraint to $\hat{H}$.


title:
Mixed-Integer Programming for Signal Temporal Logic with Fewer Binary
  Variables

abstract:
  Signal Temporal Logic (STL) provides a convenient way of encoding complex
control objectives for robotic and cyber-physical systems. The state-of-the-art
in trajectory synthesis for STL is based on Mixed-Integer Convex Programming
(MICP). The MICP approach is sound and complete, but has limited scalability
due to exponential complexity in the number of binary variables. In this
letter, we propose a more efficient MICP encoding for STL. Our new encoding is
based on the insight that disjunction can be encoded using a logarithmic number
of binary variables and conjunction can be encoded without binary variables. We
demonstrate in simulation examples that our proposed approach significantly
outperforms the state-of-the-art for long and complex specifications.
Open-source software is available at https://stlpy.readthedocs.io.


title: Hasse principles for quadratic forms over function fields

abstract:
  We investigate the Hasse principles for isotropy and isometry of quadratic
forms over finitely generated field extensions with respect to various sets of
discrete valuations. Over purely transcendental field extensions of fields that
satisfy property $\mathscr{A}_i(2)$ for some $i$, we find numerous
counterexamples to the Hasse principle for isotropy with respect to a
relatively small set of discrete valuations. For finitely generated field
extensions $K$ of transcendence degree $r$ over an algebraically closed field
of characteristic $\ne 2$, we use the $2^r$-dimensional counterexample to the
Hasse principle for isotropy due to Auel and Suresh to obtain counterexamples
of lower dimensions with respect to the divisorial discrete valuations induced
by a variety with function field $K$.


title:
Full-stack quantum computing systems in the NISQ era: algorithm-driven
  and hardware-aware compilation techniques

abstract:
  The progress in developing quantum hardware with functional quantum
processors integrating tens of noisy qubits, together with the availability of
near-term quantum algorithms has led to the release of the first quantum
computers. These quantum computing systems already integrate different software
and hardware components of the so-called "full-stack", bridging quantum
applications to quantum devices. In this paper, we will provide an overview on
current full-stack quantum computing systems. We will emphasize the need for
tight co-design among adjacent layers as well as vertical cross-layer design to
extract the most from noisy intermediate-scale quantum (NISQ) processors which
are both error-prone and severely constrained in resources. As an example of
co-design, we will focus on the development of hardware-aware and
algorithm-driven compilation techniques.


title: A model grid for the reflected light from transition disks

abstract:
  The dust in protoplanetary disks is an important ingredient in planet
formation and can be investigated with model simulations and quantitative
imaging polarimetry of the scattered stellar light. This study explores
circumstellar disks with calculations for the intensity and polarization of the
reflected light. The photon scattering and absorption by the disk are
calculated with a Monte Carlo method for a grid of simple, rotationally
symmetric models approximated at each point by a plane-parallel dusty
atmosphere. The results of our simple disk models reproduce well the
measurements for the intensity $I/I_{\star}$, azimuthal polarization
$Q_{\varphi}/I_{\star}$, and fractional polarization $p_{\varphi}$ reported for
well-observed transition disks. They describe the dependencies of the scattered
radiation on the disk geometry and the dust scattering parameters in detail.
Particularly strong constraints on disk properties can be obtained from certain
diagnostic quantities. Therefore, they can be used as a diagnostic tool for the
analysis of quantitative measurements, specifically in constraining or even
determining the scattering properties of the dust particles in disks.
Collecting and comparing such information for many systems is required to
understand the nature of the scattering dust in planet-forming disks.


title:
Deep learning based automatic detection of offshore oil slicks using SAR
  data and contextual information

abstract:
  Ocean surface monitoring, especially oil slick detection, has become
mandatory due to its importance for oil exploration and risk prevention on
ecosystems. For years, the detection task has been performed manually by
photo-interpreters using Synthetic Aperture Radar (SAR) images with the help of
contextual data such as wind. This tedious manual work cannot handle the
increasing amount of data collected by the available sensors and thus requires
automation. Literature reports conventional and semi-automated detection
methods that generally focus either on oil slicks originating from
anthropogenic (spills) or natural (seeps) sources on limited data collections.
As an extension, this paper presents the automation of offshore oil slicks on
an extensive database with both kinds of slicks. It builds upon the slick
annotations of specialized photo-interpreters on Sentinel-1 SAR data for 4
years over 3 exploration and monitoring areas worldwide. All the considered SAR
images and related annotation relate to real oil slick monitoring scenarios.
Further, wind estimation is systematically computed to enrich the data
collection. Paper contributions are the following : (i) a performance
comparison of two deep learning approaches: semantic segmentation using
FC-DenseNet and instance segmentation using Mask-RCNN. (ii) the introduction of
meteorological information (wind speed) is deemed valuable for oil slick
detection in the performance evaluation. The main results of this study show
the effectiveness of slick detection by deep learning approaches, in particular
FC-DenseNet, which captures more than 92% of oil instances in our test set.
Furthermore, a strong correlation between model performances and contextual
information such as slick size and wind speed is demonstrated in the
performance evaluation. This work opens perspectives to design models that can
fuse SAR and wind information to reduce the false alarm rate.


title:
Low Voltage Customer Phase Identification Methods Based on Smart Meter
  Data

abstract:
  The increased deployment of distributed energy generation and the integration
of new, large electric loads such as electric vehicles and heat pumps challenge
the correct and reliable operation of low voltage distribution systems. To
tackle potential problems, active management solutions are proposed in the
literature, which require distribution system models that include the phase
connectivity of all the consumers in the network. However, information on the
phase connectivity is in practice often unavailable. In this work, several
voltage and power measurement-based phase identification methods from the
literature are implemented. A consistent comparison of the methods is made
across different smart meter accuracy classes and smart meter penetration
levels using publicly available data. Furthermore, a novel method is proposed
that makes use of ensemble learning and that can combine data from different
measurement campaigns. The results indicate that generally better results are
obtained with voltage data compared to power data from smart meters of the same
accuracy class. If power data is available too, the novel ensemble method can
improve the accuracy of the phase identification obtained from voltage data
alone.


title: The Alperin-McKay conjecture for the prime 2

abstract:
  In this paper we consider the inductive Alperin-McKay condition for
quasi-isolated 2-blocks of exceptional groups of Lie type. Thereby, we complete
the proof of the Alperin-McKay conjecture for the prime 2.


title: Boson Stars

abstract:
  We review particle-like configurations of complex scalar field, localized by
gravity, so-called boson stars. In the simplest case, these solutions posses
spherical symmetry, they may arise in the massive Einstein-Klein-Gordon theory
with global $U(1)$ symmetry, as gravitationally bounded lumps of scalar
condensate. Further, there are spinning axially symmetric boson stars which
possess non-zero angular momentum, and a variety of non-trivial multipolar
stationary configurations without any continuous symmetries. In this short
overview we discuss important dynamic properties of the boson stars,
concentrating on recent results on the construction of multicomponent
constellations of boson stars.


title: Online greedy identification of linear dynamical systems

abstract:
  This work addresses the problem of exploration in an unknown environment. For
linear dynamical systems, we use an experimental design framework and introduce
an online greedy policy where the control maximizes the information of the next
step. In a setting with a limited number of experimental trials, our algorithm
has low complexity and shows experimentally competitive performances compared
to more elaborate gradient-based methods.


title:
A quarter century of spectroscopic monitoring of the nearby M dwarf Gl
  514. A super-Earth on an eccentric orbit moving in and out of the habitable
  zone

abstract:
  We investigated the presence of planetary companions around the nearby (7.6
pc) and bright ($V=9$ mag) early-type M dwarf Gl 514, analysing 540 radial
velocities collected over nearly 25 years with the HIRES, HARPS, and CARMENES
spectrographs. The data are affected by time-correlated signals at the level of
2-3 ms$^{-1}$ due to stellar activity, that we filtered out testing three
different models based on Gaussian process regression. As a sanity cross-check,
we repeated the analyses using HARPS radial velocities extracted with three
different algorithms. We used HIRES radial velocities and Hipparcos-Gaia
astrometry to put constraints on the presence of long-period companions, and we
analysed TESS photometric data. We found strong evidence that Gl 514 hosts a
super-Earth on a likely eccentric orbit, residing in the conservative habitable
zone for nearly $34\%$ of its orbital period. The planet Gl 514 b has minimum
mass $m_b\sin i_b=5.2\pm0.9$ $M_{\rm Earth}$, orbital period
$P_b=140.43\pm0.41$ days, and eccentricity $e_b=0.45^{+0.15}_{-0.14}$. No
evidence for transits is found in the TESS light curve. There is no evidence
for a longer period companion in the radial velocities and, based on
astrometry, we can rule out a $\sim0.2$ $M_{\rm Jup}$ planet at a distance of
$\sim3-10$ au, and massive giant planets/brown dwarfs out to several tens of
au. We discuss the possible presence of a second low-mass companion at a
shorter distance from the host than Gl 514 b. Gl 514 b represents an
interesting science case to study the habitability of planets on eccentric
orbits. We advocate for additional spectroscopic follow-up to get more accurate
and precise planetary parameters. Further follow-up is also needed to
investigate sub \ms and shorter period signals.


title:
EMMI: Empathic Human-Machine Interaction for Establishing Trust in
  Automated Driving

abstract:
  Highly automated vehicles represent one of the most crucial development
efforts in the automotive industry. In addition to the use of research
vehicles, production vehicles for the general public are realistic in the near
future. However, to fully exploit the benefits of these systems, it is
fundamental that users have an appropriate level of trust in automation. Recent
studies indicate that more research is needed in this area. Furthermore, beyond
the management of user trust, the system should also convey a perceptible added
value to realize not only trust, but also acceptance and thus use. The EMMI
project pushes both, the management of user trust while conveying added value
to the user. Therefore, an advanced socio-emotional user model for estimating
user trust and various user-centered HMI systems with unique UX are being
developed. Together, the systems are employed to induce changes in the users'
trust in automated vehicles.


title:
One-point correlation functions of the two-periodic weighted Aztec
  diamond in mesoscopic limit

abstract:
  One-point correlation functions were studied for the Aztec diamond with
alternating weights in the limit when the size of the diamond tends to infinity
in such a way that the correlation radius in the ordered region is of the same
order as the linear size of the region.


title: The Eisenstein cycles and Manin Drinfeld properties

abstract:
  Consider a subgroup of finite index of modular group. We give an analytic
criterion for a cuspidal divisor to be torsion in the Jacobian of the
corresponding modular curve. By BelyI theorem, such a criterion would apply to
any curve over a number field. Our main tool is the explicit description, in
terms of modular symbols, of what we call Eisenstein cycles. The latter are
representations of relative homology classes over which integration of any
holomorphic differential forms vanishes. Our approach relies in an essential
way on the specific case , where we can consider convenient generalized
Jacobians instead of Jacobian. The Eisenstein classes are the real part of
certain homology classes with complex coefficients. The imaginary part of those
classes are related to the scattering constants attached to Eisenstein series.
Finally, we illustrate our theory by considering Fermat curves.


title:
Communication Efficient Curvature Aided Primal-dual Algorithms for
  Distributed Optimization

abstract:
  This paper presents a family of primal-dual algorithms for distributed convex
composite problems. We consider the setting of a network of agents that
cooperatively minimize a global objective function composed of a sum of local
functions plus a regularizer. Through use of intermediate consensus variables,
we remove the need for inner-communication loops between agents when computing
curvature guided updates. A general scheme is presented which unifies the
analysis for a plethora of computing choices, including gradient descent,
Newton updates, and BFGS updates. Our analysis establishes sublinear
convergence to the optimal solution under convex objective functions with
Lipschitz continuous gradient, as well as linear convergence rate when the
local functions are assumed to be strongly convex. Moreover, we explicitly
characterize the acceleration due to curvature information. Last but not least,
we present an asynchronous implementation for the proposed algorithms, which
removes the need for a central clock, with a linear convergence rate
established in expectation under strongly convex objectives. We ascertain the
effectiveness of the proposed methods with numerical experiments on benchmark
datasets.


title: A Decade of Time Crystals: Quo Vadis?

abstract:
  Ten years ago, the new era of time crystals began. Time crystals are systems
that behave in the time dimension like ordinary space crystals do in space
dimensions. We present a brief history of a decade of research on time
crystals, describe current research directions, indicate challenges, and
discuss some future perspectives for condensed matter physics in the time
domain.

categories: cond-mat.quant-gas cond-mat.dis-nn cond-mat.mes-hall quant-ph


title: Empathy-Centric Design At Scale

abstract:
  EmpathiCH aims at bringing together and blend different expertise to develop
new research agenda in the context of "Empathy-Centric Design at Scale". The
main research question is to investigate how new technologies can contribute to
the elicitation of empathy across and within multiple stakeholders at scale;
and how empathy can be used to design solutions to societal problems that are
not only effective but also balanced, inclusive, and aware of their effect on
society. Through presentations, participatory sessions, and a living experiment
-- where data about the peoples' interactions is collected throughout the event
-- we aim to make this workshop the ideal venue to foster collaboration, build
networks, and shape the future direction of "Empathy-Centric Design at Scale".


title:
Proof of the equivalence of the symplectic forms derived from the
  canonical and the covariant phase space formalisms

abstract:
  We prove that, for any theory defined over a space-time with boundary, the
symplectic form derived in the covariant phase space is equivalent to the one
derived from the canonical formalism.


title: Galaxy rotation favors prolate dark matter haloes

abstract:
  The flattening rotation velocity $v(r)\to {\rm constant}$ found by Vera Rubin
and collaborators and very apparent in the SPARC galaxy-rotation data coincides
with Kepler's law in one less dimension. Thus, it is naturally reproduced by
elongated dark matter distributions with the axis of prolateness perpendicular
to the galactic plane. This theoretical understanding is borne out by the
detailed fits to the rotation data that we here report: for equal dark matter
profile, elongated distributions provide smaller $\chi^2$ than purely spherical
ones. We also propose to use the geometric mean of the individual halo
ellipticities, as opposed to their arithmetic average, because $s=c/a\in
(0,\infty)$ corresponds to spherical haloes for $s=1$, so that the usually
reported average is skewed towards oblateness and fails to reveal the large
majority of prolate haloes. Several independently coded fitting exercises
concur in yielding $s<1$ for most of the database entries and the oblate
exceptions are understood and classified. This likely prolateness is of
consequence for the estimated dark matter density near Earth.


title:
Next-to-leading order QCD calculation of $B_c$ to charmonium tensor form
  factors

abstract:
  We present a next-to-leading order (NLO) QCD corrections to $B_c\to \eta_c$
and $B_c\to J/\psi$ tensor form factors within nonrelativistic QCD (NRQCD)
framework. The full analytical results for $B_c$ to S-wave charmonium tensor
form factors are obtained. We also studied the asymptotic behaviours of tensor
form factors in heavy bottom quark limit. A compact expression for tensor form
factors are given analytically in the heavy bottom quark limit. The relation
among different form factors is also analyzed especially at large momentum
recoil point. The numerical results for the $B_c$ to charmonium tensor form
factors in all the physical region are given in the end.


title:
Efficient Deep Neural Network Accelerator Using Controlled Ferroelectric
  Domain Dynamics

abstract:
  The current work reports an efficient deep neural network (DNN) accelerator
where synaptic weight elements are controlled by ferroelectric domain dynamics.
An integrated device-to-algorithm framework for benchmarking novel synaptic
devices is used. In P(VDF-TrFE) based ferroelectric tunnel junctions, analog
conductance states are measured using a custom pulsing protocol and associated
custom circuits and array architectures for DNN training is simulated. Our
results show precise control of polarization switching dynamics in
multi-domain, polycrystalline ferroelectric thin films can produce considerable
weight update linearity in metal-ferroelectric-semiconductor (MFS) tunnel
junctions. Ultrafast switching and low junction current in these devices offer
extremely energy efficient operation. Through an integrated platform of
hardware development, characterization and modelling, we predict the available
conductance range where linearity is expected under identical potentiating and
depressing pulses for efficient DNN training and inference tasks. As an
example, an analog crossbar based DNN accelerator with MFS junctions as
synaptic weight elements showed ~ 93% training accuracy on large MNIST
handwritten digit dataset while for cropped images, a 95% accuracy is achieved.
One observed challenge is rather limited dynamic conductance range while
operating under identical potentiating and depressing pulses below 1V.
Investigation is underway for improving the dynamic conductance range without
losing the weight update linearity.


title: Tracing the role of AGB stars in the Galactic Fluorine budget

abstract:
  The cosmic origin of fluorine is still under debate. Asymptotic giant branch
(AGB) stars are among the few suggested candidates to efficiently synthesis F
in our Galaxy, however their relative contribution is not clear. In this paper,
we briefly review the theoretical studies from stellar yield models of the F
synthesis and chemical equilibrium models of the F-containing molecules in the
outflow around AGB stars. Previous detections of the F-bearing species towards
AGB and post-AGB stars are also highlighted. We suggest that high-resolution
ALMA observations of the AlF, one of the two main carriers of F in the outflow
of AGB stars, can provide a reliable tracer of the F-budget in AGB stars. This
will be helpful to quantify the role of AGB stars in the Galactic F budget.


title:
First experimental results of the spatial resolution of RSD pad arrays
  read out with a 16-ch board

abstract:
  Resistive Silicon Detectors (RSD, also known as AC-LGAD) are innovative
silicon sensors, based on the LGAD technology, characterized by a continuous
gain layer that spreads across the whole sensor active area. RSDs are very
promising tracking detectors, thanks to the combination of the built-in signal
sharing with the internal charge multiplication, which allows large signals to
be seen over multiple read-out channels. This work presents the first
experimental results obtained from a 3$\times$4 array with 200~\mum~pitch,
coming from the RSD2 production manufactured by FBK, read out with a 16-ch
digitizer. A machine learning model has been trained, with experimental data
taken with a precise TCT laser setup, and then used to predict the laser shot
positions, finding a spatial resolution of $\sim$~5.5~\mum.


title:
CRUSH: Contextually Regularized and User anchored Self-supervised Hate
  speech Detection

abstract:
  The last decade has witnessed a surge in the interaction of people through
social networking platforms. While there are several positive aspects of these
social platforms, the proliferation has led them to become the breeding ground
for cyber-bullying and hate speech. Recent advances in NLP have often been used
to mitigate the spread of such hateful content. Since the task of hate speech
detection is usually applicable in the context of social networks, we introduce
CRUSH, a framework for hate speech detection using user-anchored
self-supervision and contextual regularization. Our proposed approach secures ~
1-12% improvement in test set metrics over best performing previous approaches
on two types of tasks and multiple popular english social media datasets.


title:
Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A
  Machine Learning Approach

abstract:
  Coverage and capacity are the important metrics for performance evaluation in
wireless networks, while the coverage and capacity have several conflicting
relationships, e.g. high transmit power contributes to large coverage but high
inter-cell interference reduces the capacity performance. Therefore, in order
to strike a balance between the coverage and capacity, a novel model is
proposed for the coverage and capacity optimization of simultaneously
transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs)
assisted networks. To solve the coverage and capacity optimization (CCO)
problem, a machine learning-based multi-objective optimization algorithm, i.e.,
the multi-objective proximal policy optimization (MO-PPO) algorithm, is
proposed. In this algorithm, a loss function-based update strategy is the core
point, which is able to calculate weights for both loss functions of coverage
and capacity by a min-norm solver at each update. The numerical results
demonstrate that the investigated update strategy outperforms the fixed
weight-based MO algorithms.


title: Chaotic instability in the BFSS matrix model

abstract:
  Chaotic scattering is a manifestation of transient chaos realized by the
scattering with non-integrable potential. When the initial position is taken in
the potential, a particle initially exhibits chaotic motion, but escapes
outside after a certain period of time. The time to stay inside the potential
can be seen as lifetime and this escape process may be regarded as a kind of
instability. The process of this type exists in the
Banks-Fischler-Shenker-Susskind (BFSS) matrix model in which the potential has
flat directions. We discuss this chaotic instability by reducing the system
with an ansatz to a simple dynamical system and present the associated fractal
structure. We also show the singular behavior of the time delay function and
compute the fractal dimension. This chaotic instability is the basic mechanism
by which membranes are unstable, which is also common to supermembranes at
quantum level.


title: Flavour anomalies and dark matter assisted unification in $SO(10)$ GUT

abstract:
  With the recent experimental hint of new physics from flavor physics
anomalies, neutrino mass and dark matter, we consider a minimal extension of SM
with a scalar leptoquark and a fermion triplet. The scalar leptoquark with
couplings to leptons and quarks can explain lepton flavor non-universality
observables $R_K$, $R_{K^{(*)}}$, $R_{D^{(*)}}$ and $R_{J/\psi}$. Neutral
component of fermion triplet provides current abundance of dark matter in the
Universe. The interesting feature of the proposal is that the minimal addition
of these phenomenologically rich particles (scalar leptoquark and fermion
triplet) assist in realizing the unification of the gauge couplings associated
with the strong and electroweak forces of standard model when embedded in the
non-supersymmetric $SO(10)$ grand unified theory. We discuss on unification
mass scale and the corresponding proton decay constraints while taking into
account the GUT threshold corrections.


title: A dusty compact object bridging galaxies and quasars at cosmic dawn

abstract:
  Understanding how super-massive black holes form and grow in the early
Universe has become a major challenge since the discovery of luminous quasars
only 700 million years after the Big Bang. Simulations indicate an evolutionary
sequence of dust-reddened quasars emerging from heavily dust-obscured
starbursts that then transition to unobscured luminous quasars by expelling gas
and dust. Although the last phase has been identified out to a redshift of 7.6,
a transitioning quasar has not been found at similar redshifts owing to their
faintness at optical and near-infrared wavelengths. Here we report observations
of an ultraviolet compact object, GNz7q, associated with a dust-enshrouded
starburst at a redshift of z=7.1899+/-0.0005. The host galaxy is more luminous
in dust emission than any other known object at this epoch, forming 1,600 solar
masses of stars per year within a central radius of 480 parsec. A red point
source in the far-ultraviolet is identified in deep, high-resolution imaging
and slitless spectroscopy. GNz7q is extremely faint in X-rays, which indicates
the emergence of a uniquely ultraviolet compact star-forming region or a
Compton-thick super-Eddington black-hole accretion disk at the dusty starburst
core. In the latter case, the observed properties are consistent with
predictions from cosmological simulations and suggest that GNz7q is an
antecedent to unobscured luminous quasars at later epochs.


title:
Tuning the Magnetization of Manganese (II) Carbonate by Intracrystalline
  Amino Acids

abstract:
  Incorporation of organic molecules into the lattice of inorganic crystalline
hosts is a common phenomenon in biomineralization, and has been shown to alter
various properties of the crystalline host. Taking this phenomenon as our
source of inspiration, we show herein that incorporation of specific single
amino acids into the lattice of manganese (II) carbonate strongly alters its
inherent magnetic properties. At room temperature, the magnetic susceptibility
of the amino-acid-incorporating paramagnetic MnCO3 decreases, following a
simple rule of mixtures. When cooled below the N\'eel temperature, however, the
opposite trend is observed, namely an increase in magnetic susceptibility
measured in a high magnetic field. Such an increase, accompanied by a drastic
change in the N'eel phase transformation temperature, results from a decrease
in MnCO3 orbital overlapping and the weakening of superexchange interactions.


title: Analogue metric in a black bounce background

abstract:
  The conventional approach of embedding an effective acoustic metric for sound
motion in a background flat Minkowski space-time, has recently been extended to
incorporate more general curved background metrics, which might contain a black
hole. Though the observational aspects of these kinds of acoustics horizons,
including the sonic shadow structure and quasi normal modes have received
significant attention in the literature, it leaves room for discussions about
embedding in more general classes of curved background space-times without
optical horizons. Here we propose and study a new class of acoustic metrics
that is embedded in a black-bounce space-time, thereby giving a suitable
tuneable system to understand possible observational effects of the presence or
absence of acoustic horizons. After showing that the metric can represent five
types of different effective backgrounds for sound motion, including a novel
"acoustic wormhole - optical wormhole" branch, we discuss how the distinctive
features of sonic shadows can appear even in the absence of any acoustic
horizon due to the wormhole throat present in the acoustic metric.


title: Creating good quality meshes from smooth implicit surfaces

abstract:
  Visualization of implicit surfaces is an actively researched topic. While
raytracing can produce high quality images, it is not well suited for creating
a quick preview of the surface. Indirect algorithms (e.g. Marching Cubes)
create an easily renderable triangle mesh, but the result is often not
sufficiently well-structured for a good approximation of differential surface
quantities (normals, curvatures, etc.). Post-processing methods usually have a
considerable computational overhead, and high quality is not guaranteed. We
propose a tessellation algorithm to create nearly isotropic meshes, using
multi-sided implicit surfaces.


title: Trajectory-based Algorithm Selection with Warm-starting

abstract:
  Landscape-aware algorithm selection approaches have so far mostly been
relying on landscape feature extraction as a preprocessing step, independent of
the execution of optimization algorithms in the portfolio. This introduces a
significant overhead in computational cost for many practical applications, as
features are extracted and computed via sampling and evaluating the problem
instance at hand, similarly to what the optimization algorithm would perform
anyway within its search trajectory. As suggested in Jankovic et al. (EvoAPPs
2021), trajectory-based algorithm selection circumvents the problem of costly
feature extraction by computing landscape features from points that a solver
sampled and evaluated during the optimization process. Features computed in
this manner are used to train algorithm performance regression models, upon
which a per-run algorithm selector is then built.
  In this work, we apply the trajectory-based approach onto a portfolio of five
algorithms. We study the quality and accuracy of performance regression and
algorithm selection models in the scenario of predicting different algorithm
performances after a fixed budget of function evaluations. We rely on landscape
features of the problem instance computed using one portion of the
aforementioned budget of the same function evaluations. Moreover, we consider
the possibility of switching between the solvers once, which requires them to
be warm-started, i.e. when we switch, the second solver continues the
optimization process already being initialized appropriately by making use of
the information collected by the first solver. In this new context, we show
promising performance of the trajectory-based per-run algorithm selection with
warm-starting.


title:
Estimation of the Parameters of the Spiral Pattern in the Galaxy Based
  on a Sample of Classical Cepheids

abstract:
  We consider a sample of Galactic classical Cepheids with highly accurate
estimates of their distances taken from Skowron et al., where they were
determined based on the period-luminosity relation. We have refined the
geometric characteristics of two spiral arm segments-the Carina-Sagittarius and
Outer ones. For this purpose, we have selected 269 Cepheids belonging to the
Carina-Sagittarius arm with ages in the range 80-120 Myr. From them we have
estimated the pitch angle of the spiral pattern $i=-11.9\pm0.2^\circ$ and the
position of this arm $a_0=7.32\pm0.05$ kpc for the adopted $R_0=8.1\pm0.1$ kpc.
In the Outer arm we have selected 343 Cepheids with ages in the range 120-300
Myr. From them we have found $i=-11.5\pm0.5^\circ$ and $a_0=12.89\pm0.06$ kpc.
Adhering to the model of a grand-design spiral pattern in the Galaxy with one
pitch angle for all arms, we can conclude that this angle is close to
$-12^\circ$.


title:
Universality of the least singular value and singular vector
  delocalization for levy non-symmetric matrices

abstract:
  In this paper we consider $N \times N $ matrices $D_{N}$ with i.i.d. entries
all following an $a-$stable law divided by $N^{1/a}$. We prove that the least
singular value of $D_{N}$, multiplied by $N$, tends to the same law as in the
Gaussian case, for almost all $a \in (0,2)$. This is proven by considering the
symmetrization of the matrix $D_{N}$ and using a version of the three step
strategy, a well known strategy in the random matrix theory literature. In
order to apply the three step strategy, we also prove an isotropic local law
for the symmetrization of matrices after slightly perturbing them by a Gaussian
matrix with a similar structure. The isotropic local law is proven for a
general class of matrices that satisfy some regularity assumption. We also
prove the complete delocalization for the left and right singular vectors of
$D_{N}$ at small energy, i.e., for energies at a small interval around $0$.


title: Donor-acceptor discrete optical emission in 2D perovskites

abstract:
  Two-dimensional (2D) van der Waals nanomaterials have attracted considerable
attention for potential use in photonic and optoelectronic applications in the
nanoscale, due to their outstanding electrical and optical properties,
differing from their bulk state. Currently, 2D perovskite belonging to this
group of nanomaterials is widely studied for a wide range of optoelectronic
applications. Thanks to their excitonic properties, 2D perovskites are also
promising materials for photonics and nonlinear devices working at room
temperature. Nevertheless, strong excitonic effects can reduce the photocurrent
characteristics when using thinner perovskites phases. In this work, we present
solid experimental evidence for the presence of single donor-acceptor pair
optical transitions in 2D Lead Halide Perovskites, characterized by sub meV
linewidths ($\simeq 120 \mu eV$) and long decay times (5-8 ns).
Micro-photoluminescence evidence is supported by detailed Photoemission
measurements, and a model simulation. The association of Phenethylammonium with
Methylammonium cations, the latter molecule being only present in 2D Halide
Perovskites with thicker phases $n \geq 2$, has been identified as the source
of the donor-acceptor pair formation, corresponding to the displacement of lead
atoms and their replacement by methylamonium. Our seminal study of discrete
Donor-Acceptor Pair (DAP) sharp and bright optical transitions in 2D Lead
Halide Perovskites opens new routes to implement DAP as the carrier sources for
novel designs of optoelectronic devices with 2D perovskites, and will foster
the development of future outstanding properties in non-linear quantum
technologies.


title: Regularity estimates and open problems in kinetic equations

abstract:
  We survey some new results regarding a priori regularity estimates for the
Boltzmann and Landau equations conditional to the boundedness of the associated
macroscopic quantities. We also discuss some open problems in the area. In
particular, we describe some ideas related to the global well posedness problem
for the space-homogeneous Landau equation for Coulomb potentials.


title:
Sound Event Triage: Detecting Sound Events Considering Priority of
  Classes

abstract:
  We propose a new task for sound event detection (SED): sound event triage
(SET). The goal of SET is to detect a high-priority event while allowing
misdetections of low-priority events where the extent of priority is given for
each event class. In conventional methods of SED for targeting a specific sound
event class, only information on types of target sound can be treated. To
flexibly control more wealth of information on the target event, the proposed
SET exploits not only types of target sound but also the extent to which each
target sound is detected with priority. To implement SET, we apply a method
that allows the system input of the priority of sound events to be detected,
which is based on the class-level loss-conditional training. Results of the
experiment using the URBAN--SED dataset reveal that our SET scheme achieves
reasonable detection performance in terms of frame-based and intersection-based
F-scores. In particular, the proposed method of SET outperforms the
conventional SED method by around 10 percentage points for some events.


title:
De-IReps: Searching for improved Re-parameterizing Architecture based on
  Differentiable Evolution Strategy

abstract:
  In recent years, neural architecture search (NAS) has shown great
competitiveness in many fields and re-parameterization techniques have started
to appear in the field of architectural search. However, most edge devices do
not adapt well to networks, especially the multi-branch structure, which is
searched by NAS. Therefore, in this work we design a search space that covers
almost all re-parameterization operations. In this search space, multiple-path
networks can be unconditionally re-parameterized into single-path networks.
Thus, enhancing the usefulness of traditional nas. Meanwhile we summarize the
characteristics of the re-parameterization search space and propose a
differentiable evolutionary strategy (DES) to explore the re-parameterization
search space. We visualize the features of the searched architecture and give
our explanation for the appearance of this architecture. In this work, we can
achieve efficient search and find better network structures. Respectively, we
completed the architecture search on CIFAR-10 with the test accuracy of 96.64%
(IrepResNet-18) and 95.65% (IrepVGG-16) and on ImageNet with the test accuracy
of 77.92% (Irep-ResNet-50).


title: Receding Neuron Importances for Structured Pruning

abstract:
  Structured pruning efficiently compresses networks by identifying and
removing unimportant neurons. While this can be elegantly achieved by applying
sparsity-inducing regularisation on BatchNorm parameters, an L1 penalty would
shrink all scaling factors rather than just those of superfluous neurons. To
tackle this issue, we introduce a simple BatchNorm variation with bounded
scaling parameters, based on which we design a novel regularisation term that
suppresses only neurons with low importance. Under our method, the weights of
unnecessary neurons effectively recede, producing a polarised bimodal
distribution of importances. We show that neural networks trained this way can
be pruned to a larger extent and with less deterioration. We one-shot prune VGG
and ResNet architectures at different ratios on CIFAR and ImagenNet datasets.
In the case of VGG-style networks, our method significantly outperforms
existing approaches particularly under a severe pruning regime.


title:
Directional dynamics of $\mathbb{Z}_+\times \mathbb{Z}$-actions
  generated by 1D-CA and the shift map

abstract:
  In this short paper, we compute the directional sequence entropy for of
$\mathbb{Z}_+\times \mathbb{Z}$-actions generated by cellular automata and the
shift map. Meanwhile, we study the directional dynamics of this system. As a
corollary, we prove that there exists a sequence such that for any direction,
some of the systems above have positive directional sequence entropy. Moreover,
with help of mean ergodic theory for directional weak mixing systems, we obtain
a result of number theory about combinatorial numbers.


title: An isoperimetric inequality for surfaces formed from spherical polygons

abstract:
  We give a new proof of an isoperimetric inequality for a family of closed
surfaces, which have Gaussian curvature identically equal to one wherever the
surface is smooth. These surfaces are formed from a convex, spherical polygon,
with each vertex of the polygon leading to a non-smooth point on the surface.
For example, the surface formed from a spherical lune is a surface of
revolution, with two non-smooth tips. Combined with a straightforward
approximation argument, this inequality was first proved by B\'erard, Besson,
and Gallot, where they provide a generalization of the L\'evy-Gromov
isoperimetric inequality. The inequality implies an isoperimetric inequality
for geodesically convex subsets of the sphere, and, using a Faber-Krahn
theorem, it also implies a lower bound on the first Dirichlet eigenvalue of a
region of a given area on the closed surfaces. Via approximation, we convert
this into a lower bound on the first Dirichlet-Neumann eigenvalue of domains
contained in geodesically convex subsets of the sphere.


title: Flexible Multiple-Objective Reinforcement Learning for Chip Placement

abstract:
  Recently, successful applications of reinforcement learning to chip placement
have emerged. Pretrained models are necessary to improve efficiency and
effectiveness. Currently, the weights of objective metrics (e.g., wirelength,
congestion, and timing) are fixed during pretraining. However, fixed-weighed
models cannot generate the diversity of placements required for engineers to
accommodate changing requirements as they arise. This paper proposes flexible
multiple-objective reinforcement learning (MORL) to support objective functions
with inference-time variable weights using just a single pretrained model. Our
macro placement results show that MORL can generate the Pareto frontier of
multiple objectives effectively.


title:
Evidence for neutron star triaxial free precession in Her X-1 from
  Fermi/GBM pulse period measurements

abstract:
  Her X-1/HZ Her is one of the best studied accreting X-ray pulsars. In
addition to the pulsating and orbital periods, the X-ray and optical light
curves of the source exhibit an almost periodic 35-day variability caused by a
precessing accretion disk. The nature of the observed long-term stability of
the 35-day cycle has been debatable. The X-ray pulse frequency of Her X-1
measured by the Fermi/GBM demonstrates periodical variations with X-ray flux at
the Main-on state of the source. We explain the observed periodic
sub-microsecond pulse frequency changes by the free precession of a triaxial
neutron star with parameters previously inferred from an independent analysis
of the X-ray pulse evolution over the 35-day cycle. In the Fermi/GBM data, we
identified several time intervals with a duration of half a year or longer
where the neutron star precession period describing the pulse frequency
variations does not change. We found that the NS precession period varies
within one per cent in different intervals. Such variations in the free
precession period on a year time scale can be explained by <1% changes in the
fractional difference between the triaxial neutron star's moments of inertia
due to the accreted mass readjustment or variable internal coupling of the
neutron star crust with the core.


title: Floquet engineering of excitons in semiconductor quantum dots

abstract:
  Within the Floquet theory of periodically driven quantum systems, we
demonstrate that a high-frequency electromagnetic field can be used as an
effective tool to control excitonic properties of semiconductor quantum dots
(QDs). It is shown, particularly, that the field both decreases the exciton
binding energy and dynamically stabilizes the exciton, increasing its radiative
lifetime. The developed theory can serve as a basis for the ultrafast method to
tune spectral characteristics of the QD-based photon emitters by a
high-frequency field.


title:
Precise control of optical phase and coherent synthesis in femtosecond
  laser based optical frequency combs

abstract:
  Optical frequency combs are laser sources which are capable of generating
discrete, equal-spaced and highly coherent comb modes. Optical frequency comb
technique provides a significant bridge to transfer the stability between
optical frequency and radio frequency. The advances of this technology greatly
promote the development of precision spectroscopy, optical time/frequency
transfer, optical frequency division, long-distance transfer of time/frequency
references and high-precision distance measurement. Benefiting from the wide
spectral outputs, femtosecond lasers have become the best choice for the
fulfillment of optical frequency combs. Within the precise control of the
repetition frequency and carrier-envelope offset frequency of the pulse train
from femtosecond lasers, a stable optical frequency comb both in the time
domain and frequency domain can be obtained. This dissertation presents the
precise control of repetition rate, carrier-envelope offset frequency and
coherent pulse synthesis in optical frequency combs.