title:
Knowledge Graph and Accurate Portrait Construction of Scientific and
  Technological Academic Conferences

abstract:
  In recent years, with the continuous progress of science and technology, the
number of scientific research achievements is increasing day by day, as the
exchange platform and medium of scientific research achievements, the
scientific and technological academic conferences have become more and more
abundant. The convening of scientific and technological academic conferences
will bring large number of academic papers, researchers, research institutions
and other data, and the massive data brings difficulties for researchers to
obtain valuable information. Therefore, it is of great significance to use deep
learning technology to mine the core information in the data of scientific and
technological academic conferences, and to realize a knowledge graph and
accurate portrait system of scientific and technological academic conferences,
so that researchers can obtain scientific research information faster.


title: Muon $(g-2)$ and Thermal WIMP DM in ${\rm U(1)}_{L_\mu-L_\tau} $ Models

abstract:
  ${\rm U(1)}_{L_\mu - L_\tau} \equiv {\rm U(1)}_X$ model is anomaly free
within the Standard Model (SM) fermion content, and can accommodate the muon
$(g-2)$ data for $M_{Z'} \sim O(10-100)$ MeV and $g_X \sim (4 - 8) \times
10^{-4}$. WIMP type thermal dark matter (DM) can be also introduced for $M_{Z'}
\sim 2 M_{\rm DM}$, if DM pair annihilations into the SM particles occur only
through the $s$-channel $Z'$ exchange. In this work, we show that this tight
correlation between $M_{Z'}$ and $M_{\rm DM}$ can be completely evaded both for
scalar and fermionic DM, if we %consider include the contributions from dark
Higgs boson ($H_1$). Dark Higgs boson plays a crucial role in DM phenomenology,
not only for generation of dark photon mass, but also opening new channels for
DM pair annihilations into the final states involving dark Higgs boson, such as
dark Higgs pair as well as $Z' Z'$ through dark Higgs exchange in the
$s$-channel, and co-annihilation into $Z' H_1$ in case of inelastic DM. Thus
dark Higgs boson will dissect the strong correlation $M_{Z'} \sim 2 M_{\rm
DM}$, and much wider mass range is allowed for $U(1)_X$-charged complex scalar
and Dirac fermion DM, still explaining the muon $(g-2)$. We consider both
generic $U(1)_X$ breaking as well as ${\rm U(1)}_X \rightarrow Z_2$ (and also
into $Z_3$ only for scalar DM case).


title:
Anti-Adversarially Manipulated Attributions for Weakly Supervised
  Semantic Segmentation and Object Localization

abstract:
  Obtaining accurate pixel-level localization from class labels is a crucial
process in weakly supervised semantic segmentation and object localization.
Attribution maps from a trained classifier are widely used to provide
pixel-level localization, but their focus tends to be restricted to a small
discriminative region of the target object. An AdvCAM is an attribution map of
an image that is manipulated to increase the classification score produced by a
classifier before the final softmax or sigmoid layer. This manipulation is
realized in an anti-adversarial manner, so that the original image is perturbed
along pixel gradients in directions opposite to those used in an adversarial
attack. This process enhances non-discriminative yet class-relevant features,
which make an insufficient contribution to previous attribution maps, so that
the resulting AdvCAM identifies more regions of the target object. In addition,
we introduce a new regularization procedure that inhibits the incorrect
attribution of regions unrelated to the target object and the excessive
concentration of attributions on a small region of the target object. Our
method achieves a new state-of-the-art performance in weakly and
semi-supervised semantic segmentation, on both the PASCAL VOC 2012 and MS COCO
2014 datasets. In weakly supervised object localization, it achieves a new
state-of-the-art performance on the CUB-200-2011 and ImageNet-1K datasets.


title:
Methods of Informational Trends Analytics and Fake News Detection on
  Twitter

abstract:
  In the paper, different approaches for the analysis of news trends on Twitter
has been considered. For the analysis and case study, informational trends on
Twitter caused by Russian invasion of Ukraine in 2022 year have been studied. A
deep learning approach for fake news detection has been analyzed. The use of
the theory of frequent itemsets and association rules, graph theory for news
trends analytics have been considered.


title:
JORLDY: a fully customizable open source framework for reinforcement
  learning

abstract:
  Recently, Reinforcement Learning (RL) has been actively researched in both
academic and industrial fields. However, there exist only a few RL frameworks
which are developed for researchers or students who want to study RL. In
response, we propose an open-source RL framework "Join Our Reinforcement
Learning framework for Developing Yours" (JORLDY). JORLDY provides more than 20
widely used RL algorithms which are implemented with Pytorch. Also, JORLDY
supports multiple RL environments which include OpenAI gym, Unity ML-Agents,
Mujoco, Super Mario Bros and Procgen. Moreover, the algorithmic components such
as agent, network, environment can be freely customized, so that the users can
easily modify and append algorithmic components. We expect that JORLDY will
support various RL research and contribute further advance the field of RL. The
source code of JORLDY is provided on the following Github:
https://github.com/kakaoenterprise/JORLDY


title: Box distance and observable distance via optimal transport

abstract:
  On the set of all metric measure spaces, we have two important metrics, the
box metric and the observable metric, both introduced by M. Gromov. We obtain
the representation of these metrics by using transport plan. In addition, we
prove the existence of optimal transport plans of these metrics.


title:
Immuno-Histo X-ray Phase Contrast Tomography: New 3D imaging technique
  for molecular tomography

abstract:
  The still unmet ability of following the fate of specific cells or molecules
within the whole body would give an outstanding breakthrough in the
comprehension of disease mechanisms and in the monitoring of therapeutic
approaches. Our idea is to push forward the bio-nanotechnology to a level where
it serves the most advanced 3D bio-medical imaging to provide a multi-scale
imaging ranging from the whole organ down to the cellular level, enabling
high-resolution visualization of disease-relevant cells within the whole
disease-altered biological context. We present here the first proof-of-concept
of a novel tomography procedure, Immuno-Histo-X-ray Phase Contrast Tomography
(XPCT) that combines cutting-edge XPCT, which provides detailed image of the
whole organ, with molecular imaging at the cellular level, identifying the
relevant cells via an immunohistochemistry-based approach. We combine metal
nanoparticles and a single-domain antibody that target relevant cells. Our
results lay the foundation for a new generation of 3D-bio-medical X-ray
imaging.


title:
Advanced self-similar solutions of regular and irregular diffusion
  equations

abstract:
  We study the diffusion equation with an appropriate change of variables. This
equation is in general a partial differential equation (PDE). With the
self-similar and related Ansat\"atze we transform the PDE of diffusion to an
ordinary differential equation. The solutions of the PDE belong to a family of
functions which are presented for the case of infinite horizon. In the
presentation we accentuate the physically reasonable solutions. We also study
time dependent diffusion phenomena, where the spreading may vary in time. To
describe the process we consider time dependent diffusion coefficients. The
obtained analytic solutions all can be expressed with Kummer's or
Whittaker-type of functions.


title:
Exponential and logarithm of multivector in low dimensional (n=p+q<3)
  Clifford algebras

abstract:
  Closed form expressions for a multivector exponential and logarithm are
presented in real Clifford geometric algebras Cl(p,q)when n=p+q=1 (complex and
hyperbolic numbers) and n=2 (Hamilton, split and conectorine quaternions).
Starting from Cl(0,1) and Cl(1,0) algebras wherein square of a basis vector is
either -1 or +1, we have generalized exponential and logarithm formulas to 2D
quaternionic algebras, Cl(0,2), Cl(1,1), and Cl(2,0). The sectors in the
multivector coefficient space where 2D logarithm exists are found. They are
related with a square root of the multivector.


title:
ProFuse: Physical Multi-Band Structural Decomposition of Galaxies and
  the Mass-Size-Age Plane

abstract:
  We present the new ProFuse R package, a simultaneous spectral (ultraviolet to
far infrared) and spatial structural decomposition tool that produces physical
models of galaxies and their components. This combines the functionality of the
recently released ProFound (for automatic source extraction), ProFit (for
extended source profiling) and ProSpect (for stellar population modelling)
software packages. The key novelty of ProFuse is that it generates images using
a self-consistent model for the star formation and metallicity history of the
bulge and disk separately, and uses target images across a range of wavelengths
to define the model likelihood and optimise our physical galaxy reconstruction.
The first part of the paper explores the ProFuse approach in detail, and
compares results to published structural and stellar population properties. The
latter part of the paper applies ProFuse to 6,664 z < 0.06 GAMA galaxies. Using
re-processed ugriZYJHKs imaging we extract structural and stellar population
properties for bulges and disks in parallel. As well as producing true stellar
mass based mass-size relationships, we further extend this correlation to
explore the third dimensions of age and gas phase metallicity. The disks in
particular demonstrate strong co-dependency between mass-size-age in a well
defined plane, where at a given disk stellar mass younger disks tend to be
larger. These findings are in broad agreement with work at higher redshift
suggesting disks that formed earlier are physically smaller.


title: PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining

abstract:
  Open-source process mining provides many algorithms for the analysis of event
data which could be used to analyze mainstream processes (e.g., O2C, P2P, CRM).
However, compared to commercial tools, they lack the performance and struggle
to analyze large amounts of data. This paper presents PM4Py-GPU, a Python
process mining library based on the NVIDIA RAPIDS framework. Thanks to the
dataframe columnar storage and the high level of parallelism, a significant
speed-up is achieved on classic process mining computations and processing
activities.


title:
Three-point functions of a fermionic higher-spin current in 4D conformal
  field theory

abstract:
  We investigate the properties of a four-dimensional conformal field theory
possessing a fermionic higher-spin current $Q_{\alpha(2k) \dot{\alpha}}$. Using
a computational approach, we examine the number of independent tensor
structures contained in the three-point correlation functions of two fermionic
higher-spin currents with the conserved vector current $V_{m}$, and with the
energy-momentum tensor $T_{m n}$. In particular, the $k = 1$ case corresponds
to a "supersymmetry-like" current, that is, a fermionic conserved current with
identical properties to the supersymmetry current which appears in $\mathcal{N}
= 1$ superconformal field theories. However, we show that in general, the
three-point correlation functions $\langle QQV \rangle$ and $\langle QQT
\rangle$ are not consistent with $\mathcal{N} = 1$ supersymmetry.


title:
Confusing Image Quality Assessment: Towards Better Augmented Reality
  Experience

abstract:
  With the development of multimedia technology, Augmented Reality (AR) has
become a promising next-generation mobile platform. The primary value of AR is
to promote the fusion of digital contents and real-world environments, however,
studies on how this fusion will influence the Quality of Experience (QoE) of
these two components are lacking. To achieve better QoE of AR, whose two layers
are influenced by each other, it is important to evaluate its perceptual
quality first. In this paper, we consider AR technology as the superimposition
of virtual scenes and real scenes, and introduce visual confusion as its basic
theory. A more general problem is first proposed, which is evaluating the
perceptual quality of superimposed images, i.e., confusing image quality
assessment. A ConFusing Image Quality Assessment (CFIQA) database is
established, which includes 600 reference images and 300 distorted images
generated by mixing reference images in pairs. Then a subjective quality
perception study and an objective model evaluation experiment are conducted
towards attaining a better understanding of how humans perceive the confusing
images. An objective metric termed CFIQA is also proposed to better evaluate
the confusing image quality. Moreover, an extended ARIQA study is further
conducted based on the CFIQA study. We establish an ARIQA database to better
simulate the real AR application scenarios, which contains 20 AR reference
images, 20 background (BG) reference images, and 560 distorted images generated
from AR and BG references, as well as the correspondingly collected subjective
quality ratings. We also design three types of full-reference (FR) IQA metrics
to study whether we should consider the visual confusion when designing
corresponding IQA algorithms. An ARIQA metric is finally proposed for better
evaluating the perceptual quality of AR images.


title: Entropic transfer operators

abstract:
  We propose a new concept for the regularization and discretization of
transfer operators in dynamical systems. Our approach is based on the
entropically regularized optimal transport between two probability measures. In
particular, we use optimal transport plans in order to construct a
finite-dimensional approximation of some transfer operator which can be
analysed computationally. We prove that the spectrum of the discretized
operator converges to the one of the regularized original operator, give a
detailed analysis of the relation between the discretized and the original
peripheral spectrum for a rotation map on the $n$-torus and provide code for
three numerical experiments, including one based on the raw trajectory data of
a small biomolecule from which its dominant conformations are recovered.


title: NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias

abstract:
  Media news framing bias can increase political polarization and undermine
civil society. The need for automatic mitigation methods is therefore growing.
We propose a new task, a neutral summary generation from multiple news articles
of the varying political leanings to facilitate balanced and unbiased news
reading. In this paper, we first collect a new dataset, illustrate insights
about framing bias through a case study, and propose a new effective metric and
model (NeuS-TITLE) for the task. Based on our discovery that title provides a
good signal for framing bias, we present NeuS-TITLE that learns to neutralize
news content in hierarchical order from title to article. Our hierarchical
multi-task learning is achieved by formatting our hierarchical data pair
(title, article) sequentially with identifier-tokens ("TITLE=>", "ARTICLE=>")
and fine-tuning the auto-regressive decoder with the standard negative
log-likelihood objective. We then analyze and point out the remaining
challenges and future directions. One of the most interesting observations is
that neural NLG models can hallucinate not only factually inaccurate or
unverifiable content but also politically biased content.


title:
PICASSO: Unleashing the Potential of GPU-centric Training for
  Wide-and-deep Recommender Systems

abstract:
  The development of personalized recommendation has significantly improved the
accuracy of information matching and the revenue of e-commerce platforms.
Recently, it has 2 trends: 1) recommender systems must be trained timely to
cope with ever-growing new products and ever-changing user interests from
online marketing and social network; 2) SOTA recommendation models introduce
DNN modules to improve prediction accuracy. Traditional CPU-based recommender
systems cannot meet these two trends, and GPU- centric training has become a
trending approach. However, we observe that GPU devices in training recommender
systems are underutilized, and they cannot attain an expected throughput
improvement as what it has achieved in CV and NLP areas. This issue can be
explained by two characteristics of these recommendation models: First, they
contain up to a thousand input feature fields, introducing fragmentary and
memory-intensive operations; Second, the multiple constituent feature
interaction submodules introduce substantial small-sized compute kernels. To
remove this roadblock to the development of recommender systems, we propose a
novel framework named PICASSO to accelerate the training of recommendation
models on commodity hardware. Specifically, we conduct a systematic analysis to
reveal the bottlenecks encountered in training recommendation models. We
leverage the model structure and data distribution to unleash the potential of
hardware through our packing, interleaving, and caching optimization.
Experiments show that PICASSO increases the hardware utilization by an order of
magnitude on the basis of SOTA baselines and brings up to 6x throughput
improvement for a variety of industrial recommendation models. Using the same
hardware budget in production, PICASSO on average shortens the walltime of
daily training tasks by 7 hours, significantly reducing the delay of continuous
delivery.


title: The Compact Genetic Algorithm Struggles on Cliff Functions

abstract:
  The compact genetic algorithm (cGA) is an non-elitist estimation of
distribution algorithm which has shown to be able to deal with difficult
multimodal fitness landscapes that are hard to solve by elitist algorithms. In
this paper, we investigate the cGA on the CLIFF function for which it has been
shown recently that non-elitist evolutionary algorithms and artificial immune
systems optimize it in expected polynomial time. We point out that the cGA
faces major difficulties when solving the CLIFF function and investigate its
dynamics both experimentally and theoretically around the cliff. Our
experimental results indicate that the cGA requires exponential time for all
values of the update strength $K$. We show theoretically that, under sensible
assumptions, there is a negative drift when sampling around the location of the
cliff. Experiments further suggest that there is a phase transition for $K$
where the expected optimization time drops from $n^{\Theta(n)}$ to
$2^{\Theta(n)}$.


title:
Evaluating Vision Transformer Methods for Deep Reinforcement Learning
  from Pixels

abstract:
  Vision Transformers (ViT) have recently demonstrated the significant
potential of transformer architectures for computer vision. To what extent can
image-based deep reinforcement learning also benefit from ViT architectures, as
compared to standard convolutional neural network (CNN) architectures? To
answer this question, we evaluate ViT training methods for image-based
reinforcement learning (RL) control tasks and compare these results to a
leading convolutional-network architecture method, RAD. For training the ViT
encoder, we consider several recently-proposed self-supervised losses that are
treated as auxiliary tasks, as well as a baseline with no additional loss
terms. We find that the CNN architectures trained using RAD still generally
provide superior performance. For the ViT methods, all three types of auxiliary
tasks that we consider provide a benefit over plain ViT training. Furthermore,
ViT masking-based tasks are found to significantly outperform ViT
contrastive-learning.


title:
Application of QUBO solver using black-box optimization to structural
  design for resonance avoidance

abstract:
  Quadratic unconstrained binary optimization (QUBO) solvers can be applied to
design an optimal structure to avoid resonance. QUBO algorithms that work on a
classical or quantum device have succeeded in some industrial applications.
However, their applications are still limited due to the difficulty of
transforming from the original optimization problem to QUBO. Recently,
black-box optimization (BBO) methods have been proposed to tackle this issue
using a machine learning technique and a Bayesian treatment for combinatorial
optimization. We employed the BBO methods to design a printed circuit board for
resonance avoidance. This design problem is formulated to maximize natural
frequency and simultaneously minimize the number of mounting points. The
natural frequency, which is the bottleneck for the QUBO formulation, is
approximated to a quadratic model in the BBO method. We demonstrated that BBO
using a factorization machine shows good performance in both the calculation
time and the success probability of finding the optimal solution. Our results
can open up QUBO solvers' potential for other applications in structural
designs.


title:
Same Author or Just Same Topic? Towards Content-Independent Style
  Representations

abstract:
  Linguistic style is an integral component of language. Recent advances in the
development of style representations have increasingly used training objectives
from authorship verification (AV): Do two texts have the same author? The
assumption underlying the AV training task (same author approximates same
writing style) enables self-supervised and, thus, extensive training. However,
a good performance on the AV task does not ensure good "general-purpose" style
representations. For example, as the same author might typically write about
certain topics, representations trained on AV might also encode content
information instead of style alone. We introduce a variation of the AV training
task that controls for content using conversation or domain labels. We evaluate
whether known style dimensions are represented and preferred over content
information through an original variation to the recently proposed STEL
framework. We find that representations trained by controlling for conversation
are better than representations trained with domain or no content control at
representing style independent from content.


title:
No Token Left Behind: Explainability-Aided Image Classification and
  Generation

abstract:
  The application of zero-shot learning in computer vision has been
revolutionized by the use of image-text matching models. The most notable
example, CLIP, has been widely used for both zero-shot classification and
guiding generative models with a text prompt. However, the zero-shot use of
CLIP is unstable with respect to the phrasing of the input text, making it
necessary to carefully engineer the prompts used. We find that this instability
stems from a selective similarity score, which is based only on a subset of the
semantically meaningful input tokens. To mitigate it, we present a novel
explainability-based approach, which adds a loss term to ensure that CLIP
focuses on all relevant semantic parts of the input, in addition to employing
the CLIP similarity loss used in previous works. When applied to one-shot
classification through prompt engineering, our method yields an improvement in
the recognition rate, without additional training or fine-tuning. Additionally,
we show that CLIP guidance of generative models using our method significantly
improves the generated images. Finally, we demonstrate a novel use of CLIP
guidance for text-based image generation with spatial conditioning on object
location, by requiring the image explainability heatmap for each object to be
confined to a pre-determined bounding box.


title: Curvature measures and soap bubbles beyond convexity

abstract:
  Extending the celebrated results of Alexandrov (1958) and Korevaar-Ros (1988)
for smooth sets, as well as the results of Schneider (1979) and the first
author (1999) for arbitrary convex bodies, we obtain for the first time the
characterization of the isoperimetric sets of a uniformly convex smooth
finite-dimensional normed space (i.e. Wulff shapes) in the non-smooth and
non-convex setting, based on the natural geometric condition involving the
curvature measures. More specifically we show, under a natural mean-convexity
assumption, that finite unions of disjoint Wulff shapes are the only sets of
positive reach $ A \subseteq \mathbf{R}^{n+1} $ with finite and positive volume
such that, for some $ k \in \{0, \ldots , n-1\}$, the $ k $-th generalized
curvature measure $ \Theta^\phi_{k}(A, \cdot) $, which is defined on the unit
normal bundle of $ A $ with respect to the relative geometry induced by $ \phi
$, is proportional to $ \Theta^\phi_{n}(A, \cdot)$. If $ k = n-1 $ the
conclusion holds for all sets of positive reach with finite and positive
volume. We also prove a related sharp result about the removability of the
singularities. This result is based on the extension of the notion of a normal
boundary point, originally introduced by Busemann and Feller (1936) for
arbitrary convex bodies, to sets of positive reach.
  These results are new even in the Euclidean space.
  Several auxiliary and related results are also proved, which are of
independent interest. They include the extension of the classical Steiner--Weyl
tube formula to arbitrary closed sets in a finite dimensional uniformly convex
normed vector space and a general formula for the derivative of the localized
volume function, which extends and complements recent results of
Chambolle--Lussardi--Villa (2021).


title:
A-DRIVE: Autonomous Deadlock Detection and Recovery at Road
  Intersections for Connected and Automated Vehicles

abstract:
  Connected and Automated Vehicles (CAVs) are highly expected to improve
traffic throughput and safety at road intersections, single-track lanes, and
construction zones. However, multiple CAVs can block each other and create a
mutual deadlock around these road segments (i) when vehicle systems have a
failure, such as a communication failure, control failure, or localization
failure and/or (ii) when vehicles use a long shared road segment. In this
paper, we present an Autonomous Deadlock Detection and Recovery Protocol at
Intersections for Automated Vehicles named A-DRIVE that is a decentralized and
time-sensitive technique to improve traffic throughput and shorten worst-case
recovery time. To enable the deadlock recovery with automated vehicles and with
human-driven vehicles, A-DRIVE includes two components: V2V communication-based
A-DRIVE and Local perception-based A-DRIVE. V2V communication-based A-DRIVE is
designed for homogeneous traffic environments in which all the vehicles are
connected and automated. Local perception-based A-DRIVE is for mixed traffic,
where CAVs, non-connected automated vehicles, and human-driven vehicles
co-exist and cooperate with one another. Since these two components are not
exclusive, CAVs inclusively and seamlessly use them in practice. Finally, our
simulation results show that A-DRIVE improves traffic throughput compared to a
baseline protocol.


title:
Category-Aware Transformer Network for Better Human-Object Interaction
  Detection

abstract:
  Human-Object Interactions (HOI) detection, which aims to localize a human and
a relevant object while recognizing their interaction, is crucial for
understanding a still image. Recently, transformer-based models have
significantly advanced the progress of HOI detection. However, the capability
of these models has not been fully explored since the Object Query of the model
is always simply initialized as just zeros, which would affect the performance.
In this paper, we try to study the issue of promoting transformer-based HOI
detectors by initializing the Object Query with category-aware semantic
information. To this end, we innovatively propose the Category-Aware
Transformer Network (CATN). Specifically, the Object Query would be initialized
via category priors represented by an external object detection model to yield
better performance. Moreover, such category priors can be further used for
enhancing the representation ability of features via the attention mechanism.
We have firstly verified our idea via the Oracle experiment by initializing the
Object Query with the groundtruth category information. And then extensive
experiments have been conducted to show that a HOI detection model equipped
with our idea outperforms the baseline by a large margin to achieve a new
state-of-the-art result.


title:
Hybrid electro-optical trap for experiments with levitated particles in
  vacuum

abstract:
  We confine a microparticle in a hybrid potential created by a Paul trap and a
dual-beam optical trap. We transfer the particle between the Paul trap and the
optical trap at different pressures and study the influence of feedback cooling
on the transfer process. This technique provides a path for experiments with
optically levitated particles in ultra-high vacuum and in potentials with
complex structure.


title:
Permutation-Invariant Relational Network for Multi-person 3D Pose
  Estimation

abstract:
  Recovering multi-person 3D poses from a single RGB image is a severely
ill-conditioned problem due not only to the inherent 2D-3D depth ambiguity but
also because of inter-person occlusions and body truncations. Recent works have
shown promising results by simultaneously reasoning for different people but in
all cases within a local neighborhood. An interesting exception is PI-Net,
which introduces a self-attention block to reason for all people in the image
at the same time and refine potentially noisy initial 3D poses. However, the
proposed methodology requires defining one of the individuals as a reference,
and the outcome of the algorithm is sensitive to this choice. In this paper, we
model people interactions at a whole, independently of their number, and in a
permutation-invariant manner building upon the Set Transformer. We leverage on
this representation to refine the initial 3D poses estimated by off-the-shelf
detectors. A thorough evaluation demonstrates that our approach is able to
boost the performance of the initially estimated 3D poses by large margins,
achieving state-of-the-art results on MuPoTS-3D, CMU Panoptic and NBA2K
datasets. Additionally, the proposed module is computationally efficient and
can be used as a drop-in complement for any 3D pose detector in multi-people
scenes.


title: Zero-shot Cross-lingual Conversational Semantic Role Labeling

abstract:
  While conversational semantic role labeling (CSRL) has shown its usefulness
on Chinese conversational tasks, it is still under-explored in non-Chinese
languages due to the lack of multilingual CSRL annotations for the parser
training. To avoid expensive data collection and error-propagation of
translation-based methods, we present a simple but effective approach to
perform zero-shot cross-lingual CSRL. Our model implicitly learns
language-agnostic, conversational structure-aware and semantically rich
representations with the hierarchical encoders and elaborately designed
pre-training objectives. Experimental results show that our model outperforms
all baselines by large margins on two newly collected English CSRL test sets.
More importantly, we confirm the usefulness of CSRL to non-Chinese
conversational tasks such as the question-in-context rewriting task in English
and the multi-turn dialogue response generation tasks in English, German and
Japanese by incorporating the CSRL information into the downstream
conversation-based models. We believe this finding is significant and will
facilitate the research of non-Chinese dialogue tasks which suffer the problems
of ellipsis and anaphora.


title:
Low-Complexity Sum-Capacity Maximization for Intelligent Reflecting
  Surface-Aided MIMO Systems

abstract:
  Reducing computational complexity is crucial in optimizing the phase shifts
of Intelligent Reflecting Surface (IRS) systems since IRS-assisted
communication systems are generally deployed with a large number of reflecting
elements (REs). This letter proposes a low-complexity algorithm, designated as
Dimension-wise Sinusoidal Maximization (DSM), to obtain the optimal IRS phase
shifts that maximize the sum capacity of a MIMO network. The algorithm exploits
the fact that the objective function for the optimization problem is sinusoidal
w.r.t. the phase shift of each RE. The numerical results show that DSM achieves
a near-maximal sum rate and faster convergence speed than two other benchmark
methods.


title:
ConSLT: A Token-level Contrastive Framework for Sign Language
  Translation

abstract:
  Sign language translation (SLT) is an important technology that can bridge
the communication gap between the deaf and the hearing people. SLT task is
essentially a low-resource problem due to the scarcity of publicly available
parallel data. To this end, inspired by the success of neural machine
translation methods based on contrastive learning, we propose ConSLT, a novel
token-level \textbf{Con}trastive learning framework for \textbf{S}ign
\textbf{L}anguage \textbf{T}ranslation. Unlike previous contrastive learning
based works whose goal is to obtain better sentence representation, ConSLT aims
to learn effective token representation by pushing apart tokens from different
sentences. Concretely, our model follows the two-stage SLT method. First, in
the recoginition stage, we use a state-of-the-art continuous sign language
recognition model to recognize glosses from sign frames. Then, in the
translation stage, we adopt the Transformer framework while introducing
contrastive learning. Specifically, we pass each sign glosses to the
Transformer model twice to obtain two different hidden layer representations
for each token as "positive examples" and randomly sample K tokens that are not
in the current sentence from the vocabulary as "negative examples" for each
token. Experimental results demonstrate that ConSLT achieves new
state-of-the-art performance on PHOENIX14T dataset, with +1.48 BLEU
improvements.


title: A Class of Isochronous and Non-Isochronous Nonlinear Oscillators

abstract:
  In this work, we present a method of generating a class of nonlinear ordinary
differential equations (ODEs), representing the dynamics of appropriate
nonlinear oscillators, that have the characteristics of either amplitude
independent frequency of oscillations or amplitude dependent frequency of
oscillations from the integrals of the simple harmonic oscillator equation. To
achieve this, we consider the case where the integrals are in the same form
both for the linear and the nonlinear oscillators in either of the cases. We
also discuss the method of deriving the associated integrals and the general
solution in harmonic form for both the types. We demonstrate the applicability
of this method up to 2N coupled first order nonlinear ODEs in both the cases.
Further, we illustrate the theory with an example in each case.


title:
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture
  Search

abstract:
  The key challenge in neural architecture search (NAS) is designing how to
explore wisely in the huge search space. We propose a new NAS method called
TNAS (NAS with trees), which improves search efficiency by exploring only a
small number of architectures while also achieving a higher search accuracy.
TNAS introduces an architecture tree and a binary operation tree, to factorize
the search space and substantially reduce the exploration size. TNAS performs a
modified bi-level Breadth-First Search in the proposed trees to discover a
high-performance architecture. Impressively, TNAS finds the global optimal
architecture on CIFAR-10 with test accuracy of 94.37\% in four GPU hours in
NAS-Bench-201. The average test accuracy is 94.35\%, which outperforms the
state-of-the-art. Code is available at:
\url{https://github.com/guochengqian/TNAS}.


title:
Deep-quantile-regression-based surrogate model for joint
  chance-constrained optimal power flow with renewable generation

abstract:
  Joint chance-constrained optimal power flow (JCC-OPF) is a promising tool to
manage uncertainties from distributed renewable generation. However, most
existing works are based on power flow equations, which require accurate
network parameters that may be unobservable in many distribution systems. To
address this issue, this paper proposes a learning-based surrogate model for
JCC-OPF with renewable generation. This model equivalently converts joint
chance constraints in quantile-based forms and introduces deep quantile
regression to replicate them, in which a multi-layer perceptron (MLP) is
trained with a special loss function to predict the quantile of constraint
violations. Another MLP is trained to predict the expected power loss. Then,
the JCC-OPF can be formulated without network parameters by reformulating these
two MLPs into mixed-integer linear constraints. To further improve its
performance, two pre-processing steps, i.e., data augmentation and calibration,
are developed. The former trains a simulator to generate more training samples
for enhancing the prediction accuracy of MLPs. The latter designs a positive
parameter to calibrate the predictions of MLPs so that the feasibility of
solutions can be guaranteed. Numerical experiments based on the IEEE 33- and
123-bus systems validate that the proposed model can achieve desirable
feasibility and optimality simultaneously with no need for network parameters.


title: A graphical construction of free Markov categories

abstract:
  We describe how to perform surgeries on Joyal-Street style diagrams and
thereby construct free Markov categories. We also show that the construction is
functorial over ordered directed acyclic graphs.


title: The Rank-One Theorem on $\mathrm{RCD}$ spaces

abstract:
  We extend Alberti's Rank-One Theorem to $\mathrm{RCD}(K,N)$ metric measure
spaces.


title:
Active and Passive Collection of SSH key material for cyber threat
  intelligence

abstract:
  This paper describes a system for storing historical forensic artefacts
collected from SSH connections. This system exposes a REST API in a similar
fashion as passive DNS databases, malware hash registries, and SSL notaries
with the goal of supporting incident investigations and monitoring of
infrastructure.


title:
Stability of the ball under volume preserving fractional mean curvature
  flow

abstract:
  We consider the volume constrained fractional mean curvature flow of a nearly
spherical set, and prove long time existence and asymptotic convergence to a
ball. The result applies in particular to convex initial data, under the
assumption of global existence. Similarly, we show exponential convergence to a
constant for the fractional mean curvature flow of a periodic graph.


title: Calculating the $p$-canonical basis of Hecke algebras

abstract:
  We describe an algorithm for computing the $p$-canonical basis of the Hecke
algebra, or one of its antispherical modules. The algorithm does not operate in
the Hecke category directly, but rather uses a faithful embedding of the Hecke
category inside a semisimple category to build a "model" for indecomposable
objects and bases of their morphism spaces. Inside this semisimple category,
objects are sequences of Coxeter group elements, and morphisms are (sparse)
matrices over a fraction field, making it quite amenable to computations. This
strategy works for the full Hecke category over any base field, but in the
antispherical case we must instead work over $\mathbb{Z}_{(p)}$ and use an
idempotent lifting argument to deduce the result for a field of characteristic
$p > 0$. We also describe a less sophisticated algorithm which is much more
suited to the case of finite groups. We provide complete implementations of
both algorithms in the MAGMA computer algebra system.


title: Forming Iron-rich Planets with Giant Impacts

abstract:
  We investigate mantle stripping giant impacts (GI) between super-Earths with
masses between 1 M$_{\oplus}$ and 20 M$_{\oplus}$. We infer new scaling laws
for the mass of the largest fragment and its iron mass fraction, as well as
updated fitting coefficients for the critical specific impact energy for
catastrophic disruption, $Q_{RD}^{*}$. With these scaling laws, we derive
equations that relate the impact conditions, i.e., target mass, impact velocity
and impactor-to-target mass ratio, to the mass and iron mass fraction of the
largest fragment. This allows one to predict collision outcomes without
performing a large suite of simulations. Using these equations we present the
maximum and minimum planetary iron mass fraction as a result of collisional
stripping of its mantle for a given range of impact conditions. We also infer
the radius for a given mass and composition using interior structure models and
compare our results to observations of metal-rich exoplanets. We find good
agreement between the data and the simulated planets suggesting that GI could
have played a key role in their formation. Furthermore, using our scaling laws
we can further constrain the impact conditions that favour their masses and
compositions. Finally, we present a flexible and easy-to-use tool that allows
one to predict mass and composition of a planet after a GI for an arbitrary
range of impact conditions which in turn allows to assess the role of GI in
observed planetary systems.


title:
Geometry of submanifolds of all classes of third-order ODEs as a
  Riemannian manifold

abstract:
  In this paper, we prove that any surface corresponding to linear second-order
ODEs as a submanifold is minimal in all classes of third-order ODEs $y'''=f(x,
y, p, q)$ as a Riemannian manifold where $y'=p$ and $y''=q$, if and only if
$q_{yy}=0$. Moreover, we will see the linear second-order ODE with general form
$y''=\pm y+\beta(x)$ is the only case that is defined a minimal surface and is
also totally geodesic.


title:
Thermodynamic geometry and complexity of black holes in theories with
  broken translational invariance

abstract:
  The relationship between thermodynamics and the Lloyd bound on the
holographic complexity for a black hole has been of interest. We consider $D$
dimensional anti-de Sitter black holes with hyperbolic geometry as well as
black holes with momentum relaxation that have a minimum for temperature and
mass. We show that the singular points of the thermodynamic curvature of the
black holes, as thermodynamic systems, correspond to the zero points of the
action and volume complexity at the Lloyd bound. For such black holes with a
single horizon, the complexity of volume and the complexity of action at
minimum mass and minimum temperature are zero, respectively. We show that the
thermodynamic curvature diverges at these minimal values. Because of the
behaviour of action complexity and thermodynamic curvature at minimum
temperature, we propose the action complexity as an order parameter of the
black holes as thermodynamic systems. Also, we derive the critical exponent
related to the thermodynamic curvature in different dimensions.


title:
Multi-User Wireless Communications with Holographic MIMO Surfaces: A
  Convenient Channel Model and Spectral Efficiency Analysis

abstract:
  The multi-user Holographic Multiple-Input and Multiple-Output Surface
(MU-HMIMOS) paradigm, which is capable of realizing large continuous apertures
with minimal power consumption and of shaping radio wave propagation at will,
has been recently considered as an energy-efficient solution for future
wireless networks. The tractable channel modeling of MU-HMIMOS signal
propagation is one of the most critical challenges, mainly due to the coupling
effect induced by the excessively large number of closely spaced patch
antennas. In this paper, we focus on this challenge for downlink communications
and model the electromagnetic channel in the wavenumber domain using the
Fourier plane wave representation. Based on the proposed model, we devise a
Zero-Forcing (ZF) precoding scheme, capitalizing on the sampled channel
variance that depends on the number and spacing of the HMIMOS patch antennas,
and perform a spectral efficiency analysis. Our simulation results showcase
that the more patch antennas and the larger their spacing is, the performance
of the considered MU-HMIMOS system improves. In addition, it is demonstrated
that our theoretical performance expressions approximate sufficiently well the
simulated spectral efficiency, even for the highly correlated cases, thus
verifying the effectiveness and robustness of the presented analytical
framework.


title:
Polarimetry of M-type asteroids in the context of their surface
  composition

abstract:
  Aims. We aim to investigate how polarimetric observations can improve our
understanding of the nature and diversity of M/X-type asteroids. Methods.
Polarimetric observations of the selected M/X-type asteroids were carried out
at the Tohoku 0.6-m telescope at Haleakala Observatory, Hawaii (simultaneously
in BVR filters), the 2-m telescope of the Bulgarian National Astronomical
Observatory in Rozhen (in R filter), and the 2.15-m telescope of the Complejo
Astron\'omico El Leoncito (CASLEO), Argentina (in V filter). We analysed the
polarimetric characteristics of M/X-type asteroids along with the available
data obtained by other techniques. Results. New polarimetric observations of 22
M/X-type asteroids combined with published observations provide a data set of
41 asteroids for which the depth of a negative polarisation branch and/or
inversion angle were determined. We found that the depth of the negative
polarisation branch tends to increase with decreasing steepness of the
near-infrared spectra. Asteroids with a deeper negative polarisation branch
tend to have a higher radar circular polarisation ratio. We show that, based on
the relationship of the depth of the negative polarisation branch and inversion
angle, two main sub-types can be distinguished among M-type asteroids. We
suggest that these groups may be related to different surface compositions
similar to (1) irons and stony-irons and (2) enstatite and iron-rich
carbonaceous chondrites.


title: A Unified Perspective on Deep Equilibrium Finding

abstract:
  Extensive-form games provide a versatile framework for modeling interactions
of multiple agents subjected to imperfect observations and stochastic events.
In recent years, two paradigms, policy space response oracles (PSRO) and
counterfactual regret minimization (CFR), showed that extensive-form games may
indeed be solved efficiently. Both of them are capable of leveraging deep
neural networks to tackle the scalability issues inherent to extensive-form
games and we refer to them as deep equilibrium-finding algorithms. Even though
PSRO and CFR share some similarities, they are often regarded as distinct and
the answer to the question of which is superior to the other remains ambiguous.
Instead of answering this question directly, in this work we propose a unified
perspective on deep equilibrium finding that generalizes both PSRO and CFR. Our
four main contributions include: i) a novel response oracle (RO) which computes
Q values as well as reaching probability values and baseline values; ii) two
transform modules -- a pre-transform and a post-transform -- represented by
neural networks transforming the outputs of RO to a latent additive space
(LAS), and then the LAS to action probabilities for execution; iii) two average
oracles -- local average oracle (LAO) and global average oracle (GAO) -- where
LAO operates on LAS and GAO is used for evaluation only; and iv) a novel method
inspired by fictitious play that optimizes the transform modules and average
oracles, and automatically selects the optimal combination of components of the
two frameworks. Experiments on Leduc poker game demonstrate that our approach
can outperform both frameworks.


title: Exceptional cavity quantum electrodynamics

abstract:
  An open quantum system operated at the spectral singularities where
dimensionality reduces, known as exceptional points (EPs), demonstrates
distinguishing behavior from the Hermitian counterpart. Based on the recently
proposed microcavity with exceptional surface (ES), we report and explain the
peculiar quantum dynamics in atom-photon interaction associated with EPs:
cavity transparency, decoherence suppression beyond the limitation of
Jaynes-Cummings (JC) system, and the population trapping of lossy cavity. An
analytical description of the local density of states (LDOS) for ES microcavity
is derived from an equivalent cavity quantum electrodynamics (QED) model, which
goes beyond the single-excitation approximation and allows exploring the
quantum effects of EPs on multiphoton process by parametrizing the extended
cascaded quantum master equation. It reveals that a square Lorentzian term in
LDOS induced by second-order EPs interferes with the linear Lorentzian profile,
giving rise to cavity transparency for atom with special transition frequency
in the weak coupling regime. This additional contribution from EPs also breaks
the limit on dissipation rate of JC system bounded by bare components,
resulting in the decoherence suppression with anomalously small decay rate of
the Rabi oscillation and the long-time dynamics. Remarkably, we find that the
cavity population can be partially trapped at EPs, achieved by forming a bound
dressed state in the limiting case of vanishing atom decay. Our work unveils
the exotic phenomena unique to EPs in cavity QED systems, which opens the door
for controlling light-matter interaction at the quantum level through
non-Hermiticity, and holds great potential in building high-performance
quantum-optics devices.


title:
Optimized SC-F-LOAM: Optimized Fast LiDAR Odometry and Mapping Using
  Scan Context

abstract:
  LiDAR odometry can achieve accurate vehicle pose estimation for short driving
range or in small-scale environments, but for long driving range or in
large-scale environments, the accuracy deteriorates as a result of cumulative
estimation errors. This drawback necessitates the inclusion of loop closure
detection in a SLAM framework to suppress the adverse effects of cumulative
errors. To improve the accuracy of pose estimation, we propose a new
LiDAR-based SLAM method which uses F-LOAM as LiDAR odometry, Scan Context for
loop closure detection, and GTSAM for global optimization. In our approach, an
adaptive distance threshold (instead of a fixed threshold) is employed for loop
closure detection, which achieves more accurate loop closure detection results.
Besides, a feature-based matching method is used in our approach to compute
vehicle pose transformations between loop closure point cloud pairs, instead of
using the raw point cloud obtained by the LiDAR sensor, which significantly
reduces the computation time. The KITTI dataset and a UGV platform are used for
verifications of our method, and the experimental results demonstrate that the
proposed method outperforms typical LiDAR odometry/SLAM methods in the
literature. Our code is made publicly available for the benefit of the
community.


title: Search for Dispersed Pulses at Declinations from +56o to +87o

abstract:
  The survey of northern hemisphere were made at a frequency of 111 MHz. The
total accumulation time at each point of the sky was at least one hour. 75
sources of pulse emission were detected. More then 80% of these sources are
known pulsars observed in the side lobes of the antenna. From one to several
hundreds pulses were detected in twelve known pulsars. For four pulsars
(J0157+6212, J1910+5655, J2337+6151, J2354+6155) the narrowness of the
strongest pulses and the ratio of peak flux densities in the strongest pulse
and in the average profile indicate that they can be pulsars with giant pulses.
One new rotating radio transient (RRAT) J0812+8626 with a dispersion measure
DM=40.25 pc/cm3 was detected.


title:
Finite time blow-up for some parabolic systems arising in turbulence
  theory

abstract:
  We study a class of non-linear parabolic systems relevant in turbulence
theory. Those systems can be viewed as simplified versions of the Prandtl
one-equation and Kolmogorov two-equation models of turbulence.
  We restrict our attention to the case of one space dimension. We consider
initial data for which the diffusion coefficients may vanish. We prove that,
under this condition, those systems are locally well-posed in the class of
Sobolev spaces of high enough regularity, but also that there exist smooth
initial data for which the corresponding solutions blow up in finite time.
  We are able to put in evidence two different types of blow-up mechanism. In
addition, the results are extended to the case of transport-diffusion systems,
namely to the case when convection is taken into account.


title:
The Placebo Effect of Artificial Intelligence in Human-Computer
  Interaction

abstract:
  In medicine, patients can obtain real benefits from a sham treatment. These
benefits are known as the placebo effect. We report two experiments (Experiment
I: N=369; Experiment II: N=100) demonstrating a placebo effect in adaptive
interfaces. Participants were asked to solve word puzzles while being supported
by no system or an adaptive AI interface. All participants experienced the same
word puzzle difficulty and had no support from an AI throughout the
experiments. Our results showed that the belief of receiving adaptive AI
support increases expectations regarding the participant's own task
performance, sustained after interaction. These expectations were positively
correlated to performance, as indicated by the number of solved word puzzles.
We integrate our findings into technological acceptance theories and discuss
implications for the future assessment of AI-based user interfaces and novel
technologies. We argue that system descriptions can elicit placebo effects
through user expectations biasing the results of user-centered studies.


title: Dynamics and Kinematics of the EUV Wave Event on 6 May 2019

abstract:
  We present here the kinematics of the EUV wave associated with a GOES
M1.0-class solar flare, which originates in NOAA AR 12740. The event is
thoroughly observed with Atmospheric Imaging Assembly (AIA) onboard Solar
Dynamics Observatory (SDO) with high spatio-temporal resolutions. This event
displays many features of EUV waves, which are very decisive for the
understanding of the nature of EUV waves. These features include: a fast-mode
wave, a pseudo wave, a slow-mode wave and stationary fronts, probably due to
mode conversion. One fast-mode wave also propagates towards the coronal hole
situated close to the north pole and the wave speed does not change when it
encounters the coronal hole. We intend to provide self-consistent
interpretations for all these different features.


title: Assessment of Massively Multilingual Sentiment Classifiers

abstract:
  Models are increasing in size and complexity in the hunt for SOTA. But what
if those 2\% increase in performance does not make a difference in a production
use case? Maybe benefits from a smaller, faster model outweigh those slight
performance gains. Also, equally good performance across languages in
multilingual tasks is more important than SOTA results on a single one. We
present the biggest, unified, multilingual collection of sentiment analysis
datasets. We use these to assess 11 models and 80 high-quality sentiment
datasets (out of 342 raw datasets collected) in 27 languages and included
results on the internally annotated datasets. We deeply evaluate multiple
setups, including fine-tuning transformer-based models for measuring
performance. We compare results in numerous dimensions addressing the imbalance
in both languages coverage and dataset sizes. Finally, we present some best
practices for working with such a massive collection of datasets and models
from a multilingual perspective.


title: Value-based Practical Reasoning: Modal Logic + Argumentation

abstract:
  Autonomous agents are supposed to be able to finish tasks or achieve goals
that are assigned by their users through performing a sequence of actions.
Since there might exist multiple plans that an agent can follow and each plan
might promote or demote different values along each action, the agent should be
able to resolve the conflicts between them and evaluate which plan he should
follow. In this paper, we develop a logic-based framework that combines modal
logic and argumentation for value-based practical reasoning with plans. Modal
logic is used as a technique to represent and verify whether a plan with its
local properties of value promotion or demotion can be followed to achieve an
agent's goal. We then propose an argumentation-based approach that allows an
agent to reason about his plans in the form of supporting or objecting to a
plan using the verification results.


title: Bootstrap Cointegration Tests in ARDL Models

abstract:
  The paper proposes a new bootstrap approach to the Pesaran, Shin and Smith's
bound tests in a conditional equilibrium correction model with the aim to
overcome some typical drawbacks of the latter, such as inconclusive inference
and distortion in size. The bootstrap tests are worked out under several data
generating processes, including degenerate cases. Monte Carlo simulations
confirm the better performance of the bootstrap tests with respect to bound
ones and to the asymptotic F test on the independent variables of the ARDL
model. It is also proved that any inference carried out in misspecified models,
such as unconditional ARDLs, may be misleading. Empirical applications
highlight the importance of employing the appropriate specification and provide
definitive answers to the inconclusive inference of the bound tests when
exploring the long-term equilibrium relationship between economic variables.


title:
Concept validation of a high dynamic range point-diffraction
  interferometer for wavefront sensing in adaptive optics

abstract:
  The direct detection and imaging of exoplanets requires the use of
high-contrast adaptive optics (AO). In these systems quasi-static aberrations
need to be highly corrected and calibrated. In order to achieve this, the
pupil-modulated point-diffraction interferometer (m-PDI), was presented in an
earlier paper. This present paper focuses on m-PDI concept validation through
three experiments. First, the instrument's accuracy and dynamic range are
characterised by measuring the spatial transfer function at all spatial
frequencies and at different amplitudes. Then, using visible monochromatic
light, an adaptive optics control loop is closed on the system's systematic
bias to test for precision and completeness. In a central section of the pupil
with 72% of the total radius the residual error is 7.7nm-rms. Finally, the
control loop is run using polychromatic light with a spectral FWHM of 77nm
around the R-band. The control loop shows no drop in performance with respect
to the monochromatic case, reaching a final Strehl ratio larger than 0.7.


title: Linear Moment Models to Approximate Knudsen Layers

abstract:
  We propose a well-posed Maxwell-type boundary condition for the linear moment
system in half-space. As a reduction of the Boltzmann equation, the moment
equations are available to model Knudsen layers near a solid wall, where proper
boundary conditions should be prescribed. Utilizing an orthogonal
decomposition, we separate the part with a damping term from the system and
then impose a new class of Maxwell-type boundary conditions on it. Due to the
new boundary condition, we show that the half-space boundary value problem
admits a unique solution with explicit expressions. Instantly, the
well-posedness of the linear moment system is achieved. We apply the procedure
to classical flow problems with the Shakhov collision term, such as the
velocity slip and temperature jump problems. The model can capture Knudsen
layers with very high accuracy using only a few moments.


title: Recent Results On Nonlinear Elliptic Free Boundary Problems

abstract:
  In this paper we give an overview of some recent and older results concerning
free boundary problems governed by elliptic operators.


title: Dynamical charge susceptibility in non-equilibrium double quantum dots

abstract:
  Double quantum dots are one of the promising two-state quantum systems for
realizing qubits. In the quest of successfully manipulating and reading
information in qubit systems, it is of prime interest to control the charge
response of the system to a gate voltage, as filled in by the dynamical charge
susceptibility. We theoretically study this quantity for a non-equilibrium
double quantum dot by using the functional integral approach and derive its
general analytical expression. One highlights the existence of two lines of
maxima as a function of the dot level energies, each of them being split under
the action of a bias voltage. In the low frequency limit, we derive the
capacitance and the charge relaxation resistance of the equivalent quantum
RC-circuit with a notable difference in the range of variation for R depending
on whether the system is connected in series or in parallel. By incorporating
an additional triplet state in order to describe the situation of a double
quantum dot with spin, we obtain results for the resonator phase response which
are in qualitative agreement with recent experimental observations in spin
qubit systems. These results show the wealth of information brought by the
knowledge of dynamical charge susceptibility in double quantum dots with
potential applications for spin qubits.


title:
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature
  Aggregation and Pyramid Decoders

abstract:
  Semantic segmentation of point clouds generates comprehensive understanding
of scenes through densely predicting the category for each point. Due to the
unicity of receptive field, semantic segmentation of point clouds remains
challenging for the expression of multi-receptive field features, which brings
about the misclassification of instances with similar spatial structures. In
this paper, we propose a graph convolutional network DGFA-Net rooted in dilated
graph feature aggregation (DGFA), guided by multi-basis aggregation loss
(MALoss) calculated through Pyramid Decoders. To configure multi-receptive
field features, DGFA which takes the proposed dilated graph convolution
(DGConv) as its basic building block, is designed to aggregate multi-scale
feature representation by capturing dilated graphs with various receptive
regions. By simultaneously considering penalizing the receptive field
information with point sets of different resolutions as calculation bases, we
introduce Pyramid Decoders driven by MALoss for the diversity of receptive
field bases. Combining these two aspects, DGFA-Net significantly improves the
segmentation performance of instances with similar spatial structures.
Experiments on S3DIS, ShapeNetPart and Toronto-3D show that DGFA-Net
outperforms the baseline approach, achieving a new state-of-the-art
segmentation performance.


title:
Linearization of transition functions along a certain class of Levi-flat
  hypersurfaces

abstract:
  We pose a normal form of transition functions along some Levi-flat
hypersurfaces obtained by suspension. By focusing on methods in circle dynamics
and linearization theorems, we give a sufficient condition to obtain a normal
form as a geometrical analogue of Arnol'd's linearization theorem.


title:
Birational boundedness of rationally connected log Calabi-Yau pairs with
  fixed index

abstract:
  We show that the set of rationally connected projective varieties $X$ of a
fixed dimension such that $(X,B)$ is klt, and $-l(K_X+B)$ is Cartier and nef
for some fixed positive integer $l$, is bounded modulo flops.


title: On quasi-stationary Mean Field Games of Controls

abstract:
  In Mean Field Games of Controls, the dynamics of the single agent is
influenced not only by the distribution of the agents, as in the classical
theory, but also by the distribution of their optimal strategies. In this
paper, we study quasi-stationary Mean Field Games of Controls, which differs
from the standard case in the strategy-choice mechanism of the agent: it cannot
predict the evolution of the population, but chooses its strategy only on the
basis of the information available at the given instant of time, without
anticipating. We prove existence and uniqueness for the solution of the
corresponding quasi-stationary Mean Field Games system under different sets of
hypotheses and we provide some examples of models which fall within these
hypotheses.


title:
The GAPS Programme at TNG XXXIII. HARPS-N detects multiple atomic
  species in emission from the dayside of KELT-20b

abstract:
  The detection of lines in emission in planetary atmospheres provides direct
evidence of temperature inversion. We confirm the trend of ultra-hot Jupiters
orbiting A-type stars showing temperature inversions on their daysides, by
detecting metals emission lines in the dayside of KELT-20b. We first detect the
planetary emission by using the G2 stellar mask of the HARPS-N pipeline, which
is mainly composed of neutral iron lines, as a template. Using neutral iron
templates, we perform a retrieval of the atmospheric temperature-pressure
profile of the planet, confirming a thermal inversion. Then we create models of
planetary emission of different species using the retrieved inverted
temperature-pressure profile. By using the cross-correlation technique, we
detect FeI, FeII and CrI at signal-to-noise ratio levels of 7.1, 3.9 and 3.6,
respectively. The latter is detected for the first time in emission in the
atmosphere of an exoplanet. Contrary to FeI, FeII and CrI are detected only
after the occultation and not before, hinting for different atmospheric
properties in view on the pre- and post- occultation orbital phases. A further
retrieval of the temperature-pressure profile performed independently on the
pre- and post- occultation phases, while not highly significant, points to a
steeper thermal inversion in the post-occultation.


title:
A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis
  System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic View

abstract:
  As a disease with malignant potential, hydatidiform mole (HM) is one of the
most common gestational trophoblastic diseases. For pathologists, the HM
section of hydrops lesions is an important basis for diagnosis. In pathology
departments, the diverse microscopic manifestations of HM lesions and the
limited view under the microscope mean that physicians with extensive
diagnostic experience are required to prevent missed diagnosis and
misdiagnosis. Feature extraction can significantly improve the accuracy and
speed of the diagnostic process. As a remarkable diagnosis assisting
technology, computer-aided diagnosis (CAD) has been widely used in clinical
practice. We constructed a deep-learning-based CAD system to identify HM
hydrops lesions in the microscopic view in real-time. The system consists of
three modules; the image mosaic module and edge extension module process the
image to improve the outcome of the hydrops lesion recognition module, which
adopts a semantic segmentation network, our novel compound loss function, and a
stepwise training function in order to achieve the best performance in
identifying hydrops lesions. We evaluated our system using an HM hydrops
dataset. Experiments show that our system is able to respond in real-time and
correctly display the entire microscopic view with accurately labeled HM
hydrops lesions.


title:
Commonality in Natural Images Rescues GANs: Pretraining GANs with
  Generic and Privacy-free Synthetic Data

abstract:
  Transfer learning for GANs successfully improves generation performance under
low-shot regimes. However, existing studies show that the pretrained model
using a single benchmark dataset is not generalized to various target datasets.
More importantly, the pretrained model can be vulnerable to copyright or
privacy risks as membership inference attack advances. To resolve both issues,
we propose an effective and unbiased data synthesizer, namely Primitives-PS,
inspired by the generic characteristics of natural images. Specifically, we
utilize 1) the generic statistics on the frequency magnitude spectrum, 2) the
elementary shape (i.e., image composition via elementary shapes) for
representing the structure information, and 3) the existence of saliency as
prior. Since our synthesizer only considers the generic properties of natural
images, the single model pretrained on our dataset can be consistently
transferred to various target datasets, and even outperforms the previous
methods pretrained with the natural images in terms of Fr'echet inception
distance. Extensive analysis, ablation study, and evaluations demonstrate that
each component of our data synthesizer is effective, and provide insights on
the desirable nature of the pretrained model for the transferability of GANs.


title: A Cahn-Hilliard model coupled to viscoelasticity with large deformations

abstract:
  We propose a new class of phase field models coupled to viscoelasticity with
large deformations, obtained from a diffuse interface mixture model composed by
a phase with elastic properties and a liquid phase. The model is formulated in
the Eulerian configuration and it is derived by imposing the mass balance for
the mixture components and the momentum balance that comes from a generalized
form of the principle of virtual powers. The latter considers the presence of a
system of microforces and microstresses associated to the microscopic
interactions between the mixture's constituents together with a system of
macroforces and macrostresses associated to their viscoelastic behavior, taking
into account also the friction between the phases. The free energy density of
the system is given as the sum of a Cahn-Hilliard term and an elastic
polyconvex term, with a coupling between the phase field variable and the
elastic deformation gradient in the elastic contribution. General constitutive
assumptions complying with a mechanical version of the second law of
thermodynamics in isothermal situations are taken. We study the global
existence of a weak solution for a simplified and regularized version of the
general model, which considers an incompressible elastic free energy of
Neo-Hookean type with elastic coefficients depending on the phase field
variable. The regularization is properly designed to deal with the coupling
between the phase field variable and the elastic deformation gradient in the
elastic energy density. The analysis is made both in two and three space
dimensions.


title:
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text
  Classification

abstract:
  Text classification struggles to generalize to unseen classes with very few
labeled text instances per class. In such a few-shot learning (FSL) setting,
metric-based meta-learning approaches have shown promising results. Previous
studies mainly aim to derive a prototype representation for each class.
However, they neglect that it is challenging-yet-unnecessary to construct a
compact representation which expresses the entire meaning for each class. They
also ignore the importance to capture the inter-dependency between query and
the support set for few-shot text classification. To deal with these issues, we
propose a meta-learning based method MGIMN which performs instance-wise
comparison followed by aggregation to generate class-wise matching vectors
instead of prototype learning. The key of instance-wise comparison is the
interactive matching within the class-specific context and episode-specific
context. Extensive experiments demonstrate that the proposed method
significantly outperforms the existing state-of-the-art approaches, under both
the standard FSL and generalized FSL settings.


title:
Populations of highly variable X-ray sources in the XMM$-$Newton slew
  survey

abstract:
  We present the identifications of a flux-limited sample of highly variable
X-ray sources on long time-scales from the second catalogue of the XMM$-$Newton
SLew survey (XMMSL2). The carefully constructed sample, comprising 265 sources
(2.5 per cent) selected from the XMMSL2 clean catalogue, displayed X-ray
variability of a factor of more than 10 in 0.2$-$2 keV compared to the ROSAT
All Sky Survey. Of the sample sources, 94.3 per cent are identified. The
identification procedure follows a series of cross-matches with astronomical
data bases and multiwavelength catalogues to refine the source position and
identify counterparts to the X-ray sources. Assignment of source type utilizes
a combination of indicators including counterparts offset, parallax
measurement, spectral colours, X-ray luminosity, and light-curve behaviour. We
identified 40 per cent of the variables with stars, 10 per cent with accreting
binaries, and at least 30.4 per cent with active galactic nuclei. The rest of
the variables are identified as galaxies. It is found that the mean effective
temperatures of the highly variable stars are lower than those of less variable
stars. Our sample of highly variable AGN tend to have lower black hole masses,
redshifts, and marginally lower soft X-ray luminosities compared to the less
variable ones, while no difference was found in the Eddington ratio
distributions. Five flaring events are tidal disruption events published
previously. This study has significantly increased the number of variable
sources in XMMSL2 with identifications and provides greater insight on the
nature of many of the sources, enabling further studies of highly variable
X-ray sources.


title: Reinforcement Re-ranking with 2D Grid-based Recommendation Panels

abstract:
  Modern recommender systems usually present items as one-dimensional ranking
list. Recently there is a trend in e-commerce that the recommended items are
organized as two-dimensional grid-based panels where users can view the items
in both vertical and horizontal directions. Presenting items in grid-based
result panels poses new challenges to recommender systems because existing
models are all designed to output sequential lists while the slots in a
grid-based panel have no explicit order. Directly converting the item rankings
into grids (e.g., pre-defining an order on the slots)overlooks the
user-specific behavioral patterns on grid-based pan-els and inevitably hurts
the user experiences. To address this issue, we propose a novel Markov decision
process (MDP) to place the items in 2D grid-based result panels at the final
re-ranking stage of the recommender systems. The model, referred to as
Panel-MDP, takes an initial item ranking from the early stages as the input.
Then, it defines the MDP discrete time steps as the ranks in the initial
ranking list, and the actions as the slots in the grid-based panels, plus a
NULL action. At each time step, Panel-MDP sequentially takes an action of
selecting one slot for placing an item of the initial ranking list, or
discarding the item if NULL action is selected. The process is continued until
all of the slots are filled. The reinforcement learning algorithm of DQN is
employed to implement and learn the parameters in the Panel-MDP. Experiments on
a dataset collected from a widely-used e-commerce app demonstrated the
superiority ofPanel-MDP in terms of recommending 2D grid-based result panels.


title:
Machine learning based event classification for the energy-differential
  measurement of the $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reactions

abstract:
  The paper explores the feasibility of using machine learning techniques, in
particular neural networks, for classification of the experimental data from
the joint $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reaction cross section
measurement from the neutron time of flight facility n_TOF at CERN. Each
relevant $\Delta E$-$E$ pair of strips from two segmented silicon telescopes is
treated separately and afforded its own dedicated neural network. An important
part of the procedure is a careful preparation of training datasets, based on
the raw data from Geant4 simulations. Instead of using these raw data for the
training of neural networks, we divide a relevant 3-parameter space into
discrete voxels, classify each voxel according to a particle/reaction type and
submit these voxels to a training procedure. The classification capabilities of
the structurally optimized and trained neural networks are found to be superior
to those of the manually selected cuts.


title:
Segmentation Network with Compound Loss Function for Hydatidiform Mole
  Hydrops Lesion Recognition

abstract:
  Pathological morphology diagnosis is the standard diagnosis method of
hydatidiform mole. As a disease with malignant potential, the hydatidiform mole
section of hydrops lesions is an important basis for diagnosis. Due to
incomplete lesion development, early hydatidiform mole is difficult to
distinguish, resulting in a low accuracy of clinical diagnosis. As a remarkable
machine learning technology, image semantic segmentation networks have been
used in many medical image recognition tasks. We developed a hydatidiform mole
hydrops lesion segmentation model based on a novel loss function and training
method. The model consists of different networks that segment the section image
at the pixel and lesion levels. Our compound loss function assign weights to
the segmentation results of the two levels to calculate the loss. We then
propose a stagewise training method to combine the advantages of various loss
functions at different levels. We evaluate our method on a hydatidiform mole
hydrops dataset. Experiments show that the proposed model with our loss
function and training method has good recognition performance under different
segmentation metrics.


title: Wellposedness of NLS in Modulation Spaces

abstract:
  We prove new local and global well-posedness results for the cubic
one-dimensional nonlinear Schr\"odinger equation in modulation spaces. Local
results are obtained via multilinear interpolation. Global results are proven
using conserved quantities based on the complete integrability of the equation,
persistence of regularity, and by separating off the time evolution of finitely
many Picard iterates.


title:
On the fluctuation-dissipation of the oxide trapped charge in a MOSFET
  operated down to deep cryogenic temperatures

abstract:
  An analysis of the oxide trapped charge noise in a MOSFET operated down to
deep cryogenic temperatures is proposed. To this end, a revisited derivation of
the interface trap conductance Gp and oxide trapped charge noise SQt at the
SiO2/Si MOS interface is conducted under very low temperature condition, where
Fermi-Dirac statistics applies. A new relation between the oxide trapped charge
noise SQt and the interface trap conductance Gp is established, showing the
inadequacy of the Nyquist relation at very low temperature. Finally, a new
formula for the oxide trapped charge 1/f noise, going beyond the classical
Boltzmann expression, is developed in terms of oxide trap density and effective
temperature accounting for degenerate statistics.


title: HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation

abstract:
  Knowledge graph (KG) plays an increasingly important role to improve the
recommendation performance and interpretability. A recent technical trend is to
design end-to-end models based on information propagation schemes. However,
existing propagation-based methods fail to (1) model the underlying
hierarchical structures and relations, and (2) capture the high-order
collaborative signals of items for learning high-quality user and item
representations.
  In this paper, we propose a new model, called Hierarchy-Aware Knowledge Gated
Network (HAKG), to tackle the aforementioned problems. Technically, we model
users and items (that are captured by a user-item graph), as well as entities
and relations (that are captured in a KG) in hyperbolic space, and design a
hyperbolic aggregation scheme to gather relational contexts over KG. Meanwhile,
we introduce a novel angle constraint to preserve characteristics of items in
the embedding space. Furthermore, we propose a dual item embeddings design to
represent and propagate collaborative signals and knowledge associations
separately, and leverage the gated aggregation to distill discriminative
information for better capturing user behavior patterns. Experimental results
on three benchmark datasets show that, HAKG achieves significant improvement
over the state-of-the-art methods like CKAN, Hyper-Know, and KGIN. Further
analyses on the learned hyperbolic embeddings confirm that HAKG offers
meaningful insights into the hierarchies of data.


title: Constrained Shortest Path and Hierarchical Structures

abstract:
  The Constraint Shortest Path (CSP) problem is as follows. An $n$-vertex graph
is given, each edge/arc assigned two weights. Let us call them "cost" and
"length" for definiteness. Finding a min-cost upper-bounded length path between
a given pair of vertices is required. The problem is NP-hard even when the
lengths of all edges are the same. Therefore, various approximation algorithms
have been proposed in the literature for it. The constraint on path length can
be accounted for by considering one edge weight equals to a linear combination
of cost and length. By varying the multiplier value in a linear combination, a
feasible solution delivers a minimum to the function with new weights. At the
same time, Dijkstra's algorithm or its modifications are used to construct the
shortest path with the current weights of the edges. However, with
insufficiently large graphs, this approach may turn out to be time-consuming.
In this article, we propose to look for a solution, not in the original graph
but specially constructed hierarchical structures (HS). We show that the
shortest path in the HS is constructed with $O(m)$-time complexity, where $m$
is the number of edges/arcs of the graph, and the approximate solution in the
case of integer costs and lengths of the edges is found with $O(m\log n)$-time
complexity. The a priori estimate of the algorithm's accuracy turned out to
depend on the parameters of the problem and can be significant. Therefore, to
evaluate the algorithm's effectiveness, we conducted a numerical experiment on
the graphs of roads of megalopolis and randomly constructed unit-disk graphs
(UDGs). The numerical experiment results show that in the HS, a solution close
to optimal one is built 10--100 times faster than in the methods which use
Dijkstra's algorithm to build a min-weight path in the original graph.


title:
Predicting solution scattering patterns with explicit-solvent molecular
  simulations

abstract:
  Small-angle X-ray or neutron scattering (SAXS/SANS/SAS) is widely used to
obtain structural information on biomolecules or soft-matter complexes in
solution. Deriving a molecular interpretation of the scattering signals
requires methods for predicting SAS patterns from a given atomistic structural
model. Such SAS predictions are non-trivial because the patterns are influenced
by the hydration layer of the solute, the excluded solvent, and by thermal
fluctuations. Many computationally efficient methods use simplified, implicit
models for the hydration layer and excluded solvent, leading to some
uncertainties and to free parameters that require fitting against experimental
data. SAS predictions based on explicit-solvent molecular dynamics (MD)
simulations overcome such limitations at the price of an increased
computational cost. To rationalize the need for explicit-solvent methods, we
first review the approximations underlying implicit-solvent methods. Next, we
describe the theory behind explicit-solvent SAS predictions that are easily
accessible via the WAXSiS web server. We present the workflow for computing SAS
pattern from a given molecular dynamics trajectory with a freely available via
a modified version of the GROMACS simulations software, coined GROMACS-SWAXS,
which implements the WAXSiS method. Practical considerations for running
routine explicit-solvent SAS predictions are discussed.


title:
IC-GVINS: A Robust, Real-time, INS-Centric GNSS-Visual-Inertial
  Navigation System for Wheeled Robot

abstract:
  In this letter, we present a robust, real-time, inertial navigation system
(INS)-Centric GNSS-Visual-Inertial navigation system (IC-GVINS) for wheeled
robot, in which the precise INS is fully utilized in both the state estimation
and visual process. To improve the system robustness, the INS information is
employed during the whole keyframe-based visual process, with strict
outlier-culling strategy. GNSS is adopted to perform an accurate and convenient
initialization of the IC-GVINS, and is further employed to achieve absolute
positioning in large-scale environments. The IMU, visual, and GNSS measurements
are tightly fused within the framework of factor graph optimization. Dedicated
experiments were conducted to evaluate the robustness and accuracy of the
IC-GVINS on a wheeled robot. The IC-GVINS demonstrates superior robustness in
various visual-degenerated scenes with moving objects. Compared to the
state-of-the-art visual-inertial navigation systems, the proposed method yields
improved robustness and accuracy in various environments. We open source our
codes combined with the dataset on GitHub


title: A General Compressive Sensing Construct using Density Evolution

abstract:
  This paper proposes a general framework to design a sparse sensing matrix
$\ensuremath{\mathbf{A}}\in \mathbb{R}^{m\times n}$, in a linear measurement
system $\ensuremath{\mathbf{y}} = \ensuremath{\mathbf{Ax}}^{\natural} +
\ensuremath{\mathbf{w}}$, where $\ensuremath{\mathbf{y}} \in \mathbb{R}^m$,
$\ensuremath{\mathbf{x}}^{\natural}\in \RR^n$, and $\ensuremath{\mathbf{w}}$
denote the measurements, the signal with certain structures, and the
measurement noise, respectively. By viewing the signal reconstruction from the
measurements as a message passing algorithm over a graphical model, we leverage
tools from coding theory in the design of low density parity check codes,
namely the density evolution, and provide a framework for the design of matrix
$\ensuremath{\mathbf{A}}$. Particularly, compared to the previous methods, our
proposed framework enjoys the following desirable properties: ($i$)
Universality: the design supports both regular sensing and preferential
sensing, and incorporates them in a single framework; ($ii$) Flexibility: the
framework can easily adapt the design of $\bA$ to a signal
$\ensuremath{\mathbf{x}}^{\natural}$ with different underlying structures. As
an illustration, we consider the $\ell_1$ regularizer, which correspond to
Lasso, for both the regular sensing and preferential sensing scheme.
Noteworthy, our framework can reproduce the classical result of Lasso, i.e.,
$m\geq c_0 k\log(n/k)$ (the regular sensing) with regular design after proper
distribution approximation, where $c_0 > 0$ is some fixed constant. We also
provide numerical experiments to confirm the analytical results and demonstrate
the superiority of our framework whenever a preferential treatment of a
sub-block of vector $\bx^{\natural}$ is required.


title: Online Frank-Wolfe with Unknown Delays

abstract:
  The online Frank-Wolfe (OFW) method has gained much popularity for online
convex optimization due to its projection-free property. Previous studies
showed that for convex losses, OFW attains $O(T^{3/4})$ regret over general
sets and $O(T^{2/3})$ regret over strongly convex sets, and if losses are
strongly convex, these bounds can be improved to $O(T^{2/3})$ and
$O(\sqrt{T})$, respectively. However, they assumed that each gradient queried
by OFW is revealed immediately, which may not hold in practice. In this paper,
we consider a more practical setting where gradients arrive with arbitrary and
unknown delays, and propose delayed OFW which generalizes OFW to this setting.
The main idea is to perform an update similar to OFW after receiving any
gradient, and play the latest decision for each round. We first show that for
convex losses, delayed OFW achieves $O(T^{3/4}+dT^{1/4})$ regret over general
sets and $O(T^{2/3}+dT^{1/3})$ regret over strongly convex sets, where $d$ is
the maximum delay. Furthermore, we prove that for strongly convex losses,
delayed OFW attains $O(T^{2/3}+d\log T)$ regret over general sets and
$O(\sqrt{T}+d\log T)$ regret over strongly convex sets. Compared with regret
bounds in the non-delayed setting, our results imply that the proposed method
is robust to a relatively large amount of delay.


title:
Multistream neural architectures for cued-speech recognition using a
  pre-trained visual feature extractor and constrained CTC decoding

abstract:
  This paper proposes a simple and effective approach for automatic recognition
of Cued Speech (CS), a visual communication tool that helps people with hearing
impairment to understand spoken language with the help of hand gestures that
can uniquely identify the uttered phonemes in complement to lipreading. The
proposed approach is based on a pre-trained hand and lips tracker used for
visual feature extraction and a phonetic decoder based on a multistream
recurrent neural network trained with connectionist temporal classification
loss and combined with a pronunciation lexicon. The proposed system is
evaluated on an updated version of the French CS dataset CSF18 for which the
phonetic transcription has been manually checked and corrected. With a decoding
accuracy at the phonetic level of 70.88%, the proposed system outperforms our
previous CNN-HMM decoder and competes with more complex baselines.


title:
Non-symmetric wormholes and localized Big Rip singularities in
  Einstein-Weyl gravity

abstract:
  The inclusion of the Weyl squared term in the gravitational action is one of
the most simple, yet non trivial modifications to General Relativity at high
energies. Nevertheless the study of the spherically-symmetric vacuum solutions
of this theory has received much attention only in recent times. A new type of
asymptotically flat wormhole which does not match symmetrically at a finite
radius with another sheet of the spacetime is presented. The outer spacetime is
characterized by a newtonian potential with a Yukawa correction, and has
gravitational properties that can be arbitrarily close to the ones of a
Schwarzschild black hole. The internal spacetime instead possesses a
singularity at $r=\infty$ with the topology of a 2-dimensional sphere. The
expansion scalar of geodesics reaching this singularity diverges in a finite
amount of proper time, with a striking resemblance with the future singularity
of the Big Rip cosmological scenario. In terms of the external Yukawa hair and
mass $M$, these new wormholes fill a large region of the two-dimensional
parameter space of physical solutions with $M>0$. On the contrary black holes,
both of Schwarzschild and non-Schwarzschild nature, are confined on a line. We
argue that this type of wormholes are ideal black hole mimickers.


title:
Derivation of an effective rheology for dilute suspensions of
  micro-swimmers

abstract:
  We provide a rigorous derivation of an effective rheology for dilute viscous
suspensions of self-propelled particles. We derive at the limit an effective
Stokes system with two additional terms. The first term corresponds to an
effective viscosity that matches the famous Einstein's formula $\mu_{eff}=(1+
\frac{5}{2} \lambda)\mu$. The second additional term derived is an effective
active stress $\sigma_1$ that appears as a forcing term $\lambda \, \text{div}
\, \sigma_1$ in the fluid equation. We then investigate the effects of this
term depending on the micro-swimmers nature and their distribution in the
fluid.


title: Bimodal Camera Pose Prediction for Endoscopy

abstract:
  Deducing the 3D structure of endoscopic scenes from images remains extremely
challenging. In addition to deformation and view-dependent lighting, tubular
structures like the colon present problems stemming from the self-occluding,
repetitive anatomical structures. In this paper, we propose SimCol, a synthetic
dataset for camera pose estimation in colonoscopy and a novel method that
explicitly learns a bimodal distribution to predict the endoscope pose. Our
dataset replicates real colonoscope motion and highlights drawbacks of existing
methods. We publish 18k RGB images from simulated colonoscopy with
corresponding depth and camera poses and make our data generation environment
in Unity publicly available. We evaluate different camera pose prediction
methods and demonstrate that, when trained on our data, they generalize to real
colonoscopy sequences and our bimodal approach outperforms prior unimodal work.


title: Assessing hierarchies by their consistent segmentations

abstract:
  Recent segmentation approaches start by creating a hierarchy of nested image
partitions, and then specify a segmentation from it, usually, by choosing one
horizontal cut. Our first contribution is to describe several different ways,
some of them new, for specifying segmentations using the hierarchy regions.
Then we consider the best hierarchy-induced segmentation, in which the segments
are specified by a limited number, k, of hierarchy nodes/regions. The number of
hierarchy-induced segmentations grows exponentially with the hierarchy size,
implying that exhaustive search is unfeasible. We focus on a common quality
measure, the Jaccard index (known also as IoU). Optimizing the Jaccard index is
highly nontrivial. Yet, we propose an efficient optimization * This work was
done when the first author was with the Math dept. Technion, Israel.


title:
Non-Convex Optimization with Certificates and Fast Rates Through Kernel
  Sums of Squares

abstract:
  We consider potentially non-convex optimization problems, for which optimal
rates of approximation depend on the dimension of the parameter space and the
smoothness of the function to be optimized. In this paper, we propose an
algorithm that achieves close to optimal a priori computational guarantees,
while also providing a posteriori certificates of optimality. Our general
formulation builds on infinite-dimensional sums-of-squares and Fourier
analysis, and is instantiated on the minimization of multivariate periodic
functions.


title:
A new artificial photosynthetic system coupling photovoltaic
  electrocatalysis with photothermal catalysis

abstract:
  In this work, we present a novel artificial photosynthetic paradigm with
square meter (m2) level scalable production by integrating photovoltaic
electrolytic water splitting device and solar heating CO2 hydrogenation device,
successfully achieving the synergy of 1 sun driven 19.4% solar to chemical
energy efficiency (STC) for CO production (2.7 times higher than state of the
art of large-sized artificial photosynthetic systems) with a low cost
(equivalent to 1/7 of reported artificial photosynthetic systems). Furthermore,
the outdoor artificial photosynthetic demonstration with 1.268 m2 of scale
exhibits the CO generation amount of 258.4 L per day, the STC of ~15.5% for CO
production in winter, which could recover the cost within 833 suuny days of
operation by selling CO.


title: External control of a genetic toggle switch via Reinforcement Learning

abstract:
  We investigate the problem of using a learning-based strategy to stabilize a
synthetic toggle switch via an external control approach. To overcome the data
efficiency problem that would render the algorithm unfeasible for practical use
in synthetic biology, we adopt a sim-to-real paradigm where the policy is
learnt via training on a simplified model of the toggle switch and it is then
subsequently exploited to control a more realistic model of the switch
parameterized from in-vivo experiments. Our in-silico experiments confirm the
viability of the approach suggesting its potential use for in-vivo control
implementations.


title: Consistent Estimators for Nonlinear Vessel Models

abstract:
  In this work, the issue of obtaining consistent parameter estimators for
nonlinear regression models where the regressors are second-order modulus
functions is explored. It is shown that consistent instrumental variable
estimators can be obtained by estimating first and second-order moments of
non-additive environmental disturbances' probability distributions as nuisance
parameters in parallel to the sought-after model parameters, conducting
experiments with a static excitation offset of sufficient amplitude and forcing
the instruments to have zero mean. The proposed method is evaluated in a
simulation example with a model of a marine surface vessel.


title: Drazin-Inverse and heat capacity for driven random walks on the ring

abstract:
  We give a graphical representation for the Drazin-inverse of the backward
generator $L$ for biased random walkers on $\mathbb{Z}_N$. From the
matrix-forest theorem an algorithm is constructed for computing the
pseudo-potential $V$, solution of $LV = f$ for any function $f$ on
$\mathbb{Z}_N$ having zero stationary expectation. As an application to thermal
physics, we give the nonequilibrium heat capacity as function of bias and
temperature. Finally, we discuss the diffusion limit $N\uparrow \infty$.


title:
Gerstenhaber structure on Hochschild cohomology of the Fomin-Kirillov
  algebra on 3 generators

abstract:
  The goal of this article is to compute the Gerstenhaber bracket of the
Hochschild cohomology of the Fomin-Kirillov algebra on three generators over a
field of characteristic different from $2$ and $3$. This is in part based on a
general method we introduce to easily compute the Gerstenhaber bracket between
elements of $\operatorname{HH}^{0}(A)$ and elements of
$\operatorname{HH}^{n}(A)$ for $n \in \mathbb{N}_{0}$, the method by M.
Su\'arez-\'Alvarez to calculate the Gerstenhaber bracket between elements of
$\operatorname{HH}^{1}(A)$ and elements of $\operatorname{HH}^{n}(A)$ for any
$n \in \mathbb{N}_{0}$, as well as an elementary result that allows to compute
the remaining brackets from the previous ones. We also show that the
Gerstenhaber bracket of $\operatorname{HH}^{\bullet}(A)$ is not induced by any
Batalin-Vilkovisky generator.