title: Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation

abstract:
  Few-shot segmentation enables the model to recognize unseen classes with few
annotated examples. Most existing methods adopt prototype learning
architecture, where support prototype vectors are expanded and concatenated
with query features to perform conditional segmentation. However, such
framework potentially focuses more on query features while may neglect the
similarity between support and query features. This paper proposes a
contrastive enhancement approach using latent prototypes to leverage latent
classes and raise the utilization of similarity information between prototype
and query features. Specifically, a latent prototype sampling module is
proposed to generate pseudo-mask and novel prototypes based on features
similarity. The module conveniently conducts end-to-end learning and has no
strong dependence on clustering numbers like cluster-based method. Besides, a
contrastive enhancement module is developed to drive models to provide
different predictions with the same query features. Our method can be used as
an auxiliary module to flexibly integrate into other baselines for a better
segmentation performance. Extensive experiments show our approach remarkably
improves the performance of state-of-the-art methods for 1-shot and 5-shot
segmentation, especially outperforming baseline by 5.9% and 7.3% for 5-shot
task on Pascal-5^i and COCO-20^i. Source code is available at
https://github.com/zhaoxiaoyu1995/CELP-Pytorch


title:
Critical percolation threshold restricts late-summer Arctic sea ice melt
  pond coverage

abstract:
  During the summer, vast regions of Arctic sea ice are covered by meltwater
ponds that significantly lower the ice reflectivity and accelerate melting.
Ponds develop over the melt season through an initial rapid growth stage
followed by drainage through macroscopic holes. Recent analysis of melt pond
photographs indicates that late-summer ponds exist near the critical
percolation threshold, a special pond coverage fraction below which the ponds
are largely disconnected and above which they are highly connected. Here, we
show that the percolation threshold, a statistical property of ice topography,
constrains pond evolution due to pond drainage through macroscopic holes. We
show that it sets the approximate upper limit and scales pond coverage
throughout its evolution after the beginning of drainage. Furthermore, we show
that the rescaled pond coverage during drainage is a universal function of a
single non-dimensional parameter, $\eta$, roughly interpreted as the number of
drainage holes per characteristic area of the surface. This universal curve
allows us to formulate an equation for pond coverage time-evolution during and
after pond drainage that captures the dependence on environmental parameters
and is supported by observations on undeformed first-year ice. This equation
reveals that pond coverage is highly sensitive to environmental parameters,
suggesting that modeling uncertainties could be reduced by more directly
parameterizing the ponds' natural parameter, $\eta$. Our work uncovers
previously unrecognized constraints on melt pond physics and places ponds
within a broader context of phase transitions and critical phenomena.
Therefore, it holds promise for improving ice-albedo parameterizations in
large-scale models.

categories: nlin.PS nlin.AO physics.ao-ph physics.flu-dyn physics.geo-ph


title:
Quantum algorithm for neural network enhanced multi-class parallel
  classification

abstract:
  Using the properties of quantum superposition, we propose a quantum
classification algorithm to efficiently perform multi-class classification
tasks, where the training data are loaded into parameterized operators which
are applied to the basis of the quantum state in quantum circuit composed by
\emph{sample register} and \emph{label register}, and the parameters of quantum
gates are optimized by a hybrid quantum-classical method, which is composed of
a trainable quantum circuit and a gradient-based classical optimizer. After
several quantum-to-class repetitions, the quantum state is optimal that the
state in \emph{sample register} is the same as that in \emph{label register}.
%A structure of loading data many times is performed as a quantum version of
neural network to improve the expression ability of quantum circuit. For a
classification task of $L$-class, the analysis shows that the space and time
complexity of the quantum circuit are $O(L*logL)$ and $O(logL)$, respectively.
The numerical simulation results of 2-class task and 5-class task show that the
proposed algorithm has a higher classification accuracy, faster convergence and
higher expression ability. The classification accuracy and the speed of
converging can also be improved by increasing the number times of applying
multi-qubit controlled operators on the quantum circuit, especially for
multiple classes classification.


title: COLA: Consistent Learning with Opponent-Learning Awareness

abstract:
  Learning in general-sum games can be unstable and often leads to socially
undesirable, Pareto-dominated outcomes. To mitigate this, Learning with
Opponent-Learning Awareness (LOLA) introduced opponent shaping to this setting,
by accounting for the agent's influence on the anticipated learning steps of
other agents. However, the original LOLA formulation (and follow-up work) is
inconsistent because LOLA models other agents as naive learners rather than
LOLA agents. In previous work, this inconsistency was suggested as a cause of
LOLA's failure to preserve stable fixed points (SFPs). First, we formalize
consistency and show that higher-order LOLA (HOLA) solves LOLA's inconsistency
problem if it converges. Second, we correct a claim made in the literature, by
proving that, contrary to Sch\"afer and Anandkumar (2019), Competitive Gradient
Descent (CGD) does not recover HOLA as a series expansion. Hence, CGD also does
not solve the consistency problem. Third, we propose a new method called
Consistent LOLA (COLA), which learns update functions that are consistent under
mutual opponent shaping. It requires no more than second-order derivatives and
learns consistent update functions even when HOLA fails to converge. However,
we also prove that even consistent update functions do not preserve SFPs,
contradicting the hypothesis that this shortcoming is caused by LOLA's
inconsistency. Finally, in an empirical evaluation on a set of general-sum
games, we find that COLA finds prosocial solutions and that it converges under
a wider range of learning rates than HOLA and LOLA. We support the latter
finding with a theoretical result for a simple game.


title: VoViT: Low Latency Graph-based Audio-Visual Voice Separation Transformer

abstract:
  This paper presents an audio-visual approach for voice separation which
outperforms state-of-the-art methods at a low latency in two scenarios: speech
and singing voice. The model is based on a two-stage network. Motion cues are
obtained with a lightweight graph convolutional network that processes face
landmarks. Then, both audio and motion features are fed to an audio-visual
transformer which produces a fairly good estimation of the isolated target
source. In a second stage, the predominant voice is enhanced with an audio-only
network. We present different ablation studies and comparison to
state-of-the-art methods. Finally, we explore the transferability of models
trained for speech separation in the task of singing voice separation. The
demos, code, and weights will be made publicly available at
https://ipcv.github.io/VoViT/


title:
Low-rank approximation of continuous functions in Sobolev spaces with
  dominating mixed smoothness

abstract:
  Let $\Omega_i\subset\mathbb{R}^{n_i}$, $i=1,\ldots,m$, be given domains. In
this article, we study the low-rank approximation with respect to
$L^2(\Omega_1\times\dots\times\Omega_m)$ of functions from Sobolev spaces with
dominating mixed smoothness. To this end, we first estimate the rank of a
bivariate approximation, i.e., the rank of the continuous singular value
decomposition. In comparison to the case of functions from Sobolev spaces with
isotropic smoothness, compare \cite{GH14,GH19}, we obtain improved results due
to the additional mixed smoothness. This convergence result is then used to
study the tensor train decomposition as a method to construct multivariate
low-rank approximations of functions from Sobolev spaces with dominating mixed
smoothness. We show that this approach is able to beat the curse of dimension.


title:
Populist Discourse and Entrepreneurship: The Role of Political Ideology
  and Institutions

abstract:
  Using institutional economic theory as our guiding framework, we develop a
model to describe how populist discourse by a nation's political leader
influences entrepreneurship. We hypothesize that populist discourse reduces
entrepreneurship by creating regime uncertainty concerning the future stability
of the institutional environment, resulting in entrepreneurs anticipating
higher future transaction costs. Our model highlights two important factors
that moderate the relationship. First, is the strength of political checks and
balances, which we hypothesize weakens the negative relationship between
populist discourse and entrepreneurship by providing entrepreneurs with greater
confidence that the actions of a populist will be constrained. Second, the
political ideology of the leader moderates the relationship between populist
discourse and entrepreneurship. The anti-capitalistic rhetoric of left-wing
populism will create greater regime uncertainty than right-wing populism, which
is often accompanied by rhetoric critical of free trade and foreigners but also
supportive of business interests. The effect of centrist populism, which is
accompanied by a mix of contradictory and often moderate ideas that make it
difficult to discern future transaction costs, will have a weaker negative
effect on entrepreneurship than either left-wing or right-wing populism. We
empirically test our model using a multi-level design and a dataset comprised
of more than 780,000 individuals in 33 countries over the period 2002-2016. Our
analysis largely supports our theory regarding the moderating role of ideology.
Still, surprisingly, our findings suggest that the negative effect of populism
on entrepreneurship is greater in nations with stronger checks and balances.


title:
Cavity Quantum Electrodynamics Effects of Optically Cooled
  Nitrogen-Vacancy Centers Coupled to a High Frequency Microwave Resonator

abstract:
  Recent experiments demonstrated the cooling of a microwave mode of a
high-quality dielectric resonator coupled to optically cooled nitrogen-vacancy
(NV) spins in diamond. Our recent theoretical study [arXiv:2110.10950] pointed
out the cooled NV spins can be used to realize cavity quantum electrodynamics
effects (C-QED) at room temperature. In this article, we propose to modify the
setup used in a recent diamond maser experiment [Nature 55, 493-496 (2018)],
which features a higher spin transition frequency, a lower spin-dephasing rate
and a stronger NV spins-resonator coupling, to realize better microwave mode
cooling and the room-temperature CQED effects. To describe more precisely the
optical spin cooling and the collective spin-resonator coupling, we extend the
standard Jaynes-Cumming model to account for the rich electronic and spin
levels of the NV centers. Our calculations show that for the proposed setup it
is possible to cool the microwave mode from $293$ K (room temperature) to $116$
K, which is about $72$ K lower than the previous records, and to study the
intriguing dynamics of the CQED effects under the weak-to-strong coupling
transition by varying the laser power. With simple modifications, our model can
be applied to, e.g., other solid-state spins or triplet spins of pentacene
molecules, and to investigate other effects, such as the operations of pulsed
and continuous-wave masing.


title:
Deterministic Dynamic Stackelberg Games: Time-Consistent Open-Loop
  Solution

abstract:
  In this paper, the known deterministic linear-quadratic Stackelberg game is
revisited, whose open-loop Stackelberg solution actually possesses the nature
of time inconsistency. To handle this time inconsistency, {a two-tier game
framework is introduced, where the upper-tier game works according to
Stackelberg's scenario with a leader and a follower, and two lower-tier
intertemporal games give the follower's and leader's equilibrium response
mappings that mimic the notion of time-consistent open-loop equilibrium control
in existing literature. The resulting open-loop equilibrium solution of the
two-tier game} is shown to be weakly time-consistent in the sense that the
adopted policies will no longer be denied in the future only if past policies
are consistent with the equilibrium policies. On the existence and uniqueness
of such a solution, necessary and sufficient conditions are obtained, which are
characterized via the solutions of several Riccati-like equations.


title:
A solvable 3D Kondo lattice exhibiting odd-frequency pairing and order
  fractionalization

abstract:
  The Kondo lattice model plays a key role in our understanding of quantum
materials, but a lack of small parameters has posed a long-standing problem. We
present a 3 dimensional S= 1/2 Kondo lattice model describing a spin liquid
within an electron sea. Strong correlations in the spin liquid are treated
exactly, enabling a controlled analytical approach. Like a Peierls or BCS
phase, a logarithmically divergent susceptibility leads to an instability into
a new phase at arbitrarily small Kondo coupling. Our solution captures a
plethora of emergent phenomena, including odd-frequency pairing, pair density
wave formation and order fractionalization. The ground-state state is a pair
density wave with a fractionalized charge e, S = 1/2 order parameter, formed
between electrons and Majorana fermions.


title: Blowup polynomials and delta-matroids of graphs

abstract:
  For every finite simple connected graph $G = (V,E)$, we introduce an
invariant, its blowup-polynomial $p_G(\{ n_v : v \in V \})$. This is obtained
by dividing the determinant of the distance matrix of its blowup graph $G[{\bf
n}]$ (containing $n_v$ copies of $v$) by an exponential factor. We show that
$p_G({\bf n})$ is indeed a polynomial function in the sizes $n_v$, which is
moreover multi-affine and real-stable. This associates a hitherto unexplored
delta-matroid to each graph $G$; and we provide a second unexplored one for
each tree. As another consequence, we obtain a new characterization of complete
multipartite graphs, via the homogenization at $-1$ of $p_G$ being
completely/strongly log-concave, i.e., Lorentzian. (These results extend to
weighted graphs.) Finally, we show $p_G$ is indeed a graph invariant, i.e.,
$p_G$ and its symmetries (in the variables ${\bf n}$) recover $G$ and its
isometries, respectively.


title: Basics of Fourier Analysis for High-Energy Astronomy

abstract:
  The analysis of time variability, whether fast variations on time scales well
below the second or slow changes over years, is becoming more and more
important in high-energy astronomy. Many sophisticated tools are available for
data analysis and complex practical aspects are described in technical papers.
Here, we present the basic concepts upon which all these techniques are based.
It is intended as a condensed primer of Fourier analysis, dealing with
fundamental aspects that can be examined in detailed elsewhere. It is not
intended to be a presentation of detailed Fourier tools for data analysis, but
the reader will find the theoretical basis to understand available analysis
techniques.


title:
Comparing representations of biological data learned with different AI
  paradigms, augmenting and cropping strategies

abstract:
  Recent advances in computer vision and robotics enabled automated large-scale
biological image analysis. Various machine learning approaches have been
successfully applied to phenotypic profiling. However, it remains unclear how
they compare in terms of biological feature extraction. In this study, we
propose a simple CNN architecture and implement 4 different representation
learning approaches. We train 16 deep learning setups on the 770k cancer cell
images dataset under identical conditions, using different augmenting and
cropping strategies. We compare the learned representations by evaluating
multiple metrics for each of three downstream tasks: i) distance-based
similarity analysis of known drugs, ii) classification of drugs versus
controls, iii) clustering within cell lines. We also compare training times and
memory usage. Among all tested setups, multi-crops and random augmentations
generally improved performance across tasks, as expected. Strikingly,
self-supervised (implicit contrastive learning) models showed competitive
performance being up to 11 times faster to train. Self-supervised regularized
learning required the most of memory and computation to deliver arguably the
most informative features. We observe that no single combination of augmenting
and cropping strategies consistently results in top performance across tasks
and recommend prospective research directions.


title: Comfortable place for quantum walkers on finite path

abstract:
  We consider the stationary state of a quantum walk on the finite path, where
the sink and source are set at the left and right boundaries. The quantum coin
is uniformly placed at every vertex of the path graph. At every time step, a
new quantum walker penetrates into the internal from the left boundary and also
some existing quantum walkers in the internal goes out to the sinks located in
the left and right boundaries. The square modulus of the stationary state at
each vertex is regarded as the comfortability for a quantum walker to this
vertex in this paper. We show the weak convergence theorem for the scaled limit
distribution of the comfortability in the limit of the length of the path.


title: Explaining Classifiers by Constructing Familiar Concepts

abstract:
  Interpreting a large number of neurons in deep learning is difficult. Our
proposed `CLAssifier-DECoder' architecture (ClaDec) facilitates the
understanding of the output of an arbitrary layer of neurons or subsets
thereof. It uses a decoder that transforms the incomprehensible representation
of the given neurons to a representation that is more similar to the domain a
human is familiar with. In an image recognition problem, one can recognize what
information (or concepts) a layer maintains by contrasting reconstructed images
of ClaDec with those of a conventional auto-encoder(AE) serving as reference.
An extension of ClaDec allows trading comprehensibility and fidelity. We
evaluate our approach for image classification using convolutional neural
networks. We show that reconstructed visualizations using encodings from a
classifier capture more relevant classification information than conventional
AEs. This holds although AEs contain more information on the original input.
Our user study highlights that even non-experts can identify a diverse set of
concepts contained in images that are relevant (or irrelevant) for the
classifier. We also compare against saliency based methods that focus on pixel
relevance rather than concepts. We show that ClaDec tends to highlight more
relevant input areas to classification though outcomes depend on classifier
architecture. Code is at \url{https://github.com/JohnTailor/ClaDec}


title: Localised bulging of an inflated rubber tube with fixed ends

abstract:
  When a rubber tube with free ends is inflated under volume control, the
pressure will first reach a maximum and then decrease monotonically to approach
a constant asymptote. The pressure maximum corresponds to the initiation of a
localised bulge and is predicted by a bifurcation condition, whereas the
asymptote is the Maxwell pressure corresponding to a "two-phase" propagation
state. In contrast, when the tube is first pre-stretched and then has its ends
fixed during subsequent inflation, the pressure versus bulge amplitude has both
a maximum and a minimum, and the behaviour on the right ascending branch has
previously not been fully understood. We show that for all values of
pre-stretch and tube length, the ascending branches all converge to a single
curve that is only dependent on the ratio of tube thickness to the outer
radius. This curve represents the Maxwell state to be approached in each case
(if Euler buckling or axisymmetric wrinkling does not occur first), but this
state is pressure-dependent in contrast with the free-ends case. We also
demonstrate experimentally that localized bulging cannot occur when the
pre-stretch is sufficiently large and investigate what strain-energy functions
can predict this observed phenomenon.


title:
Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection
  for English and Arabic Using Transformers and Data Augmentation

abstract:
  This paper describes our submission to SemEval-2022 Task 6 on sarcasm
detection and its five subtasks for English and Arabic. Sarcasm conveys a
meaning which contradicts the literal meaning, and it is mainly found on social
networks. It has a significant role in understanding the intention of the user.
For detecting sarcasm, we used deep learning techniques based on transformers
due to its success in the field of Natural Language Processing (NLP) without
the need for feature engineering. The datasets were taken from tweets. We
created new datasets by augmenting with external data or by using word
embeddings and repetition of instances. Experiments were done on the datasets
with different types of preprocessing because it is crucial in this task. The
rank of our team was consistent across four subtasks (fourth rank in three
subtasks and sixth rank in one subtask); whereas other teams might be in the
top ranks for some subtasks but rank drastically less in other subtasks. This
implies the robustness and stability of the models and the techniques we used.


title:
North-South type dynamics of relative atoroidal automorphisms of free
  groups on a relative space of currents

abstract:
  This paper, which is the second of a series of three papers, studies
dynamical properties of elements of $\mathrm{Out}(F_{\tt n})$, the outer
automorphism group of a nonabelian free group $F_{\tt n}$. We prove that, for
every exponentially growing outer automorphism of $F_{\tt n}$, there exists a
preferred compact topological space, the space of currents relative to a
malnomal subgroup system, on which $\phi$ acts by homeomorphism with a
North-South dynamics behavior.


title:
Quantification of Occlusion Handling Capability of a 3D Human Pose
  Estimation Framework

abstract:
  3D human pose estimation using monocular images is an important yet
challenging task. Existing 3D pose detection methods exhibit excellent
performance under normal conditions however their performance may degrade due
to occlusion. Recently some occlusion aware methods have also been proposed,
however, the occlusion handling capability of these networks has not yet been
thoroughly investigated. In the current work, we propose an occlusion-guided 3D
human pose estimation framework and quantify its occlusion handling capability
by using different protocols. The proposed method estimates more accurate 3D
human poses using 2D skeletons with missing joints as input. Missing joints are
handled by introducing occlusion guidance that provides extra information about
the absence or presence of a joint. Temporal information has also been
exploited to better estimate the missing joints. A large number of experiments
are performed for the quantification of occlusion handling capability of the
proposed method on three publicly available datasets in various settings
including random missing joints, fixed body parts missing, and complete frames
missing, using mean per joint position error criterion. In addition to that,
the quality of the predicted 3D poses is also evaluated using action
classification performance as a criterion. 3D poses estimated by the proposed
method achieved significantly improved action recognition performance in the
presence of missing joints. Our experiments demonstrate the effectiveness of
the proposed framework for handling the missing joints as well as
quantification of the occlusion handling capability of the deep neural
networks.


title:
A study on joint modeling and data augmentation of multi-modalities for
  audio-visual scene classification

abstract:
  In this paper, we propose two techniques, namely joint modeling and data
augmentation, to improve system performances for audio-visual scene
classification (AVSC). We employ pre-trained networks trained only on image
data sets to extract video embedding; whereas for audio embedding models, we
decide to train them from scratch. We explore different neural network
architectures for joint modeling to effectively combine the video and audio
modalities. Moreover, data augmentation strategies are investigated to increase
audio-visual training set size. For the video modality the effectiveness of
several operations in RandAugment is verified. An audio-video joint mixup
scheme is proposed to further improve AVSC performances. Evaluated on the
development set of TAU Urban Audio Visual Scenes 2021, our final system can
achieve the best accuracy of 94.2% among all single AVSC systems submitted to
DCASE 2021 Task 1b.


title: Biological Sequence Design with GFlowNets

abstract:
  Design of de novo biological sequences with desired properties, like protein
and DNA sequences, often involves an active loop with several rounds of
molecule ideation and expensive wet-lab evaluations. These experiments can
consist of multiple stages, with increasing levels of precision and cost of
evaluation, where candidates are filtered. This makes the diversity of proposed
candidates a key consideration in the ideation phase. In this work, we propose
an active learning algorithm leveraging epistemic uncertainty estimation and
the recently proposed GFlowNets as a generator of diverse candidate solutions,
with the objective to obtain a diverse batch of useful (as defined by some
utility function, for example, the predicted anti-microbial activity of a
peptide) and informative candidates after each round. We also propose a scheme
to incorporate existing labeled datasets of candidates, in addition to a reward
function, to speed up learning in GFlowNets. We present empirical results on
several biological sequence design tasks, and we find that our method generates
more diverse and novel batches with high scoring candidates compared to
existing approaches.


title: Some Recent Results on Ricci-Based Gravity Theories

abstract:
  Metric-affine theories in which the gravity Lagrangian is built using
(projectively invariant) contractions of the Ricci tensor with itself and with
the metric (Ricci-Based Gravity theories, or RBGs for short) are reviewed. The
goal is to provide a contextualized and coherent presentation of some recent
results. In particular, we focus on the correspondence that exists between the
field equations of these theories and those of general relativity, and comment
on how this can be used to build new solutions of physical interest. We also
discuss the formalism of junction conditions in the $f(R)$ case, and provide a
brief summary on current experimental and observational bounds on model
parameters.


title:
xTag: Mitigating Use-After-Free Vulnerabilities via Software-Based
  Pointer Tagging on Intel x86-64

abstract:
  Memory safety in complex applications implemented in unsafe programming
languages such as C/C++ is still an unresolved problem in practice. Many
different types of defenses have been proposed in the past to mitigate this
problem. The most promising next step is a tighter integration of the hardware
and software level: modern mitigation techniques are either accelerated using
hardware extensions or implemented in the hardware by extensions of the ISA. In
particular, memory tagging, as proposed by ARM or SPARC, promises to solve many
issues for practical memory safety. Unfortunately, Intel x86-64, which
represents the most important ISA for both the desktop and server domain, lacks
support for hardware-accelerated memory tagging, so memory tagging is not
considered practical for this platform.
  In this paper, we present the design and implementation of an efficient,
software-only pointer tagging scheme for Intel x86-64 based on a novel metadata
embedding scheme. The basic idea is to alias multiple virtual pages to one
physical page so that we can efficiently embed tag bits into a pointer.
Furthermore, we introduce several optimizations that significantly reduce the
performance impact of this approach to memory tagging. Based on this scheme, we
propose a novel use-after-free mitigation scheme, called xTag, that offers
better performance and strong security properties compared to state-of-the-art
methods. We also show how double-free vulnerabilities can be mitigated. Our
approach is highly compatible, allowing pointers to be passed back and forth
between instrumented and non-instrumented code without losing metadata, and it
is even compatible with inline assembly. We conclude that building exploit
mitigation mechanisms on top of our memory tagging scheme is feasible on Intel
x86-64, as demonstrated by the effective prevention of use-after-free bugs in
the Firefox web browser.


title: An Efficient Polyp Segmentation Network

abstract:
  Cancer is a disease that occurs as a result of the uncontrolled division and
proliferation of cells. Colon cancer is one of the most common types of cancer
in the world. Polyps that can be seen in the large intestine can cause cancer
if not removed with early intervention. Deep learning and image segmentation
techniques are used to minimize the number of polyps that goes unnoticed by the
experts during these interventions. Although these techniques perform well in
terms of accuracy, they require too many parameters. We propose a new model to
address this problem. Our proposed model requires fewer parameters as well as
outperforms the state-of-the-art models. We use EfficientNetB0 for the encoder
part, as it performs well in various tasks while requiring fewer parameters. We
use partial decoder, which is used to reduce the number of parameters while
achieving high accuracy in segmentation. Since polyps have variable appearances
and sizes, we use an asymmetric convolution block instead of a classic
convolution block. Then, we weight each feature map using a squeeze and
excitation block to improve our segmentation results. We used different splits
of Kvasir and CVC-ClinicDB datasets for training, validation, and testing,
while we use CVC- ColonDB, ETIS, and Endoscene datasets for testing. Our model
outperforms state-of-art models with a Dice metric of %71.8 on the ColonDB test
dataset, %89.3 on the EndoScene test dataset, and %74.8 on the ETIS test
dataset while requiring fewer parameters. Our model requires 2.626.337
parameters in total while the closest model in the state-of-the-art is U-Net++
with 9.042.177 parameters.


title:
Dynamics of single-mode nonclassicalities and quantum correlations in
  the Jaynes-Cummings model

abstract:
  Dynamics of atom-field correlations and single-mode nonclassicalities present
in the resonant Jaynes-Cummings model are investigated using negativity and
entanglement potential for a set of initial states. The study has revealed the
interplay between three different types of nonclassicality present in the model
and established that the nonclassicality is continuously exchanged between the
field and atom through the atom-field correlations. Further, it is observed
that the entanglement potential does not capture all the single-mode
nonclassicality and there exists some residual nonclassicality in the reduced
single-mode states at the output of the beam splitter which is not captured by
the entanglement in which single-mode nonclassicality is quantitatively mapped
in Asboth's criterion. Additional layers of beam splitters are added to deplete
all the nonclassicality and to reveal that almost all the residual
nonclassicality is captured with three layers of beam splitters. Further, the
reduced states of the atom and field have zero (non-zero) quantum coherence in
the Fock basis when the atom-field correlations are maximum if the field (or
atom) has zero (non-zero) quantum coherence initially.


title:
Graph-based Reinforcement Learning meets Mixed Integer Programs: An
  application to 3D robot assembly discovery

abstract:
  Robot assembly discovery is a challenging problem that lives at the
intersection of resource allocation and motion planning. The goal is to combine
a predefined set of objects to form something new while considering task
execution with the robot-in-the-loop. In this work, we tackle the problem of
building arbitrary, predefined target structures entirely from scratch using a
set of Tetris-like building blocks and a robotic manipulator. Our novel
hierarchical approach aims at efficiently decomposing the overall task into
three feasible levels that benefit mutually from each other. On the high level,
we run a classical mixed-integer program for global optimization of block-type
selection and the blocks' final poses to recreate the desired shape. Its output
is then exploited to efficiently guide the exploration of an underlying
reinforcement learning (RL) policy. This RL policy draws its generalization
properties from a flexible graph-based representation that is learned through
Q-learning and can be refined with search. Moreover, it accounts for the
necessary conditions of structural stability and robotic feasibility that
cannot be effectively reflected in the previous layer. Lastly, a grasp and
motion planner transforms the desired assembly commands into robot joint
movements. We demonstrate the performance of the proposed method on a set of
competitive simulated robot assembly discovery environments and report
performance and robustness gains compared to an unstructured end-to-end
approach. Videos are available at https://sites.google.com/view/rl-meets-milp .


title:
Few Shot Generative Model Adaption via Relaxed Spatial Structural
  Alignment

abstract:
  Training a generative adversarial network (GAN) with limited data has been a
challenging task. A feasible solution is to start with a GAN well-trained on a
large scale source domain and adapt it to the target domain with a few samples,
termed as few shot generative model adaption. However, existing methods are
prone to model overfitting and collapse in extremely few shot setting (less
than 10). To solve this problem, we propose a relaxed spatial structural
alignment method to calibrate the target generative models during the adaption.
We design a cross-domain spatial structural consistency loss comprising the
self-correlation and disturbance correlation consistency loss. It helps align
the spatial structural information between the synthesis image pairs of the
source and target domains. To relax the cross-domain alignment, we compress the
original latent space of generative models to a subspace. Image pairs generated
from the subspace are pulled closer. Qualitative and quantitative experiments
show that our method consistently surpasses the state-of-the-art methods in few
shot setting.


title:
Reactive collisions between electrons and BeT$^+$: Complete set of
  thermal rate coefficients up to 5000 K

abstract:
  Rate coefficients for the dissociative recombination, vibrational excitation
and vibrational de-excitation of the BeT$^{+}$ ion for all vibrational levels
of its ground electronic state ($
X\ensuremath{^{1}\Sigma^{+}},v_{i}^{+}=0,\dots,27$) are reported, including in
the calculation the contribution of super-excited states of the BeT complex
pertaining to three electronic symmetries - $^{2}\Pi$, $^{2}\Sigma^{+}$, and
$^{2}\Delta$. These data are suitable for the kinetic modeling of beryllium and
tritium containing plasma, as encountered in magnetic fusion devices with
beryllium walls (JET, ITER). In the present study we restrict ourselves to
incident electron energies from 10$^{-3}$ up to $2.7$ eV, and to electron
temperatures between $100$ and $5000$ K, respectively. Together with our
earlier and closely related studies on the BeH$^{+}$ and BeD$^{+}$ systems,
this present work completes the isotopic coverage for the beryllium monohydride
ions. The vibrational energy (rather than the vibrational quantum state) is
identified as a proper isotopic similarity parameter, e.g., for reduced but
still isotopically correct plasma chemistry models.


title:
On the complexity of invariant polynomials under the action of finite
  reflection groups

abstract:
  Let $G$ be a finite reflection group and $\mathbb{K}[x_1, \dots, x_n]$ be a
multivariate polynomial ring over a field $\mathbb{K}$. Let $\mathbb{K}[x_1,
\dots, x_n]^G$ be a set containing all invariant polynomials under the action
of $G$. Then the Chevalley-Shephard-Todd theorem states that there exists a
sequence of homogeneous polynomials $\eta_1, \dots, \eta_n$ such that for any
polynomial $f$ in $\mathbb{K}[x_1, \dots, x_n]^G$, there exists a unique
polynomial $f_{\rm new}$ in $\mathbb{K}[e_1, \dots, e_n]$, where $e_1, \dots,
e_n$ are new variables, such that $f_{\mathrm{new}}(\eta_1, \dots, \eta_n) =
f(x_1, \dots, x_n)$. In this paper, we study the arithmetic complexity to
compute $f_{\mathrm{new}}$ knowing $f$ and $(\eta_1, \dots, \eta_n)$. Our
algorithm works for any finite reflection group $G$ and any set of generators
$(\eta_1, \dots, \eta_n)$. Previously such a result was only known for $G$ a
symmetric group or a directed product of symmetric groups and $\eta_1, \dots,
\eta_n$ elementary symmetric functions.


title:
Why we should interpret density matrices as moment matrices: the case of
  (in)distinguishable particles and the emergence of classical reality

abstract:
  We introduce a formulation of quantum theory (QT) as a general probabilistic
theory but expressed via quasi-expectation operators (QEOs). This formulation
provides a direct interpretation of density matrices as quasi-moment matrices.
Using QEOs, we will provide a series of representation theorems, a' la de
Finetti, relating a classical probability mass function (satisfying certain
symmetries) to a quasi-expectation operator. We will show that QT for both
distinguishable and indistinguishable particles can be formulated in this way.
Although particles indistinguishability is considered a truly "weird" quantum
phenomenon, it is not special. We will show that finitely exchangeable
probabilities for a classical dice are as weird as QT. Using this connection,
we will rederive the first and second quantisation in QT for bosons through the
classical statistical concept of exchangeable random variables. Using this
approach, we will show how classical reality emerges in QT as the number of
identical bosons increases (similar to what happens for finitely exchangeable
sequences of rolls of a classical dice).


title:
The Generalized Fractional NU Method for the Diatomic Molecules in the
  Deng-Fan Model

abstract:
  A solution of the fractional N-dimensional radial Schrodinger equation with
the Deng-Fan potential is investigated by the generalized fractional NU method.
The analytical formulas of energy eigenvalues and corresponding eigen functions
for the Deng-Fan potential are generated. Furthermore, the current results are
applied to several diatomic molecules for the Deng-Fan potential as well as the
shifted Deng Fan potential. For both the Deng-Fan potential and its shifted
potential, the effect of the fractional parameter on the energy levels of
various diatomic molecules is examined numerically and graphically. We found
that the energy eigenvalues are gradually improved when the fractional
parameter increases. The energy spectra of various diatomic molecules are also
evaluated in three-dimensional space and higher dimensions. It is worthy to
note that the energy spectrum raises as the number of dimensions increases. In
addition, the dependence of the energy spectra of the Deng-Fan potential and
its shifted potential on the reduced mass, screening parameter, equilibrium
bond length, rotational and vibrational quantum numbers is illustrated. To
validate our findings, we estimate the energy levels of the Deng-Fan potential
and shifted Deng-Fan potential at the classical case for various diatomic
molecules and found that they are entirely compatible with earlier studies.


title: Some multivariable Rado numbers

abstract:
  The Rado number of an equation is a Ramsey-theoretic quantity associated to
the equation. Let $\mathcal{E}$ be a linear equation. Denote by
$\operatorname{R}_r(\mathcal{E})$ the minimal integer, if it exists, such that
any $r$-coloring of $[1,\operatorname{R}_r(\mathcal{E})]$ must admit a
monochromatic solution to $\mathcal{E}$. In this paper, we give upper and lower
bounds for the Rado number of $\sum_{i=1}^{m-2}x_i+kx_{m-1}=\ell x_{m}$, and
some exact values are also given. Furthermore, we derive some results for the
cases that $\ell=m=4$ and $m=5, \ell=k+i \ (1\leq i\leq 5)$. As a
generalization, the \emph{$r$-color Rado numbers} for linear equations
$\mathcal{E}_1,\mathcal{E}_2,...,\mathcal{E}_r$ is defined as the minimal
integer, if it exists, such that any $r$-coloring of
$[1,\operatorname{R}_r(\mathcal{E}_1,\mathcal{E}_2,...,\mathcal{E}_r)]$ must
admit a monochromatic solution to some $\mathcal{E}_i$, where $1\leq i\leq r$.
A lower bound for
$\operatorname{R}_r(\mathcal{E}_1,\mathcal{E}_2,...,\mathcal{E}_r)$ and the
exact values of $\operatorname{R}_2(x+y=z,\ell x=y)=5k$ and
$\operatorname{R}_2(x+y=z, x+a=y)$ was given by Lov\'{a}sz Local Lemma.


title:
Fermionic wave functions and Grassmann fields as possible sources of
  dark energy

abstract:
  We study a cosmological model with a fermionic field which can be interpreted
as a source of dark energy in the universe. Two different approaches were
considered, the first one with the fermion field represented by a standard
wave-function and the second one where the field is a Grassmann variable. For
this last case, in the massless limit, the intrinsic grassmannian property of
the field leads always to a negative pressure contribution, irrespective the
specific form of the potential. Both cases leads to a dark energy contribution
of the fermionic sector. The models are totally compatible with recent
cosmological data from Supernovae and Hubble parameter measurements. A brief
study of linear evolution of density perturbations shows that some of the small
scale problems related to standard model can be at least alleviated.


title: Follow-up observations of the binary system $\gamma$ Cep

abstract:
  We present new imaging and spectroscopic observations of the exoplanet host
star $\gamma$ Cep A, and of its low-mass stellar companion $\gamma$ Cep B. We
used AstraLux/CAHA to follow the orbital motion of the companion around its
primary, whose radial velocity was determined with spectra of the star, taken
with the spectrograph FLECHAS at the University Observatory Jena. We measured
the astrometry of $\gamma$ Cep B relative to its primary in all AstraLux images
and determined its apparent SDSS i'-band photometry, for which we obtained
i'=9.84$\pm$0.17 mag. Using stellar evolutionary models and the Gaia parallax
of the exoplanet host star, we derived the mass of $\gamma$ Cep B to be
0.39$\pm$0.03 M${_\odot}$. This is in good agreement with the mass of the
companion, derived from its NIR photometry, given in the literature. With the
detection limit, reached in our AstraLux images, we explored the detection
space of potential additional companions in the $\gamma$ Cep binary system. In
the background limited region at angular separations larger then 5 '' (or 69 au
of proj. separation) companions down to 0.11 M$_\odot$ are detectable around
$\gamma$ Cep A. The radial FoV, fully covered in our AstraLux images, exhibits
a radius of 11.2 ''. This allows the detection of companions with proj.
separations up to 155 au. However, except for $\gamma$ Cep B no additional
companions could be imaged around the exoplanet host star. We redetermined the
orbital solution of the $\gamma$ Cep binary system with the new AstraLux
astrometry of $\gamma$ Cep B and the additional radial velocities of $\gamma$
Cep A, obtained from our FLECHAS spectroscopy of the star, combined with
astrometric and radial velocity data from the literature. The determined
orbital elements were used to derive the system parameters and to calculate
specific future ephemeris for this intriguing exoplanet host binary star
system.


title:
YouTube-GDD: A challenging gun detection dataset with rich contextual
  information

abstract:
  An automatic gun detection system can detect potential gun-related violence
at an early stage that is of paramount importance for citizens security. In the
whole system, object detection algorithm is the key to perceive the environment
so that the system can detect dangerous objects such as pistols and rifles.
However, mainstream deep learning-based object detection algorithms depend
heavily on large-scale high-quality annotated samples, and the existing gun
datasets are characterized by low resolution, little contextual information and
little data volume. To promote the development of security, this work presents
a new challenging dataset called YouTube Gun Detection Dataset (YouTube-GDD).
Our dataset is collected from 343 high-definition YouTube videos and contains
5000 well-chosen images, in which 16064 instances of gun and 9046 instances of
person are annotated. Compared to other datasets, YouTube-GDD is "dynamic",
containing rich contextual information and recording shape changes of the gun
during shooting. To build a baseline for gun detection, we evaluate YOLOv5 on
YouTube-GDD and analyze the influence of additional related annotated
information on gun detection. YouTube-GDD and subsequent updates will be
released at https://github.com/UCAS-GYX/YouTube-GDD.


title:
NeReF: Neural Refractive Field for Fluid Surface Reconstruction and
  Implicit Representation

abstract:
  Existing neural reconstruction schemes such as Neural Radiance Field (NeRF)
are largely focused on modeling opaque objects. We present a novel neural
refractive field(NeReF) to recover wavefront of transparent fluids by
simultaneously estimating the surface position and normal of the fluid front.
Unlike prior arts that treat the reconstruction target as a single layer of the
surface, NeReF is specifically formulated to recover a volumetric normal field
with its corresponding density field. A query ray will be refracted by NeReF
according to its accumulated refractive point and normal, and we employ the
correspondences and uniqueness of refracted ray for NeReF optimization. We show
NeReF, as a global optimization scheme, can more robustly tackle refraction
distortions detrimental to traditional methods for correspondence matching.
Furthermore, the continuous NeReF representation of wavefront enables view
synthesis as well as normal integration. We validate our approach on both
synthetic and real data and show it is particularly suitable for sparse
multi-view acquisition. We hence build a small light field array and experiment
on various surface shapes to demonstrate high fidelity NeReF reconstruction.


title: An $\ell_1$-norm-mass inequality for complete manifolds

abstract:
  We generalize an inequality of Besson-Courtois-Gallot about volume and
simplicial volume of closed manifolds to the $\ell_1$-norm of all the homology
classes of complete manifolds. The inequality involves the critical exponent of
the fundamental group of the manifold and the mass of the homology classes.


title: Motron: Multimodal Probabilistic Human Motion Forecasting

abstract:
  Autonomous systems and humans are increasingly sharing the same space. Robots
work side by side or even hand in hand with humans to balance each other's
limitations. Such cooperative interactions are ever more sophisticated. Thus,
the ability to reason not just about a human's center of gravity position, but
also its granular motion is an important prerequisite for human-robot
interaction. Though, many algorithms ignore the multimodal nature of humans or
neglect uncertainty in their motion forecasts. We present Motron, a multimodal,
probabilistic, graph-structured model, that captures human's multimodality
using probabilistic methods while being able to output deterministic
maximum-likelihood motions and corresponding confidence values for each mode.
Our model aims to be tightly integrated with the robotic
planning-control-interaction loop; outputting physically feasible human motions
and being computationally efficient. We demonstrate the performance of our
model on several challenging real-world motion forecasting datasets,
outperforming a wide array of generative/variational methods while providing
state-of-the-art single-output motions if required. Both using significantly
less computational power than state-of-the art algorithms.


title:
Evaluating a strategy for measuring deformations of the primary
  reflector of the Green Bank telescope using a terrestrial laser scanner

abstract:
  Astronomical observations in the molecule rich 3 mm window using large
reflector antennas provide a unique view of the Universe. To efficiently carry
out these observations gravitational and thermal deformations have to be
corrected. Terrestrial laser scanners have been used to measure the
deformations in large reflector antennas due to gravity, but have not yet been
used for measuring thermal deformations. In this work we investigate the use of
a terrestrial laser scanner to measure thermal deformations on the primary
reflector of the Green Bank Telescope (GBT). Our method involves the use of
differential measurements to reduce the systematic effects of the terrestrial
laser scanner. We use the active surface of the primary reflector of the GBT to
validate our method and explore its limitations. We find that when using
differential measurements it is possible to accurately measure deformations
corresponding to different Zernike polynomials down to an amplitude of 60
$\mu$m. The difference between the amplitudes of known deformations and those
measured are $<140~\mu$m when the wind speed is $\lesssim2$ m s$^{-1}$. From
these differences we estimate that it should be possible to bring the surface
error of the GBT down to $240\pm6~\mu$m. This suggests that using a commercial
off-the-shelf terrestrial laser scanner it is possible to measure deformations
induced by thermal gradients on a large parabolic reflector.


title: Physicality, Modeling and Making in a Computational Physics Class

abstract:
  Computation is intertwined with essentially all aspects of physics research
and is invaluable for physicists' careers. Despite its disciplinary importance,
integration of computation into physics education remains a challenge and,
moreover, has tended to be constructed narrowly as a route to solving physics
problems. Here, we broaden Physics Education Research's conception of
computation by constructing an epistemic \emph{metamodel} -- a model of
modeling -- incorporating insights on computational modeling from the
philosophy of science and prior work. The metamodel is formulated in terms of
practices, things physicists do, and how these inform one another. We
operationalize this metamodel in an educational environment that incorporates
making, the creation of shared physical and digital artifacts, intended to
promote students' agency, creativity and self-expression alongside doing
physics. We present a content analysis of student work from initial
implementations of this approach to illustrate the very complex epistemic
maneuvers students make as they engaged in computational modeling. We
demonstrate how our metamodel can be used to understand student practices, and
conclude with implications of the metamodel for instruction and future
research.


title:
Bots don't Vote, but They Surely Bother! A Study of Anomalous Accounts
  in a National Referendum

abstract:
  The Web contains several social media platforms for discussion, exchange of
ideas, and content publishing. These platforms are used by people, but also by
distributed agents known as bots. Although bots have existed for decades, with
many of them being benevolent, their influence in propagating and generating
deceptive information in the last years has increased. Here we present a
characterization of the discussion on Twitter about the 2020 Chilean
constitutional referendum. The characterization uses a profile-oriented
analysis that enables the isolation of anomalous content using machine
learning. As result, we obtain a characterization that matches national vote
turnout, and we measure how anomalous accounts (some of which are automated
bots) produce content and interact promoting (false) information.


title: Cybersecurity Playbook Sharing with STIX 2.1

abstract:
  Understanding that interoperable machine-readable security playbooks will
become a fundamental component of defenders' arsenal to decrease attack
detection and response times, it is time to consider their position in sharing
efforts. This report documents the process of extending Structured Threat
Information eXpression (STIX) version 2.1, using the available extension
definition mechanism, to enable sharing machine-readable security playbooks
and, in particular, Collaborative Automated Course of Action Operations (CACAO)
playbooks.


title: Linearisability of divergence-free fields along invariant 2-tori

abstract:
  We find conditions under which the restriction of a divergence-free vector
field $B$ to an invariant toroidal surface $S$ is linearisable. The main
results are similar in conclusion to Arnold's Structure Theorems but require
weaker assumptions than the commutation $[B,\nabla\times B] = 0$. Relaxing the
need for a first integral of $B$ (also known as a flux function), we assume the
existence of a solution $u : S \to \mathbb{R}$ to the cohomological equation
$B|_S(u) = \partial_n B$ on a toroidal surface $S$ mutually invariant to $B$
and $\nabla \times B$. The right hand side $\partial_n B$ is a normal surface
derivative available to vector fields tangent to $S$. In this situation, we
show that the field $B$ on $S$ is either identically zero or nowhere vanishing
with $B|_S/\|B\|^2 |_S$ being linearisable. We are calling the latter the
semi-linearisability of $B$ (with proportionality $\|B\|^2 |_S$). The
non-vanishing property relies on Bers' results in pseudo-analytic function
theory about a generalised Laplace-Beltrami equation arising from Witten
cohomology deformation. With the use of de Rham cohomology, we also point out a
Diophantine integral condition where one can conclude that $B|_S$ itself is
linearisable. The linearisability of $B|_S$ is fundamental to the so-called
magnetic coordinates, which are central to the theory of magnetically confined
plasmas.


title: A Jacobian Free Deterministic Method for Solving Inverse Problems

abstract:
  An effective numerical method is presented for optimizing model parameters
that can be applied to any type of system of non-linear equations and any
number of data-points, which does not require explicit formulation of the
objective function or its partial derivatives. The numerics are reduced to
solving a non-linear least squares problem, which uses the Levenberg-Marquardt
algorithm and the Jacobian is approximated by applying rank-one updates using
Broyden's method. An advantage of this methodology over conventional approaches
is that the partial derivatives of the objective function do not have to be
analytically calculated. For instance, there may be situations where one cannot
formulate the partial derivatives, such as cases involving an objective
function that itself contains a nested optimization problem. Moreover, a line
search algorithm is also described that ensures that the Armijo conditions are
satisfied and that convergence is assured, which makes the success of the
approach insensitive to the initial estimates of the model parameters. The
foregoing numerical methods are described with respect to the development of
the Optima software to solve inverse problems, which are reduced to non-linear
least squares problems. This computational approach has proven to be
particularly useful at solving inverse problems of very complex physical models
that cannot be optimized directly in a practical way.


title: Sharp Rosenthal-type inequalities for mixtures and log-concave variables

abstract:
  We obtain Rosenthal-type inequalities with sharp constants for moments of
sums of independent random variables which are mixtures of a fixed
distribution. We also identify extremisers in log-concave settings when the
moments of summands are individually constrained.


title:
Fermionic and Bosonic Greybody Factors as well as Quasinormal Modes for
  Charged Taub NUT Black Holes

abstract:
  The paper studies the spinorial wave equations, namely the Dirac and the
Klein Gordon equations, as well as the greybody radiations and quasinormal
modes (QNMs) of the charged Taub NUT black hole (CTNBH). To obtain fermionic
greybody factors (GFs) and QNMs, we study the charged fermions by employing the
Dirac equation. To this end, we use a null tetrad in the Newman-Penrose
formalism. Then, we separate the Dirac equation into radial and angular sets.
Using the obtained radial equations, we convert them into the typical one
dimensional Schr\"{o}dinger like wave equations with the aid of tortoise
coordinate and derive the effective potentials. For bosonic GFs and QNMs, we
study the Klein Gordon equation in the CTNBH geometry and obtain the radial
equation. We then derive the effective potential and investigate the effect of
NUT parameter on it. We show that while the fermionic QNMs and GFs individually
increase with the increasing NUT parameter, the increase of bosonic GFs with
increasing NUT parameter is overwhelmingly greater than that of the bosonic
QNMs.


title: Reply to Galilean invariance without superluminal particles

abstract:
  In a recent comment [arXiv:2112.05658], Grudka and W\'ojcik object to some of
our earlier claims concerning a link between fundamental postulates of quantum
theory and special relativity extended to superluminal frames of reference. In
this short reply we refute these criticisms.


title: A Reply to "On Salum's Algorithm for X3SAT"

abstract: This paper is a reply to "On Salum's Algorithm for X3SAT" (arXiv:2104.02886)


title:
Kink dynamics under odd perturbations for (1+1)-scalar field models with
  one internal mode

abstract:
  We consider odd symmetric (1+1)-scalar field models with one internal mode.
Under natural and robust assumptions, including the Fermi golden rule, we prove
the asymptotic stability of the kink by odd perturbations in the energy space.
For example, the result applies to the $\phi^4$ model with odd symmetry and
some of its perturbations.


title:
Nonadiabatic vibronic effects in single-molecule junctions: A
  theoretical study using the hierarchical equations of motion approach

abstract:
  The interaction between electronic and vibrational degrees of freedom is an
important mechanism in nonequilibrium charge transport through molecular
nanojunctions. While adiabatic polaron-type coupling has been studied in great
detail, new transport phenomena arise for nonadiabatic coupling scenarios
corresponding to a breakdown of the Born-Oppenheimer approximation. Employing
the numerically exact hierarchical equations of motion approach, we analyze the
effect of nonadiabatic electronic-vibrational coupling on electron transport in
molecular junctions considering a series of models with increasing complexity.
The results reveal a significant influence of nonadiabatic coupling on the
transport characteristics and a variety of interesting effects, including
negative differential conductance. The underlying mechanisms are analyzed in
detail.


title: The structure of the linearizer of a connected complex Lie group

abstract:
  A theorem of Morimoto asserts that a connected abelian complex Lie group $A$
can be decomposed into a direct product of a group on which all holomorphic
functions are constant, a finite number of copies of $\mathbb{C}^\times$, and a
vector group. Here we prove that in the case when $A$ is a complex linearizer
of a connected complex Lie group, the last factor in the product is trivial.


title: Runtime Enforcement of Hyperproperties

abstract:
  An enforcement mechanism monitors a reactive system for undesired behavior at
runtime and corrects the system's output in case it violates the given
specification. In this paper, we study the enforcement problem for
hyperproperties, i.e., properties that relate multiple computation traces to
each other. We elaborate the notion of sound and transparent enforcement
mechanisms for hyperproperties in two trace input models: 1) the parallel trace
input model, where the number of traces is known a-priori and all traces are
produced and processed in parallel and 2) the sequential trace input model,
where traces are processed sequentially and no a-priori bound on the number of
traces is known. For both models, we study enforcement algorithms for
specifications given as formulas in universally quantified HyperLTL, a temporal
logic for hyperproperties. For the parallel model, we describe an enforcement
mechanism based on parity games. For the sequential model, we show that
enforcement is in general undecidable and present algorithms for reasonable
simplifications of the problem (partial guarantees or the restriction to safety
properties). Furthermore, we report on experimental results of our prototype
implementation for the parallel model.


title:
Mu-synthesis PID Control of Full-Car with Parallel Active Link
  Suspension Under Variable Payload

abstract:
  This paper presents a combined mu-synthesis PID control scheme, employing a
frequency separation paradigm, for a recently proposed novel active suspension,
the Parallel Active Link Suspension (PALS). The developed mu-synthesis control
scheme is superior to the conventional H-infinity control, previously designed
for the PALS, in terms of ride comfort and road holding (higher frequency
dynamics), with important realistic uncertainties, such as in vehicle payload,
taken into account. The developed PID control method is applied to guarantee
good chassis attitude control capabilities and minimization of pitch and roll
motions (low frequency dynamics). A multi-objective control method, which
merges the aforementioned PID and mu-synthesis-based controls is further
introduced to achieve simultaneously the low frequency mitigation of attitude
motions and the high frequency vibration suppression of the vehicle. A
seven-degree-of-freedom Sport Utility Vehicle (SUV) full car model with PALS,
is employed in this work to test the synthesized controller by nonlinear
simulations with different ISO-defined road events and variable vehicle
payload. The results demonstrate the control scheme's significant robustness
and performance, as compared to the conventional passive suspension as well as
the actively controlled PALS by conventional H-infinity control, achieved for a
wide range of vehicle payload considered in the investigation.


title:
Numerical solution of optimal control of atherosclerosis using direct
  and indirect methods with shooting/collocation approach

abstract:
  We present a direct numerical method for the solution of an optimal control
problem controlling the growth of LDL, HDL and plaque. The optimal control
problem is constrained with a system of coupled nonlinear free and mixed
boundary partial differential equations consisting of three parabolics one
elliptic and one ordinary differential equations. In the first step, the
original problem is transformed from a free boundary problem into a fixed one
and from the mixed boundary condition to a Neumann one. Then, employing a fixed
point-collocation method, we solve the optimal control problem. In each step of
the fixed point iteration, the problem is changed to a linear one and then, the
equations are solved using the collocation method bringing about an NLP which
is solved using sequential quadratic programming. Then, the obtained solution
is verified using indirect methods originating from the first-order optimality
conditions. Numerical results are considered to illustrate the efficiency of
methods.


title:
Quantitative characterisation of the layered structure within
  lithium-ion batteries using ultrasonic resonance

abstract:
  Lithium-ion batteries (LIBs) are becoming an important energy storage
solution to achieve carbon neutrality, but it remains challenging to
characterise their internal states for the assurance of performance, durability
and safety. This work reports a simple but powerful non-destructive
characterisation technique, based on the formation of ultrasonic resonance from
the repetitive layers within LIBs. A physical model is developed from the
ground up, to interpret the results from standard experimental ultrasonic
measurement setups. As output, the method delivers a range of critical pieces
of information about the inner structure of LIBs, such as the number of layers,
the average thicknesses of electrodes, the image of internal layers, and the
states of charge variations across individual layers. This enables the
quantitative tracking of internal cell properties, potentially providing new
means of quality control during production processes, and tracking the states
of health and charge during operation.


title:
The principle of maximum entropy and the distribution of mass in
  galaxies

abstract:
  We do not have a final answer to the question of why galaxies choose a
particular internal mass distribution. Here we examine whether the distribution
is set by thermodynamic equilibrium (TE). Traditionally, TE is discarded for a
number of reasons including the inefficiency of two-body collisions to
thermalize the mass distribution in a Hubble time, and the fact that the mass
distribution maximizing the classical Boltzmann-Gibbs entropy is unphysical.
These arguments are questionable. In particular, when the Tsallis entropy that
describes self-gravitating systems is used to define TE, the mass distributions
that result (i.e., the polytropes) are physically sensible. This work spells
out this and other arguments for TE, and presents the polytropes and their
properties. It puts forward empirical evidence for the mass distribution
observed in galaxies to be consistent with polytropes. It compares polytropes
with Sersic functions and it shows how the DM halos resulting from cosmological
numerical simulations become polytropes when efficient collisions are allowed.
It also discusses pathways to thermalization bypassing two-body collisions. It
finally outlines future developments including deciphering whether or not DM
particles collide efficiently.


title: About Isogenies between some $K3$ surfaces

abstract:
  We study some 2 and 3-isogenies on the singular $K3$ surface $Y_{10}$ of
discriminant 72 and belonging to the Ap\'ery-Fermi pencil $(Y_k)$, and find on
it many interesting properties. For example some of its elliptic fibrations
with 3-torsion section induce by 3-isogeny either an elliptic fibration of
$Y_2$, the unique $K3$ surface of discriminant 8, or an elliptic fibration of
other $K3$ surfaces of discriminant 72.


title:
Deposition of nanosized amino acid functionalized bismuth oxido clusters
  on gold surfaces

abstract:
  Bismuth compounds are of growing interest with regard to potential
applications in catalysis, medicine and electronics, for which their
environmentally benign nature is one of the key factors. The most common
starting material is bismuth nitrate, which easily hydrolyses to give a large
number of condensation products. The so-called bismuth subnitrates are composed
of bismuth oxido clusters of varying composition and nuclearity. One reason
that hampers the further development of bismuth oxido-based materials, however
is the low solubility of the subnitrates, which makes targeted immobilisation
on substrates challenging. We present an approach towards solubilisation of
bismuth oxido clusters by introducing an amino carboxylate as functional group
and a study of the growth mode of these atom-precise nanoclusters on gold
surfaces. For this purpose the bismuth oxido cluster
[Bi38O45(NO3)20(dmso)28](NO3)4*4dmso (dmso=dimethyl sulfoxide) was reacted with
the sodium salt of tert-butyloxycabonyl(Boc)-protected phenylalanine (Phe) to
give the soluble and chiral nanocluster [Bi38O45(Boc-Phe)24(dmso)9]. The
hydrodynamic diameter of the cluster was estimated with (1.4-1.6) nm (in CH3CN)
and (2.2 nm-2.9) nm (in Ethanol) based on dynamic light scattering (DLS). The
full exchange of the nitrates by the amino carboxylates was proven by NMR and
FTIR as well as elemental analysis (EA) and XPS. The solubility of the bismuth
oxido cluster in a protic as well as an aprotic polar organic solvent and the
growth mode of the clusters on Au upon spin-, dip-, and drop-coating on gold
surfaces were studied. Successful deposition of bismuth oxido cluster was
proven by powder XRD, FTIR, and XPS while the microstructure of the resulting
films was investigated as a function of the deposition method and the solvent
used by SEM, AFM, and optical microscopy.


title:
Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human
  Activity Recognition

abstract:
  Sensor-based human activity recognition (HAR) is a paramount technology in
the Internet of Things services. HAR using representation learning, which
automatically learns a feature representation from raw data, is the mainstream
method because it is difficult to interpret relevant information from raw
sensor data to design meaningful features. Ensemble learning is a robust
approach to improve generalization performance; however, deep ensemble learning
requires various procedures, such as data partitioning and training multiple
models, which are time-consuming and computationally expensive. In this study,
we propose Easy Ensemble (EE) for HAR, which enables the easy implementation of
deep ensemble learning in a single model. In addition, we propose input masking
as a method for diversifying the input for EE. Experiments on a benchmark
dataset for HAR demonstrated the effectiveness of EE and input masking and
their characteristics compared with conventional ensemble learning methods.


title:
Asymptotic normality in linear regression with approximately sparse
  structure

abstract:
  In this paper we study the asymptotic normality in high-dimensional linear
regression. We focus on the case where the covariance matrix of the regression
variables has a KMS structure, in asymptotic settings where the number of
predictors, $p$, is proportional to the number of observations, $n$. The main
result of the paper is the derivation of the exact asymptotic distribution for
the suitably centered and normalized squared norm of the product between
predictor matrix, $\mathbb{X}$, and outcome variable, $Y$, i.e. the statistic
$\|\mathbb{X}'Y\|_{2}^{2}$. Additionally, we consider a specific case of
approximate sparsity of the model parameter vector $\beta$ and perform a
Monte-Carlo simulation study. The simulation results suggest that the statistic
approaches the limiting distribution fairly quickly even under high variable
multi-correlation and relatively small number of observations, suggesting
possible applications to the construction of statistical testing procedures for
the real-world data and related problems.


title: Introduction to Automatic Backward Filtering Forward Guiding

abstract:
  In this document I aim to give an informal treatment of automatic Backward
Filtering Forward Guiding, a general algorithm for conditional sampling from a
Markov process on a directed acyclic graph. I'll show that the underlying ideas
can be understood with a basic background in probability and statistics. The
more technical treatment is the paper Automatic backward filtering forward
guiding for Markov processes and graphical models (Van der Meulen and Schauer,
2021). I specifically assume some background knowledge on likelihood based
inference and Bayesian statistics. The final sections are more demanding and
assume familiarity with continuous-time stochastic processes constructed from
their infinitesimal generator.
  Clearly, all work discussed here is the result of research carried out over
the past decade together with various collaborators, most importantly Moritz
Schauer (Chalmers University of Technology and University of Gothenburg,
Sweden). Section 8 is based on joint work with Marcin Mider (Trium Analysis
Online GmbH, Germany) and Frank Sch\"afer (University of Basel, Switzerland) as
well.


title:
Robust Local Preserving and Global Aligning Network for Adversarial
  Domain Adaptation

abstract:
  Unsupervised domain adaptation (UDA) requires source domain samples with
clean ground truth labels during training. Accurately labeling a large number
of source domain samples is time-consuming and laborious. An alternative is to
utilize samples with noisy labels for training. However, training with noisy
labels can greatly reduce the performance of UDA. In this paper, we address the
problem that learning UDA models only with access to noisy labels and propose a
novel method called robust local preserving and global aligning network
(RLPGA). RLPGA improves the robustness of the label noise from two aspects. One
is learning a classifier by a robust informative-theoretic-based loss function.
The other is constructing two adjacency weight matrices and two negative weight
matrices by the proposed local preserving module to preserve the local topology
structures of input data. We conduct theoretical analysis on the robustness of
the proposed RLPGA and prove that the robust informative-theoretic-based loss
and the local preserving module are beneficial to reduce the empirical risk of
the target domain. A series of empirical studies show the effectiveness of our
proposed RLPGA.


title: Breathing skyrmions in chiral antiferromagnets

abstract:
  Breathing oscillations of skyrmions in chiral antiferromagnets can be excited
by a temporal modification of the Dzyaloshinskii-Moriya interaction or
magnetocrystalline anisotropy strength. We employ an adiabatic approximation
and derive a formula for the potential that directly implies breathing
oscillations. We study the nonlinear regime and the features of larger
amplitude oscillations. We show that there is a maximum amplitude supported by
the potential. As a consequence, we predict theoretically and observe
numerically skyrmion annihilation events due to excitation of large amplitude
breathing oscillations. The process is efficient when the skyrmion is mildly
excited so that its radius initially grows, while the annihilation event is
eventually induced by the internal breathing dynamics. We reveal the
counter-intuitive property that the skyrmion possesses a nonzero kinetic energy
at the moment of its annihilation. Finally, the frequency of small amplitude
breathing oscillations is determined.


title: Trie-based ranking of quantum many-body states

abstract:
  Ranking bit patterns -- finding the index of a given pattern in an ordered
sequence -- is a major bottleneck scaling up numerical quantum many-body
calculations, as fermionic and hard-core bosonic states translate naturally to
bit patterns. Traditionally, ranking is done by bisectioning search, which has
poor cache performance on modern machines. We instead propose to use tries
(prefix trees), thereby achieving a two- to ten-fold speed-up in numerical
experiments with only moderate memory overhead. For the important problem of
ranking permutations, the corresponding tries can be compressed. These
compressed "staggered" lookups allow for a considerable speed-up while
retaining the memory requirements of prior algorithms based on the
combinatorial number system.


title: AI for Next Generation Computing: Emerging Trends and Future Directions

abstract:
  Autonomic computing investigates how systems can achieve (user) specified
control outcomes on their own, without the intervention of a human operator.
Autonomic computing fundamentals have been substantially influenced by those of
control theory for closed and open-loop systems. In practice, complex systems
may exhibit a number of concurrent and inter-dependent control loops. Despite
research into autonomic models for managing computer resources, ranging from
individual resources (e.g., web servers) to a resource ensemble (e.g., multiple
resources within a data center), research into integrating Artificial
Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and
performance at scale continues to be a fundamental challenge. The integration
of AI/ML to achieve such autonomic and self-management of systems can be
achieved at different levels of granularity, from full to human-in-the-loop
automation. In this article, leading academics, researchers, practitioners,
engineers, and scientists in the fields of cloud computing, AI/ML, and quantum
computing join to discuss current research and potential future directions for
these fields. Further, we discuss challenges and opportunities for leveraging
AI and ML in next generation computing for emerging computing paradigms,
including cloud, fog, edge, serverless and quantum computing environments.


title: Robustly-reliable learners under poisoning attacks

abstract:
  Data poisoning attacks, in which an adversary corrupts a training set with
the goal of inducing specific desired mistakes, have raised substantial
concern: even just the possibility of such an attack can make a user no longer
trust the results of a learning system. In this work, we show how to achieve
strong robustness guarantees in the face of such attacks across multiple axes.
  We provide robustly-reliable predictions, in which the predicted label is
guaranteed to be correct so long as the adversary has not exceeded a given
corruption budget, even in the presence of instance targeted attacks, where the
adversary knows the test example in advance and aims to cause a specific
failure on that example. Our guarantees are substantially stronger than those
in prior approaches, which were only able to provide certificates that the
prediction of the learning algorithm does not change, as opposed to certifying
that the prediction is correct, as we are able to achieve in our work.
Remarkably, we provide a complete characterization of learnability in this
setting, in particular, nearly-tight matching upper and lower bounds on the
region that can be certified, as well as efficient algorithms for computing
this region given an ERM oracle. Moreover, for the case of linear separators
over logconcave distributions, we provide efficient truly polynomial time
algorithms (i.e., non-oracle algorithms) for such robustly-reliable
predictions.
  We also extend these results to the active setting where the algorithm
adaptively asks for labels of specific informative examples, and the difficulty
is that the adversary might even be adaptive to this interaction, as well as to
the agnostic learning setting where there is no perfect classifier even over
the uncorrupted data.


title: Studying $\Delta L=2$ Lepton Flavor Violation with Muons

abstract:
  Flavor violating processes in the lepton sector have highly suppressed
branching ratios in the standard model. Thus, observation of lepton flavor
violation (LFV) constitutes a clear indication of physics beyond the standard
model (BSM). We review new physics searches in the processes that violate the
conservation of lepton (muon) flavor by two units with muonia and
muonium--antimuonium oscillations.


title:
Feedforward PID Control of Full-Car with Parallel Active Link Suspension
  for Improved Chassis Attitude Stabilization

abstract:
  PID control is commonly utilized in an active suspension system to achieve
desirable chassis attitude, where, due to delays, feedback information has much
difficulty regulating the roll and pitch behavior, and stabilizing the chassis
attitude, which may result in roll over when the vehicle steers at a large
longitudinal velocity. To address the problem of the feedback delays in chassis
attitude stabilization, in this paper, a feedforward control strategy is
proposed to combine with a previously developed PID control scheme in the
recently introduced Parallel Active Link Suspension (PALS). Numerical
simulations with a nonlinear multi-body vehicle model are performed, where a
set of ISO driving maneuvers are tested. Results demonstrate the
feedforward-based control scheme has improved suspension performance as
compared to the conventional PID control, with faster speed of convergence in
brake in a turn and step steer maneuvers, and surviving the fishhook maneuver
(although displaying two-wheel lift-off) with 50 mph maneuver entrance speed at
which conventional PID control rolls over.


title:
Localization Schemes: A Framework for Proving Mixing Bounds for Markov
  Chains

abstract:
  Two recent and seemingly-unrelated techniques for proving mixing bounds for
Markov chains are: (i) the framework of Spectral Independence, introduced by
Anari, Liu and Oveis Gharan, and its numerous extensions, which have given rise
to several breakthroughs in the analysis of mixing times of discrete Markov
chains and (ii) the Stochastic Localization technique which has proven useful
in establishing mixing and expansion bounds for both log-concave measures and
for measures on the discrete hypercube. In this paper, we introduce a framework
which connects ideas from both techniques. Our framework unifies, simplifies
and extends those two techniques. In its center is the concept of a
localization scheme which, to every probability measure, assigns a martingale
of probability measures which localize in space as time evolves. As it turns
out, to every such scheme corresponds a Markov chain, and many chains of
interest appear naturally in this framework. This viewpoint provides tools for
deriving mixing bounds for the dynamics through the analysis of the
corresponding localization process. Generalizations of concepts of Spectral
Independence and Entropic Independence naturally arise from our definitions,
and in particular we recover the main theorems in the spectral and entropic
independence frameworks via simple martingale arguments (completely bypassing
the need to use the theory of high-dimensional expanders). We demonstrate the
strength of our proposed machinery by giving short and (arguably) simpler
proofs to many mixing bounds in the recent literature, including giving the
first $O(n \log n)$ bound for the mixing time of Glauber dynamics on the
hardcore-model (of arbitrary degree) in the tree-uniqueness regime.


title: Weakly Flux-Tunable Superconducting Qubit

abstract:
  Flux-tunable qubits are a useful resource for superconducting quantum
processors. They can be used to perform cPhase gates, facilitate fast reset
protocols, avoid qubit-frequency collisions in large processors, and enable
certain fast readout schemes. However, flux-tunable qubits suffer from a
trade-off between their tunability range and sensitivity to flux noise.
Optimizing this trade-off is particularly important for enabling fast,
high-fidelity, all-microwave cross-resonance gates in large, high-coherence
processors. This is mainly because cross-resonance gates set stringent
conditions on the frequency landscape of neighboring qubits, which are
difficult to satisfy with non-tunable transmons due to their relatively large
fabrication imprecision. To solve this problem, we realize a coherent,
flux-tunable, transmon-like qubit, which exhibits a frequency tunability range
as small as 43 MHz, and whose frequency, anharmonicity and tunability range are
set by a few experimentally achievable design parameters. Such a weakly tunable
qubit is useful for avoiding frequency collisions in a large lattice while
limiting its susceptibility to flux noise.


title: On the intrinsic dimensionality of Covid-19 data: a global perspective

abstract:
  This paper aims to develop a global perspective of the complexity of the
relationship between the standardised per-capita growth rate of Covid-19 cases,
deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a
country's stringency of lockdown policies. To achieve our goal, we use a
heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture
model, called Hidalgo. We identify that the Covid-19 dataset may project onto
two low-dimensional manifolds without significant information loss. The low
dimensionality suggests strong dependency among the standardised growth rates
of cases and deaths per capita and the OxCGRT Covid-19 Stringency Index for a
country over 2020-2021. Given the low dimensional structure, it may be feasible
to model observable Covid-19 dynamics with few parameters. Importantly, we
identify spatial autocorrelation in the intrinsic dimension distribution
worldwide. Moreover, we highlight that high-income countries are more likely to
lie on low-dimensional manifolds, likely arising from aging populations,
comorbidities, and increased per capita mortality burden from Covid-19.
Finally, we temporally stratify the dataset to examine the intrinsic dimension
at a more granular level throughout the Covid-19 pandemic.


title:
Curriculum-based Reinforcement Learning for Distribution System Critical
  Load Restoration

abstract:
  This paper focuses on the critical load restoration problem in distribution
systems following major outages. To provide fast online response and optimal
sequential decision-making support, a reinforcement learning (RL) based
approach is proposed to optimize the restoration. Due to the complexities
stemming from the large policy search space, renewable uncertainty, and
nonlinearity in a complex grid control problem, directly applying RL algorithms
to train a satisfactory policy requires extensive tuning to be successful. To
address this challenge, this paper leverages the curriculum learning (CL)
technique to design a training curriculum involving a simpler steppingstone
problem that guides the RL agent to learn to solve the original hard problem in
a progressive and more efficient manner. We demonstrate that compared with
direct learning, CL facilitates controller training to achieve better
performance. In the experiments, to study realistic scenarios where renewable
forecasts used for decision-making are in general imperfect, the trained RL
controllers are compared with two model predictive controllers (MPCs) using
renewable forecasts with different error levels and observe how these
controllers can hedge against the uncertainty. Results show that RL controllers
are less susceptible to forecast errors than the baseline MPCs and can provide
a more reliable restoration process.


title: Is the dark energy equation of state parameter singular?

abstract:
  A dark energy with a negative energy density in the past can simultaneously
address various cosmological tensions, and if it is to be positive today to
drive the observed acceleration of the universe, we show that, it should have a
pole in its equation of state parameter. More precisely, in a spatially uniform
universe, a perfect fluid (submitting to the usual continuity equation of local
energy conservation) whose energy density $\rho(z)$ vanishes at an isolated
zero $z=z_p$, necessarily has a pole in its equation of state parameter $w(z)$
at $z_p$, and, $w(z)$ diverges to positive infinity in the limit $z\to z_p^+$
and it diverges to negative infinity in the limit $z\to z_p^-$ -- we assume
that $z_p$ is not an accumulation point for poles of $w(z)$. However, the
converse statement that this kind of a pole of $w(z)$ corresponds to a
vanishing energy density at that point, is not true as we show by a
counterexample.


title:
Emergence of spin singlets with inhomogeneous gaps in the kagome
  Heisenberg antiferromagnets Zn-barlowite and herbertsmithite

abstract:
  The kagome Heisenberg antiferromagnet formed by frustrated spins arranged in
a lattice of corner-sharing triangles is a prime candidate for hosting a
quantum spin liquid (QSL) ground state consisting of entangled spin singlets.
But the existence of various competing states makes a convincing theoretical
prediction of the QSL ground state difficult, calling for experimental clues
from model materials. The kagome lattice materials Zn-barlowite
ZnCu$_{3}$(OD)$_{6}$FBr and herbertsmithite ZnCu$_{3}$(OD)$_{6}$Cl$_2$ do not
exhibit long range order, and they are considered the best realizations of the
kagome Heisenberg antiferromagnet known to date. Here we use $^{63}$Cu nuclear
quadrupole resonance combined with the inverse Laplace transform (ILT) to probe
locally the inhomogeneity of delicate quantum ground states affected by
disorder. We present direct evidence for the gradual emergence of spin singlets
with spatially varying excitation gaps, but even at temperatures far below the
super-exchange energy scale their fraction is limited to approximately 60\% of
the total spins. Theoretical models need to incorporate the role of disorder to
account for the observed inhomogeneously gapped behaviour.


title: Fluid Flow Induced Deformation of a Boundary Hair

abstract:
  The deformation of a dense carpet of hair due to Stokes flow in a channel can
be described by a nonlinear integro-differential equation for the shape of a
single hair, which possesses several solutions for a given choice of
parameters. While being posed in a previous study and bearing resemblance to
the pendulum problem from mechanics, this equation has not been analytically
solved until now. Despite the presence on an integral with a nonlinear
functional dependence on the dependent variable, the system is integrable. We
compare the analytically obtained solution to a finite-difference numerical
approach, identify the physically realizable solution branch, and briefly study
the solution structure through a conserved energy-like quantity. Time-dependent
fluid-structure interactions are a rich and complex subject to investigate and
we argue that the solution discussed herein can be used as a basis for
understanding these systems.


title:
Commutative algebras of Toeplitz operators on the Bergman space
  revisited: Spectral theorem approach

abstract:
  For three standard models of commutative algebras generated by Toeplitz
operators in the weighted analytic Bergman space on the unit disk, we find
their representations as the algebras of bounded functions of certain unbounded
self-adjoint operators. We discuss main properties of these representation and,
especially, describe relations between properties of the spectral function of
Toeplitz operators in the spectral representation and properties of the
symbols.


title: Strong decays of excited $ 2^+ $ charmed mesons

abstract:
  The new charmed resonance $ D_2^*(3000) $ was observed by the LHCb
Collaboration in $ B $ decays. In this paper, by assigning it as four possible
excited states in the $ 2^+ $ family, we use the instantaneous Bethe-Salpeter
method to calculate their OZI-allowed two-body strong decays. The results of $
1^3F_2 $ and $ 3^3P_2 $ states deviate from the present experimental
observation while $ 2^3P_2 $ and $ 2^3F_2 $ are in the error range. Our study
also reveals that the widths of these states depend strongly on the masses. Due
to the large uncertainties and different mass input, variable models get
inconsistent conclusions at the moment. The analysis in this work can provide
essential assistance for future measurements and investigations.


title:
Distributed Control using Reinforcement Learning with
  Temporal-Logic-Based Reward Shaping

abstract:
  We present a computational framework for synthesis of distributed control
strategies for a heterogeneous team of robots in a partially observable
environment. The goal is to cooperatively satisfy specifications given as
Truncated Linear Temporal Logic (TLTL) formulas. Our approach formulates the
synthesis problem as a stochastic game and employs a policy graph method to
find a control strategy with memory for each agent. We construct the stochastic
game on the product between the team transition system and a finite state
automaton (FSA) that tracks the satisfaction of the TLTL formula. We use the
quantitative semantics of TLTL as the reward of the game, and further reshape
it using the FSA to guide and accelerate the learning process. Simulation
results demonstrate the efficacy of the proposed solution under demanding task
specifications and the effectiveness of reward shaping in significantly
accelerating the speed of learning.


title:
Analysis of a JWST NIRSpec Lab Time Series: Characterizing Systematics,
  Recovering Exoplanet Transit Spectroscopy, and Constraining a Noise Floor

abstract:
  The James Webb Space Telescope's NIRSpec instrument will unveil the nature of
exoplanet atmospheres across the wealth of planet types, from temperate
terrestrial worlds to ultrahot Jupiters. In particular, the 0.6-5.3 micron
PRISM mode is especially well-suited for efficient spectroscopic exoplanet
observations spanning a number of important spectral features. We analyze a
lab-measured NIRSpec PRISM mode Bright Object Time Series (BOTS) observation
from the perspective of a JWST user to understand the instrument performance
and detector properties. We create two realistic transiting exoplanet time
series observations by performing injection-recovery tests on the lab-measured
data to quantify the effects of real instrument jitter, drift, intrapixel
sensitivity variations, and 1/$f$ noise on measured transmission spectra. By
fitting the time series systematics simultaneously with the injected transit,
we can obtain more realistic transit depth uncertainties that take into account
noise sources that are currently not modeled by traditional exposure time
calculators. We find that sources of systematic noise related to intrapixel
sensitivity variations and PSF motions are apparent in the data at the level of
a few hundred ppm, but can be effectively detrended using a low-order
polynomial with detector position. We recover the injected spectral features of
GJ 436 b and TRAPPIST-1 d, and place a 3-sigma upper limit on the detector
noise floor of 14 ppm. We find that the noise floor is consistent with <10 ppm
at the 1.7-sigma level, which bodes well for future observations of challenging
targets with faint atmospheric signatures.


title: Bispectrum-window convolution via Hankel transform

abstract:
  We present a method to perform the exact convolution of the model prediction
for bispectrum multipoles in redshift space with the survey window function. We
extend a widely applied method for the power spectrum convolution to the
bispectrum, taking advantage of a 2D-FFTlog algorithm. As a preliminary test of
its accuracy, we consider the toy model of a spherical window function in real
space. This setup provides an analytical evaluation of the 3-point function of
the window, and therefore it allows to isolate and quantify possible systematic
errors of the method. We find that our implementation of the convolution in
terms of a mixing matrix shows differences at the percent level in comparison
to the measurements from a very large set of mock halo catalogs. It is also
able to recover unbiased constraints on halo bias parameters in a likelihood
analysis of a set of numerical simulations with a total volume of $100\, h^{-3}
\, {\rm Gpc}^3$. For the level of accuracy required by these tests, the
multiplication with the mixing matrix is performed in the time of one second or
less.


title:
Optical force and torque on small particles induced by polarization
  singularities

abstract:
  Optical forces in the near fields have important applications in on-chip
optical manipulations of small particles and molecules. Here, we report a study
of optical force and torque on small particles induced by the optical
polarization singularities of a gold cylinder. We show that the scattering of
the cylinder generates both electric and magnetic C lines (i.e., lines of
polarization singularities) in the near fields, and the C lines can induce
complex force and torque on a dielectric/magnetic particle. The force and
torque manifest dramatic spatial variations with interesting symmetry
properties, providing rich degrees of freedom for near-field optical
manipulations. The study, for the first time to our knowledge, uncovers the
effect of optical polarization singularities on light-induced force and torque
on small particles. The results contribute to the understanding of chiral
light-matter interactions and can find applications in on-chip optical
manipulations and optical sensing.


title: Variational methods for simulation-based inference

abstract:
  We present Sequential Neural Variational Inference (SNVI), an approach to
perform Bayesian inference in models with intractable likelihoods. SNVI
combines likelihood-estimation (or likelihood-ratio-estimation) with
variational inference to achieve a scalable simulation-based inference
approach. SNVI maintains the flexibility of likelihood(-ratio) estimation to
allow arbitrary proposals for simulations, while simultaneously providing a
functional estimate of the posterior distribution without requiring MCMC
sampling. We present several variants of SNVI and demonstrate that they are
substantially more computationally efficient than previous algorithms, without
loss of accuracy on benchmark tasks. We apply SNVI to a neuroscience model of
the pyloric network in the crab and demonstrate that it can infer the posterior
distribution with one order of magnitude fewer simulations than previously
reported. SNVI vastly reduces the computational cost of simulation-based
inference while maintaining accuracy and flexibility, making it possible to
tackle problems that were previously inaccessible.


title: Occupancy Map Prediction for Improved Indoor Robot Navigation

abstract:
  In the typical path planning pipeline for a ground robot, we build a map
(e.g., an occupancy grid) of the environment as the robot moves around. While
navigating indoors, a ground robot's knowledge about the environment may be
limited by the occlusions in its surroundings. Therefore, the map will have
many as-yet-unknown regions that may need to be avoided by a conservative
planner. Instead, if a robot is able to correctly infer what its surroundings
and occluded regions look like, the navigation can be further optimized. In
this work, we propose an approach using pix2pix and UNet to infer the occupancy
grid in unseen areas near the robot as an image-to-image translation task. Our
approach simplifies the task of occupancy map prediction for the deep learning
network and reduces the amount of data required compared to similar existing
methods. We show that the predicted map improves the navigation time in
simulations over the existing approaches.