title:
TASAC: a twin-actor reinforcement learning framework with stochastic
  policy for batch process control

abstract:
  Due to their complex nonlinear dynamics and batch-to-batch variability, batch
processes pose a challenge for process control. Due to the absence of accurate
models and resulting plant-model mismatch, these problems become harder to
address for advanced model-based control strategies. Reinforcement Learning
(RL), wherein an agent learns the policy by directly interacting with the
environment, offers a potential alternative in this context. RL frameworks with
actor-critic architecture have recently become popular for controlling systems
where state and action spaces are continuous. It has been shown that an
ensemble of actor and critic networks further helps the agent learn better
policies due to the enhanced exploration due to simultaneous policy learning.
To this end, the current study proposes a stochastic actor-critic RL algorithm,
termed Twin Actor Soft Actor-Critic (TASAC), by incorporating an ensemble of
actors for learning, in a maximum entropy framework, for batch process control.


title: Boolean automata isolated cycles and tangential double-cycles dynamics

abstract:
  Our daily social and political life is more and more impacted by social
networks. The functioning of our living bodies is deeply dependent on
biological regulation networks such as neural, genetic, and protein networks.
And the physical world in which we evolve, is also structured by systems of
interacting particles. Interaction networks can be seen in all spheres of
existence that concern us, and yet, our understanding of interaction networks
remains severely limited by our present lack of both theoretical and applied
insight into their clockworks. In the past, efforts at understanding
interaction networks have mostly been directed towards applications. This has
happened at the expense of developing understanding of the generic and
fundamental aspects of interaction networks. Intrinsic properties of
interaction networks (eg the ways in which they transmit information along
entities, their ability to produce this or that kind of global dynamical
behaviour depending on local interactions) are thus still not well understood.
Lack of fundamental knowledge tends to limit the innovating power of
applications. Without more theoretical fundamental knowledge, applications
cannot evolve deeply and become more impacting. Hence, it is necessary to
better apprehend and comprehend the intrinsic properties of interaction
networks, notably the relations between their architecture and their dynamics
and how they are affected by and set in time. In this chapter, we use the
elementary mathematical model of Boolean automata networks as a formal
archetype of interaction networks. We survey results concerning the role of
feedback cycles and the role of intersections between feedback cycles, in
shaping the asymptotic dynamical behaviours of interaction networks.


title:
SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based
  Convolutions

abstract:
  Event-based sensors are drawing increasing attention due to their high
temporal resolution, low power consumption, and low bandwidth. To efficiently
extract semantically meaningful information from sparse data streams produced
by such sensors, we present a 4.5TOP/s/W digital accelerator capable of
performing 4-bits-quantized event-based convolutional neural networks (eCNN).
Compared to standard convolutional engines, our accelerator performs a number
of operations proportional to the number of events contained into the input
data stream, ultimately achieving a high energy-to-information processing
proportionality. On the IBM-DVS-Gesture dataset, we report 80uJ/inf to
261uJ/inf, respectively, when the input activity is 1.2% and 4.9%. Our
accelerator consumes 0.221pJ/SOP, to the best of our knowledge it is the lowest
energy/OP reported on a digital neuromorphic engine.


title: Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds

abstract:
  Dense captioning in 3D point clouds is an emerging vision-and-language task
involving object-level 3D scene understanding. Apart from coarse semantic class
prediction and bounding box regression as in traditional 3D object detection,
3D dense captioning aims at producing a further and finer instance-level label
of natural language description on visual appearance and spatial relations for
each scene object of interest. To detect and describe objects in a scene,
following the spirit of neural machine translation, we propose a
transformer-based encoder-decoder architecture, namely SpaCap3D, to transform
objects into descriptions, where we especially investigate the relative
spatiality of objects in 3D scenes and design a spatiality-guided encoder via a
token-to-token spatial relation learning objective and an object-centric
decoder for precise and spatiality-enhanced object caption generation.
Evaluated on two benchmark datasets, ScanRefer and ReferIt3D, our proposed
SpaCap3D outperforms the baseline method Scan2Cap by 4.94% and 9.61% in
CIDEr@0.5IoU, respectively. Our project page with source code and supplementary
files is available at https://SpaCap3D.github.io/ .


title:
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained
  Visual Recognition

abstract:
  One-shot fine-grained visual recognition often suffers from the problem of
having few training examples for new fine-grained classes. To alleviate this
problem, off-the-shelf image generation techniques based on Generative
Adversarial Networks (GANs) can potentially create additional training images.
However, these GAN-generated images are often not helpful for actually
improving the accuracy of one-shot fine-grained recognition. In this paper, we
propose a meta-learning framework to combine generated images with original
images, so that the resulting "hybrid" training images improve one-shot
learning. Specifically, the generic image generator is updated by a few
training instances of novel classes, and a Meta Image Reinforcing Network
(MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as
image reinforcement. Our experiments demonstrate consistent improvement over
baselines on one-shot fine-grained image classification benchmarks.
Furthermore, our analysis shows that the reinforced images have more diversity
compared to the original and GAN-generated images.


title: Implicit Channel Charting with Application to UAV-aided Localization

abstract:
  Traditional localization algorithms based on features such as time difference
of arrival are impaired by non-line of sight propagation, which negatively
affects the consistency that they expect among distance estimates. Instead,
fingerprinting localization is robust to these propagation conditions but
requires the costly collection of large data sets. To alleviate these
limitations, the present paper capitalizes on the recently-proposed notion of
channel charting to learn the geometry of the space that contains the channel
state information (CSI) measurements collected by the nodes to be localized.
The proposed algorithm utilizes a deep neural network that learns distances
between pairs of nodes using their measured CSI. Unlike standard channel
charting approaches, this algorithm directly works with the physical geometry
and therefore only implicitly learns the geometry of the radio domain.
Simulation results demonstrate that the proposed algorithm outperforms its
competitors and allows accurate localization in emergency scenarios using an
unmanned aerial vehicle.


title: The mixed search game against an agile and visible fugitive is monotone

abstract:
  We consider the mixed search game against an agile and visible fugitive. This
is the variant of the classic fugitive search game on graphs where searchers
may be placed to (or removed from) the vertices or slide along edges. Moreover,
the fugitive resides on the edges of the graph and can move at any time along
unguarded paths. The mixed search number against an agile and visible fugitive
of a graph $G$, denoted $avms(G)$, is the minimum number of searchers required
to capture to fugitive in this graph searching variant. Our main result is that
this graph searching variant is monotone in the sense that the number of
searchers required for a successful search strategy does not increase if we
restrict the search strategies to those that do not permit the fugitive to
visit an already clean edge. This means that mixed search strategies against an
agile and visible fugitive can be polynomially certified, and therefore that
the problem of deciding, given a graph $G$ and an integer $k,$ whether
$avms(G)\leq k$ is in NP. Our proof is based on the introduction of the notion
of tight bramble, that serves as an obstruction for the corresponding search
parameter. Our results imply that for a graph $G$, $avms(G)$ is equal to the
Cartesian tree product number of $G$ that is the minimum $k$ for which $G$ is a
minor of the Cartesian product of a tree and a clique on $k$ vertices.


title:
Constructing Trinomial Models Based on Cubature Method on Wiener Space:
  Applications to Pricing Financial Derivatives

abstract:
  This contribution deals with an extension to our developed novel cubature
methods of degrees 5 on Wiener space. In our previous studies, we have shown
that the cubature formula is exact for all multiple Stratonovich integrals up
to dimension equal to the degree. In fact, cubature method reduces solving a
stochastic differential equation to solving a finite set of ordinary
differential equations. Now, we apply the above methods to construct trinomial
models and to price different financial derivatives. We will compare our
numerical solutions with the Black's and Black--Scholes models' analytical
solutions. The constructed model has practical usage in pricing American-style
derivatives and can be extended to more sophisticated stochastic market models.


title: Successive approximations of $\pi$ using Euler Beta functions

abstract:
  In this didactic note, we describe a procedure to derive successive
approximations of $\pi$ using Euler Beta functions. It is an interesting
exercise for undergraduate students, since it involves polynomial roots,
integral calculations, inequalities and Euler Beta functions.


title: A Multigraph Approach for Performing the Quantum Schur Transform

abstract:
  We take inspiration from the Okounkov-Vershik approach to the representation
theory of the symmetric groups to develop a new way of understanding how the
Schur-Weyl duality can be used to perform the Quantum Schur Transform. The
Quantum Schur Transform is a unitary change of basis transformation between the
computational basis of $(\mathbb{C}^d)^{\otimes n}$ and the Schur-Weyl basis of
$(\mathbb{C}^d)^{\otimes n}$. We describe a new multigraph, which we call the
Schur-Weyl-Young graph, that represents both standard Weyl tableaux and
standard Young tableaux in the same diagram. We suggest a major improvement on
Louck's formula for calculating the transition amplitudes between two standard
Weyl tableaux appearing in adjacent levels of the Schur-Weyl-Young graph for
the case $d=2$, merely by looking at the entries in the two tableaux. The key
theoretical component that underpins our results is the discovery of a
branching rule for the Schur-Weyl states, which we call the Schur-Weyl
branching rule. This branching rule allows us to perform the change of basis
transformation described above in a straightforward manner for any $n$ and $d$.


title: Universum-inspired Supervised Contrastive Learning

abstract:
  Mixup is an efficient data augmentation method which generates additional
samples through respective convex combinations of original data points and
labels. Although being theoretically dependent on data properties, Mixup is
reported to perform well as a regularizer and calibrator contributing reliable
robustness and generalization to neural network training. In this paper,
inspired by Universum Learning which uses out-of-class samples to assist the
target tasks, we investigate Mixup from a largely under-explored perspective -
the potential to generate in-domain samples that belong to none of the target
classes, that is, universum. We find that in the framework of supervised
contrastive learning, universum-style Mixup produces surprisingly high-quality
hard negatives, greatly relieving the need for a large batch size in
contrastive learning. With these findings, we propose Universum-inspired
Contrastive learning (UniCon), which incorporates Mixup strategy to generate
universum data as g-negatives and pushes them apart from anchor samples of the
target classes. Our approach not only improves Mixup with hard labels, but also
innovates a novel measure to generate universum data. With a linear classifier
on the learned representations, our method achieves 81.68% top-1 accuracy on
CIFAR-100, surpassing the state of art by a significant margin of 5% with a
much smaller batch size, typically, 256 in UniCon vs. 1024 in SupCon using
ResNet-50.


title:
Microscopic and Macroscopic Effects in the Decoherence of Neutrino
  Oscillations

abstract:
  We present a generic structure (the layer structure) for decoherence effects
in neutrino oscillations, which includes decoherence from quantum mechanical
and classical uncertainties. The calculation is done by combining the concept
of open quantum system and quantum field theory, forming a structure composed
of phase spaces from microscopic to macroscopic level. Having information loss
at different levels, quantum mechanical uncertainties parameterize decoherence
by an intrinsic mass eigenstate separation effect, while decoherence for
classical uncertainties is typically dominated by a statistical averaging
effect. With the help of the layer structure, we classify the former as state
decoherence (SD) and the latter as phase decoherence (PD), then further
conclude that both SD and PD result from phase wash-out effects of different
phase structures on different layers. Such effects admit for simple numerical
calculations of decoherence for a given width and shape of uncertainties. While
our structure is generic, so are the uncertainties, nonetheless, a few notable
ones are: the wavepacket size of the external particles, the effective
interaction volume at production and detection, the energy reconstruction model
and the neutrino production profile. Furthermore, we estimate the experimental
sensitivities for SD and PD parameterized by the uncertainty parameters, for
reactor neutrinos and decay-at-rest neutrinos, using a traditional rate
measuring method and a novel phase measuring method.


title:
Observation of $1/k^4$-tails in the asymptotic momentum distribution of
  Bose polarons

abstract:
  We measure the asymptotic momentum density in a Bose-Einstein condensate with
dilute spin impurities, after an expansion in the presence of interactions. In
the absence of impurities, we confirm the theoretical scenario of C. Qu {\it et
al.} [Phys. Rev. A {\bf 94}, 063635 (2016)] according to which signatures of
the quantum depletion at large momenta vanish during an expansion of an
interacting Bose-Einstein condensate. When impurities are present, we observe
tails decaying as $1/k^4$ at large momentum $k$ in the condensate and in the
impurity cloud. These results highlight the key role played by impurities when
present, a possibility that had not been considered in our previous work R.
Chang {\it et al.} [Phys. Rev. Lett. {\bf 117}, 235303 (2016)]. We show that
the algebraic tails originate from the impurity-BEC interaction, but that their
amplitudes greatly exceed those expected for the in-trap contact of
weakly-interacting Bose polarons. This discrepancy may be due to the
non-trivial dynamics of the expansion in the presence of bath-impurity
interactions.


title: Graph Gain: A Concave-Hull Based Volumetric Gain for Robotic Exploration

abstract:
  The existing volumetric gain for robotic exploration is calculated in the 3D
occupancy map, while the sampling-based exploration method is extended in the
reachable (free) space. The inconsistency between them makes the existing
calculation of volumetric gain inappropriate for a complete exploration of the
environment. To address this issue, we propose a concave-hull based volumetric
gain in a sampling-based exploration framework. The concave hull is constructed
based on the viewpoints generated by Rapidly-exploring Random Tree (RRT) and
the nodes that fail to expand. All space outside this concave hull is
considered unknown. The volumetric gain is calculated based on the viewpoints
configuration rather than using the occupancy map. With the new volumetric
gain, robots can avoid inefficient or even erroneous exploration behavior
caused by the inappropriateness of existing volumetric gain calculation
methods. Our exploration method is evaluated against the existing
state-of-the-art RRT-based method in a benchmark environment. In the evaluated
environment, the average running time of our method is about 38.4% of the
existing state-of-the-art method and our method is more robust.


title: The subharmonic bifurcation of Stokes waves on vorticity flow

abstract:
  We consider a family of Stokes waves on vorticity flow parameterized by a
parameter. For large value of the parameter the Stokes waves approach the
Stokes extreme wave. We prove that there are infinitely many subharmonic
bifurcation points on such branches.


title: Quantum Semi-Supervised Kernel Learning

abstract:
  Quantum computing leverages quantum effects to build algorithms that are
faster then their classical variants. In machine learning, for a given model
architecture, the speed of training the model is typically determined by the
size of the training dataset. Thus, quantum machine learning methods have the
potential to facilitate learning using extremely large datasets. While the
availability of data for training machine learning models is steadily
increasing, oftentimes it is much easier to collect feature vectors that to
obtain the corresponding labels. One of the approaches for addressing this
issue is to use semi-supervised learning, which leverages not only the labeled
samples, but also unlabeled feature vectors. Here, we present a quantum machine
learning algorithm for training Semi-Supervised Kernel Support Vector Machines.
The algorithm uses recent advances in quantum sample-based Hamiltonian
simulation to extend the existing Quantum LS-SVM algorithm to handle the
semi-supervised term in the loss. Through a theoretical study of the
algorithm's computational complexity, we show that it maintains the same
speedup as the fully-supervised Quantum LS-SVM.


title: Softer than soft: diving into squishy granular matter

abstract:
  Softer than soft, squishy granular matter is composed of grains capable of
significantly changing their shape (typically larger than 10% of deformation)
without tearing or breaking. Because of the difficulty to test these materials
experimentally and numerically, such a family of discrete systems remains
largely ignored in the granular matter physics field despite being commonly
found in nature and industry. Either from a numerical, experimental, or
analytical point of view, the study of highly deformable granular matter
involves several challenges covering, for instance: ($i$) the need to include a
large diversity of grain rheology, ($ii$) the need to consider \dc{large
material} deformations, and ($iii$) the analysis upon the effects the large
body distortion has on the global scale. In this article, we propose a thorough
definition of these squishy granular systems, and we summarize the upcoming
challenges in their study.


title: Attractor landscapes in Boolean networks with firing memory

abstract:
  In this paper we study the dynamical behavior of Boolean networks with firing
memory, namely Boolean networks whose vertices are updated synchronously
depending on their proper Boolean local transition functions so that each
vertex remains at its firing state a finite number of steps. We prove in
particular that these networks have the same computational power than the
classical ones, ie any Boolean network with firing memory composed of $m$
vertices can be simulated by a Boolean network by adding vertices. We also
prove general results on specific classes of networks. For instance, we show
that the existence of at least one delay greater than 1 in disjunctive networks
makes such networks have only fixed points as attractors. Moreover, for
arbitrary networks composed of two vertices, we characterize the delay phase
space, \ie the delay values such that networks admits limit cycles or fixed
points. Finally, we analyze two classical biological models by introducing
delays: the model of the immune control of the $\lambda$-phage and that of the
genetic control of the floral morphogenesis of the plant \emph{Arabidopsis
thaliana}.


title:
Persona-Guided Planning for Controlling the Protagonist's Persona in
  Story Generation

abstract:
  Endowing the protagonist with a specific personality is essential for writing
an engaging story. In this paper, we aim to control the protagonist's persona
in story generation, i.e., generating a story from a leading context and a
persona description, where the protagonist should exhibit the specified
personality through a coherent event sequence. Considering that personas are
usually embodied implicitly and sparsely in stories, we propose a
planning-based generation model named CONPER to explicitly model the
relationship between personas and events. CONPER first plans events of the
protagonist's behavior which are motivated by the specified persona through
predicting one target sentence, then plans the plot as a sequence of keywords
with the guidance of the predicted persona-related events and commonsense
knowledge, and finally generates the whole story. Both automatic and manual
evaluation results demonstrate that CONPER outperforms state-of-the-art
baselines for generating more coherent and persona-controllable stories.


title:
SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across
  Drone and Satellite

abstract:
  The purpose of cross-view image matching is to match images acquired from the
different platforms of the same target scene and then help positioning system
to infer the location of the target scene. With the rapid development of drone
technology, how to help Drone positioning or navigation through cross-view
matching technology has become a challenging research topic. However, the
accuracy of current cross-view matching models is still low, mainly because the
existing public datasets do not include the differences in images obtained by
drones at different heights, and the types of scenes are relatively
homogeneous, which makes the models unable to adapt to complex and changing
scenes. We propose a new cross-view dataset, SUES-200, to address these
issues.SUES-200 contains images acquired by the drone at four flight heights
and the corresponding satellite view images under the same target scene. To our
knowledge, SUES-200 is the first dataset that considers the differences
generated by aerial photography of drones at different flight heights. In
addition, we build a pipeline for efficient training testing and evaluation of
cross-view matching models. Then, we comprehensively evaluate the performance
of feature extractors with different CNN architectures on SUES-200 through an
evaluation system for cross-view matching models and propose a robust baseline
model. The experimental results show that SUES-200 can help the model learn
features with high discrimination at different heights. Evaluating indicators
of the matching system improves as the drone flight height gets higher because
the drone camera pose and the surrounding environment have less influence on
aerial photography.


title: The Euler's formula and root formula of Clifford Algebra Cl2

abstract:
  In this paper, we express Euler's formula and De Moivre's formula for
Clifford algebra Cl2 and find nth roots of an element in Clifford algebra Cl2.


title:
Global well--posedness and large time behavior of classical solutions to
  a generic compressible two-fluid model

abstract:
  In this paper, we investigate a generic compressible two-fluid model with
common pressure ($P^+=P^-$) in $\mathbb{R}^3$. Under some smallness
assumptions, Evje-Wang-Wen [Arch Rational Mech Anal 221:1285--1316, 2016]
obtained the global solution and its optimal decay rate for the 3D compressible
two-fluid model with unequal pressures $P^+\neq P^-$. More precisely, the
capillary pressure $f(\alpha^-\rho^-)=P^+-P^-\neq 0$ is taken into account, and
is assumed to be a strictly decreasing function near the equilibrium. As
indicated by Evje-Wang-Wen, this assumption played an key role in their
analysis and appeared to have an essential stabilization effect on the model.
However, global well-posedness of the 3D compressible two-fluid model with
common pressure has been a challenging open problem due to the fact that the
system is partially dissipative and its nonlinear structure is very terrible.
In the present work, by exploiting the dissipation structure of the model and
making full use of several key observations, we establish global existence and
large time behavior of classical solutions to the 3D compressible two-fluid
model with common pressure. One of key observations here is that to closure the
higher-order energy estimates of non-dissipative variables (i.e, fraction
densities $\alpha_{\pm}\rho_\pm$), we will introduce the linear combination of
two velocities ($u^\pm$): $v=(2\mu^++\lambda^+)u^+-(2\mu^-+\lambda^-)u^-$ and
explore its good regularity, which is particularly better than ones of two
velocities themselves.


title:
Crescent: Taming Memory Irregularities for Accelerating Deep Point Cloud
  Analytics

abstract:
  3D perception in point clouds is transforming the perception ability of
future intelligent machines. Point cloud algorithms, however, are plagued by
irregular memory accesses, leading to massive inefficiencies in the memory
sub-system, which bottlenecks the overall efficiency. This paper proposes
Crescent, an algorithm-hardware co-design system that tames the irregularities
in deep point cloud analytics while achieving high accuracy. To that end, we
introduce two approximation techniques, approximate neighbor search and
selectively bank conflict elision, that "regularize" the DRAM and SRAM memory
accesses. Doing so, however, necessarily introduces accuracy loss, which we
mitigate by a new network training procedure that integrates approximation into
the network training process. In essence, our training procedure trains models
that are conditioned upon a specific approximate setting and, thus, retain a
high accuracy. Experiments show that Crescent doubles the performance and
halves the energy consumption compared to an optimized baseline accelerator
with < 1% accuracy loss. The code of our paper is available at:
https://github.com/horizon-research/crescent.


title: Seeing double through dependent optics

abstract:
  Tambara modules are strong profunctors between monoidal categories. They've
been defined by Tambara in the context of representation theory, but quickly
found their way in applications when it was understood Tambara modules provide
a useful encoding of modular data accessors known as mixed optics. To suit the
needs of these applications, Tambara theory has been extended to profunctors
between categories receiving an action of a monoidal category. Motivated by the
generalization of optics to dependently-typed contexts, we sketch a further
extension of the theory of Tambara modules in the setting of actions of double
categories (thus doubly indexed categories), by defining them as horizontal
natural transformations. The theorems and constructions in Pastro-Street theory
relevant to profunctor representation theorem for mixed optics are reobtained
in this context. This reproduces the definition of dependent optics recently
put forward by Vertechi and Milewski, and hinted at by previous work of the
author and his collaborators.


title: Nuclear spectra from low-energy interactions

abstract:
  A method to describe spectra starting from nuclear density functionals is
explored. The idea is based on postulating an effective Hamiltonian that
reproduces the stiffness associated with collective modes. The method defines a
simple form of such an effective Hamiltonian and a mapping to go from a density
functional to the corresponding Hamiltonian. In order to test the method, the
Hamiltonian is constrained using a Skyrme functional and solved with the
generator-coordinate method to describe low-lying levels and electromagnetic
transitions in $^{48,49,50,52}$Cr and $^{24}$Mg.


title:
Tweets2Stance: Users stance detection exploiting Zero-Shot Learning
  Algorithms on Tweets

abstract:
  In the last years there has been a growing attention towards predicting the
political orientation of active social media users, being this of great help to
study political forecasts, opinion dynamics modeling and users polarization.
Existing approaches, mainly targeting Twitter users, rely on content-based
analysis or are based on a mixture of content, network and communication
analysis. The recent research perspective exploits the fact that a user's
political affinity mainly depends on his/her positions on major political and
social issues, thus shifting the focus on detecting the stance of users through
user-generated content shared on social networks. The work herein described
focuses on a completely unsupervised stance detection framework that predicts
the user's stance about specific social-political statements by exploiting
content-based analysis of its Twitter timeline. The ground-truth user's stance
may come from Voting Advice Applications, online tools that help citizens to
identify their political leanings by comparing their political preferences with
party political stances. Starting from the knowledge of the agreement level of
six parties on 20 different statements, the objective of the study is to
predict the stance of a Party p in regard to each statement s exploiting what
the Twitter Party account wrote on Twitter. To this end we propose
Tweets2Stance (T2S), a novel and totally unsupervised stance detector framework
which relies on the zero-shot learning technique to quickly and accurately
operate on non-labeled data. Interestingly, T2S can be applied to any social
media user for any context of interest, not limited to the political one.
Results obtained from multiple experiments show that, although the general
maximum F1 value is 0.4, T2S can correctly predict the stance with a general
minimum MAE of 1.13, which is a great achievement considering the task
complexity.


title:
Gas-atomized particles of giant magnetocaloric compound HoB2 for
  magnetic hydrogen liquefiers

abstract:
  The processing of promising magnetocaloric materials into spheres is one of
the important issues on developing high-performance magnetic refrigeration
systems. In the present study, we achieved in producing spherical particles of
a giant magnetocaloric compound HoB2 by a crucible-free gas atomization
process, despite its high melting point of 2350 C. The particle size
distribution ranges from 100 to 710 micrometers centered at 212-355 micrometers
with the highest yield of 14-20wt% of total melted electrode, which is suitable
for magnetic refrigeration systems. The majority of the resulting particles are
mostly spherical with no contamination during the processing, while unique
microstructures are observed on the surface and inside. These spherical
particles exhibit sharp magnetic transitions and huge magnetic entropy change
of 0.34 J cm^{-3} K^{-1} for a magnetic field change of 5 T at 15.5 K. The high
sphericality and the high magnetocaloric performance suggest that the HoB2
gas-atomized particles have good potential as magnetic refrigerants for use in
magnetic refrigerators for hydrogen liquefaction.


title: About block-parallel Boolean networks: a position paper

abstract:
  In automata networks, it is well known that the way entities update their
states over time has a major impact on their dynamics. In particular, depending
on the chosen update schedule, the underlying dynamical systems may exhibit
more or less asymptotic dynamical behaviours such as fixed points or limit
cycles. Since such mathematical models have been used in the framework of
biological networks modelling, the question of choosing appropriate update
schedules has arised soon. In this note, focusing on Boolean networks, our aim
is to emphasise that the adequate way of thinking regulations and genetic
expression over time is certainly not to consider a wall segregating
synchronicity from asynchronicity because they actually complement rather well.
In particular, we highlight that specific update schedules, namely
block-parallel update schedules, whose intrinsic features are still not known
from a theoretical point of view, admit realistic and pertinent properties in
the context of biological modelling and deserve certainly more attention from
the community.


title:
EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through
  Learning Offsets and Clustering Bandwidths

abstract:
  A systematic analysis of the cell behavior requires automated approaches for
cell segmentation and tracking. While deep learning has been successfully
applied for the task of cell segmentation, there are few approaches for
simultaneous cell segmentation and tracking using deep learning. Here, we
present EmbedTrack, a single convolutional neural network for simultaneous cell
segmentation and tracking which predicts easy to interpret embeddings. As
embeddings, offsets of cell pixels to their cell center and bandwidths are
learned. We benchmark our approach on nine 2D data sets from the Cell Tracking
Challenge, where our approach performs on seven out of nine data sets within
the top 3 contestants including three top 1 performances. The source code is
publicly available at https://git.scc.kit.edu/kit-loe-ge/embedtrack.


title:
Identifying Chinese Opinion Expressions with Extremely-Noisy
  Crowdsourcing Annotations

abstract:
  Recent works of opinion expression identification (OEI) rely heavily on the
quality and scale of the manually-constructed training corpus, which could be
extremely difficult to satisfy. Crowdsourcing is one practical solution for
this problem, aiming to create a large-scale but quality-unguaranteed corpus.
In this work, we investigate Chinese OEI with extremely-noisy crowdsourcing
annotations, constructing a dataset at a very low cost. Following zhang et al.
(2021), we train the annotator-adapter model by regarding all annotations as
gold-standard in terms of crowd annotators, and test the model by using a
synthetic expert, which is a mixture of all annotators. As this
annotator-mixture for testing is never modeled explicitly in the training
phase, we propose to generate synthetic training samples by a pertinent mixup
strategy to make the training and testing highly consistent. The simulation
experiments on our constructed dataset show that crowdsourcing is highly
promising for OEI, and our proposed annotator-mixup can further enhance the
crowdsourcing modeling.


title:
Equivalent definitions of Arthur packets for real quasisplit unitary
  groups

abstract:
  Mok has defined Arthur packets for quasisplit unitary groups. His definition
follows Arthur's work on classical groups and relies on harmonic analysis. For
real groups there is an alternative definition of Arthur packets, due to
Adams-Barbasch-Vogan. It relies on sheaf-theoretic techniques instead of
harmonic analysis. We prove that these two definitions of Arthur packets are
equivalent in the case of real quasisplit unitary groups.


title:
Hierarchical Label-wise Attention Transformer Model for Explainable ICD
  Coding

abstract:
  International Classification of Diseases (ICD) coding plays an important role
in systematically classifying morbidity and mortality data. In this study, we
propose a hierarchical label-wise attention Transformer model (HiLAT) for the
explainable prediction of ICD codes from clinical documents. HiLAT firstly
fine-tunes a pretrained Transformer model to represent the tokens of clinical
documents. We subsequently employ a two-level hierarchical label-wise attention
mechanism that creates label-specific document representations. These
representations are in turn used by a feed-forward neural network to predict
whether a specific ICD code is assigned to the input clinical document of
interest. We evaluate HiLAT using hospital discharge summaries and their
corresponding ICD-9 codes from the MIMIC-III database. To investigate the
performance of different types of Transformer models, we develop
ClinicalplusXLNet, which conducts continual pretraining from XLNet-Base using
all the MIMIC-III clinical notes. The experiment results show that the F1
scores of the HiLAT+ClinicalplusXLNet outperform the previous state-of-the-art
models for the top-50 most frequent ICD-9 codes from MIMIC-III. Visualisations
of attention weights present a potential explainability tool for checking the
face validity of ICD code predictions.


title: A Summary of the ALQAC 2021 Competition

abstract:
  We summarize the evaluation of the first Automated Legal Question Answering
Competition (ALQAC 2021). The competition this year contains three tasks, which
aims at processing the statute law document, which are Legal Text Information
Retrieval (Task 1), Legal Text Entailment Prediction (Task 2), and Legal Text
Question Answering (Task 3). The final goal of these tasks is to build a system
that can automatically determine whether a particular statement is lawful.
There is no limit to the approaches of the participating teams. This year,
there are 5 teams participating in Task 1, 6 teams participating in Task 2, and
5 teams participating in Task 3. There are in total 36 runs submitted to the
organizer. In this paper, we summarize each team's approaches, official
results, and some discussion about the competition. Only results of the teams
who successfully submit their approach description paper are reported in this
paper.


title: Determinants from homomorphisms

abstract:
  We give a new combinatorial explanation for well-known relations between
determinants and traces of matrix powers. Such relations can be used to obtain
polynomial-time and poly-logarithmic space algorithms for the determinant. Our
new explanation avoids linear-algebraic arguments and instead exploits a
classical connection between subgraph and homomorphism counts.


title: Genus 2 Goeritz Equivalence in $S^3$

abstract:
  The Goeritz group of a genus $g$ Heegaard splitting of a 3-manifold is the
group of isotopy classes of orientation-preserving automorphisms of the
manifold that preserve the Heegaard splitting. In the context of the standard
genus 2 Heegaard splitting of $S^3$, we introduce the concept of Goeritz
equivalence of curves, present two algebraic obstructions to Goeritz
equivalence of simple closed curves that are straightforward to compute, and
provide families of examples demonstrating how these obstructions may be used.


title:
The diffraction volume for square-shaped samples in X-ray diffraction
  with high spatial resolution

abstract:
  X-ray diffraction with high spatial resolution is a prerequisite for the
characterization of (poly)-crystalline materials on micro- or nanoscopic
scales. This can be achieved by utilizing a focused X-ray beam and scanning of
the sample. However, due to the penetration of the X-rays into the material,
the exact location of diffraction within the sample is ambiguous. Here, we
utilize numerical simulations to compute the spatially resolved diffraction
volume in order to investigate these ambiguities. We demonstrate that partial
depth sensitivity can be achieved by rotating the sample.


title: The heavy tetra-quark states after the discovery of tetra-charm states

abstract:
  This paper is devoted to the study of exclusive charm and bottom aggregates
which are suitable, at least partially, for a non-relativistic description.
Starting from a simple choice of quarkonia and tetra-quark wave functions we
study their production cross sections, total widths and more interesting decay
properties.
  In particular, we compute the production cross sections of the ground state
scalar quarkonia getting results compatible with the values appearing in the
literature. We also compute the production cross sections of the lower energy
states of the charm and bottom tetra-quarks, their decay widths and the decay
rates in two $\mu\bar\mu$ pairs and in the $q\bar q\mu\bar\mu$ channel.
  Our results on the tetra-charm are consistent with the data published by the
LHCb Collaboration and those on the tetra-bottom show a reasonable agreement
with the indications published by the CMS Collaboration. Our cross section
analyses are based on interpolations based on the gluon-gluon luminosity grids
published by the NNPDF and CTEQ Collaborations. The hard amplitude calculations
are based on QCD in the tree approximation. We think that a careful improvement
of our method and a refinement of the experimental data after the foreseen
luminosity increase at the LHC will make possible valuable tests of QCD at the
intermediate energies.


title:
Regularized randomized iterative algorithms for factorized linear
  systems

abstract:
  Randomized iterative algorithms for solving a factorized linear system,
$\mathbf A\mathbf B\mathbf x=\mathbf b$ with $\mathbf A\in{\mathbb{R}}^{m\times
\ell}$, $\mathbf B\in{\mathbb{R}}^{\ell\times n}$, and $\mathbf
b\in{\mathbb{R}}^m$, have recently been proposed. They take advantage of the
factorized form and avoid forming the matrix $\mathbf C=\mathbf A\mathbf B$
explicitly. However, they can only find the minimum norm (least squares)
solution. In contrast, the regularized randomized Kaczmarz (RRK) algorithm can
find solutions with certain structures from consistent linear systems. In this
work, by combining the randomized Kaczmarz algorithm or the randomized
Gauss--Seidel algorithm with the RRK algorithm, we propose two novel
regularized randomized iterative algorithms to find (least squares) solutions
with certain structures of $\mathbf A\mathbf B\mathbf x=\mathbf b$. We prove
linear convergence of the new algorithms. Computed examples are given to
illustrate that the new algorithms can find sparse (least squares) solutions of
$\mathbf A\mathbf B\mathbf x=\mathbf b$ and can be better than the existing
randomized iterative algorithms for the corresponding full linear system
$\mathbf C\mathbf x=\mathbf b$ with $\mathbf C=\mathbf A\mathbf B$.


title: Matrix-Scaled Consensus

abstract:
  This letter proposes a matrix-scaled consensus algorithm, which generalizes
the scaled consensus algorithm in \cite{Roy2015scaled}. In (scalar) scaled
consensus algorithms, the agents' states do not converge to a common value, but
to different points along a straight line in the state space, which depends on
the scaling factors and the initial states of the agents. {In the matrix-scaled
consensus algorithm, a positive/negative definite matrix weight is assigned to
each agent. Each agent updates its state based on the product of the sum of
relative matrix scaled states and the sign of the matrix weight. Under the
proposed algorithm, each agent asymptotically converges to a final point
differing with a common consensus point by the inverse of its own scaling
matrix. Thus, the final states of the agents are not restricted to a straight
line but can be located at almost any point in the state-space.} Convergence
analysis of matrix-scaled consensus for single and double-integrator agents are
studied in detail. Simulation results are given to support the analysis.


title: On the interplay between optomechanics and the dynamical Casimir effect

abstract:
  We develop a model of a quantum field confined within a cavity with a movable
wall where the position of the wall is quantized. We obtain a full description
of the dynamics of both the quantum field and the confining wall depending on
the initial state of the whole system. Both the reaction and backreaction of
the field on the wall, and the wall on the field, can be taken into account, as
well as external driving forces on both the cavity and the wall. The model
exactly reproduces the resonant cavity mode stimulation due to the periodic
motion of the mirror (dynamical Casimir effect), as well as the standard
radiation pressure effects on the quantized wall (optomechanics). The model
also accounts for the interplay of the two scenarios. In addition, modulated
drive of the wall can be used to induce specific single- and two-mode quantum
gates on selected modes of the field. Finally, the time evolution of the
radiation force shows the interplay between static and dynamical Casimir
effect.


title:
Toward Astrometric Constraints on a Supermassive Black Hole Binary in
  the Early-Type Galaxy NGC\,4472

abstract:
  The merger of two galaxies, each hosting a supermassive black hole (SMBH) of
mass $10^6$\,M$_{\odot}$ or more, could yield a bound SMBH binary. For the
early-type galaxy NGC\,4472, we study how astrometry with a next-generation
Very Large Array (ngVLA) could be used to monitor the reflex motion of the
primary SMBH of mass $M_{\rm pri}$, as it is tugged on by the secondary SMBH of
mass $M_{\rm sec}$. Casting the orbit of the putative SMBH binary in terms of
its period $P$, semimajor axis $a_{\rm bin}$, and mass ratio $q = M_{\rm sec} /
M_{\rm pri} \le 1$, we find the following: (1) Orbits with fiducial periods of
$P = 4$\,yr and 40\,yr could be spatially resolved and monitored. (2) For a
95\% accuracy of $2\,\mu$as per monitoring epoch, sub-parsec values of $a_{\rm
bin}$ could be accessed over a range of mass ratios notionally encompassing
major ($q > \frac{1}{4}$) and minor ($q < \frac{1}{4}$) galaxy mergers. (3) If
no reflex motion is detected for $M_{\rm pri}$ after 1(10)\,yr of monitoring, a
SMBH binary with period $P = 4(40)$\,yr and mass ratio $q > 0.01(0.003)$ could
be excluded. This would suggest no present-day evidence for a past major merger
like that recently simulated, where scouring by a $q \sim 1$ SMBH binary formed
a stellar core with kinematic traits like those of NGC\,4472. (4) Astrometric
monitoring could independently check the upper limits on $q$ from searches for
continuous gravitational waves from NGC\,4472.


title:
The role of the thermodynamic factor on the intrinsic and tracer
  diffusivities in binary mixtures

abstract:
  One of the Darken equations gives a relationship between the intrinsic and
the tracer diffusion coefficients, $D_A$ and $D_A^*$, of species $A$ in a solid
binary mixture. In its original version, the equation reads $D_A = D_A^*
\Gamma$, with $\Gamma$ the thermodynamic factor. The question addressed in this
paper is how $D_A$ and $D_A^*$ depend separately on $\Gamma$. Using a recent
result for transition probabilities in terms of the excess chemical potential
(M. Di Muro and M. Hoyuelos, Phys. Rev. E 104, 044104, 2021), it is shown that
the intrinsic diffusivity does not depend on $\Gamma$. This approach simplifies
a previous theoretical analysis that reaches the same result. Experimental
results of diffusion in a Ni-Pd and Fe-Pd alloys (M. J. H. van Dal et al., Acta
mater. 48, 385, 2000) are used to check the theory.


title:
Revealing interactions between HVDC cross-area flows and frequency
  stability with explainable AI

abstract:
  The energy transition introduces more volatile energy sources into the power
grids. In this context, power transfer between different synchronous areas
through High Voltage Direct Current (HVDC) links becomes increasingly
important. Such links can balance volatile generation by enabling long-distance
transport or by leveraging their fast control behavior. Here, we investigate
the interaction of power imbalances - represented through the power grid
frequency - and power flows on HVDC links between synchronous areas in Europe.
We use explainable machine learning to identify key dependencies and
disentangle the interaction of critical features. Our results show that
market-based HVDC flows introduce deterministic frequency deviations, which
however can be mitigated through strict ramping limits. Moreover, varying HVDC
operation modes strongly affect the interaction with the grid. In particular,
we show that load-frequency control via HVDC links can both have control-like
or disturbance-like impacts on frequency stability.


title: Super-Horizon modes and cosmic expansion

abstract:
  The large scale structures observed in cosmic maps correspond to
super-horizon (causally disconnected) perturbations in the early Universe that
are usually attributed to scale invariant adiabatic modes from cosmic
Inflation. Here, we interpret discrepant measurements of the expansion rate
$H_0$ (the so called Hubble tension) as super-horizon perturbations and show
that they are neither adiabatic nor scale invariant. We argue that these
measurements indicate that cosmic expansion originates from gravitational
collapse and bounce, rather than from a singular Big Bang. This explains the
observed cosmic acceleration and large scale structure without the need of Dark
Energy or Inflation.


title:
Pathways through Conspiracy: The Evolution of Conspiracy Radicalization
  through Engagement in Online Conspiracy Discussions

abstract:
  The disruptive offline mobilization of participants in online conspiracy
theory (CT) discussions has highlighted the importance of understanding how
online users may form radicalized conspiracy beliefs. While prior work
researched the factors leading up to joining online CT discussions and provided
theories of how conspiracy beliefs form, we have little understanding of how
conspiracy radicalization evolves after users join CT discussion communities.
In this paper, we provide the empirical modeling of various radicalization
phases in online CT discussion participants. To unpack how conspiracy
engagement is related to radicalization, we first characterize the users'
journey through CT discussions via conspiracy engagement pathways.
Specifically, by studying 36K Reddit users through their 169M contributions, we
uncover four distinct pathways of conspiracy engagement: steady high,
increasing, decreasing, and steady low. We further model three successive
stages of radicalization guided by prior theoretical works. Specific
sub-populations of users, namely those on steady high and increasing conspiracy
engagement pathways, progress successively through various radicalization
stages. In contrast, users on the decreasing engagement pathway show distinct
behavior: they limit their CT discussions to specialized topics, participate in
diverse discussion groups, and show reduced conformity with conspiracy
subreddits. By examining users who disengage from online CT discussions, this
paper provides promising insights about conspiracy recovery process.


title: Rational homology ribbon cobordism is a partial order

abstract:
  We show that ribbon rational homology cobordism is a partial order within the
class of irreducible 3-manifolds. This makes essential use of the methods
recently employed by Ian Agol to show that ribbon knot concordance is a partial
order.


title:
Diverse Instance Discovery: Vision-Transformer for Instance-Aware
  Multi-Label Image Recognition

abstract:
  Previous works on multi-label image recognition (MLIR) usually use CNNs as a
starting point for research. In this paper, we take pure Vision Transformer
(ViT) as the research base and make full use of the advantages of Transformer
with long-range dependency modeling to circumvent the disadvantages of CNNs
limited to local receptive field. However, for multi-label images containing
multiple objects from different categories, scales, and spatial relations, it
is not optimal to use global information alone. Our goal is to leverage ViT's
patch tokens and self-attention mechanism to mine rich instances in multi-label
images, named diverse instance discovery (DiD). To this end, we propose a
semantic category-aware module and a spatial relationship-aware module,
respectively, and then combine the two by a re-constraint strategy to obtain
instance-aware attention maps. Finally, we propose a weakly supervised object
localization-based approach to extract multi-scale local features, to form a
multi-view pipeline. Our method requires only weakly supervised information at
the label level, no additional knowledge injection or other strongly supervised
information is required. Experiments on three benchmark datasets show that our
method significantly outperforms previous works and achieves state-of-the-art
results under fair experimental comparisons.


title: Would Magnonic Circuits Outperform CMOS Counterparts?

abstract:
  In the early stages of a novel technology development, it is difficult to
provide a comprehensive assessment of its potential capabilities and impact.
Nevertheless, some preliminary estimates can be drawn and are certainly of
great interest and in this paper we follow this line of reasoning within the
framework of the Spin Wave (SW) computing paradigm. In particular, we are
interested in assessing the technological development horizon that needs to be
reached in order to unleash the full SW paradigm potential such that SW
circuits can outperform CMOS counterparts in terms of energy consumption. In
view of the zero power SWs propagation through ferromagnetic waveguides, the
overall SW circuit power consumption is determined by the one associated to SWs
generation and sensing by means of transducers. While current antenna based
transducers are clearly power hungry recent developments indicate that
magneto-electric (ME) cells have a great potential for ultra-low power SW
generation and sensing. Given that MEs have been only proposed at the
conceptual level and no actual experimental demonstration has been reported we
cannot evaluate the impact of their utilization on the SW circuit energy
consumption. However, we can perform a reverse engineering alike analysis to
determine ME delay and power consumption upper bounds that can place SW
circuits in the leading position. To this end, we utilize a 32-bit Brent-Kung
Adder (BKA) as discussion vehicle and compute the maximum ME delay and power
consumption that could potentially enable a SW implementation able to
outperform its 7nm CMOS counterpart. We evaluate different BKA SW
implementations that rely on conversion or normalization gate cascading and
consider continuous or pulsed SW generation scenarios. 31nW is the maximum
transducer power consumption for which a 32-bit BKA SW implementation can
outperform its 7nm CMOS counterpart.


title:
Particle formation in oxymethylene ethers (OMEn, n = 2-4) / ethylene
  premixed flames

abstract:
  Alternative synthetic fuels can be produced by renewable energy sources and
represent a potential route for solving long-term energy storage. Among them,
oxygenated fuels have the advantage of significantly reducing pollutant
emissions and can therefore be used as carbon-neutral substitute fuels for
transportation. In this work, the sooting propensity of different oxymethylene
ethers (OMEn) is investigated using a combined experimental and numerical study
on a series of burner-stabilized premixed flames under mild to severe sooting
conditions. Herein, mixtures of ethylene in combination with the three
individual OMEn for n=2-4 are compared in terms of soot formation behavior with
pure ethylene flames. The kinetic mechanism from Sun et al. (Proc. Combust.
Inst. 36, 1269-1278, 2017) for oxymethylene ether OMEn combustion with n=1-3 is
extended to include OME4 decomposition and combustion kinetics. In the
numerical simulations, the kinetic mechanism is combined with a detailed
quadvariate soot model which uses the Conditional Quadrature Method of Moments
and validated with Laser-Induced Fluorescence (LIF) and Laser-Induced
Incandescence (LII) measurements. It is observed, that the three investigated
OMEn with n=2-4 show similar sooting behavior, mainly reducing larger
aggregates while not significantly affecting the formation of smaller
particles. Furthermore, the extent of soot reduction is comparable among the
three OMEn. The trends and overall reduction are very well captured by the
model. The modeling results are analyzed through reaction path analyses and
sensitivity studies which show the important role of OMEn decomposition and the
formation of CH2O under these rich conditions to reduce species relevant for
soot formation. This is at the base of the negligible observed differences in
terms of soot formation for the different OMEn fuels.


title: Universality of the Buchdahl sphere

abstract:
  Buchdahl sphere, the limiting stable isotropic stellar structure without
exotic matter, plays a very important role, as far as the near horizon physics
is concerned. This is because, the radius of the Buchdahl sphere lies between
the horizon and the photon sphere, such that the signal arising from
gravitational wave and black hole shadow will mimic that of a black hole. Here,
we show certain universal properties associated with the Buchdahl sphere, in
the sense that these properties will not change even if electric charge is
included in the stellar structure. Moreover, these universal properties
transcend general relativity and holds good in pure Lovelock theories of
gravity as well. Using these universalities, we have proposed a Buchdahl limit
for rotating stellar configuration. Finally, the universality of the Buchdahl
limit in terms of the gravitational and non-gravitational field energies and
also in terms of the photon sphere is also discussed.


title: The folding of art: avoiding one's past in finite space

abstract:
  Through-out human history the new generations have sought to create their own
artistic style while trying to avoid repeating, for example, earlier
generations' music. If we assume that this search occurs in a multi-dimensional
but confined space of creativity, this gives rise to highly complex dynamics.
We present a very simple mathematical model with two parameters which can serve
as a generic representation for past avoidance in finite space and is
qualitatively distinct from earlier dynamical systems. In the presented
radially confined form, the trajectory preserves its complexity while
retracting to the vicinity of a hypersurface of constant radius when considered
in higher dimension.


title:
Analysing Opportunity Cost of Care Work using Mixed Effects Random
  Forests under Aggregated Census Data

abstract:
  Reliable estimators of the spatial distribution of socio-economic indicators
are essential for evidence-based policy-making. As sample sizes are small for
highly disaggregated domains, the accuracy of the direct estimates is reduced.
To overcome this problem small area estimation approaches are promising. In
this work we propose a small area methodology using machine learning methods.
The semi-parametric framework of mixed effects random forest combines the
advantages of random forests (robustness against outliers and implicit
model-selection) with the ability to model hierarchical dependencies. Existing
random forest-based methods require access to auxiliary information on
population-level. We present a methodology that deals with the lack of
population micro-data. Our strategy adaptively incorporates aggregated
auxiliary information through calibration-weights - based on empirical
likelihood - for the estimation of area-level means. In addition to our point
estimator, we provide a non-parametric bootstrap estimator measuring its
uncertainty. The performance of the proposed point estimator and its
uncertainty measure is studied in model-based simulations. Finally, the
proposed methodology is applied to the $2011$ Socio-Economic Panel and
aggregate census information from the same year to estimate the average
opportunity cost of care work for $96$ regional planning regions in Germany.


title: Quantum R\'{e}nyi Entropy Functionals for Bosonic Gaussian Systems

abstract:
  In this study, the quantum R\'{e}nyi entropy power inequality of order $p>1$
and power $\kappa$ is suggested as a quantum analog of the classical
R\'{e}nyi-$p$ entropy power inequality. To derive this inequality, we first
exploit the Wehrl-$p$ entropy power inequality on bosonic Gaussian systems via
the mixing operation of quantum convolution, which is a generalized
beamsplitter operation. This observation directly provides a quantum
R\'{e}nyi-$p$ entropy power inequality over a quasi-probability distribution
for $D$-mode bosonic Gaussian regimes. The proposed inequality is expected to
be useful for the nontrivial computing of quantum channel capacities,
particularly universal upper bounds on bosonic Gaussian quantum channels.


title:
Powers of Hamiltonian cycles in randomly augmented Dirac graphs -- the
  complete collection

abstract:
  We study the powers of Hamiltonian cycles in randomly augmented Dirac graphs,
that is, $n$-vertex graphs $G$ with minimum degree at least
$(1/2+\varepsilon)n$ for some $\varepsilon>0$. For any such graph and every
integer $m\ge2$, we very accurately estimate the threshold probability $p=p(n)$
for the event that the random augmentation $G\cup G(n,p)$ contains the $m$-th
power of a Hamiltonian cycle.


title:
Group sequential methods for interim monitoring of randomized clinical
  trials with time-lagged outcome

abstract:
  The primary analysis in two-arm clinical trials usually involves inference on
a scalar treatment effect parameter; e.g., depending on the outcome, the
difference of treatment-specific means, risk difference, risk ratio, or odds
ratio. Most clinical trials are monitored for the possibility of early
stopping. Because ordinarily the outcome on any given subject can be
ascertained only after some time lag, at the time of an interim analysis, among
the subjects already enrolled, the outcome is known for only a subset and is
effectively censored for those who have not been enrolled sufficiently long for
it to be observed. Typically, the interim analysis is based only on the data
from subjects for whom the outcome has been ascertained. A goal of an interim
analysis is to stop the trial as soon as the evidence is strong enough to do
so, suggesting that the analysis ideally should make the most efficient use of
all available data, thus including information on censoring as well as other
baseline and time-dependent covariates in a principled way. A general group
sequential framework is proposed for clinical trials with a time-lagged
outcome. Treatment effect estimators that take account of censoring and
incorporate covariate information at an interim analysis are derived using
semiparametric theory and are demonstrated to lead to stronger evidence for
early stopping than standard approaches. The associated test statistics are
shown to have the independent increments structure, so that standard software
can be used to obtain stopping boundaries.


title:
Embracing AWKWARD! Real-time Adjustment of Reactive Plans Using Social
  Norms

abstract:
  This paper presents the AWKWARD agent architecture for the development of
agents in Multi-Agent Systems. AWKWARD agents can have their plans
re-configured in real time to align with social role requirements under
changing environmental and social circumstances. The proposed hybrid
architecture makes use of Behaviour Oriented Design (BOD) to develop agents
with reactive planning and of the well-established OperA framework to provide
organisational, social, and interaction definitions in order to validate and
adjust agents' behaviours. Together, OperA and BOD can achieve real-time
adjustment of agent plans for evolving social roles, while providing the
additional benefit of transparency into the interactions that drive this
behavioural change in individual agents. We present this architecture to
motivate the bridging between traditional symbolic- and behaviour-based AI
communities, where such combined solutions can help MAS researchers in their
pursuit of building stronger, more robust intelligent agent teams. We use
DOTA2, a game where success is heavily dependent on social interactions, as a
medium to demonstrate a sample implementation of our proposed hybrid
architecture.


title: Quantum Davidson Algorithm for Excited States

abstract:
  Excited states properties are essential for many chemical and physical
phenomena, such as charge separation and light emission. However, existing
quantum algorithms mainly focus on the ground state, including quantum phase
estimation and variational quantum eigensolver (VQE). Even though there are
extensions of VQE-type methods for excited states, these methods suffer from
optimization problems. Alternatively, the quantum Krylov subspace (QKS) concept
has been proposed to calculate ground and excited states, providing a low-cost
alternative to quantum phase estimation. However, current QKS methods rely on a
pre-generated subspace via real- or imaginary-time evolutions. Such subspace is
not compact and may suffer from slow convergence and/or numerical stability
problems, resulting in deep circuits. In this work, we develop an economic QKS
algorithm, namely the quantum Davidson (QDavidson) algorithm, by leveraging the
iterative growth of Krylov subspace and pre-conditioner within the Davidson
scheme. The residues of eigenstates are used to expand the Krylov subspace,
leading to a compact Krylov subspace close to the exact solutions.
Consequently, fast convergence is achieved compared to other QKS methods (e.g.,
quantum Lanczos). The newly proposed QDavidson algorithm is employed to study
the excited-state properties of various systems, including the Heisenberg spin
model and real molecules on quantum simulators. Compared to the existing QKS
method, the QDavidson algorithm converges fast and results in a much shallower
circuit, making QDavidson a practical algorithm for computing both ground and
excited states properties on quantum computers.


title:
Comparing Bayes factors and hierarchical inference for testing general
  relativity with gravitational waves

abstract:
  In the context of testing general relativity with gravitational waves,
constraints obtained with multiple events are typically combined either through
a hierarchical formalism or though a combined multiplicative Bayes factor. We
show that the well-known dependence of Bayes factors on the analysis priors in
regions of the parameter space without likelihood support can lead to strong
confidence in favor of incorrect conclusions when one employs the
multiplicative Bayes factor. Bayes factors $\mathcal{O}(1)$ are ambivalent as
they depend sensitively on the analysis priors, which are rarely set in a
principled way; additionally, combined Bayes factors $>\mathcal{O}(10^3)$ can
be obtained in favor of the incorrect conclusion depending on the analysis
priors when many $\mathcal{O}(1)$ Bayes factors are multiplied, and
specifically when the priors are much wider than the underlying population. The
hierarchical analysis that instead infers the ensemble distribution of the
individual beyond-general-relativity constraints does not suffer from this
problem, and generically converges to favor the correct conclusion. Rather than
a naive multiplication, a more reliable Bayes factor can be computed from the
hierarchical analysis. We present a number of toy models showing that the
practice of multiplying Bayes Factors can lead to incorrect conclusions.


title:
An Evaluation of Intra-Transaction Parallelism in Actor-Relational
  Database Systems

abstract:
  Over the past decade, we have witnessed a dramatic evolution in main-memory
capacity and multi-core parallelism of server hardware. To leverage this
hardware potential, multi-core in-memory OLTP database systems have been
extensively re-designed. The core objective of this re-design has been scaling
up sequential execution of OLTP transactions, wherein alternative database
architectures have been explored to eliminate system bottlenecks and overheads
impeding inter-transaction parallelism to fully manifest. However,
intra-transaction parallelism has been largely ignored by this previous work.
We conjecture this situation to have developed because OLTP workloads are
sometimes deemed to have far too little intra-transactional parallelism and,
even when this kind of parallelism is available, program analyses to recognize
it in arbitrary stored procedures are considered too brittle to be used as a
general tool. Recently, however, a new concept of actor-relational database
systems has been proposed, raising hopes that application developers can
specify intra-transaction parallelism by modeling database applications as
communicating actors. In this scenario, a natural question is whether an
actor-relational database system with an asynchronous programming model can
adequately expose the intra-transaction parallelism available in application
logic to modern multi-core hardware. Towards that aim, we conduct in this paper
an experimental evaluation of the factors that affect intra-transaction
parallelism in the prototype actor-relational database system REACTDB, with an
OLTP application designed with task-level parallelism in mind, and with the
system running on a multi-core machine.


title:
Superfluid drag between excitonic polaritons and superconducting
  electron gas

abstract:
  The Andreev-Bashkin effect, or superfluid drag, is predicted in a system of
Bose-condensed excitonic polaritons in optical microcavity coupled by
electron-exciton interaction with a superconducting layer. Two possible setups
with spatially indirect dipole excitons or direct excitons are considered. The
drag density characterizing a magnitude of this effect is found by many-body
calculations with taking into account dynamical screening of electron-exciton
interaction. For the superconducting electronic layer, we assume the recently
proposed polaritonic mechanism of Cooper pairing, although the preexisting
thin-film superconductor should also demonstrate the effect. According to our
calculations, the drag density can reach considerable values in realistic
conditions, with excitonic and electronic layers made from GaAs-based quantum
wells or two-dimensional transition metal dichalcogenides. The predicted
nondissipative drag could be strong enough to be observable as induction of a
supercurrent in the electronic layer by a superfluid flow of polaritons.


title: Explicit and efficient error estimation for convex minimization problems

abstract:
  We combine a systematic approach for deriving general a posteriori error
estimates for convex minimization problems based on convex duality relations
with a recently derived generalized Marini formula. The a posteriori error
estimates are essentially constant-free and apply to a large class of
variational problems including the $p$-Dirichlet problem, as well as degenerate
minimization, obstacle and image de-noising problems. In addition, these a
posteriori error estimates are based on a comparison to a given non-conforming
finite element solution. For the $p$-Dirichlet problem, these a posteriori
error bounds are equivalent to residual type a posteriori error bounds and,
hence, reliable and efficient.


title:
Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic
  Images in Facial Capture Pipelines

abstract:
  We propose an end-to-end pipeline for both building and tracking 3D facial
models from personalized in-the-wild (cellphone, webcam, youtube clips, etc.)
video data. First, we present a method for automatic data curation and
retrieval based on a hierarchical clustering framework typical of collision
detection algorithms in traditional computer graphics pipelines. Subsequently,
we utilize synthetic turntables and leverage deepfake technology in order to
build a synthetic multi-view stereo pipeline for appearance capture that is
robust to imperfect synthetic geometry and image misalignment. The resulting
model is fit with an animation rig, which is then used to track facial
performances. Notably, our novel use of deepfake technology enables us to
perform robust tracking of in-the-wild data using differentiable renderers
despite a significant synthetic-to-real domain gap. Finally, we outline how we
train a motion capture regressor, leveraging the aforementioned techniques to
avoid the need for real-world ground truth data and/or a high-end calibrated
camera capture setup.


title: A New Polar Code Design Based on Reciprocal Channel Approximation

abstract:
  This paper revisits polar code design for a binary-input additive white
Gaussian noise (BI-AWGN) channel when successive cancellation (SC) decoding is
applied at the receiver. We focus on the reciprocal channel approximation
(RCA), which is often adopted in the design of low-density parity-check (LDPC)
codes. In order to apply RCA to polar code design for various codeword lengths,
we derive rigorous closed-form approximations that are valid over a wide range
of SNR over an AWGN channel, for both the mutual information of BPSK signaling
and the corresponding reciprocal channel mapping. As a result, the
computational complexity required for evaluating channel polarization is thus
equivalent to that based on the popular Gaussian approximation (GA) approach.
Simulation results show that the proposed polar code design based on RCA
outperforms those based on GA as well as the so-called improved GA (IGA)
approach, especially as the codeword length is increased. Furthermore, the
RCA-based design yields a better block error rate (BLER) estimate compared to
GA-based approaches.


title:
Large population limit of the spectrum of killed birth-and-death
  processes

abstract:
  We consider a general class of birth-and-death processes with state space
$\{0,1,2,3,\ldots\}$ which describes the size of a population going eventually
to extinction with probability one. We obtain the complete spectrum of the
generator of the process killed at $0$ in the large population limit, that is,
we scale the process by a parameter $K$, and take the limit $K\to+\infty$. We
assume that the differential equation $\mathrm{d} x/\mathrm{d} t=b(x)-d(x)$
describing the infinite population limit (in any finite-time interval) has a
repulsive fixed point at $0$, and an attractive fixed point $x_*>0$. We prove
that, asymptotically, the spectrum is the superposition of two spectra. One is
the spectrum of the generator of an Ornstein-Uhlenbeck process, which is
$n(b'(x_*)-d'(x_*))$, $n\ge 0$. The other one is the spectrum of a
continuous-time binary branching process conditioned on non-extinction, and is
given by $n(d'(0)-b'(0))$, $n\ge 1$. A major difficulty is that different
scales and function spaces are involved. We work at the level of the
eigenfunctions that we split over different regions, and study their asymptotic
dependence on $K$ in each region. In particular, we prove that the spectral gap
goes to $\min\big\{b'(0)-d'(0),\,d'(x_*)-b'(x_*)\big\}$. This work complements
a previous work of ours in which we studied the approximation of the
quasi-stationary distribution and of the mean time to extinction.


title: E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR

abstract:
  Improving the performance of end-to-end ASR models on long utterances ranging
from minutes to hours in length is an ongoing challenge in speech recognition.
A common solution is to segment the audio in advance using a separate voice
activity detector (VAD) that decides segment boundary locations based purely on
acoustic speech/non-speech information. VAD segmenters, however, may be
sub-optimal for real-world speech where, e.g., a complete sentence that should
be taken as a whole may contain hesitations in the middle ("set an alarm for...
5 o'clock").
  We propose to replace the VAD with an end-to-end ASR model capable of
predicting segment boundaries in a streaming fashion, allowing the segmentation
decision to be conditioned not only on better acoustic features but also on
semantic features from the decoded text with negligible extra computation. In
experiments on real world long-form audio (YouTube) with lengths of up to 30
minutes, we demonstrate 8.5% relative WER improvement and 250 ms reduction in
median end-of-segment latency compared to the VAD segmenter baseline on a
state-of-the-art Conformer RNN-T model.


title:
PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale
  Point Cloud Upsampling

abstract:
  High-quality point clouds have practical significance for point-based
rendering, semantic understanding, and surface reconstruction. Upsampling
sparse, noisy and nonuniform point clouds for a denser and more regular
approximation of target objects is a desirable but challenging task. Most
existing methods duplicate point features for upsampling, constraining the
upsampling scales at a fixed rate. In this work, the flexible upsampling rates
are achieved via edge vector based affine combinations, and a novel design of
Edge Vector based Approximation for Flexible-scale Point clouds Upsampling
(PU-EVA) is proposed. The edge vector based approximation encodes the
neighboring connectivity via affine combinations based on edge vectors, and
restricts the approximation error within the second-order term of Taylor's
Expansion. The EVA upsampling decouples the upsampling scales with network
architecture, achieving the flexible upsampling rates in one-time training.
Qualitative and quantitative evaluations demonstrate that the proposed PU-EVA
outperforms the state-of-the-art in terms of proximity-to-surface, distribution
uniformity, and geometric details preservation.


title:
Machine Learning methods to estimate observational properties of galaxy
  clusters in large volume cosmological N-body simulations

abstract:
  In this paper we study the applicability of a set of supervised machine
learning (ML) models specifically trained to infer observed related properties
of the baryonic component (stars and gas) from a set of features of dark matter
only cluster-size halos. The training set is built from THE THREE HUNDRED
project which consists of a series of zoomed hydrodynamical simulations of
cluster-size regions extracted from the 1 Gpc volume MultiDark dark-matter only
simulation (MDPL2). We use as target variables a set of baryonic properties for
the intra cluster gas and stars derived from the hydrodynamical simulations and
correlate them with the properties of the dark matter halos from the MDPL2
N-body simulation. The different ML models are trained from this database and
subsequently used to infer the same baryonic properties for the whole range of
cluster-size halos identified in the MDPL2. We also test the robustness of the
predictions of the models against mass resolution of the dark matter halos and
conclude that their inferred baryonic properties are rather insensitive to
their DM properties which are resolved with almost an order of magnitude
smaller number of particles. We conclude that the ML models presented in this
paper can be used as an accurate and computationally efficient tool for
populating cluster-size halos with observational related baryonic properties in
large volume N-body simulations making them more valuable for comparison with
full sky galaxy cluster surveys at different wavelengths. We make the best ML
trained model publicly available.


title:
Impact of initial outbreak locations on transmission risk of infectious
  diseases in an intra-urban area

abstract:
  Infectious diseases usually originate from a specific location within a city.
Due to the heterogenous distribution of population and public facilities, and
the structural heterogeneity of human mobility network embedded in space,
infectious diseases break out at different locations would cause different
transmission risk and control difficulty. This study aims to investigate the
impact of initial outbreak locations on the risk of spatiotemporal transmission
and reveal the driving force behind high-risk outbreak locations. First,
integrating mobile phone location data, we built a SLIR
(susceptible-latent-infectious-removed)-based meta-population model to simulate
the spreading process of an infectious disease (i.e., COVID-19) across
fine-grained intra-urban regions (i.e., 649 communities of Shenzhen City,
China). Based on the simulation model, we evaluated the transmission risk
caused by different initial outbreak locations by proposing three indexes
including the number of infected cases (CaseNum), the number of affected
regions (RegionNum), and the spatial diffusion range (SpatialRange). Finally,
we investigated the contribution of different influential factors to the
transmission risk via machine learning models. Results indicates that different
initial outbreak locations would cause similar CaseNum but different RegionNum
and SpatialRange. To avoid the epidemic spread quickly to more regions, it is
necessary to prevent epidemic breaking out in locations with high
population-mobility flow density. While to avoid epidemic spread to larger
spatial range, remote regions with long daily trip distance of residents need
attention. Those findings can help understand the transmission risk and driving
force of initial outbreak locations within cities and make precise prevention
and control strategies in advance.


title: A note on the dilation of a certain family of tetrablock contractions

abstract:
  We find explicit tetrablock isometric dilation for every member $(A_\alpha,
B, P)$ of a family of tetrablock contractions indexed by a parameter $\alpha$
in the closed unit disc (only the first operator of the tetrablock contraction
depends on the parameter). The dilation space is the same for any member of the
family and the dilation is not minimal. Explicit dilation for the adjoint
tetrablock contraction $(A_\alpha^*, B^*, P^*)$ for every member of the family
mentioned above is constructed as well. This family includes the example
considered in Section 5 of \cite{Pal}. Taking cue from this construction and
using Toeplitz operators on $H^2_{\mathbb D}(\mathcal D_P)$, we obtain
necessary and sufficient conditions for a tetrablock contraction to have a
certain type of tetrablock isometric dilation.


title: Deterministic Self-Adjusting Tree Networks Using Rotor Walks

abstract:
  We revisit the design of self-adjusting single-source tree networks. The
problem can be seen as a generalization of the classic list update problem to
trees, and finds applications in reconfigurable datacenter networks. We are
given a fixed balanced binary tree T connecting n nodes V = {v_1, ... , v_n}. A
source node v_0, attached to the root of the tree, issues communication
requests to nodes in V, in an online and adversarial manner; the access cost of
a request to a node v, is given by the current depth of v in T. The online
algorithm can try to reduce the access cost by performing swap operations, with
which the position of a node is exchanged with the position of its parent in
the tree; a swap operation costs one unit. The objective is to design an online
algorithm which minimizes the total access cost plus adjustment cost
(swapping). Avin et al. recently presented Random-Push, a constant competitive
online algorithm for this problem, based on random walks, together with an
analysis exploiting the most recently used (MRU) property of random walks.
  We study analytically and empirically, online algorithms for this problem. In
particular, we explore how to derandomize Random-Push. We consider a simple
derandomized algorithm which we call Rotor-Push, as its behavior is reminiscent
of rotor walks. We first prove that Rotor-Push is constant competitive: its
competitive ratio is 12 and hence by a factor of five lower than the best
existing competitive ratio. In contrast to Random-Push, the algorithm does not
feature the MRU property, which requires a new analysis. We present a
significantly improved and simpler analysis for the randomized algorithm,
showing that it is 16-competitive. We compare empirically all self-adjusting
single-source tree networks, using synthetic and real data with varying
locality and observe that Rotor-Push and Random-Push have almost identical
performance.


title: Decomposition of Triple Collinear Splitting Functions

abstract:
  In the kinematic region where three particles $i$, $j$, $k$ are collinear,
the multi-parton scattering amplitudes factorise into a product of a triple
collinear splitting function and a multi-parton scattering amplitude with two
fewer particles. These triple collinear splitting functions contain both
iterated single unresolved contributions, and genuine double unresolved
contributions. We make this explicit by rewriting the known triple collinear
splitting functions in terms of products of two-particle splitting functions,
and a remainder that is explicitly finite when any two of $\{i,j,k\}$ are
collinear. We analyse all of the single unresolved singularities present in the
remainder.


title:
Reference Vector Adaptation and Mating Selection Strategy via Adaptive
  Resonance Theory-based Clustering for Many-objective Optimization

abstract:
  Decomposition-based multiobjective evolutionary algorithms (MOEAs) with
clustering-based reference vector adaptation show good optimization performance
for many-objective optimization problems (MaOPs). Especially, algorithms that
employ a clustering algorithm with a topological structure (i.e., a network
composed of nodes and edges) show superior optimization performance to other
MOEAs for MaOPs with irregular Pareto optimal fronts (PFs). These algorithms,
however, do not effectively utilize information of the topological structure in
the search process. Moreover, the clustering algorithms typically used in
conventional studies have limited clustering performance, inhibiting the
ability to extract useful information for the search process. This paper
proposes an adaptive reference vector-guided evolutionary algorithm using an
adaptive resonance theory-based clustering with a topological structure. The
proposed algorithm utilizes the information of the topological structure not
only for reference vector adaptation but also for mating selection. The
proposed algorithm is compared with 8 state-of-the-art MOEAs on 78 test
problems. Experimental results reveal the outstanding optimization performance
of the proposed algorithm over the others on MaOPs with various properties.


title: FaithDial: A Faithful Benchmark for Information-Seeking Dialogue

abstract:
  The goal of information-seeking dialogue is to respond to seeker queries with
natural language utterances that are grounded on knowledge sources. However,
dialogue systems often produce unsupported utterances, a phenomenon known as
hallucination. Dziri et al. (2022)'s investigation of hallucinations has
revealed that existing knowledge-grounded benchmarks are contaminated with
hallucinated responses at an alarming level (>60% of the responses) and models
trained on this data amplify hallucinations even further (>80% of the
responses). To mitigate this behavior, we adopt a data-centric solution and
create FaithDial, a new benchmark for hallucination-free dialogues, by editing
hallucinated responses in the Wizard of Wikipedia (WoW) benchmark. We observe
that FaithDial is more faithful than WoW while also maintaining engaging
conversations. We show that FaithDial can serve as a training signal for: i) a
hallucination critic, which discriminates whether an utterance is faithful or
not, and boosts the performance by 21.1 F1 score on the BEGIN benchmark
compared to existing datasets for dialogue coherence; ii) high-quality dialogue
generation. We benchmark a series of state-of-the-art models and propose an
auxiliary contrastive objective that achieves the highest level of faithfulness
and abstractiveness based on several automated metrics. Further, we find that
the benefits of FaithDial generalize to zero-shot transfer on other datasets,
such as CMU-Dog and TopicalChat. Finally, human evaluation reveals that
responses generated by models trained on FaithDial are perceived as more
interpretable, cooperative, and engaging.


title: Vector spaces with a dense-codense generic submodule

abstract:
  We study expansions of a vector space $V$ over a field $\mathbb F$, possibly
with extra structure, with a generic submodule over a subring of $\mathbb F$.
We construct a natural expansion by existentially defined functions so that the
expansion in the extended language satisfies quantifier elimination. We show
that this expansion preserves tame model theoretic properties such as
stability, NIP, NTP$_1$, NTP$_2$ and NSOP$_1$. We also study induced
independence relations in the expansion.


title:
The Boltzmann Policy Distribution: Accounting for Systematic
  Suboptimality in Human Models

abstract:
  Models of human behavior for prediction and collaboration tend to fall into
two categories: ones that learn from large amounts of data via imitation
learning, and ones that assume human behavior to be noisily-optimal for some
reward function. The former are very useful, but only when it is possible to
gather a lot of human data in the target environment and distribution. The
advantage of the latter type, which includes Boltzmann rationality, is the
ability to make accurate predictions in new environments without extensive data
when humans are actually close to optimal. However, these models fail when
humans exhibit systematic suboptimality, i.e. when their deviations from
optimal behavior are not independent, but instead consistent over time. Our key
insight is that systematic suboptimality can be modeled by predicting policies,
which couple action choices over time, instead of trajectories. We introduce
the Boltzmann policy distribution (BPD), which serves as a prior over human
policies and adapts via Bayesian inference to capture systematic deviations by
observing human actions during a single episode. The BPD is difficult to
compute and represent because policies lie in a high-dimensional continuous
space, but we leverage tools from generative and sequence models to enable
efficient sampling and inference. We show that the BPD enables prediction of
human behavior and human-AI collaboration equally as well as imitation
learning-based human models while using far less data.


title:
iCAR: Bridging Image Classification and Image-text Alignment for Visual
  Recognition

abstract:
  Image classification, which classifies images by pre-defined categories, has
been the dominant approach to visual representation learning over the last
decade. Visual learning through image-text alignment, however, has emerged to
show promising performance, especially for zero-shot recognition. We believe
that these two learning tasks are complementary, and suggest combining them for
better visual learning. We propose a deep fusion method with three adaptations
that effectively bridge two learning tasks, rather than shallow fusion through
naive multi-task learning. First, we modify the previous common practice in
image classification, a linear classifier, with a cosine classifier which shows
comparable performance. Second, we convert the image classification problem
from learning parametric category classifier weights to learning a text encoder
as a meta network to generate category classifier weights. The learnt text
encoder is shared between image classification and image-text alignment. Third,
we enrich each class name with a description to avoid confusion between classes
and make the classification method closer to the image-text alignment. We prove
that this deep fusion approach performs better on a variety of visual
recognition tasks and setups than the individual learning or shallow fusion
approach, from zero-shot/few-shot image classification, such as the Kornblith
12-dataset benchmark, to downstream tasks of action recognition, semantic
segmentation, and object detection in fine-tuning and open-vocabulary settings.
The code will be available at https://github.com/weiyx16/iCAR.


title:
Spreading fronts of wetting liquid droplets: microscopic simulations and
  universal fluctuations

abstract:
  We have used kinetic Monte Carlo (kMC) simulations of a lattice gas to study
front fluctuations in the spreading of a non-volatile liquid droplet onto a
solid substrate. Our results are consistent with a diffusive growth law for the
radius of the precursor layer, $R \sim t^{\delta}$, with $\delta \approx 1/2$
in all the conditions considered for temperature and substrate wettability, in
good agreement with previous studies. The fluctuations of the front exhibit
kinetic roughening properties with exponent values which depend on temperature
$T$, but become $T$-independent for sufficiently high $T$. Moreover, strong
evidences of intrinsic anomalous scaling have been found, characterized by
different values of the roughness exponent at short and large length scales.
Although such a behavior differs from the scaling properties of the
one-dimensional Kardar-Parisi-Zhang (KPZ) universality class, the front
covariance and the probability distribution function of front fluctuations
found in our kMC simulations do display KPZ behavior, agreeing with simulations
of a continuum height equation proposed in this context. However, this equation
does not feature intrinsic anomalous scaling, at variance with the discrete
model.


title:
Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose
  Estimation

abstract:
  A high-resolution network exhibits remarkable capability in extracting
multi-scale features for human pose estimation, but fails to capture long-range
interactions between joints and has high computational complexity. To address
these problems, we present a Dynamic lightweight High-Resolution Network
(Dite-HRNet), which can efficiently extract multi-scale contextual information
and model long-range spatial dependency for human pose estimation.
Specifically, we propose two methods, dynamic split convolution and adaptive
context modeling, and embed them into two novel lightweight blocks, which are
named dynamic multi-scale context block and dynamic global context block. These
two blocks, as the basic component units of our Dite-HRNet, are specially
designed for the high-resolution networks to make full use of the parallel
multi-resolution architecture. Experimental results show that the proposed
network achieves superior performance on both COCO and MPII human pose
estimation datasets, surpassing the state-of-the-art lightweight networks. Code
is available at: \url{https://github.com/ZiyiZhang27/Dite-HRNet}.


title:
Time symmetric electrodynamics, electric charge conservation, and the
  Lorenz gauge

abstract:
  We show how the Lorenz gauge condition is related to the electric charge
conservation, in a universe where electrically charged point particles are
created and annihilated. We derive our results using time symmetric
electrodynamics, relying on observations made by Jacov Frenkel (in 1925) and
John Archibald Wheeler (in 1940).


title: Testing a varying-$\Lambda$ model for dark energy within CPC framework

abstract:
  The CPC (Covariant Physical Couplings) framework is a modified gravity set up
assuming Einstein Field Equations wherein the quantities $\{G,c,\Lambda\}$ are
promoted to spacetime functions. Bianchi identity and the requirement of
stress-energy tensor conservation entangle the possible variations of the
couplings $\{G,c,\Lambda\}$, which are forced to co-vary as dictated by the
General Constraint (GC). In this paper we explore a cosmological model wherein
$G$, $c$ and $\Lambda$ are functions of the redshift respecting the GC of the
CPC framework. We assume a linear parameterization of $\Lambda$ in terms of the
scale factor $a$. We use the ansatz $\dot{G}/G = \sigma \left( \dot{c}/c
\right)$ with $\sigma =$ constant to deduce the functional forms of $c=c(z)$
and $G=G(z)$. We show that this varying-$\{G,c,\Lambda\}$ model fits SNe Ia
data and $H(z)$ data with $\sigma = 3$. The model parameters can be constrained
to describe dark energy at the background level.


title:
Tag-Based Attention Guided Bottom-Up Approach for Video Instance
  Segmentation

abstract:
  Video Instance Segmentation is a fundamental computer vision task that deals
with segmenting and tracking object instances across a video sequence. Most
existing methods typically accomplish this task by employing a multi-stage
top-down approach that usually involves separate networks to detect and segment
objects in each frame, followed by associating these detections in consecutive
frames using a learned tracking head. In this work, however, we introduce a
simple end-to-end trainable bottom-up approach to achieve instance mask
predictions at the pixel-level granularity, instead of the typical
region-proposals-based approach. Unlike contemporary frame-based models, our
network pipeline processes an input video clip as a single 3D volume to
incorporate temporal information. The central idea of our formulation is to
solve the video instance segmentation task as a tag assignment problem, such
that generating distinct tag values essentially separates individual object
instances across the video sequence (here each tag could be any arbitrary value
between 0 and 1). To this end, we propose a novel spatio-temporal tagging loss
that allows for sufficient separation of different objects as well as necessary
identification of different instances of the same object. Furthermore, we
present a tag-based attention module that improves instance tags, while
concurrently learning instance propagation within a video. Evaluations
demonstrate that our method provides competitive results on YouTube-VIS and
DAVIS-19 datasets, and has minimum run-time compared to other state-of-the-art
performance methods.


title: From Books to Knowledge Graphs

abstract:
  The digital transformation of the scientific publishing industry has led to
dramatic improvements in content discoverability and information analytics.
Unfortunately, these improvements have not been uniform across research areas.
The scientific literature in the arts, humanities and social sciences (AHSS)
still lags behind, in part due to the scale of analog backlogs, the persisting
importance of national languages, and a publisher ecosystem made of many, small
or medium enterprises. We propose a bottom-up approach to support publishers in
creating and maintaining their own publication knowledge graphs in the open
domain. We do so by releasing a pipeline able to extract structured information
from the bibliographies and indexes of AHSS publications, disambiguate,
normalize and export it as linked data. We test the proposed pipeline on
Brill's Classics collection, and release an implementation in open source for
further use and improvement.