title: Automorphism groups of random substitution subshifts

abstract:
  We prove that for a suitably nice class of random substitutions, their
corresponding subshifts have automorphism groups that contain an infinite
simple subgroup and a copy of the automorphism group of a full shift. Hence,
they are countable, non-amenable and non-residually finite. To show this, we
introduce the concept of shuffles and generalised shuffles for random
substitutions, as well as a local version of recognisability for random
substitutions that will be of independent interest. Without recognisability, we
need a more refined notion of recognisable words in order to understand their
automorphisms. We show that the existence of a single recognisable word is
often enough to embed the automorphism group of a full shift in the
automorphism group of the random substitution subshift.


title:
Dynamic structure factor of the antiferromagnetic Kitaev model in large
  magnetic fields

abstract:
  We investigate the dynamic structure factor of the antiferromagnetic Kitaev
honeycomb model in a magnetic field by applying perturbative continuous unitary
transformations about the high-field limit. One- and two-quasi-particle
properties of the dressed elementary spin flip excitations of the high-field
polarized phase are calculated which account for most of the spectral weight in
the dynamic structure factor. We discuss the evolution of spectral features in
these quasi-particle sectors in terms of one-quasi-particle dispersions,
two-quasi-particle continua, the formation of anti-bound states, and
quasi-particle decay. In particular, a comparably strong spectral feature above
the upper edge of the upmost two-quasi-particle continuum represents three
anti-bound states which form due to nearest-neighbor density-density
interactions.


title:
Probing quantum entanglement from magnetic-sublevels populations: beyond
  spin squeezing inequalities

abstract:
  Spin squeezing inequalities (SSI) represent a major tool to probe quantum
entanglement among a collection of few-level atoms, and are based on collective
spin measurements and their fluctuations. Yet, for atomic ensembles of spin-$j$
atoms and ultracold spinor gases, many experiments can image the populations in
all Zeeman sublevels $s=-j, -j+1, \dots, j$, potentially revealing finer
features of quantum entanglement not captured by SSI. Here we present a
systematic approach which exploits Zeeman-sublevel population measurements in
order to construct novel entanglement criteria, and illustrate our approach on
ground states of spin-1 and spin-2 Bose-Einstein condensates. Beyond these
specific examples, our approach allows one to infer, in a systematic manner,
the optimal permutationally-invariant entanglement witness for any given set of
collective measurements in an ensemble of $d$-level quantum systems.


title: Subordinacy Theory on Star-Like Graphs

abstract:
  We study Jacobi matrices on star-like graphs, which are graphs that are given
by the pasting of a finite number of half-lines to a compact graph.
Specifically, we extend subordinacy theory to this type of graphs, that is, we
find a connection between asymptotic properties of solutions to the eigenvalue
equations and continuity properties of the spectral measure with respect to the
Lebesgue measure. We also use this theory in order to derive results regarding
the multiplicity of the singular spectrum.


title:
Existence criterion of such a cycle that the set of vertices beyond the
  cycle is independent

abstract:
  We prove that every 2-connected graph with $\delta(G) \geqslant \frac{v(G) +
2}{3}$ has such a cycle that the set of vertices beyond the cycle is
independent.


title:
Modeling Target-Side Morphology in Neural Machine Translation: A
  Comparison of Strategies

abstract:
  Morphologically rich languages pose difficulties to machine translation.
Machine translation engines that rely on statistical learning from parallel
training data, such as state-of-the-art neural systems, face challenges
especially with rich morphology on the output language side. Key challenges of
rich target-side morphology in data-driven machine translation include: (1) A
large amount of differently inflected word surface forms entails a larger
vocabulary and thus data sparsity. (2) Some inflected forms of infrequent terms
typically do not appear in the training corpus, which makes closed-vocabulary
systems unable to generate these unobserved variants. (3) Linguistic agreement
requires the system to correctly match the grammatical categories between
inflected word forms in the output sentence, both in terms of target-side
morpho-syntactic wellformedness and semantic adequacy with respect to the
input.
  In this paper, we re-investigate two target-side linguistic processing
techniques: a lemma-tag strategy and a linguistically informed word
segmentation strategy. Our experiments are conducted on a English-German
translation task under three training corpus conditions of different
magnitudes. We find that a stronger Transformer baseline leaves less room for
improvement than a shallow-RNN encoder-decoder model when translating
in-domain. However, we find that linguistic modeling of target-side morphology
does benefit the Transformer model when the same system is applied to
out-of-domain input text. We also successfully apply our approach to English to
Czech translation.


title:
Feature extraction using Spectral Clustering for Gene Function
  Prediction using Hierarchical Multi-label Classification

abstract:
  Gene annotation addresses the problem of predicting unknown associations
between gene and functions (e.g., biological processes) of a specific organism.
Despite recent advances, the cost and time demanded by annotation procedures
that rely largely on in vivo biological experiments remain prohibitively high.
This paper presents a novel in silico approach for to the annotation problem
that combines cluster analysis and hierarchical multi-label classification
(HMC). The approach uses spectral clustering to extract new features from the
gene co-expression network (GCN) and enrich the prediction task. HMC is used to
build multiple estimators that consider the hierarchical structure of gene
functions. The proposed approach is applied to a case study on Zea mays, one of
the most dominant and productive crops in the world. The results illustrate how
in silico approaches are key to reduce the time and costs of gene annotation.
More specifically, they highlight the importance of: (i) building new features
that represent the structure of gene relationships in GCNs to annotate genes;
and (ii) taking into account the structure of biological processes to obtain
consistent predictions.


title: Quantized Guessing Random Additive Noise Decoding

abstract:
  We introduce a soft-detection variant of Guessing Random Additive Noise
Decoding (GRAND) called Quantized GRAND (QGRAND) that can efficiently decode
any moderate redundancy block-code of any length in an algorithm that is
suitable for highly parallelized implementation in hardware. QGRAND can avail
of any level of quantized soft information, is established to be almost
capacity achieving, and is shown to provide near maximum likelihood decoding
performance when provided with five or more bits of soft information per
received bit.


title: Preprocessing Reward Functions for Interpretability

abstract:
  In many real-world applications, the reward function is too complex to be
manually specified. In such cases, reward functions must instead be learned
from human feedback. Since the learned reward may fail to represent user
preferences, it is important to be able to validate the learned reward function
prior to deployment. One promising approach is to apply interpretability tools
to the reward function to spot potential deviations from the user's intention.
Existing work has applied general-purpose interpretability tools to understand
learned reward functions. We propose exploiting the intrinsic structure of
reward functions by first preprocessing them into simpler but equivalent reward
functions, which are then visualized. We introduce a general framework for such
reward preprocessing and propose concrete preprocessing algorithms. Our
empirical evaluation shows that preprocessed rewards are often significantly
easier to understand than the original reward.


title:
Quasilinear systems of first order PDEs with nonlocal Hamiltonian
  structures

abstract:
  In this paper we wonder whether a quasilinear system of PDEs of first order
admits Hamiltonian formulation with local and nonlocal operators. By using the
theory of differential coverings, we find differential-geometric conditions
necessary to write a given system with one of the three Hamiltonian operators
investigated.


title:
Efficient measurement of the time-dependent cavity field through
  compressed sensing

abstract:
  We propose a method based on compressed sensing (CS) to measure the evolution
processes of the states of a driven cavity quantum electrodynamics system. In
precisely reconstructing the coherent cavity field amplitudes, we have to
prepare the same states repetitively and each time perform one measurement with
short sampling intervals considering the quantum nature of measurement and the
Nyquist-Shannon sampling theorem. However, with the help of CS, the number of
measurements can be exponentially reduced without loss of the recovery
accuracy. We use largely detuned atoms and control their interactions with the
cavity field to modulate coherent state amplitudes according to the scheme
encoded in the sensing matrix. The simulation results show that the CS method
efficiently recovers the amplitudes of the coherent cavity field even in the
presence of noise.


title: Deformable Butterfly: A Highly Structured and Sparse Linear Transform

abstract:
  We introduce a new kind of linear transform named Deformable Butterfly
(DeBut) that generalizes the conventional butterfly matrices and can be adapted
to various input-output dimensions. It inherits the fine-to-coarse-grained
learnable hierarchy of traditional butterflies and when deployed to neural
networks, the prominent structures and sparsity in a DeBut layer constitutes a
new way for network compression. We apply DeBut as a drop-in replacement of
standard fully connected and convolutional layers, and demonstrate its
superiority in homogenizing a neural network and rendering it favorable
properties such as light weight and low inference complexity, without
compromising accuracy. The natural complexity-accuracy tradeoff arising from
the myriad deformations of a DeBut layer also opens up new rooms for analytical
and practical research. The codes and Appendix are publicly available at:
https://github.com/ruilin0212/DeBut.


title: Linear progress in fibres

abstract:
  A fibered hyperbolic 3-manifold induces a map from the hyperbolic plane to
hyperbolic 3-space, the respective universal covers of the fibre and the
manifold. The induced map is an embedding that is exponentially distorted in
terms of the individual metrics. In this article, we begin a study of the
distortion along typical rays in the fibre. We verify that a typical ray in the
hyperbolic plane makes linear progress in the ambient metric in hyperbolic
3-space. We formulate the proof in terms of some soft aspects of the geometry
and basic ergodic theory. This enables us to extend the result to analogous
contexts that correspond to certain extensions of closed surface groups. These
include surface group extensions that are Gromov hyperbolic, the universal
curve over a Teichm\"uller disc, and the extension induced by the Birman exact
sequence.


title:
Neural Networks with Divisive normalization for image segmentation with
  application in cityscapes dataset

abstract:
  One of the key problems in computer vision is adaptation: models are too
rigid to follow the variability of the inputs. The canonical computation that
explains adaptation in sensory neuroscience is divisive normalization, and it
has appealing effects on image manifolds. In this work we show that including
divisive normalization in current deep networks makes them more invariant to
non-informative changes in the images. In particular, we focus on U-Net
architectures for image segmentation. Experiments show that the inclusion of
divisive normalization in the U-Net architecture leads to better segmentation
results with respect to conventional U-Net. The gain increases steadily when
dealing with images acquired in bad weather conditions. In addition to the
results on the Cityscapes and Foggy Cityscapes datasets, we explain these
advantages through visualization of the responses: the equalization induced by
the divisive normalization leads to more invariant features to local changes in
contrast and illumination.


title: Nonparametric Conditional Local Independence Testing

abstract:
  Conditional local independence is an independence relation among continuous
time stochastic processes. It describes whether the evolution of one process is
directly influenced by another process given the histories of additional
processes, and it is important for the description and learning of causal
relations among processes. However, no nonparametric test of conditional local
independence has been available. We propose such a nonparametric test based on
double machine learning. The test is based on a functional target parameter
defined as the expectation of a stochastic integral. Under the hypothesis of
conditional local independence the stochastic integral is a zero-mean
martingale, and the target parameter is constantly equal to zero. We introduce
the test statistic as an estimator of the target parameter and show that by
using sample splitting or cross-fitting, its distributional limit is a Gaussian
martingale under the hypothesis. Its variance function can be estimated
consistently, and we derive specific univariate test statistics and their
asymptotic distributions. An example based on a marginalized Cox model with
time-dependent covariates is used throughout to illustrate the theory, and
simulations based on this example show how double machine learning as well as
sample splitting are needed for the test to work. Moreover, the simulation
study shows that when both of these techniques are used in combination, the
test works well without restrictive parametric assumptions.


title:
MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional
  Support Conversation

abstract:
  Applying existing methods to emotional support conversation -- which provides
valuable assistance to people who are in need -- has two major limitations: (a)
they generally employ a conversation-level emotion label, which is too
coarse-grained to capture user's instant mental state; (b) most of them focus
on expressing empathy in the response(s) rather than gradually reducing user's
distress. To address the problems, we propose a novel model \textbf{MISC},
which firstly infers the user's fine-grained emotional status, and then
responds skillfully using a mixture of strategy. Experimental results on the
benchmark dataset demonstrate the effectiveness of our method and reveal the
benefits of fine-grained emotion understanding as well as mixed-up strategy
modeling. Our code and data could be found in
\url{https://github.com/morecry/MISC}.


title: Personalize Web Searching Strategies Classification and Comparison

abstract:
  Personalization is becoming very important direction in semantic web search
for the users that needs to find appropriate information. In this paper, a
classification of web personalization is proposed and semantic web search tools
are investigated. Building user interest profile is essential for
personalizing. Nowadays, semantic web tools use ontologies for personalization
because of their advantages. It is important to mention that most of the
semantic web search tools use agent technologies for implementation.


title: Magnetic domain walls of the van der Waals material Fe$_3$GeTe$_2$

abstract:
  Among two-dimensional materials, Fe$_3$GeTe$_2$ has come to occupy a very
important place owing to its ferromagnetic nature with one of the highest Curie
temperatures among known van der Waals materials and the potential for hosting
skyrmions. In this combined experimental and theoretical work, we investigate
the magnetic bubble domains as well as the microscopic domain wall profile
using spin-polarized scanning tunneling microscopy in combination with
atomistic spin-dynamics simulations performed with parameters from density
functional theory calculations. We find a weak magneto-electric effect
influencing the domain wall width by the electric field in the tunneling
junction and determine the critical magnetic field for the collapse of the
bubble domains. Our findings shed light on the origins of complex magnetism
that Fe$_3$GeTe$_2$ exhibits.


title:
An Intelligent End-to-End Neural Architecture Search Framework for
  Electricity Forecasting Model Development

abstract:
  Recent years have witnessed an exponential growth in developing deep learning
(DL) models for the time-series electricity forecasting in power systems.
However, most of the proposed models are designed based on the designers'
inherent knowledge and experience without elaborating on the suitability of the
proposed neural architectures. Moreover, these models cannot be self-adjusted
to the dynamically changing data patterns due to an inflexible design of their
structures. Even though several latest studies have considered application of
the neural architecture search (NAS) technique for obtaining a network with an
optimized structure in the electricity forecasting sector, their training
process is quite time-consuming, computationally expensive and not intelligent,
indicating that the NAS application in electricity forecasting area is still at
an infancy phase. In this research study, we propose an intelligent automated
architecture search (IAAS) framework for the development of time-series
electricity forecasting models. The proposed framework contains two primary
components, i.e., network function-preserving transformation operation and
reinforcement learning (RL)-based network transformation control. In the first
component, we introduce a theoretical function-preserving transformation of
recurrent neural networks (RNN) to the literature for capturing the hidden
temporal patterns within the time-series data. In the second component, we
develop three RL-based transformation actors and a net pool to intelligently
and effectively search a high-quality neural architecture. After conducting
comprehensive experiments on two publicly-available electricity load datasets
and two wind power datasets, we demonstrate that the proposed IAAS framework
significantly outperforms the ten existing models or methods in terms of
forecasting accuracy and stability.


title: On the ray-wavefront duality

abstract:
  We investigate the behavior of the solutions of the ray equation in isotropic
media. We discuss local and global transversals and wavefronts and give
examples of rays without wavefronts.


title:
Self-Assessing Creative Problem Solving for Aspiring Software
  Developers: A Pilot Study

abstract:
  We developed a self-assessment tool for computing students in higher
education to measure their Creative Problem Solving skills. Our survey
encompasses 7 dimensions of creativity, based on existing validated scales and
conducted focus groups. These are: technical knowledge, communication,
constraints, critical thinking, curiosity, creative state of mind, and creative
techniques. Principal axis factor analysis groups the dimensions into three
overarching constructs: ability, mindset, and interaction. The results of a
pilot study (n = 269) provide evidence for its psychometric qualities, making
it a useful instrument for educational researchers to investigate students'
creative skills.


title: Equilibria of vortex type Hamiltonians on closed surfaces

abstract:
  We prove the existence of critical points of vortex type Hamiltonians
  \[
  H(p_1,\ldots, p_N)
  = \sum_{\mytop{i,j=1}{i\ne j}}^N \Ga_i\Ga_jG(p_i,p_j)+\psi(p_1,\dots,p_N)
  \] on a closed Riemannian surface $(\Si,g)$ which is not homeomorphic to the
sphere or the projective plane. Here $G$ denotes the Green function of the
Laplace-Beltrami operator in $\Si$, $\psi:\Si^N\to\R$ may be any function of
class $\cC^1$, and $\Ga_1,\dots,\Ga_N\in\R\setminus\{0\}$ are the vorticities.
The Kirchhoff-Routh Hamiltonian from fluid dynamics corresponds to $\psi =
-\sum_{i=1}^N \Ga_i^2h(p_i,p_i)$ where $h:\Si\times\Si\to\R$ is the regular
part of the Laplace-Beltrami operator. We obtain critical points
$p=(p_1,\dots,p_N)$ for arbitrary $N$ under conditions on the vorticities
$\Ga_k$. In particular we can deal with the case $\sign\Ga_i=(-1)^i$ of
counter-rotating vortices with identical vortex strengths. Combined with a
theorem from \cite{Figueroa:2022} our results yield blow-up solutions for mean
field equations.


title: A new reformulation of the Muskat problem with surface tension

abstract:
  Two formulas that connect the derivatives of the double layer potential and
of a related singular integral operator evaluated at some density $\vartheta$
to the $L_2$-adjoints of these operators evaluated at the density $\vartheta'$
are used to recast the Muskat problem with surface tension and general
viscosities as a system of equations with nonlinearities expressed in terms of
the $L_2$-adjoints of these operators. An advantage of this formulation is that
the nonlinearities appear now as a derivative. This aspect and abstract
quasilinear parabolic theory are then exploited to establish a local
well-posedness result in all subcritical Sobolev spaces $W^s_p(\mathbb{R})$
with $p\in(1,\infty)$ and $s\in (1+1/p,2)$.


title:
$p$-Generalized Probit Regression and Scalable Maximum Likelihood
  Estimation via Sketching and Coresets

abstract:
  We study the $p$-generalized probit regression model, which is a generalized
linear model for binary responses. It extends the standard probit model by
replacing its link function, the standard normal cdf, by a $p$-generalized
normal distribution for $p\in[1, \infty)$. The $p$-generalized normal
distributions \citep{Sub23} are of special interest in statistical modeling
because they fit much more flexibly to data. Their tail behavior can be
controlled by choice of the parameter $p$, which influences the model's
sensitivity to outliers. Special cases include the Laplace, the Gaussian, and
the uniform distributions. We further show how the maximum likelihood estimator
for $p$-generalized probit regression can be approximated efficiently up to a
factor of $(1+\varepsilon)$ on large data by combining sketching techniques
with importance subsampling to obtain a small data summary called coreset.


title:
Seshadri stratification for Schubert varieties and Standard Monomial
  Theory

abstract:
  Seshadri stratifications have been defined by the authors to generalize the
Lakshmibai-Seshadri paths (LS-paths) from Schubert varieties to projective
varieties with such a stratification. We investigate the Seshadri
stratification on a Schubert variety arising from its Schubert subvarieties.


title:
Improving Question Answering over Knowledge Graphs Using Graph
  Summarization

abstract:
  Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA)
automatically answer natural language questions using triples contained in a
KG. The key idea is to represent questions and entities of a KG as
low-dimensional embeddings. Previous KGQAs have attempted to represent entities
using Knowledge Graph Embedding (KGE) and Deep Learning (DL) methods. However,
KGEs are too shallow to capture the expressive features and DL methods process
each triple independently. Recently, Graph Convolutional Network (GCN) has
shown to be excellent in providing entity embeddings. However, using GCNs to
KGQAs is inefficient because GCNs treat all relations equally when aggregating
neighbourhoods. Also, a problem could occur when using previous KGQAs: in most
cases, questions often have an uncertain number of answers. To address the
above issues, we propose a graph summarization technique using Recurrent
Convolutional Neural Network (RCNN) and GCN. The combination of GCN and RCNN
ensures that the embeddings are propagated together with the relations relevant
to the question, and thus better answers. The proposed graph summarization
technique can be used to tackle the issue that KGQAs cannot answer questions
with an uncertain number of answers. In this paper, we demonstrated the
proposed technique on the most common type of questions, which is
single-relation questions. Experiments have demonstrated that the proposed
graph summarization technique using RCNN and GCN can provide better results
when compared to the GCN. The proposed graph summarization technique
significantly improves the recall of actual answers when the questions have an
uncertain number of answers.


title: Adaptive Neural Network-based OFDM Receivers

abstract:
  We propose and examine the idea of continuously adapting state-of-the-art
neural network (NN)-based orthogonal frequency division multiplex (OFDM)
receivers to current channel conditions. This online adaptation via retraining
is mainly motivated by two reasons: First, receiver design typically focuses on
the universal optimal performance for a wide range of possible channel
realizations. However, in actual applications and within short time intervals,
only a subset of these channel parameters is likely to occur, as macro
parameters, e.g., the maximum channel delay, can assumed to be static. Second,
in-the-field alterations like temporal interferences or other conditions out of
the originally intended specifications can occur on a practical (real-world)
transmission. While conventional (filter-based) systems would require
reconfiguration or additional signal processing to cope with these unforeseen
conditions, NN-based receivers can learn to mitigate previously unseen effects
even after their deployment. For this, we showcase on-the-fly adaption to
current channel conditions and temporal alterations solely based on recovered
labels from an outer forward error correction (FEC) code without any additional
piloting overhead. To underline the flexibility of the proposed adaptive
training, we showcase substantial gains for scenarios with static channel macro
parameters, for out-ofspecification usage and for interference compensation.


title:
A Visual Navigation Perspective for Category-Level Object Pose
  Estimation

abstract:
  This paper studies category-level object pose estimation based on a single
monocular image. Recent advances in pose-aware generative models have paved the
way for addressing this challenging task using analysis-by-synthesis. The idea
is to sequentially update a set of latent variables, e.g., pose, shape, and
appearance, of the generative model until the generated image best agrees with
the observation. However, convergence and efficiency are two challenges of this
inference procedure. In this paper, we take a deeper look at the inference of
analysis-by-synthesis from the perspective of visual navigation, and
investigate what is a good navigation policy for this specific task. We
evaluate three different strategies, including gradient descent, reinforcement
learning and imitation learning, via thorough comparisons in terms of
convergence, robustness and efficiency. Moreover, we show that a simple hybrid
approach leads to an effective and efficient solution. We further compare these
strategies to state-of-the-art methods, and demonstrate superior performance on
synthetic and real-world datasets leveraging off-the-shelf pose-aware
generative models.


title:
Unsupervised Learning of Temporal Abstractions with Slot-based
  Transformers

abstract:
  The discovery of reusable sub-routines simplifies decision-making and
planning in complex reinforcement learning problems. Previous approaches
propose to learn such temporal abstractions in a purely unsupervised fashion
through observing state-action trajectories gathered from executing a policy.
However, a current limitation is that they process each trajectory in an
entirely sequential manner, which prevents them from revising earlier decisions
about sub-routine boundary points in light of new incoming information. In this
work we propose SloTTAr, a fully parallel approach that integrates sequence
processing Transformers with a Slot Attention module and adaptive computation
for learning about the number of such sub-routines in an unsupervised fashion.
We demonstrate how SloTTAr is capable of outperforming strong baselines in
terms of boundary point discovery, even for sequences containing variable
amounts of sub-routines, while being up to 7x faster to train on existing
benchmarks.


title:
Embedding Recurrent Layers with Dual-Path Strategy in a Variant of
  Convolutional Network for Speaker-Independent Speech Separation

abstract:
  Speaker-independent speech separation has achieved remarkable performance in
recent years with the development of deep neural network (DNN). Various network
architectures, from traditional convolutional neural network (CNN) and
recurrent neural network (RNN) to advanced transformer, have been designed
sophistically to improve separation performance. However, the state-of-the-art
models usually suffer from several flaws related to the computation, such as
large model size, huge memory consumption and computational complexity. To find
the balance between the performance and computational efficiency and to further
explore the modeling ability of traditional network structure, we combine RNN
and a newly proposed variant of convolutional network to cope with speech
separation problem. By embedding two RNNs into basic block of this variant with
the help of dual-path strategy, the proposed network can effectively learn the
local information and global dependency. Besides, a four-staged structure
enables the separation procedure to be performed gradually at finer and finer
scales as the feature dimension increases. The experimental results on various
datasets have proven the effectiveness of the proposed method and shown that a
trade-off between the separation performance and computational efficiency is
well achieved.


title: Nowhere-zero 3-flows in nilpotently vertex-transitive graphs

abstract:
  We prove that every regular graph of valency at least four whose automorphism
group contains a nilpotent subgroup acting transitively on the vertex set
admits a nowhere-zero 3-flow.


title:
Low-threshold, highly stable colloidal quantum dot short-wave infrared
  laser enabled by suppression of trap-assisted Auger recombination

abstract:
  Pb-chalcogenide colloidal quantum dots (CQDs) are attractive materials to be
used as tuneable laser media across the infrared spectrum. However, excessive
nonradiative Auger recombination due to the presence of trap states outcompetes
light amplification by rapidly annihilating the exciton population, leading to
high gain thresholds. Here, we employ a binary blend of CQDs and ZnO
nanocrystals in order to passivate the in-gap trap states of PbS-CQD gain
medium. Using transient absorption, we measure a five-fold increase in Auger
lifetime demonstrating the suppression of trap-assisted Auger recombination. By
doing so, we achieve a two-fold reduction in amplified spontaneous emission
(ASE) threshold. Finally, by integrating our proposed binary blend to a DFB
resonator, we demonstrate single-mode lasing emission at 1650 nm with a
linewidth of 1.23 nm (0.62 meV), operating at a low lasing threshold of ~385
{\mu}J.cm-2. The Auger suppression in this system has allowed to achieve
unprecedented lasing emission stability for a CQD laser with recorded
continuous operation of 5 hours at room temperature and ambient conditions.


title:
Analyzing Search Techniques for Autotuning Image-based GPU Kernels: The
  Impact of Sample Sizes

abstract:
  Modern computing systems are increasingly more complex, with their multicore
CPUs and GPUs accelerators changing yearly, if not more often. It thus has
become very challenging to write programs that efficiently use the associated
complex memory systems and take advantage of the available parallelism.
Autotuning addresses this by optimizing parameterized code to the targeted
hardware by searching for the optimal set of parameters. Empirical autotuning
has therefore gained interest during the past decades. While new autotuning
algorithms are regularly presented and published, we will show why comparing
these autotuning algorithms is a deceptively difficult task.
  In this paper, we describe our empirical study of state-of-the-art search
techniques for autotuning by comparing them on a range of sample sizes,
benchmarks and architectures. We optimize 6 tunable parameters with a
search-space size of over 2 million. The algorithms studied include Random
Search (RS), Random Forest Regression (RF), Genetic Algorithms (GA), Bayesian
Optimization with Gaussian Processes (BO GP) and Bayesian Optimization with
Tree-Parzen Estimators (BO TPE).
  Our results on the ImageCL benchmark suite suggest that the ideal autotuning
algorithm heavily depends on the sample size. In our study, BO GP and BO TPE
outperform the other algorithms in most scenarios with sample sizes from 25 to
100. However, GA usually outperforms the others for sample sizes 200 and
beyond. We generally see the most speedup to be gained over RS in the lower
range of sample sizes (25-100). However, the algorithms more consistently
outperform RS for higher sample sizes (200-400). Hence, no single
state-of-the-art algorithm outperforms the rest for all sample sizes. Some
suggestions for future work are also included.


title:
Oscillatory banded Hessenberg matrices, multiple orthogonal polynomials
  and random walks

abstract:
  A spectral Favard theorem for oscillatory bounded banded lower Hessenberg
matrices is found. To motivate the relevance of the oscillatory character, the
spectral Favard theorem for bounded Jacobi matrices is revisited and it is
shown that after an adequate shift of the Jacobi matrix one gets an oscillatory
matrix. The large knowledge on the spectral and factorization properties of
oscillatory and totally nonnegative matrices leads to a spectral
representation, a spectral Favard theorem for these Hessenberg matrices, in
terms of sequences of multiple orthogonal polynomials of types II and I with
respect to a set of positive Lebesgue-Stieltjes measures. Using bidiagonal
factorization when certain nonnegative continued fraction is positive, the
regular oscillatory case. It is shown that oscillatory Toeplitz banded
Hessenberg matrices are regular. The spectral Favard theorem for regular
oscillatory bounded banded Hessenberg matrices is applied to Markov chains with
tetradiagonal transition matrices, i.e. beyond birth and death. In the finite
case, the Karlin-McGregor spectral representation is given, it is shown that
the random walks are recurrent and explicit expressions in terms of the
orthogonal polynomials for the stationary distributions are given. Similar
results are obtained for the countable infinite Markov chain. Now the Markov
chain is not necessarily recurrent, it is characterized in terms of the first
measure. Ergodicity of the Markov chain is discussed in terms of the existence
of a mass at $1$, which is an eigenvalue and corresponding right and left
eigenvectors are given. Finally, the fact that any oscillatory matrix is $LU$
factorizable in terms of bidiagonal matrices leads to a stochastic
factorization of the Markov transition matrix.


title:
Extracting the $\Lambda$ Polarizing Fragmentation Function from Belle
  $e^+e^-$ Data Within the TMD Formalism

abstract:
  Data from the Belle Collaboration for associated production (with a light
unpolarized hadron) and single-inclusive production of transversely polarized
$\Lambda$-hyperons in $e^+e^-$ annihilation processes allowed to extract within
a TMD approach, for the first time, the $\Lambda$ polarizing fragmentation
function. Recent theoretical developments on the computation of cross sections
for single-inclusive hadron production in $e^+e^-$ annihilation processes,
within a Soft Collinear Effective Theory approach, combined with the CSS
formalism for the double-hadron production case, can be used to perform a
renewed analysis, adopting a full TMD factorization scheme. Preliminary results
and a comparison between the two analyses are presented.


title: Spectroscopy of high pressure rubidium-noble gas mixtures

abstract:
  Spectroscopy of alkali-buffer gas mixtures at high pressures from
single-digit to several 100 bars in the regime of substantial collisional
broadening is relevant in a wide range of fields, ranging from collisional
redistribution cooling to laboratory astrophysics. Here we report on
spectroscopic measurements of dense rubidium-noble gas mixtures recorded in a
pressure cell equipped with soldered sapphire optical viewports, which allows
for the controlled realization of extreme conditions of high temperature and
high pressure in a table top laboratory experiment. In the gas cell, we have
recorded absorption and emission spectra of rubidium subject to 250 bar helium
buffer gas pressure at 500 K temperature. The spectra to good accuracy fulfill
the thermodynamic Kennard- Stepanov frequency scaling of the ratio of
absorption and emission spectral profiles. Further, the long optical path
length in the cell allowed to both record spectra of rubidium-argon mixtures at
moderate temperatures and high pressures and to observe redistributional laser
cooling in this system.


title:
Hybrid 2D-QD MoS2 PbSe Quantum Dot Broadband Photodetectors with
  High-Sensitivity and Room-Temperature Operation at 2.5 {\mu}m

abstract:
  Broadband infrared photodetectors have profound importance in diverse
applications including security, gas sensing, bioimaging, spectroscopy for food
quality, and recycling, just to name a few. Yet, these applications can
currently be served by expensive epitaxially grown photodetectors, limiting
their market potential and social impact. The use of colloidal quantum dots
(CQDs) and 2D-materials in a hybrid layout is an attractive alternative to
design low-cost CMOS-compatible infrared photodetectors. However, the spectral
sensitivity of these conventional hybrid detectors has been restricted to 2.1
um. Herein, we present a hybrid structure comprising MoS2 with PbSe CQDs to
extend their sensitivity further towards the mid-wave infrared, up to 3 um. We
achieve room temperature responsivity of 137.6 A/W and a detectivity of 7.7
10^10 Jones at 2.55 um owing to highly efficient photoexcited carrier
separation at the interface of MoS2 and PbSe in combination with an
oxide-coating to reduce dark current; the highest value yet for a PbSe based
hybrid device. These findings strongly support the successful fabrication of
hybrid devices which may pave the pathway for cost-effective, high performance,
next-generation, novel photodetectors.


title:
Nonlinear behaviour of warped discs around a central object with a
  quadrupole moment

abstract:
  The nonlinear behaviour of low-viscosity warped discs is poorly understood.
We verified a nonlinear bending-wave theory, in which fluid columns undergo
affine transformations, with direct 3D hydrodynamical simulations. We employed
a second-order Godunov-type scheme, Meshless Finite Mass (MFM), and also the
Smoothed Particle Hydrodynamics (SPH) method, with up to 128M particles. For
moderate nonlinearity, MFM maintains well the steady nonlinear warp predicted
by the affine model for a tilted inviscid disc around a central object with a
quadrupole moment. However, numerical dissipation in SPH is so severe that even
a low-amplitude nonlinear warp degrades at a resolution where MFM performs
well. A low-amplitude arbitrary warp tends to evolve towards a nonlinear steady
state. However, no such state exists in our thin disc with an angular
semi-thickness H/R = 0.02 when the outer tilt angle is beyond about 14 degrees.
The warp breaks tenuously and reconnects in adiabatic simulations, or breaks
into distinct annuli in isothermal simulations. The breaking radius lies close
to the location with the most extreme nonlinear deformation. Parametric
instability is captured only in our highest-resolution simulation, leading to
ring structures that may serve as incubators for planets around binaries.


title:
Visible-blind ZnMgO Colloidal Quantum Dot Downconverters expand Silicon
  CMOS Sensors Spectral Coverage into Ultraviolet and enable UV Band
  Discrimination

abstract:
  Selective spectral detection of ultraviolet (UV) radiation is highly
important across numerous fields from health and safety to industrial and
environmental monitoring applications. Herein, we report a non-toxic,
visible-blind, inorganic quantum dot (QD)-based sensing scheme that expands the
spectral coverage of Silicon CMOS sensors into the UV, enabling efficient UV
detection without affecting the sensor performance in the visible and UV-band
discrimination. The reported scheme employs zinc magnesium oxide (ZnMgO) QDs
with compositionally tunable absorption across UV and high photoluminescence
quantum yield (PLQY) in the visible. The efficient luminescence and large
stokes shift of these QDs have been exploited herein to act as an efficient
downconverting material that enhances the UV sensitivity of Si-photodetector
(Si-PD). A Si-PD integrated with the QDs results in a nine-fold improvement in
photoresponsivity from 0.83 mA/W to 7.5 mA/W at 260 nm. Leveraging the
tunability of these QDs we further report on a simple UV band identification
scheme, using two distinct band gap ZnMgO QDs stacked in a tandem architecture
whose spectral emission color depends on the UV-band excitation light. The
downconverting stack enables facile discrimination of UV light using a standard
CMOS image sensor (camera) or by the naked eye and avoids the use of complex
optics.


title:
On-Demand Activation of Photochromic Nanoheaters for High Color Purity
  3D Printing

abstract:
  The creation of white and multicoloured 3D-printed objects with high colour
fidelity via powder sintering processes is currently limited by discolouration
from thermal sensitizers used in the printing process. Here we circumvent this
problem by using switchable, photochromic tungsten oxide nanoparticles, which
are colourless even at high concentrations. Upon ultraviolet illumination, the
tungsten oxide nanoparticles can be reversibly activated making them highly
absorbing in the infrared. Their strong infrared absorption upon activation
renders them efficient photothermal sensitizers that can act as fusing agents
for polymer powders in sintering-based 3D printing. The WO3 nanoparticles show
fast activation times, and when mixed with polyamide powders they exhibit a
heating-to-colour-change ratio greatly exceeding other sensitizers in the
literature. Upon mixing with coloured inks, powders containing WO3 display
identical colouration to a pristine powder. This demonstrates the potential of
WO3, and photochromic nanoparticles in general as a new class of material for
advanced manufacturing.


title: Coupling from the Past for the Null Recurrent Markov Chain

abstract:
  The Doeblin Graph of a countable state space Markov chain describes the joint
pathwise evolutions of the Markov dynamics starting from all possible initial
conditions, with two paths coalescing when they reach the same point of the
state space at the same time. Its Bridge Doeblin subgraph only contains the
paths starting from a tagged point of the state space at all possible times. In
the irreducible, aperiodic, and positive recurrent case, the following results
are known: the Bridge Doeblin Graph is an infinite tree that is
unimodularizable. Moreover, it contains a single bi-infinite path, which allows
one to build a perfect sample of the stationary state of the Markov chain. The
present paper is focused on the null recurrent case. It is shown that when
assuming irreducibility and aperiodicity again, the Bridge Doeblin Graph is
either an infinite tree or a forest made of a countable collection of infinite
trees. In the first case, the infinite tree in question has a single end, is
not unimodularizable in general, but is always locally unimodular. These key
properties are used to study the stationary regime of several measure-valued
random dynamics on this Bridge Doeblin Tree. The most important ones are the
taboo random dynamics, which admits as steady state a random measure with mean
measure equal to the invariant measure of the Markov chain, and the potential
random dynamics which is a random extension of the classical potential measure,
with a mean measure equal to infinity at every point of the state space.


title:
Relic gravitons at intermediate frequencies and the expansion history of
  the Universe

abstract:
  The early expansion history of the Universe is constrained by combining the
most recent limits on the cosmic gravitons in the audio band and the claimed
evidences of the nHz domain. The simplest scenario stipulates that between the
end of inflation and the formation of light nuclei the evolution consists of a
single phase expanding at a rate that is either faster or slower than the one
of radiation. If there are instead multiple post-inflationary stages evolving
at different rates, the spectral energy density always undershoots the signals
potentially attributed to relic gravitons by the pulsar timing arrays at
intermediate frequencies but ultimately develops a local maximum. After
examining further complementary possibilities (like the presence of a secondary
stage of inflation at low-scales) we analyze the early modifications of the
effective expansion rate and argue that if the refractive index of the relic
gravitons increases during a conventional inflationary epoch the spectral
energy density is blue above the fHz and then flattens out in the $\mu$Hz
region. In this instance the signal is compatible with the unconfirmed nHz
observations, with the most recent limits of the wide-band interferometers and
with the further constraints customarily imposed on the backgrounds of relic
gravitons produced during inflation.


title: Threshold size for the emergence of a classical-like behaviour

abstract:
  In this work we design a procedure to estimate the minimum size beyond which
a system is amenable to a classical-like description, i.e. a description based
on representative points in classical phase-spaces. This is obtained by
relating quantum states to representative points via Generalized Coherent
States (GCS), and designing a POVM for GCS discrimination. Conditions upon this
discrimination are defined, such that the POVM results convey enough
information to meet our needs for reliability and precision, as gauged by two
parameters $\epsilon$, of our arbitrary choice, and $\delta$, set by the
experimental apparatus, respectively. The procedure implies a definition of
what is meant by "size" of the system, in terms of the number $N$ of elementary
constituents that provide the global algebra leading to the phase-space for the
emergent classical-like description. The above conditions on GCS discrimination
can be thus turned into $N>N_{\rm t}(\epsilon,\delta)$, where $N_{\rm
t}(\epsilon,\delta)$ is the threshold size mentioned in the title. The specific
case of a magnetic system is considered, with details of a gedanken experiment
presented and thoroughly commented. Results for pseudo-spin and bosonic systems
are also given.


title: Black hole chemistry, the cosmological constant and the embedding tensor

abstract:
  We study black-hole thermodynamics in theories that contain dimensionful
constants such as the cosmological constant or coupling constants in Wald's
formalism. The most natural way to deal with these constants is to promote them
to scalar fields introducing a (d-1)-form Lagrange multiplier that forces them
to be constant on-shell. These (d-1)-form potentials provide a dual description
of them and, in the context of superstring/supergravity theories, a
higher-dimensional origin/explanation. In the context of gauged supergravity
theories, all these constants can be collected in the embedding tensor. We show
in an explicit 4-dimensional example that the embedding tensor can also be
understood as a thermodynamical variable that occurs in the Smarr formula in a
duality-invariant fashion. This establishes an interesting link between
black-hole thermodynamics, gaugings and compactifications in the context of
superstring/supergravity theories.


title: Reduction and integrability: a geometric perspective

abstract:
  A geometric approach to integrability and reduction of dynamical system is
developed from a modern perspective. The main ingredients in such analysis are
the infinitesimal symmetries and the tensor fields that are invariant under the
given dynamics. Particular emphasis is given to the existence of invariant
volume forms and the associated Jacobi multiplier theory, and then the Hojman
symmetry theory is developed as a complement to Noether theorem and non-Noether
constants of motion. The geometric approach to Hamilton-Jacobi equation is
shown to be a particular example of the search for related field in a lower
dimensional manifold.


title: Impact of Dataset on Acoustic Models for Automatic Speech Recognition

abstract:
  In Automatic Speech Recognition, GMM-HMM had been widely used for acoustic
modelling. With the current advancement of deep learning, the Gaussian Mixture
Model (GMM) from acoustic models has been replaced with Deep Neural Network,
namely DNN-HMM Acoustic Models. The GMM models are widely used to create the
alignments of the training data for the hybrid deep neural network model, thus
making it an important task to create accurate alignments. Many factors such as
training dataset size, training data augmentation, model hyperparameters, etc.,
affect the model learning. Traditionally in machine learning, larger datasets
tend to have better performance, while smaller datasets tend to trigger
over-fitting. The collection of speech data and their accurate transcriptions
is a significant challenge that varies over different languages, and in most
cases, it might be limited to big organizations. Moreover, in the case of
available large datasets, training a model using such data requires additional
time and computing resources, which may not be available. While the data about
the accuracy of state-of-the-art ASR models on open-source datasets are
published, the study about the impact of the size of a dataset on acoustic
models is not readily available. This work aims to investigate the impact of
dataset size variations on the performance of various GMM-HMM Acoustic Models
and their respective computational costs.


title: Continual Test-Time Domain Adaptation

abstract:
  Test-time domain adaptation aims to adapt a source pre-trained model to a
target domain without using any source data. Existing works mainly consider the
case where the target domain is static. However, real-world machine perception
systems are running in non-stationary and continually changing environments
where the target domain distribution can change over time. Existing methods,
which are mostly based on self-training and entropy regularization, can suffer
from these non-stationary environments. Due to the distribution shift over time
in the target domain, pseudo-labels become unreliable. The noisy pseudo-labels
can further lead to error accumulation and catastrophic forgetting. To tackle
these issues, we propose a continual test-time adaptation approach~(CoTTA)
which comprises two parts. Firstly, we propose to reduce the error accumulation
by using weight-averaged and augmentation-averaged predictions which are often
more accurate. On the other hand, to avoid catastrophic forgetting, we propose
to stochastically restore a small part of the neurons to the source pre-trained
weights during each iteration to help preserve source knowledge in the
long-term. The proposed method enables the long-term adaptation for all
parameters in the network. CoTTA is easy to implement and can be readily
incorporated in off-the-shelf pre-trained models. We demonstrate the
effectiveness of our approach on four classification tasks and a segmentation
task for continual test-time adaptation, on which we outperform existing
methods. Our code is available at \url{https://qin.ee/cotta}.


title: ILoveEye: Eyeliner Makeup Guidance System with Eye Shape Features

abstract:
  Drawing eyeliner is not an easy task for whom lacks experience in eye makeup.
Everyone has a unique pair of eyes, so they need to draw eyeliner in a style
that suits their eyes. We proposed ILoveEye, an interactive system that
supports eye-makeup novices to draw natural and suitable eyeliner. The proposed
system analyzes the shape of the user's eyes and classifies the eye types from
camera frame. The system can recommend the eyeliner style to the user based on
the designed recommendation rules. Then, the system can generate the original
patterns corresponding to the eyeliner style, and the user can draw the
eyeliner while observing the real-time makeup guidance. The user evaluation
experiments are conducted to verify that the proposed ILoveEye system can help
some users to draw reasonable eyeliner based on the specific eye shapes and
improve their eye makeup skills.


title: Integrable quadratic structures in peakon models

abstract:
  We propose realizations of the Poisson structures for the Lax representations
of three integrable $n$-body peakon equations, Camassa--Holm,
Degasperis--Procesi and Novikov. The Poisson structures derived from the
integrability structures of the continuous equations yield quadratic forms for
the $r$-matrix representation, with the Toda molecule classical $r$-matrix
playing a prominent role.
  We look for a linear form for the $r$-matrix representation. Aside from the
Camassa--Holm case, where the structure is already known, the two other cases
do not allow such a presentation, with the noticeable exception of the Novikov
model at $n=2$.
  Generalized Hamiltonians obtained from the canonical Sklyanin trace formula
for quadratic structures are derived in the three cases.


title: Continuous quantum gate sets and pulse class meta-optimization

abstract:
  Reducing the circuit depth of quantum circuits is a crucial bottleneck to
enabling quantum technology. This depth is inversely proportional to the number
of available quantum gates that have been synthesised. Moreover, quantum gate
synthesis and control problems exhibit a vast range of external parameter
dependencies, both physical and application-specific. In this article we
address the possibility of learning families of optimal control pulses which
depend adaptively on various parameters, in order to obtain a global optimal
mapping from the space of potential parameter values to the control space, and
hence continuous classes of gates. Our proposed method is tested on different
experimentally relevant quantum gates and proves capable of producing
high-fidelity pulses even in presence of multiple variable or uncertain
parameters with wide ranges.


title: Fast Hybrid Image Retargeting

abstract:
  Image retargeting changes the aspect ratio of images while aiming to preserve
content and minimise noticeable distortion. Fast and high-quality methods are
particularly relevant at present, due to the large variety of image and display
aspect ratios. We propose a retargeting method that quantifies and limits
warping distortions with the use of content-aware cropping. The pipeline of the
proposed approach consists of the following steps. First, an importance map of
a source image is generated using deep semantic segmentation and saliency
detection models. Then, a preliminary warping mesh is computed using axis
aligned deformations, enhanced with the use of a distortion measure to ensure
low warping deformations. Finally, the retargeted image is produced using a
content-aware cropping algorithm. In order to evaluate our method, we perform a
user study based on the RetargetMe benchmark. Experimental analyses show that
our method outperforms recent approaches, while running in a fraction of their
execution time.


title: DeepALM: Holistic Optical Network Monitoring based on Machine Learning

abstract:
  We demonstrate a machine learning-based optical network monitoring system
which can integrate fiber monitoring, predictive maintenance of optical
hardware, and security information management in a single solution.


title: Pushchino multibeam pulsar search -- II. Long-term variability of RRATs

abstract:
  Pulses from 16 previously known rotating radio transients (RRAT) have been
searched at the 110 MHz daily monitor program for 4 to 5.5 years by using the
Large-Phased-Array (LPA) at Pushchino. The total number of pulses detected in
such a long observation interval is only 90 pulses for RRAT J0640+07 or is as
high as 10,751 pulses for RRAT J0302+22. The number and amplitude of pulses
varies at a time-scales from six to twenty months for RRATs J1336+33, J1404+11,
J1848+15, J2051+12, J2105+22, and the pulse number can increase by one or two
orders of magnitude in active phases. The long-term trends are found for RRATs
J0139+33 and J0302+22, showing a 2-3 times increase in detected pulse number
over 1,959 days. Some RRATs show the annual variations on both pulse number and
pulse amplitude. It is hard to explain all these variation time scales by
refractive scintillation on the interstellar medium. The annual and semi-annual
variations are likely caused by scintillations of the inhomogeneous
interplanetary plasma. Our data show that the number of observational sessions
with no pulse detection over the threshold decreases exponentially with the
length of pulse silence.


title: Symmetry breaking in sticky collisions between ultracold molecules

abstract:
  Ultracold molecules undergo "sticky collisions" that result in loss even for
chemically nonreactive molecules. Sticking times can be enhanced by orders of
magnitude by interactions that lead to non-conservation of nuclear spin or
total angular momentum. We present a quantitative theory of the required
strength of such symmetry-breaking interactions based on classical simulation
of collision complexes. We find static electric fields as small as $10$~V/cm
can lead to non-conservation of angular momentum, while we find nuclear spin is
conserved during collisions. We also compute loss of collision complexes due to
spontaneous emission and absorption of black-body radiation, which are found to
be slow.

categories: physics.atom-ph cond-mat.quant-gas physics.chem-ph quant-ph


title: Learning Relational Rules from Rewards

abstract:
  Humans perceive the world in terms of objects and relations between them. In
fact, for any given pair of objects, there is a myriad of relations that apply
to them. How does the cognitive system learn which relations are useful to
characterize the task at hand? And how can it use these representations to
build a relational policy to interact effectively with the environment? In this
paper we proposed that this problem can be understood through the lens of a
sub-field of symbolic machine learning called relational reinforcement learning
(RRL). To demonstrate the potential of our approach, we build a simple model of
relational policy learning based on a function approximator developed in RRL.
We trained and tested our model in three Atari games that required to consider
an increasingly number of potential relations: Breakout, Pong and Demon Attack.
In each game, our model was able to select adequate relational representations
and build a relational policy incrementally. We discuss the relationship
between our model with models of relational and analogical reasoning, as well
as its limitations and future directions of research.


title:
Noninvasive mechanical investigations of free-standing SiN membranes
  patterned with one-dimensional photonic crystal structures

abstract:
  A detailed investigation of the structural and vibrational properties of
various prestressed silicon nitride membranes patterned with one-dimensional
photonic crystal structures is presented. The tensile stress-related
deformation of the structure in the vicinity of the patterned area is
determined by Atomic Force Microscopy scans, while the resonance frequencies
and quality factors of the out-of-plane membrane vibrations are measured using
optical interferometry. We show that these noninvasive measurements, combined
with results of finite element simulations, provide accurate knowledge on the
tensile stress, the elasticity modulus and the density of these nanostructured
thin films. The obtained results are interesting in two ways: first, they show
that such highly reflective thin membranes already exploited in various
photonics applications possess high-mechanical quality, which also makes them
attractive for optomechanics and sensing applications. Second, they represent a
nondestructive method to determine key material parameters which can be
applicable to a broad range of fragile nanostructured thin films.


title:
Navigable Proximity Graph-Driven Native Hybrid Queries with Structured
  and Unstructured Constraints

abstract:
  As research interest surges, vector similarity search is applied in multiple
fields, including data mining, computer vision, and information retrieval.
{Given a set of objects (e.g., a set of images) and a query object, we can
easily transform each object into a feature vector and apply the vector
similarity search to retrieve the most similar objects. However, the original
vector similarity search cannot well support \textit{hybrid queries}, where
users not only input unstructured query constraint (i.e., the feature vector of
query object) but also structured query constraint (i.e., the desired
attributes of interest). Hybrid query processing aims at identifying these
objects with similar feature vectors to query object and satisfying the given
attribute constraints. Recent efforts have attempted to answer a hybrid query
by performing attribute filtering and vector similarity search separately and
then merging the results later, which limits efficiency and accuracy because
they are not purpose-built for hybrid queries.} In this paper, we propose a
native hybrid query (NHQ) framework based on proximity graph (PG), which
provides the specialized \textit{composite index and joint pruning} modules for
hybrid queries. We easily deploy existing various PGs on this framework to
process hybrid queries efficiently. Moreover, we present two novel navigable
PGs (NPGs) with optimized edge selection and routing strategies, which obtain
better overall performance than existing PGs. After that, we deploy the
proposed NPGs in NHQ to form two hybrid query methods, which significantly
outperform the state-of-the-art competitors on all experimental datasets
(10$\times$ faster under the same \textit{Recall}), including eight public and
one in-house real-world datasets. Our code and datasets have been released at
\url{https://github.com/AshenOn3/NHQ}.


title:
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple
  Verbalizations

abstract:
  The current workflow for Information Extraction (IE) analysts involves the
definition of the entities/relations of interest and a training corpus with
annotated examples. In this demonstration we introduce a new workflow where the
analyst directly verbalizes the entities/relations, which are then used by a
Textual Entailment model to perform zero-shot IE. We present the design and
implementation of a toolkit with a user interface, as well as experiments on
four IE tasks that show that the system achieves very good performance at
zero-shot learning using only 5--15 minutes per type of a user's effort. Our
demonstration system is open-sourced at https://github.com/BBN-E/ZS4IE . A
demonstration video is available at https://vimeo.com/676138340 .


title: Making Nonlinear Systems Negative Imaginary via State Feedback

abstract:
  This paper provides a state feedback stabilization approach for nonlinear
systems of relative degree less than or equal to two by rendering them
nonlinear negative imaginary (NI) systems. Conditions are provided under which
a nonlinear system can be made a nonlinear NI system or a nonlinear output
strictly NI (OSNI) system. Roughly speaking, an affine nonlinear system which
has a normal form with relative degree less than or equal to two after possible
output transformation can be rendered nonlinear NI and nonlinear OSNI. In
addition, if the internal dynamics of the normal form is input-to-state stable,
then there exists a state feedback input that stabilizes the system. This
stabilization result is then extended to achieve stability for system with a
nonlinear NI uncertainty.


title: Formal Semantics and Formally Verified Validation for Temporal Planning

abstract:
  We present a simple and concise semantics for temporal planning. Our
semantics are developed and formalised in the logic of the interactive theorem
prover Isabelle/HOL. We derive from those semantics a validation algorithm for
temporal planning and show, using a formal proof in Isabelle/HOL, that this
validation algorithm implements our semantics. We experimentally evaluate our
verified validation algorithm and show that it is practical.


title:
A direct test for instantaneous collapse of wave functions in
  configuration space

abstract:
  Wavefunction collapse is a puzzling aspect of quantum mechanics. Designing a
test to confirm the instantaneousness of collapse has turned out to be
challenging, especially for collapse in configuration space. We propose a test
using two identical, non-local, correlated photons in an interferometer in
which a post-selection measurement of one of the photons in its location
changes the statistical behavior of the other photon, which is an arbitrarily
large distance away, and thus its detectable subsequent behavior. Analysis of
the resulting correlations constitutes a test of the instantaneousness (or
non-instantaneousness) of collapse. Connections to some of the recent proposed
models of collapse are discussed


title:
contact.engineering -- Create, analyze and publish digital surface twins
  from topography measurements across many scales

abstract:
  The optimization of surface finish to improve performance, such as adhesion,
friction, wear, fatigue life, or interfacial transport, occurs largely through
trial-and-error, despite significant advancements in the relevant science.
There are three central challenges that account for this disconnect: (1) the
challenge of integration of many different types of measurement for the same
surface to capture the multi-scale nature of roughness; (2) the technical
complexity of implementing spectral analysis methods and mechanical or
numerical models describing surface performance; (3) a lack of consistency
between researchers and industries in how surfaces are measured, quantified,
and communicated. Here we present a freely-available internet-based application
(available at https://contact.engineering) which attempts to overcome all three
challenges. First, the application enables the user to upload a set of
topography measurements from a single surface, and then integrates all of them
together to create a digital surface twin. Second, the application calculates
many of the commonly used topography metrics, such as root-mean-square
parameters, power spectral density (PSD), and autocorrelation function (ACF),
as well as implementing analytical and numerical calculations, such as boundary
element modeling (BEM) for elastic and plastic deformation. Third, the
application serves as a repository for users to securely store surfaces, and if
they choose, to share these with collaborators or even publish them (with a
digital object identifier) for all to access. The primary goal of this
application is to enable experimental researchers to quickly and easily
characterize the topography, and predict the contact properties for real-world
surfaces. An additional goal is that computer-generated topographies can be
analyzed to aid in the search for alternative surface finishes with improved
properties.


title:
Fast and computationally efficient generative adversarial network
  algorithm for unmanned aerial vehicle-based network coverage optimization

abstract:
  The challenge of dynamic traffic demand in mobile networks is tackled by
moving cells based on unmanned aerial vehicles. Considering the tremendous
potential of unmanned aerial vehicles in the future, we propose a new heuristic
algorithm for coverage optimization. The proposed algorithm is implemented
based on a conditional generative adversarial neural network, with a unique
multilayer sum-pooling loss function. To assess the performance of the proposed
approach, we compare it with the optimal core-set algorithm and quasi-optimal
spiral algorithm. Simulation results show that the proposed approach converges
to the quasi-optimal solution with a negligible difference from the global
optimum while maintaining a quadratic complexity regardless of the number of
users.


title:
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and
  Monocular 3D Object Detection Task

abstract:
  Concurrent perception datasets for autonomous driving are mainly limited to
frontal view with sensors mounted on the vehicle. None of them is designed for
the overlooked roadside perception tasks. On the other hand, the data captured
from roadside cameras have strengths over frontal-view data, which is believed
to facilitate a safer and more intelligent autonomous driving system. To
accelerate the progress of roadside perception, we present the first
high-diversity challenging Roadside Perception 3D dataset- Rope3D from a novel
view. The dataset consists of 50k images and over 1.5M 3D objects in various
scenes, which are captured under different settings including various cameras
with ambiguous mounting positions, camera specifications, viewpoints, and
different environmental conditions. We conduct strict 2D-3D joint annotation
and comprehensive data analysis, as well as set up a new 3D roadside perception
benchmark with metrics and evaluation devkit. Furthermore, we tailor the
existing frontal-view monocular 3D object detection approaches and propose to
leverage the geometry constraint to solve the inherent ambiguities caused by
various sensors, viewpoints. Our dataset is available on
https://thudair.baai.ac.cn/rope.


title: Unsupervised Pre-training for Temporal Action Localization Tasks

abstract:
  Unsupervised video representation learning has made remarkable achievements
in recent years. However, most existing methods are designed and optimized for
video classification. These pre-trained models can be sub-optimal for temporal
localization tasks due to the inherent discrepancy between video-level
classification and clip-level localization. To bridge this gap, we make the
first attempt to propose a self-supervised pretext task, coined as Pseudo
Action Localization (PAL) to Unsupervisedly Pre-train feature encoders for
Temporal Action Localization tasks (UP-TAL). Specifically, we first randomly
select temporal regions, each of which contains multiple clips, from one video
as pseudo actions and then paste them onto different temporal positions of the
other two videos. The pretext task is to align the features of pasted pseudo
action regions from two synthetic videos and maximize the agreement between
them. Compared to the existing unsupervised video representation learning
approaches, our PAL adapts better to downstream TAL tasks by introducing a
temporal equivariant contrastive learning paradigm in a temporally dense and
scale-aware manner. Extensive experiments show that PAL can utilize large-scale
unlabeled video data to significantly boost the performance of existing TAL
methods. Our codes and models will be made publicly available at
https://github.com/zhang-can/UP-TAL.


title: Learning to Adapt to Unseen Abnormal Activities under Weak Supervision

abstract:
  We present a meta-learning framework for weakly supervised anomaly detection
in videos, where the detector learns to adapt to unseen types of abnormal
activities effectively when only video-level annotations of binary labels are
available. Our work is motivated by the fact that existing methods suffer from
poor generalization to diverse unseen examples. We claim that an anomaly
detector equipped with a meta-learning scheme alleviates the limitation by
leading the model to an initialization point for better optimization. We
evaluate the performance of our framework on two challenging datasets,
UCF-Crime and ShanghaiTech. The experimental results demonstrate that our
algorithm boosts the capability to localize unseen abnormal events in a weakly
supervised setting. Besides the technical contributions, we perform the
annotation of missing labels in the UCF-Crime dataset and make our task
evaluated effectively.


title: Class-Incremental Learning for Action Recognition in Videos

abstract:
  We tackle catastrophic forgetting problem in the context of class-incremental
learning for video recognition, which has not been explored actively despite
the popularity of continual learning. Our framework addresses this challenging
task by introducing time-channel importance maps and exploiting the importance
maps for learning the representations of incoming examples via knowledge
distillation. We also incorporate a regularization scheme in our objective
function, which encourages individual features obtained from different time
steps in a video to be uncorrelated and eventually improves accuracy by
alleviating catastrophic forgetting. We evaluate the proposed approach on
brand-new splits of class-incremental action recognition benchmarks constructed
upon the UCF101, HMDB51, and Something-Something V2 datasets, and demonstrate
the effectiveness of our algorithm in comparison to the existing continual
learning methods that are originally designed for image data.


title: Repairing Group-Level Errors for DNNs Using Weighted Regularization

abstract:
  Deep Neural Networks (DNNs) have been widely used in software making
decisions impacting people's lives. However, they have been found to exhibit
severe erroneous behaviors that may lead to unfortunate outcomes. Previous work
shows that such misbehaviors often occur due to class property violations
rather than errors on a single image. Although methods for detecting such
errors have been proposed, fixing them has not been studied so far. Here, we
propose a generic method called Weighted Regularization (WR) consisting of five
concrete methods targeting the error-producing classes to fix the DNNs. In
particular, it can repair confusion error and bias error of DNN models for both
single-label and multi-label image classifications. A confusion error happens
when a given DNN model tends to confuse between two classes. Each method in WR
assigns more weights at a stage of DNN retraining or inference to mitigate the
confusion between target pair. A bias error can be fixed similarly. We evaluate
and compare the proposed methods along with baselines on six widely-used
datasets and architecture combinations. The results suggest that WR methods
have different trade-offs but under each setting at least one WR method can
greatly reduce confusion/bias errors at a very limited cost of the overall
performance.


title:
From Hurwitz numbers to Feynman diagrams: counting rooted trees in log
  gravity

abstract:
  We show that the partition function of the logarithmic sector of critical
topologically massive gravity can be expressed as a sum over rooted trees. Our
work appears as a natural extension of recently obtained results on a log
partition function that finds itself at the confluence of many theories,
relating nonunitary gravity, the plethystic programme, integrable hierarchies
of soliton equations and $\tau$-functions, Hurwitz theory and branched covering
of Riemann surfaces to the celebrated Connes-Kreimer Hopf algebra of rooted
trees and Feynman diagrams. In particular, the Hurwitz numbers arise in the
Hopf algebraic setting as coefficients of isomorphic classes of rooted trees,
and the log partition function can be interpreted as a generating function of
covering maps of rooted trees.


title: Energetical self-organization of a few strongly interacting particles

abstract:
  We study the quantum self-organization of a few interacting particles with
strong short-range interactions. The physical system is modelled via a 2D
Hubbard model with a nearest-neighbor interaction term of strength U and a
second nearest-neighbor hopping t. We find persistent many-body ground and
excited states whose structural properties are determined by the energetical
ordering occuring for vanishing hopping (t=0), when the self-organization of
the particles is determined by the interaction term only. For our analysis we
use the Euler characteristic of the network/graph grid structures formed by the
particles in real space (Fock states), which helps to quantify the energetical
ordering. We find multiple states, with integer Euler numbers whose values are
determined by the $t=0$ case, that persist/survive for $t \ne 0$ as long as the
interaction strength is large $U>>t$. The corresponding quantum phases contain
either density-wave-order (DWO) for low fillings or clustering-order (CO) for
high fillings. In addition, for the excited states we find superpositions of
Fock states whose probability amplitudes are self-tuned in a way that preserves
the integer value of the Euler characteristic from the $t=0$ case.

categories: cond-mat.str-el cond-mat.dis-nn cond-mat.mes-hall nlin.AO quant-ph


title: Quasiparticles, Flat Bands, and the Melting of Hydrodynamic Matter

abstract:
  The concept of quasiparticles -- long-lived low-energy particle-like
excitations -- has become a keystone of condensed quantum matter, where it
explains a variety of emergent many-body phenomena, such as superfluidity and
superconductivity. Here, we use quasiparticles to explain the collective
behavior of a classical system of hydrodynamically interacting particles in two
dimensions. In the disordered phase of this matter, measurements reveal a
sub-population of long-lived particle pairs. Modeling and simulation of the
ordered crystalline phase identify the pairs as quasiparticles, emerging at the
Dirac cones of the spectrum. The quasiparticles stimulate supersonic pairing
avalanches, bringing about the melting of the crystal. In hexagonal crystals,
where the intrinsic threefold symmetry of the hydrodynamic interaction matches
that of the crystal, the spectrum forms a flat band dense with ultra-slow,
low-frequency phonons whose collective interactions induce a much sharper
melting transition. Altogether, these findings demonstrate the usefulness of
concepts from quantum matter theory in understanding many-body physics in
classical dissipative settings.


title:
Lightweight Graph Convolutional Networks with Topologically Consistent
  Magnitude Pruning

abstract:
  Graph convolution networks (GCNs) are currently mainstream in learning with
irregular data. These models rely on message passing and attention mechanisms
that capture context and node-to-node relationships. With multi-head attention,
GCNs become highly accurate but oversized, and their deployment on cheap
devices requires their pruning. However, pruning at high regimes usually leads
to topologically inconsistent networks with weak generalization. In this paper,
we devise a novel method for lightweight GCN design. Our proposed approach
parses and selects subnetworks with the highest magnitudes while guaranteeing
their topological consistency. The latter is obtained by selecting only
accessible and co-accessible connections which actually contribute in the
evaluation of the selected subnetworks. Experiments conducted on the
challenging FPHA dataset show the substantial gain of our topologically
consistent pruning method especially at very high pruning regimes.


title:
EmotionNAS: Two-stream Architecture Search for Speech Emotion
  Recognition

abstract:
  Speech emotion recognition (SER) is a crucial research topic in
human-computer interactions. Existing works are mainly based on manually
designed models. Despite their great success, these methods heavily rely on
historical experience, which are time-consuming but cannot exhaust all possible
structures. To address this problem, we propose a neural architecture search
(NAS) based framework for SER, called "EmotionNAS". We take spectrogram and
wav2vec features as the inputs, followed with NAS to optimize the network
structure for these features separately. We further incorporate complementary
information in these features through decision-level fusion. Experimental
results on IEMOCAP demonstrate that our method succeeds over existing
state-of-the-art strategies on SER.


title: Automating scattering amplitudes with chirality flow

abstract:
  Recently we introduced the chirality-flow formalism, a method which builds on
the spinor-helicity formalism and is inspired by the color-flow idea in QCD.
With this formalism, Feynman rules and diagrams are simplified to the extent
that it is often possible to immediately, by hand, write down a helicity
amplitude given a Feynman diagram. In this paper we show that the method can
also speed up numerical evaluation of scattering amplitudes by considering $e^+
e^-$ going to $n$ photons in a MadGraph-based tree-level implementation. We
find that the computation time is reduced by roughly a factor ten for six
photons, and that it scales better with the number of external particles than
the default MadGraph5_aMC@NLO implementation. This performance gain is in part
attributed to the more compact Lorentz structures involved, and in part due to
a transparent choice of gauge reference vectors which reduces the number of
Feynman diagrams considered.


title:
Answering Should Self-Publishing Video Game Developers Market Their Game
  on Twitter

abstract:
  In the marketing of video games made by self-publishing, independent
developers, there is common advice to market a game using the social media
platform Twitter. However, the advice that stems from industry sources is
somewhat contradictory, and many self-publishing independent developers elect
not to use Twitter at all. This presents an opportunity for researchers to
investigate this tension and determine if using Twitter does have a causal
influence on the successful release of a game by using relatively recent
developments in causal data science techniques. In this sense, this paper
highlights these causal inference developments while simultaneously informing
self-publishing independent developers whether they should indeed market their
games using Twitter. It was found that using Twitter results in an average
increase of 85.4 reviews during release week, corresponding to a 314% positive
difference. Using Twitter also doubles the chance of reaching a critical
10-review inflection point threshold on release week. These effects, however,
are moderated by the characteristics of the game, expressed as 'tags', yet in
no case is the effect of Twitter use reduced to 0. Based on these findings, it
is advised that new, self-publishing video game developers do use Twitter to
market their games.


title: Semi-Supervised Formality Style Transfer with Consistency Training

abstract:
  Formality style transfer (FST) is a task that involves paraphrasing an
informal sentence into a formal one without altering its meaning. To address
the data-scarcity problem of existing parallel datasets, previous studies tend
to adopt a cycle-reconstruction scheme to utilize additional unlabeled data,
where the FST model mainly benefits from target-side unlabeled sentences. In
this work, we propose a simple yet effective semi-supervised framework to
better utilize source-side unlabeled sentences based on consistency training.
Specifically, our approach augments pseudo-parallel data obtained from a
source-side informal sentence by enforcing the model to generate similar
outputs for its perturbed version. Moreover, we empirically examined the
effects of various data perturbation methods and propose effective data
filtering strategies to improve our framework. Experimental results on the
GYAFC benchmark demonstrate that our approach can achieve state-of-the-art
results, even with less than 40% of the parallel data.


title:
Post-Disaster Communications: Enabling Technologies, Architectures, and
  Open Challenges

abstract:
  The number of disasters has increased over the past decade where these
calamities significantly affect the functionality of communication networks. In
the context of 6G, airborne and spaceborne networks offer hope in disaster
recovery to serve the underserved and to be resilient in calamities. Therefore,
this paper surveys the state-of-the-art literature on post-disaster wireless
communication networks and provides insights for the future establishment of
such networks. In particular, we first give an overview of the works
investigating the general procedures and strategies for counteracting any
large-scale disasters. Then, we present the possible technological solutions
for post-disaster communications, such as the recovery of the terrestrial
infrastructure, installing aerial networks, and using spaceborne networks.
Afterward, we shed light on the technological aspects of post-disaster
networks, primarily the physical and networking issues. We present the
literature on channel modeling, coverage and capacity, radio resource
management, localization, and energy efficiency in the physical layer and
discuss the integrated space-air-ground architectures, routing,
delay-tolerant/software-defined networks, and edge computing in the networking
layer. This paper also presents interesting simulation results which can
provide practical guidelines about the deployment of ad hoc network
architectures in emergency scenarios. Finally, we present several promising
research directions, namely backhauling, placement optimization of aerial base
stations, and the mobility-related aspects that come into play when deploying
aerial networks, such as planning their trajectories and the consequent
handovers.


title: Existence of groupoid models for diagrams of groupoid correspondences

abstract:
  This article continues the study of diagrams in the bicategory of \'etale
groupoid correspondences. We prove that any such diagram has a groupoid model
and that the groupoid model is a locally compact \'etale groupoid if the
diagram is locally compact and proper. A key tool for this is the relative
Stone-\v{C}ech compactification for spaces over a locally compact Hausdorff
space.


title:
The best defense is a good defense: adapting negotiation methods for
  tackling pressure over software project estimates

abstract:
  Software estimation is critical for a software project's success and a
challenging activity. We argue that estimation problems are not restricted to
the generation of estimates but also their use for commitment establishment:
project stakeholders pressure estimators to change their estimates or to accept
unrealistic commitments to attain business goals. In this study, we employed a
Design Science Research (DSR) methodology to design an artifact based on
negotiation methods, to empower software estimators in defending their
estimates and searching for alternatives to unrealistic commitments when facing
pressure. The artifact is a concrete step towards disseminating the soft skill
of negotiation among practitioners. We present the preliminary results from a
focus group that showed that practitioners from the software industry could use
the artifact in a concrete scenario when estimating and establishing
commitments about a software project. Our future steps include improving the
artifact with the suggestions from focus group participants and evaluating it
empirically in real software projects in the industry.


title:
Stability of solutions to obstacle problems with generalized Orlicz
  growth

abstract:
  We consider nonlinear equations having generalized Orlicz growth (also known
as Musielak--Orlicz growth). We prove that if differential operators
$\mathcal{A}_i$ converge locally uniformly to an operator $\mathcal{A}$, then
the sequence of solutions $(u_i)$ has a subsequence converging to solution $u$
of the limit operator in Sobolev and H\"older norms.


title:
Statistical analysis of hard X-ray radiation at PAL-XFEL facility
  performed by Hanbury Brown and Twiss interferometry

abstract:
  Hanbury Brown and Twiss interferometry experiment based on second-order
correlations was performed at PAL-XFEL facility. The statistical properties of
the X-ray radiation were studied within this experiment. Measurements were
performed at NCI beamline at 10 keV photon energy in various operation
conditions: Self-Amplified Spontaneous Emission (SASE), SASE with a
monochromator, and self-seeding regimes at 120 pC, 180 pC, and 200 pC electron
bunch charge, respectively. Statistical analysis showed short average pulse
duration from 6 fs to 9 fs depending on operation conditions. A high spatial
degree of coherence of about 70-80% was determined in spatial domain for the
SASE beams with the monochromator and self-seeding regime of operation. The
obtained values describe the statistical properties of the beams generated at
PAL-XFEL facility.


title: Models of modified $F(R,T)$ and cuscuton braneworld

abstract:
  This work deals with braneworld models in the presence of scalar fields in a
five-dimensional warped geometry with a single extra dimension of infinite
extent. We consider generalized models in the presence of the Ricci scalar, the
trace of the stress-energy tensor and the cuscuton contribution. The models
describe novel braneworld scenarios and the investigations consider distinct
possibilities, from which we show how the brane may change to become thinner,
although keeping gravitational stability and the gravity zero mode under strict
control. Moreover, we did not identify any split behavior in the warp factor in
the presence of the cuscuton and the trace of the stress-energy tensor.


title: On the structure and classification of Bernstein algebras

abstract:
  We prove that any Bernstein algebra $(A, \omega)$ is isomorphic to a
semidirect product $V \ltimes_{(\cdot, \, \Omega)} \, k$ associated to a
commutative algebra $(V, \cdot)$ such that $(x^2)^2 = 0$, for all $x\in A$ and
an idempotent endomorphism $\Omega = \Omega^2 \in {\rm End}_k (V)$ of $V$
satisfying two compatibility conditions. The set of types of $(1 +
|I|)$-dimensional Bernstein algebras is parametrized by an explicitely
constructed (using linear algebra tools) classified object. The automorphisms
group of any Bernstein algebra is described as a subgroup of the canonical
semidirect product of groups $(V, +) \ltimes {\rm GL}_k (V)$.


title:
DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio
  Representation Learning

abstract:
  Inspired by the recent progress in self-supervised learning for computer
vision, in this paper, through the DeLoRes learning framework, we introduce two
new general-purpose audio representation learning approaches, the DeLoRes-S and
DeLoRes-M. Our main objective is to make our network learn representations in a
resource-constrained setting (both data and compute), that can generalize well
across a diverse set of downstream tasks. Inspired from the Barlow Twins
objective function, we propose to learn embeddings that are invariant to
distortions of an input audio sample, while making sure that they contain
non-redundant information about the sample. To achieve this, we measure the
cross-correlation matrix between the outputs of two identical networks fed with
distorted versions of an audio segment sampled from an audio file and make it
as close to the identity matrix as possible. We call this the DeLoRes learning
framework, which we employ in different fashions with the DeLoRes-S and
DeLoRes-M. We use a combination of a small subset of the large-scale AudioSet
dataset and FSD50K for self-supervised learning and are able to learn with less
than half the parameters compared to state-of-the-art algorithms. For
evaluation, we transfer these learned representations to 11 downstream
classification tasks, including speech, music, and animal sounds, and achieve
state-of-the-art results on 7 out of 11 tasks on linear evaluation with
DeLoRes-M and show competitive results with DeLoRes-S, even when pre-trained
using only a fraction of the total data when compared to prior art. Our
transfer learning evaluation setup also shows extremely competitive results for
both DeLoRes-S and DeLoRes-M, with DeLoRes-M achieving state-of-the-art in 4
tasks.


title: Critical parameters in dynamic network modeling of sepsis

abstract:
  In this work, we propose a dynamical systems perspective on the modeling of
sepsis and its organ-damaging consequences. We develop a functional two-layer
network model for sepsis based upon the interaction of parenchymal cells and
immune cells via cytokines, and the coevolutionary dynamics of parenchymal,
immune cells, and cytokines. By means of the simple paradigmatic model of phase
oscillators in a two-layer system, we analyze the emergence of organ
threatening interactions between the dysregulated immune system and the
parenchyma. We demonstrate that the complex cellular cooperation between
parenchyma and stroma (immune layer) either in the physiological or in the
pathological case can be related to dynamical patterns of the network. In this
way we explain sepsis by the dysregulation of the healthy homeostatic state
(frequency synchronized) leading to a pathological state (desynchronized or
multifrequency cluster) in the parenchyma. We provide insight into the complex
stabilizing and destabilizing interplay of parenchyma and stroma by determining
critical interaction parameters. The coupled dynamics of parenchymal cells
(metabolism) and nonspecific immune cells (response of the innate immune
system) is represented by nodes of a duplex layer. Cytokine interaction is
modeled by adaptive coupling weights between nodes representing immune cells
(with fast adaptation timescale) and parenchymal cells (slow adaptation
timescale), and between pairs of parenchymal and immune cells in the duplex
network (fixed bidirectional coupling). The proposed model allows for a
functional description of organ dysfunction in sepsis and the recurrence risk
in a plausible pathophysiological context.


title: Bond-Selective Intensity Diffraction Tomography

abstract:
  Recovering molecular information remains a grand challenge in the widely used
holographic and computational imaging technologies. To address this challenge,
we developed a computational mid-infrared photothermal microscope, termed
Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost
brightfield microscope with an add-on pulsed light source, BS-IDT recovers both
infrared spectra and bond-selective 3D refractive index maps from
intensity-only measurements. High-fidelity infrared fingerprint spectra
extraction is validated. Volumetric chemical imaging of biological cells is
demonstrated at a speed of ~20 seconds per volume, with a lateral and axial
resolution of ~350 nm and ~1.1 micron, respectively. BS-IDT's application
potential is investigated by chemically quantifying lipids stored in cancer
cells and volumetric chemical imaging on Caenorhabditis elegans with a large
field of view (~100 micron X 100 micron).


title:
Anapole states and scattering deflection effects in anisotropic van der
  Waals nanoparticles

abstract:
  Transition metal dichalcogenides (TMDCs), belonging to the class of van der
Waals materials, are promising materials for optoelectronics and photonics. In
particular, their giant optical anisotropy may enable important optical effects
when employed in nanostructures with finite thickness. In this paper, we
theoretically and numerically study light scattering behavior from anisotropic
MoS2 nanocylinders, and highlight its distinct features advantageous over the
response of conventional silicon particles of the same shape. We establish two
remarkable phenomena, appearing in the same MoS2 particle with optimized
geometry. The first one is a pure magnetic dipole scattering associated with
the excitation of the electric-dipole anapole states. Previously reported in
core-shell hybrid (metal/dielectric) systems only, it is now demonstrated in an
all-dielectric particle. The second phenomenon is the super-deflection in the
far field: the maximum scattering may occur over a wide range of directions,
including forward-, backward- and side-scattering depending on the mutual
orientation of the MoS2 nanocylinder and the incident wave. In contrast to the
well-known Kerker and anti-Kerker effects, which appear in nanoparticles at
different frequencies, the super-deflection can be achieved by rotating the
particle at a constant frequency of incident light. Our results facilitate the
development of functional optical devices incorporating nanostructured
anisotropic TMDCs and may encourage further research in meta-optics based on
highly anisotropic materials.


title:
Node and edge control strategy identification via trap spaces in Boolean
  networks

abstract:
  The study of control mechanisms of biological systems allows for interesting
applications in bioengineering and medicine, for instance in cell reprogramming
or drug target identification. A control strategy often consists of a set of
interventions that, by fixing the values of some components, ensure that the
long term dynamics of the controlled system is in a desired state. A common
approach to control in the Boolean framework consists in checking how the fixed
values propagate through the network, to establish whether the effect of
percolating the interventions is sufficient to induce the target state.
Although methods based uniquely on value percolation allow for efficient
computation, they can miss many control strategies. Exhaustive methods for
control strategy identification, on the other hand, often entail high
computational costs. In order to increase the number of control strategies
identified while still benefiting from an efficient implementation, we
introduce a method based on value percolation that uses trap spaces, subspaces
of the state space that are closed with respect to the dynamics, and that can
usually be easily computed in biological networks. The approach allows for node
interventions, which fix the value of certain components, and edge
interventions, which fix the effect that one component has on another. The
method is implemented using Answer Set Programming, extending an existing
efficient implementation of value percolation to allow for the use of trap
spaces and edge control. The applicability of the approach is studied for
different control targets in a biological case study, identifying in all cases
new control strategies that would escape usual percolation-based methods.