title:
Bulk charge density wave and electron-phonon coupling in superconducting
  copper oxychlorides

abstract:
  Bulk charge density waves are now reported in nearly all high-temperature
superconducting cuprates, with the noticeable exception of one particular
family: the copper oxychlorides. Here, we used resonant inelastic X-ray
scattering to reveal a bulk charge density waves in these materials. Combining
resonant inelastic X-ray scattering with non-resonant inelastic X-ray
scattering, we investigate the interplay between the lattice excitations and
the charge density wave, and evidence the phonon anomalies of the Cu-O
bond-stretching mode at the charge density wave wave-vector. We propose that
such electron-phonon anomalies occur in the presence of dispersive charge
excitations emanating from the charge density wave and interacting with the
Cu-O bond-stretching phonon. Our results pave the way for future studies,
combining both bulk and surface probes, to investigate the static and dynamical
properties of the charge density wave in the copper oxychloride family.


title: In-N-Out Generative Learning for Dense Unsupervised Video Segmentation

abstract:
  In this paper, we focus on unsupervised learning for Video Object
Segmentation (VOS) which learns visual correspondence (i.e., the similarity
between pixel-level features) from unlabeled videos. Previous methods are
mainly based on the contrastive learning paradigm, which optimize either in
image level or pixel level. Image-level optimization (e.g., the spatially
pooled feature of ResNet) learns robust high-level semantics but is sub-optimal
since the pixel-level features are optimized implicitly. By contrast,
pixel-level optimization is more explicit, however, it is sensitive to the
visual quality of training data and is not robust to object deformation. To
complementarily perform these two levels of optimization in a unified
framework, we propose the In-aNd-Out (INO) generative learning from a purely
generative perspective with the help of naturally designed class tokens and
patch tokens in Vision Transformer (ViT). Specifically, for image-level
optimization, we force the out-view imagination from local to global views on
class tokens, which helps capture high-level semantics, and we name it as
out-generative learning. As to pixel-level optimization, we perform in-view
masked image modeling on patch tokens, which recovers the corrupted parts of an
image via inferring its fine-grained structure, and we term it as in-generative
learning. To discover the temporal information better, we additionally force
the inter-frame consistency from both feature and affinity matrix levels.
Extensive experiments on DAVIS-2017 val and YouTube-VOS 2018 val show that our
INO outperforms previous state-of-the-art methods by significant margins.


title:
Initial stability estimates for Ricci flow and three dimensional
  Ricci-pinched manifolds

abstract:
  This paper investigates the question of stability for a class of Ricci flows
which start at possibly non-smooth metric spaces. We show that if the initial
metric space is Reifenberg and locally bi-Lipschitz to Euclidean space, then
two solutions to the Ricci flow whose Ricci curvature is uniformly bounded from
below and whose curvature is bounded by $c\cdot t^{-1}$ converge to one another
at an exponential rate once they have been appropriately gauged. As an
application, we show that smooth three dimensional, complete, uniformly
Ricci-pinched Riemannian manifolds with bounded curvature are either compact or
flat, thus confirming a conjecture of Hamilton and Lott.


title: Cross-Modality High-Frequency Transformer for MR Image Super-Resolution

abstract:
  Improving the resolution of magnetic resonance (MR) image data is critical to
computer-aided diagnosis and brain function analysis. Higher resolution helps
to capture more detailed content, but typically induces to lower
signal-to-noise ratio and longer scanning time. To this end, MR image
super-resolution has become a widely-interested topic in recent times. Existing
works establish extensive deep models with the conventional architectures based
on convolutional neural networks (CNN). In this work, to further advance this
research field, we make an early effort to build a Transformer-based MR image
super-resolution framework, with careful designs on exploring valuable domain
prior knowledge. Specifically, we consider two-fold domain priors including the
high-frequency structure prior and the inter-modality context prior, and
establish a novel Transformer architecture, called Cross-modality
high-frequency Transformer (Cohf-T), to introduce such priors into
super-resolving the low-resolution (LR) MR images. Comprehensive experiments on
two datasets indicate that Cohf-T achieves new state-of-the-art performance.


title:
Box-counting dimension in one-dimensional random geometry of
  multiplicative cascades

abstract:
  We investigate the box-counting dimension of the image of a set $E \subset
\mathbb{R}$ under a random multiplicative cascade function $f$. The
corresponding result for Hausdorff dimension was established by Benjamini and
Schramm in the context of random geometry, and for sufficiently regular sets,
the same formula holds for box-counting dimension. However, we show that this
is far from true in general, and we compute explicitly a formula of a very
different nature that gives the almost sure box-counting dimension of the
random image $f(E)$ when the set $E$ comprises a convergent sequence. In
particular, the box-counting dimension of $f(E)$ depends more subtly on $E$
than just on its dimensions. We also obtain lower and upper bounds for the
box-counting dimension of the random images for general sets $E$.


title: Systematically Evaluation of Challenge Obfuscated APUFs

abstract:
  As a well-known physical unclonable function that can provide huge number of
challenge response pairs (CRP) with a compact design and fully compatibility
with current electronic fabrication process, the arbiter PUF (APUF) has
attracted great attention. To improve its resilience against modeling attacks,
many APUF variants have been proposed so far. Though the modeling resilience of
response obfuscated APUF variants such as XOR-APUF and lightweight secure APUF
(LSPUF) have been well studied, the challenge obfuscated APUFs (CO-APUFs) such
as feed-forward APUF (FF-APUF), and XOR-FF-APUF are less elucidated,
especially, with the deep learning (DL) methods. This work systematically
evaluates five CO-APUFs including three influential designs of FF-APUF,
XOR-FF-APUF, iPUF, one very recently design and our newly optimized design
(dubbed as OAX-FF-APUF), in terms of their reliability, uniformity (related to
uniqueness), and modeling resilience. Three DL techniques of GRU, TCN and MLP
are employed to examine these CO-APUFs' modeling resilience -- the first two
are newly explored. With computation resource of a common personal computer, we
show that all five CO-APUFs with relatively large scale can be successfully
modeled -- attacking accuracy higher or close to its reliability. The
hyper-parameter tuning of DL technique is crucial for implementing efficient
attacks. Increasing the scale of the CO-APUF is validated to be able to improve
the resilience but should be done with minimizing the reliability degradation.
As the powerful capability of DL technique affirmed by us, we recommend the DL,
specifically the MLP technique always demonstrating best efficacy, to be always
considered for examining the modeling resilience when newly composited APUFs
are devised or to a large extent, other strong PUFs are constructed.


title: Agreement or Disagreement in Noise-tolerant Mutual Learning?

abstract:
  Deep learning has made many remarkable achievements in many fields but
suffers from noisy labels in datasets. The state-of-the-art learning with noisy
label method Co-teaching and Co-teaching+ confronts the noisy label by
mutual-information between dual-network. However, the dual network always tends
to convergent which would weaken the dual-network mechanism to resist the noisy
labels. In this paper, we proposed a noise-tolerant framework named MLC in an
end-to-end manner. It adjusts the dual-network with divergent regularization to
ensure the effectiveness of the mechanism. In addition, we correct the label
distribution according to the agreement between dual-networks. The proposed
method can utilize the noisy data to improve the accuracy, generalization, and
robustness of the network. We test the proposed method on the simulate noisy
dataset MNIST, CIFAR-10, and the real-world noisy dataset Clothing1M. The
experimental result shows that our method outperforms the previous
state-of-the-art method. Besides, our method is network-free thus it is
applicable to many tasks.


title: Evolving Multi-Label Fuzzy Classifier

abstract:
  Multi-label classification has attracted much attention in the machine
learning community to address the problem of assigning single samples to more
than one class at the same time. We propose an evolving multi-label fuzzy
classifier (EFC-ML) which is able to self-adapt and self-evolve its structure
with new incoming multi-label samples in an incremental, single-pass manner. It
is based on a multi-output Takagi-Sugeno type architecture, where for each
class a separate consequent hyper-plane is defined. The learning procedure
embeds a locally weighted incremental correlation-based algorithm combined with
(conventional) recursive fuzzily weighted least squares and Lasso-based
regularization. The correlation-based part ensures that the interrelations
between class labels, a specific well-known property in multi-label
classification for improved performance, are preserved properly; the
Lasso-based regularization reduces the curse of dimensionality effects in the
case of a higher number of inputs. Antecedent learning is achieved by
product-space clustering and conducted for all class labels together, which
yields a single rule base, allowing a compact knowledge view. Furthermore, our
approach comes with an online active learning (AL) strategy for updating the
classifier on just a number of selected samples, which in turn makes the
approach applicable for scarcely labelled streams in applications, where the
annotation effort is typically expensive. Our approach was evaluated on several
data sets from the MULAN repository and showed significantly improved
classification accuracy compared to (evolving) one-versus-rest or classifier
chaining concepts. A significant result was that, due to the online AL method,
a 90\% reduction in the number of samples used for classifier updates had
little effect on the accumulated accuracy trend lines compared to a full update
in most data set cases.


title:
Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT
  Models for Code Generation

abstract:
  Neural Machine Translation (NMT) has reached a level of maturity to be
recognized as the premier method for the translation between different
languages and aroused interest in different research areas, including software
engineering. A key step to validate the robustness of the NMT models consists
in evaluating the performance of the models on adversarial inputs, i.e., inputs
obtained from the original ones by adding small amounts of perturbation.
However, when dealing with the specific task of the code generation (i.e., the
generation of code starting from a description in natural language), it has not
yet been defined an approach to validate the robustness of the NMT models. In
this work, we address the problem by identifying a set of perturbations and
metrics tailored for the robustness assessment of such models. We present a
preliminary experimental evaluation, showing what type of perturbations affect
the model the most and deriving useful insights for future directions.


title: Dressing in the Wild by Watching Dance Videos

abstract:
  While significant progress has been made in garment transfer, one of the most
applicable directions of human-centric image generation, existing works
overlook the in-the-wild imagery, presenting severe garment-person misalignment
as well as noticeable degradation in fine texture details. This paper,
therefore, attends to virtual try-on in real-world scenes and brings essential
improvements in authenticity and naturalness especially for loose garment
(e.g., skirts, formal dresses), challenging poses (e.g., cross arms, bent
legs), and cluttered backgrounds. Specifically, we find that the pixel flow
excels at handling loose garments whereas the vertex flow is preferred for hard
poses, and by combining their advantages we propose a novel generative network
called wFlow that can effectively push up garment transfer to in-the-wild
context. Moreover, former approaches require paired images for training.
Instead, we cut down the laboriousness by working on a newly constructed
large-scale video dataset named Dance50k with self-supervised cross-frame
training and an online cycle optimization. The proposed Dance50k can boost
real-world virtual dressing by covering a wide variety of garments under
dancing poses. Extensive experiments demonstrate the superiority of our wFlow
in generating realistic garment transfer results for in-the-wild images without
resorting to expensive paired datasets.


title:
Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain
  Data

abstract:
  Noise-robust speech recognition systems require large amounts of training
data including noisy speech data and corresponding transcripts to achieve
state-of-the-art performances in face of various practical environments.
However, such plenty of in-domain data is not always available in the real-life
world. In this paper, we propose a generative adversarial network to simulate
noisy spectrum from the clean spectrum (Simu-GAN), where only 10 minutes of
unparalleled in-domain noisy speech data is required as labels. Furthermore, we
also propose a dual-path speech recognition system to improve the robustness of
the system under noisy conditions. Experimental results show that the proposed
speech recognition system achieves 7.3% absolute improvement with simulated
noisy data by Simu-GAN over the best baseline in terms of word error rate
(WER).


title:
Time Reversal Precoding at SubTHz Frequencies: Experimental Results on
  Spatiotemporal Focusing

abstract:
  Due to availability of large spectrum chunks, the sub-TeraHertz (subTHz)
frequency band can support Ultra-WideBand (UWB) wireless communications, paving
the way for unprecedented increase in the wireless network capacity. This fact
is expected to be the next breakthrough for the upcoming sixth Generation (6G)
standards. However, the technology of subTHz transceivers is not yet mature
enough to apply the advanced signal processing currently being implemented for
millimeter wave wireless communications. In this paper, we consider the Time
Reversal (TR) precoding technique, which provides simple and robust processing
capable to offer highly focalized in time and space UWB waveforms, exploiting
the spatial diversity of wireless channels. We first investigate experimentally
the performance of subTHz TR focusing in complex media inside a leaking
reverberation cavity. We then combine TR with received spatial modulation to
realize data communication using a simple non-coherent receiver with two
antennas. Our results showcase the capability of TR to offer focusing in time
in the order of few nanoseconds and in space in the order of less than 1 mm.


title: Improving Persian Relation Extraction Models by Data Augmentation

abstract:
  Relation extraction that is the task of predicting semantic relation type
between entities in a sentence or document is an important task in natural
language processing. Although there are many researches and datasets for
English, Persian suffers from sufficient researches and comprehensive datasets.
The only available Persian dataset for this task is PERLEX, which is a Persian
expert-translated version of the SemEval-2010-Task-8 dataset. In this paper, we
present our augmented dataset and the results and findings of our system,
participated in the Persian relation Extraction shared task of NSURL 2021
workshop. We use PERLEX as the base dataset and enhance it by applying some
text preprocessing steps and by increasing its size via data augmentation
techniques to improve the generalization and robustness of applied models. We
then employ two different models including ParsBERT and multilingual BERT for
relation extraction on the augmented PERLEX dataset. Our best model obtained
64.67% of Macro-F1 on the test phase of the contest and it achieved 83.68% of
Macro-F1 on the test set of PERLEX.


title: syslrn: Learning What to Monitor for Efficient Anomaly Detection

abstract:
  While monitoring system behavior to detect anomalies and failures is
important, existing methods based on log-analysis can only be as good as the
information contained in the logs, and other approaches that look at the
OS-level software state introduce high overheads. We tackle the problem with
syslrn, a system that first builds an understanding of a target system offline,
and then tailors the online monitoring instrumentation based on the learned
identifiers of normal behavior. While our syslrn prototype is still preliminary
and lacks many features, we show in a case study for the monitoring of
OpenStack failures that it can outperform state-of-the-art log-analysis systems
with little overhead.


title:
Robust Single Image Dehazing Based on Consistent and Contrast-Assisted
  Reconstruction

abstract:
  Single image dehazing as a fundamental low-level vision task, is essential
for the development of robust intelligent surveillance system. In this paper,
we make an early effort to consider dehazing robustness under variational haze
density, which is a realistic while under-studied problem in the research filed
of singe image dehazing. To properly address this problem, we propose a novel
density-variational learning framework to improve the robustness of the image
dehzing model assisted by a variety of negative hazy images, to better deal
with various complex hazy scenarios. Specifically, the dehazing network is
optimized under the consistency-regularized framework with the proposed
Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the
negative information to facilitate the traditional positive-orient dehazing
objective function, by squeezing the dehazed image to its clean target from
different directions. Meanwhile, the consistency regularization keeps
consistent outputs given multi-level hazy images, thus improving the model
robustness. Extensive experimental results on two synthetic and three
real-world datasets demonstrate that our method significantly surpasses the
state-of-the-art approaches.


title:
Speech Emotion Recognition with Co-Attention based Multi-level Acoustic
  Information

abstract:
  Speech Emotion Recognition (SER) aims to help the machine to understand
human's subjective emotion from only audio information. However, extracting and
utilizing comprehensive in-depth audio information is still a challenging task.
In this paper, we propose an end-to-end speech emotion recognition system using
multi-level acoustic information with a newly designed co-attention module. We
firstly extract multi-level acoustic information, including MFCC, spectrogram,
and the embedded high-level acoustic information with CNN, BiLSTM and wav2vec2,
respectively. Then these extracted features are treated as multimodal inputs
and fused by the proposed co-attention mechanism. Experiments are carried on
the IEMOCAP dataset, and our model achieves competitive performance with two
different speaker-independent cross-validation strategies. Our code is
available on GitHub.


title:
Exact convergence rate in the central limit theorem for a branching
  process with immigration in a random environment

abstract:
  Let (Zn) be a branching process with immigration in an independent and
identically distributed random environment. Under necessary moment conditions,
we show the exact convergence rate in the central limit theorem on logZn by
using the convergence rates of the logarithm of submartingale and the result of
the corresponding random walk on the Berry-Esseen bound.


title:
Compact Token Representations with Contextual Quantization for Efficient
  Document Re-ranking

abstract:
  Transformer based re-ranking models can achieve high search relevance through
context-aware soft matching of query tokens with document tokens. To alleviate
runtime complexity of such inference, previous work has adopted a late
interaction architecture with pre-computed contextual token representations at
the cost of a large online storage. This paper proposes contextual quantization
of token embeddings by decoupling document-specific and document-independent
ranking contributions during codebook-based compression. This allows effective
online decompression and embedding composition for better search relevance.
This paper presents an evaluation of the above compact token representation
model in terms of relevance and space efficiency.


title: The Fox-Hatcher cycle and a Vassiliev invariant of order three

abstract:
  We show that the integration of a 1-cocycle I(X) of the space of long knots
in R^3 over the Fox-Hatcher 1-cycles gives rise to a Vassiliev invariant of
order exactly three. This result can be seen as a continuation of the previous
work of the second named author, proving that the integration of I(X) over the
Gramain 1-cycles is the Casson invariant, the unique nontrivial Vassiliev
invariant of order two (up to scalar multiplications). The result in the
present paper is also analogous to part of Mortier's result. Our result differs
from, but is motivated by, Mortier's one in that the 1-cocycle I(X) is given by
the configuration space integrals associated with graphs while Mortier's
cocycle is obtained in a combinatorial way.


title: Amplitudes meet Cosmology: A (Scalar) Primer

abstract:
  We review the most recent progress in our understanding of quantum mechanical
observables in cosmology in the perturbative regime. It relies on an approach
that considers them directly as functions of the data at the space-like
boundary at future infinity prescinding from the explicit time evolution. It
takes inspiration from the on-shell formulation of perturbative scattering
amplitudes developed in the past 20 years: starting with the requirement of
consistency with some fundamental principles such as causality, unitarity and
locality, it provides different ways of phrasing and extracting predictions. In
this review, we aim to provide a pedagogical treatment of the most recent
insights about the analytic structure of the perturbative quantum mechanical
observables in cosmology, its relation to fundamental principles as well as
physical processes, and how such observables and their features emerge from
novel well-defined mathematical objects with their own first principle
definition. The review is divided in three parts: Part 0 discusses the
definition of quantum mechanical observables in cosmology and some general
principles; Part I reviews the boundary approach to the analysis and
computation of the perturbative wavefunction of the universe; Part II provides
an introduction to the combinatorial-geometrical description of cosmological
processes in terms of cosmological polytopes.


title:
CNN Filter DB: An Empirical Investigation of Trained Convolutional
  Filters

abstract:
  Currently, many theoretical as well as practically relevant questions towards
the transferability and robustness of Convolutional Neural Networks (CNNs)
remain unsolved. While ongoing research efforts are engaging these problems
from various angles, in most computer vision related cases these approaches can
be generalized to investigations of the effects of distribution shifts in image
data. In this context, we propose to study the shifts in the learned weights of
trained CNN models. Here we focus on the properties of the distributions of
dominantly used 3x3 convolution filter kernels. We collected and publicly
provide a dataset with over 1.4 billion filters from hundreds of trained CNNs,
using a wide range of datasets, architectures, and vision tasks. In a first use
case of the proposed dataset, we can show highly relevant properties of many
publicly available pre-trained models for practical applications: I) We analyze
distribution shifts (or the lack thereof) between trained filters along
different axes of meta-parameters, like visual category of the dataset, task,
architecture, or layer depth. Based on these results, we conclude that model
pre-training can succeed on arbitrary datasets if they meet size and variance
conditions. II) We show that many pre-trained models contain degenerated
filters which make them less robust and less suitable for fine-tuning on target
applications.
  Data & Project website: https://github.com/paulgavrikov/cnn-filter-db


title: Balanced Multimodal Learning via On-the-fly Gradient Modulation

abstract:
  Multimodal learning helps to comprehensively understand the world, by
integrating different senses. Accordingly, multiple input modalities are
expected to boost model performance, but we actually find that they are not
fully exploited even when the multimodal model outperforms its uni-modal
counterpart. Specifically, in this paper we point out that existing multimodal
discriminative models, in which uniform objective is designed for all
modalities, could remain under-optimized uni-modal representations, caused by
another dominated modality in some scenarios, e.g., sound in blowing wind
event, vision in drawing picture event, etc. To alleviate this optimization
imbalance, we propose on-the-fly gradient modulation to adaptively control the
optimization of each modality, via monitoring the discrepancy of their
contribution towards the learning objective. Further, an extra Gaussian noise
that changes dynamically is introduced to avoid possible generalization drop
caused by gradient modulation. As a result, we achieve considerable improvement
over common fusion methods on different multimodal tasks, and this simple
strategy can also boost existing multimodal methods, which illustrates its
efficacy and versatility. The source code is available at
\url{https://github.com/GeWu-Lab/OGM-GE_CVPR2022}.


title:
On Affine Policies for Wasserstein Distributionally Robust Unit
  Commitment

abstract:
  This paper proposes a unit commitment (UC) model based on data-driven
Wasserstein distributionally robust optimization (WDRO) for power systems under
uncertainty of renewable generation as well as its tractable exact
reformulation. The proposed model is formulated as a WDRO problem relying on an
affine policy, which nests an infinite-dimensional worst-case expectation
problem and satisfies the non-anticipativity constraint. To reduce
conservativeness, we develop a novel technique that defines a subset of the
uncertainty set with a probabilistic guarantee. Subsequently, the proposed
model is recast as a semi-infinite programming problem that can be efficiently
solved using existing algorithms. Notably, the scale of this reformulation is
invariant with the sample size. As a result, a number of samples are easily
incorporated without using sophisticated decomposition algorithms. Numerical
simulations on 6- and 24-bus test systems demonstrate the economic and
computational efficiency of the proposed model.


title: AnyFace: Free-style Text-to-Face Synthesis and Manipulation

abstract:
  Existing text-to-image synthesis methods generally are only applicable to
words in the training dataset. However, human faces are so variable to be
described with limited words. So this paper proposes the first free-style
text-to-face method namely AnyFace enabling much wider open world applications
such as metaverse, social media, cosmetics, forensics, etc. AnyFace has a novel
two-stream framework for face image synthesis and manipulation given arbitrary
descriptions of the human face. Specifically, one stream performs text-to-face
generation and the other conducts face image reconstruction. Facial text and
image features are extracted using the CLIP (Contrastive Language-Image
Pre-training) encoders. And a collaborative Cross Modal Distillation (CMD)
module is designed to align the linguistic and visual features across these two
streams. Furthermore, a Diverse Triplet Loss (DT loss) is developed to model
fine-grained features and improve facial diversity. Extensive experiments on
Multi-modal CelebA-HQ and CelebAText-HQ demonstrate significant advantages of
AnyFace over state-of-the-art methods. AnyFace can achieve high-quality,
high-resolution, and high-diversity face synthesis and manipulation results
without any constraints on the number and content of input captions.


title: Iranian Modal Music (Dastgah) detection using deep neural networks

abstract:
  In this work, several deep neural networks are implemented to recognize
Iranian modal music in seven high correlated categories. The best model, which
achieved 92 percent overall accuracy, uses an architecture inspired by
autoencoder, including BiLSTM and BiGRU layers. This model is trained using the
Nava dataset, with 1786 records and up to 55 hours of music played solo by
Kamanche, Tar, Setar, Reed, and Santoor (Dulcimer). Features that have been
studied through this research contain MFCC, Chroma CENS, and Mel spectrogram.
The results indicate that MFCC carries more valuable information for detecting
Iranian modal music (Dastgah) than other sound representations. Moreover, the
architecture, which is inspired by autoencoder, is robust in distinguishing
high correlated data like Dastgahs. It also shows that because of the precise
order in Iranian Dastgah Music, Bidirectional Recurrent networks are more
efficient than any other networks that have been implemented in this study.


title:
End-to-End Compressed Video Representation Learning for Generic Event
  Boundary Detection

abstract:
  Generic event boundary detection aims to localize the generic, taxonomy-free
event boundaries that segment videos into chunks. Existing methods typically
require video frames to be decoded before feeding into the network, which
demands considerable computational power and storage space. To that end, we
propose a new end-to-end compressed video representation learning for event
boundary detection that leverages the rich information in the compressed
domain, i.e., RGB, motion vectors, residuals, and the internal group of
pictures (GOP) structure, without fully decoding the video. Specifically, we
first use the ConvNets to extract features of the I-frames in the GOPs. After
that, a light-weight spatial-channel compressed encoder is designed to compute
the feature representations of the P-frames based on the motion vectors,
residuals and representations of their dependent I-frames. A temporal
contrastive module is proposed to determine the event boundaries of video
sequences. To remedy the ambiguities of annotations and speed up the training
process, we use the Gaussian kernel to preprocess the ground-truth event
boundaries. Extensive experiments conducted on the Kinetics-GEBD dataset
demonstrate that the proposed method achieves comparable results to the
state-of-the-art methods with $4.5\times$ faster running speed.


title:
Infrared and Visible Image Fusion via Interactive Compensatory Attention
  Adversarial Learning

abstract:
  The existing generative adversarial fusion methods generally concatenate
source images and extract local features through convolution operation, without
considering their global characteristics, which tends to produce an unbalanced
result and is biased towards the infrared image or visible image. Toward this
end, we propose a novel end-to-end mode based on generative adversarial
training to achieve better fusion balance, termed as \textit{interactive
compensatory attention fusion network} (ICAFusion). In particular, in the
generator, we construct a multi-level encoder-decoder network with a triple
path, and adopt infrared and visible paths to provide additional intensity and
gradient information. Moreover, we develop interactive and compensatory
attention modules to communicate their pathwise information, and model their
long-range dependencies to generate attention maps, which can more focus on
infrared target perception and visible detail characterization, and further
increase the representation power for feature extraction and feature
reconstruction. In addition, dual discriminators are designed to identify the
similar distribution between fused result and source images, and the generator
is optimized to produce a more balanced result. Extensive experiments
illustrate that our ICAFusion obtains superior fusion performance and better
generalization ability, which precedes other advanced methods in the subjective
visual description and objective metric evaluation. Our codes will be public at
\url{https://github.com/Zhishe-Wang/ICAFusion}


title:
Dynamic-subarray with Fixed Phase Shifters for Energy-efficient
  Terahertz Hybrid Beamforming under Partial CSI

abstract:
  Terahertz (THz) communications are regarded as a pillar technology for the 6G
systems, by offering multi-ten-GHz bandwidth. To overcome the huge propagation
loss while reducing the hardware complexity, THz ultra-massive (UM) MIMO
systems with hybrid beamforming are proposed to offer high array gain. Notably,
the adjustable-phase-shifters considered in most existing hybrid beamforming
studies are power-hungry and difficult to realize in the THz band. Moreover,
due to the ultra-massive antennas, full channel-state-information (CSI) is
challenging to obtain. To address these practical concerns, in this paper, an
energy-efficient dynamic-subarray with fixed-phase-shifters (DS-FPS)
architecture is proposed for THz hybrid beamforming. To compensate for the
spectral efficiency loss caused by the fixed-phase of FPS, a switch network is
inserted to enable dynamic connections. In addition, by considering the partial
CSI, we propose a row-successive-decomposition (RSD) algorithm to design the
hybrid beamforming matrices for DS-FPS. A row-by-row (RBR) algorithm is further
proposed to reduce computational complexity. Extensive simulation results show
that, the proposed DS-FPS architecture with the RSD and RBR algorithms achieves
much higher energy efficiency than the existing architectures. Moreover, the
DS-FPS architecture with partial CSI achieves 97% spectral efficiency of that
with full CSI.


title: Spectra of weighted uniform hypertrees

abstract:
  Let $T$ be a $k$-tree equipped with a weighting function $\w: V(T)\cup
E(T)\rightarrow \C$, where $k \geq 3$. The weighted matching polynomial of the
weighted $k$-tree $(T,\w)$ is defined to be $$ \mu(T,\w,x)= \sum_{M \in
\mathcal{M}(T)}(-1)^{|M|}\prod_{e \in E(M)}\mathbf{w}(e)^k \prod_{v \in
V(T)\backslash V(M)}(x-\w(v)), $$ where $\mathcal{M}(T)$ denotes the set of
matchings (including empty set) of $T$. In this paper, we investigate the
eigenvalues of the adjacency tensor $\A(T,\w)$ of the weighted $k$-tree
$(T,\w)$. The main result provides that $\w(v)$ is an eigenvalue of $\A(T,\w)$
for every $v\in V(T)$, and if $\lambda\neq \w(v)$ for every $v\in V(T)$, then
$\lambda$ is an eigenvalue of $\A(T,\w)$ if and only if there exists a subtree
$T'$ of $T$ such that $\lambda$ is a root of $\mu(T',\w,x)$. Moreover, the
spectral radius of $\A(T,\w)$ is equal to the largest root of $\mu(T,\w,x)$
when $\w$ is real and nonnegative. The result extends a work by Clark and
Cooper ({\em On the adjacency spectra of hypertrees, Electron. J. Combin., 25
(2)(2018) $\#$P2.48}) to weighted $k$-trees. As applications, two analogues of
the above work for the Laplacian and the signless Laplacian tensors of
$k$-trees are obtained.


title:
Splitting-based randomized iterative methods for solving indefinite
  least squares problem

abstract:
  The indefinite least squares (ILS) problem is a generalization of the famous
linear least squares problem. It minimizes an indefinite quadratic form with
respect to a signature matrix. For this problem, we first propose an
impressively simple and effective splitting (SP) method according to its own
structure and prove that it converges 'unconditionally' for any initial value.
Further, to avoid implementing some matrix multiplications and calculating the
inverse of large matrix and considering the acceleration and efficiency of the
randomized strategy, we develop two randomized iterative methods on the basis
of the SP method as well as the randomized Kaczmarz, Gauss-Seidel and
coordinate descent methods, and describe their convergence properties.
Numerical results show that our three methods all have quite decent performance
in both computing time and iteration numbers compared with the latest iterative
method of the ILS problem, and also demonstrate that the two randomized methods
indeed yield significant acceleration in term of computing time.


title: Cosmological Number Counts under Disformal Transformations

abstract:
  We investigate whether true physical observables associated with the
measurements of large scale structure in the universe are frame-independent. In
particular, we study if cosmological observables such as the galaxy number
counts and weak lensing observables are invariant under the disformal
transformations. In a previous work, it was shown that this frame-invariance
holds true for the case of conformal transformations. In this work, we find
that although the cosmological number counts remain invariant under the
disformal transformations, convergence and cosmic shear associated with the
weak lensing potential are generally not invariant. Since the lightcone
structure does not remain causal under disformal transformations, photon
geodesics do not remain null anymore, and as a result, weak lensing observables
are indeed affected. We also briefly comment on the disformal invariance of
other cosmological observables.


title:
Systematics of E2 strength in the sd-shell with the valence-space
  in-medium similarity renormalization group

abstract:
  Background: Recent developments in {\it ab initio} nuclear theory demonstrate
promising results in medium- to heavy-mass nuclei. A particular challenge for
many of the many-body methodologies, however, is an accurate treatment of the
electric-quadrupole, $E2$, strength associated with collectivity. Purpose: The
valence-space in-medium similarity renormalization group (VS-IMSRG) is a
particularly powerful method for accessing medium- and high-mass nuclei but has
been found to underpredict $E2$ strengths. The purpose of this work is to
evaluate the isospin dependence of this underprediction. Methods: We perform a
systematic comparison of valence-space in-medium similarity renormalization
group (VS-IMSRG) calculations with available literature. We make use of
isoscalar and isovector contributions to the $E2$ matrix elements to assess
isoscalar and isovector contributions to the missing strength. Results: It is
found that the $E2$ strength is consistent throughout
$T_z=\left|\frac{1}{2}\right|$, $T_z=\left|1\right|$,
$T_z=\left|\frac{3}{2}\right|$ and $T_z=2$ pairs within the $sd$-shell.
Furthermore, no isovector contribution to the deficiency is identified.
Conclusions: A comparison with toy-models and coupled-cluster calculations is
used to discuss potential origins of the missing strength, which arises from
missing many-particle, many-hole excitations out of the model space. The
absence of any significant isovector contribution to the missing $E2$ strength
indicates that the $E2$ strength discrepancy, and therefore any correction, is
largely independent of the isospin of the nuclei in question.


title:
Coupled frustrated ferromagnetic and antiferromagnetic quantum spin
  chains in the quasi-one-dimensional mineral antlerite, Cu$_3$SO$_4$(OH)$_4$

abstract:
  Magnetic frustration, the competition among exchange interactions, often
leads to novel magnetic ground states with unique physical properties which can
hinge on details of interactions that are otherwise difficult to observe. Such
states are particularly interesting when it is possible to tune the balance
among the interactions to access multiple types of magnetic order. We present
antlerite, Cu$_3$SO$_4$(OH)$_4$, as a potential platform for tuning
frustration. The low-temperature magnetic state of its three-leg zigzag ladders
is a quasi-one-dimensional analog of the magnetic state recently proposed to
exhibit spinon-magnon mixing in botallackite. Density functional theory
calculations indicate that antlerite's magnetic ground state is exquisitely
sensitive to fine details of the atomic positions, with each chain
independently on the cusp of a phase transition, indicating an excellent
potential for tunability.


title:
An upper bound on topological entropy of the Bunimovich stadium billiard
  map

abstract:
  We show that the topological entropy of the billiard map in a Bunimovich
stadium is at most log(3.4908).


title:
Task-specific Inconsistency Alignment for Domain Adaptive Object
  Detection

abstract:
  Detectors trained with massive labeled data often exhibit dramatic
performance degradation in some particular scenarios with data distribution
gap. To alleviate this problem of domain shift, conventional wisdom typically
concentrates solely on reducing the discrepancy between the source and target
domains via attached domain classifiers, yet ignoring the difficulty of such
transferable features in coping with both classification and localization
subtasks in object detection. To address this issue, in this paper, we propose
Task-specific Inconsistency Alignment (TIA), by developing a new alignment
mechanism in separate task spaces, improving the performance of the detector on
both subtasks. Specifically, we add a set of auxiliary predictors for both
classification and localization branches, and exploit their behavioral
inconsistencies as finer-grained domain-specific measures. Then, we devise
task-specific losses to align such cross-domain disagreement of both subtasks.
By optimizing them individually, we are able to well approximate the category-
and boundary-wise discrepancies in each task space, and therefore narrow them
in a decoupled manner. TIA demonstrates superior results on various scenarios
to the previous state-of-the-art methods. It is also observed that both the
classification and localization capabilities of the detector are sufficiently
strengthened, further demonstrating the effectiveness of our TIA method. Code
and trained models are publicly available at https://github.com/MCG-NJU/TIA.


title: Enumeration of extended irreducible binary Goppa codes

abstract:
  The family of Goppa codes is one of the most interesting subclasses of linear
codes. As the McEliece cryptosystem often chooses a random Goppa code as its
key,knowledge of the number of inequivalent Goppa codes for fixed parameters
may facilitate in the evaluation of the security of such a cryptosystem. In
this paper we present a new approach to give an upper bound on the number of
inequivalent extended irreducible binary Goppa codes. To be more specific, let
$n>3$ be an odd prime number and $q=2^n$; let $r\geq3$ be a positive integer
satisfying $\gcd(r,n)=1$ and $\gcd\big(r,q(q^2-1)\big)=1$. We obtain an upper
bound for the number of inequivalent extended irreducible binary Goppa codes of
length $q+1$ and degree $r$.


title: Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis

abstract:
  Synthesizing a subject-specific pathology-free image from a pathological
image is valuable for algorithm development and clinical practice. In recent
years, several approaches based on the Generative Adversarial Network (GAN)
have achieved promising results in pseudo-healthy synthesis. However, the
discriminator (i.e., a classifier) in the GAN cannot accurately identify
lesions and further hampers from generating admirable pseudo-healthy images. To
address this problem, we present a new type of discriminator, the segmentor, to
accurately locate the lesions and improve the visual quality of pseudo-healthy
images. Then, we apply the generated images into medical image enhancement and
utilize the enhanced results to cope with the low contrast problem existing in
medical image segmentation. Furthermore, a reliable metric is proposed by
utilizing two attributes of label noise to measure the health of synthetic
images. Comprehensive experiments on the T2 modality of BraTS demonstrate that
the proposed method substantially outperforms the state-of-the-art methods. The
method achieves better performance than the existing methods with only 30\% of
the training data. The effectiveness of the proposed method is also
demonstrated on the LiTS and the T1 modality of BraTS. The code and the
pre-trained model of this study are publicly available at
https://github.com/Au3C2/Generator-Versus-Segmentor.


title:
A new procedure for Selective Inference with the Generalized Linear
  Lasso

abstract:
  This articles investigates the distribution of the solutions of the
generalized linear lasso (GLL), conditional on some selection event. In this
framework of post-selection inference (PSI), we provide rigorous definitions of
the selected and saturated models: two different paradigms that determine the
hypothesis being tested. Based on a conditional Maximum Likelihood Estimator
(MLE) approach, we give a procedure to obtain asymptotically valid PSI
confidence regions and testing procedures for Generalized Linear Models (GLMs).
In a second stage, we focus on the sparse logistic regression and we exhibit
conditions ensuring that our conditional MLE method is valid. We present
extensive numerical simulations supporting our theoretical results.


title:
LDKP: A Dataset for Identifying Keyphrases from Long Scientific
  Documents

abstract:
  Identifying keyphrases (KPs) from text documents is a fundamental task in
natural language processing and information retrieval. Vast majority of the
benchmark datasets for this task are from the scientific domain containing only
the document title and abstract information. This limits keyphrase extraction
(KPE) and keyphrase generation (KPG) algorithms to identify keyphrases from
human-written summaries that are often very short (approx 8 sentences). This
presents three challenges for real-world applications: human-written summaries
are unavailable for most documents, the documents are almost always long, and a
high percentage of KPs are directly found beyond the limited context of title
and abstract. Therefore, we release two extensive corpora mapping KPs of ~1.3M
and ~100K scientific articles with their fully extracted text and additional
metadata including publication venue, year, author, field of study, and
citations for facilitating research on this real-world problem.


title: End-to-End Transformer Based Model for Image Captioning

abstract:
  CNN-LSTM based architectures have played an important role in image
captioning, but limited by the training efficiency and expression ability,
researchers began to explore the CNN-Transformer based models and achieved
great success. Meanwhile, almost all recent works adopt Faster R-CNN as the
backbone encoder to extract region-level features from given images. However,
Faster R-CNN needs a pre-training on an additional dataset, which divides the
image captioning task into two stages and limits its potential applications. In
this paper, we build a pure Transformer-based model, which integrates image
captioning into one stage and realizes end-to-end training. Firstly, we adopt
SwinTransformer to replace Faster R-CNN as the backbone encoder to extract
grid-level features from given images; Then, referring to Transformer, we build
a refining encoder and a decoder. The refining encoder refines the grid
features by capturing the intra-relationship between them, and the decoder
decodes the refined features into captions word by word. Furthermore, in order
to increase the interaction between multi-modal (vision and language) features
to enhance the modeling capability, we calculate the mean pooling of grid
features as the global feature, then introduce it into refining encoder to
refine with grid features together, and add a pre-fusion process of refined
global feature and generated words in decoder. To validate the effectiveness of
our proposed model, we conduct experiments on MSCOCO dataset. The experimental
results compared to existing published works demonstrate that our model
achieves new state-of-the-art performances of 138.2% (single model) and 141.0%
(ensemble of 4 models) CIDEr scores on `Karpathy' offline test split and 136.0%
(c5) and 138.3% (c40) CIDEr scores on the official online test server. Trained
models and source code will be released.


title: Random Geometric Graph: Some recent developments and perspectives

abstract:
  The Random Geometric Graph (RGG) is a random graph model for network data
with an underlying spatial representation. Geometry endows RGGs with a rich
dependence structure and often leads to desirable properties of real-world
networks such as the small-world phenomenon and clustering. Originally
introduced to model wireless communication networks, RGGs are now very popular
with applications ranging from network user profiling to protein-protein
interactions in biology. RGGs are also of purely theoretical interest since the
underlying geometry gives rise to challenging mathematical questions. Their
resolutions involve results from probability, statistics, combinatorics or
information theory, placing RGGs at the intersection of a large span of
research communities. This paper surveys the recent developments in RGGs from
the lens of high dimensional settings and non-parametric inference. We also
explain how this model differs from classical community based random graph
models and we review recent works that try to take the best of both worlds. As
a by-product, we expose the scope of the mathematical tools used in the proofs.


title:
CUBES Phase A design overview -- The Cassegrain U-Band Efficient
  Spectrograph for the Very Large Telescope

abstract:
  We present the baseline conceptual design of the Cassegrain U-Band Efficient
Spectrograph (CUBES) for the Very Large Telescope. CUBES will provide
unprecedented sensitivity for spectroscopy on a 8 - 10 m class telescope in the
ground ultraviolet (UV), spanning a bandwidth of > 100 nm that starts at 300
nm, the shortest wavelength accessible from the ground. The design has been
optimized for end-to-end efficiency and provides a spectral resolving power of
R > 20000, that will unlock a broad range of new topics across solar system,
Galactic and extraglactic astronomy. The design also features a second,
lower-resolution (R \sim 7000) mode and has the option of a fiberlink to the
UVES instrument for simultaneous observations at longer wavelengths. Here we
present the optical, mechanical and software design of the various subsystems
of the instrument after the Phase A study of the project. We discuss the
expected performances for the layout choices and highlight some of the
performance trade-offs considered to best meet the instrument top-level
requirements. We also introduce the model-based system engineering approach
used to organize and manage the project activities and interfaces, in the
context that it is increasingly necessary to integrate such tools in the
development of complex astronomical projects.


title:
SIOD: Single Instance Annotated Per Category Per Image for Object
  Detection

abstract:
  Object detection under imperfect data receives great attention recently.
Weakly supervised object detection (WSOD) suffers from severe localization
issues due to the lack of instance-level annotation, while semi-supervised
object detection (SSOD) remains challenging led by the inter-image discrepancy
between labeled and unlabeled data. In this study, we propose the Single
Instance annotated Object Detection (SIOD), requiring only one instance
annotation for each existing category in an image. Degraded from inter-task
(WSOD) or inter-image (SSOD) discrepancies to the intra-image discrepancy, SIOD
provides more reliable and rich prior knowledge for mining the rest of
unlabeled instances and trades off the annotation cost and performance. Under
the SIOD setting, we propose a simple yet effective framework, termed
Dual-Mining (DMiner), which consists of a Similarity-based Pseudo Label
Generating module (SPLG) and a Pixel-level Group Contrastive Learning module
(PGCL). SPLG firstly mines latent instances from feature representation space
to alleviate the annotation missing problem. To avoid being misled by
inaccurate pseudo labels, we propose PGCL to boost the tolerance to false
pseudo labels. Extensive experiments on MS COCO verify the feasibility of the
SIOD setting and the superiority of the proposed method, which obtains
consistent and significant improvements compared to baseline methods and
achieves comparable results with fully supervised object detection (FSOD)
methods with only 40% instances annotated.


title:
Signing at Scale: Learning to Co-Articulate Signs for Large-Scale
  Photo-Realistic Sign Language Production

abstract:
  Sign languages are visual languages, with vocabularies as rich as their
spoken language counterparts. However, current deep-learning based Sign
Language Production (SLP) models produce under-articulated skeleton pose
sequences from constrained vocabularies and this limits applicability. To be
understandable and accepted by the deaf, an automatic SLP system must be able
to generate co-articulated photo-realistic signing sequences for large domains
of discourse.
  In this work, we tackle large-scale SLP by learning to co-articulate between
dictionary signs, a method capable of producing smooth signing while scaling to
unconstrained domains of discourse. To learn sign co-articulation, we propose a
novel Frame Selection Network (FS-Net) that improves the temporal alignment of
interpolated dictionary signs to continuous signing sequences. Additionally, we
propose SignGAN, a pose-conditioned human synthesis model that produces
photo-realistic sign language videos direct from skeleton pose. We propose a
novel keypoint-based loss function which improves the quality of synthesized
hand images.
  We evaluate our SLP model on the large-scale meineDGS (mDGS) corpus,
conducting extensive user evaluation showing our FS-Net approach improves
co-articulation of interpolated dictionary signs. Additionally, we show that
SignGAN significantly outperforms all baseline methods for quantitative
metrics, human perceptual studies and native deaf signer comprehension.


title:
Online Continual Learning on a Contaminated Data Stream with Blurry Task
  Boundaries

abstract:
  Learning under a continuously changing data distribution with incorrect
labels is a desirable real-world problem yet challenging. A large body of
continual learning (CL) methods, however, assumes data streams with clean
labels, and online learning scenarios under noisy data streams are yet
underexplored. We consider a more practical CL task setup of an online learning
from blurry data stream with corrupted labels, where existing CL methods
struggle. To address the task, we first argue the importance of both diversity
and purity of examples in the episodic memory of continual learning models. To
balance diversity and purity in the episodic memory, we propose a novel
strategy to manage and use the memory by a unified approach of label noise
aware diverse sampling and robust learning with semi-supervised learning. Our
empirical validations on four real-world or synthetic noise datasets (CIFAR10
and 100, mini-WebVision, and Food-101N) exhibit that our method significantly
outperforms prior arts in this realistic and challenging continual learning
scenario. Code and data splits are available in
https://github.com/clovaai/puridiver.


title: Magnetohydrostatic Modeling of the Solar Atmosphere

abstract:
  Understanding structures and evolutions of the magnetic fields and plasma in
multiple layers on the Sun is very important. A force-free magnetic field which
is an accurate approximation of the solar corona due to the low plasma $\beta$
has been widely studied and used to model the coronal magnetic structure. While
the force-freeness assumption is well satisfied in the solar corona, the lower
atmosphere is not force-free given the high plasma $\beta$. Therefore, a
magnetohydrostatic (MHS) equilibrium which takes into account plasma forces,
such as pressure gradient and gravitational force, is considered to be more
appropriate to describe the lower atmosphere. This paper reviews both
analytical and numerical extrapolation methods based on the MHS assumption for
calculating the magnetic fields and plasma in the solar atmosphere from
measured magnetograms.


title:
New measurement of the electric quadrupole amplitude in atomic thallium
  $6P_{1/2}\rightarrow6P_{3/2}$ transition using electromagnetically induced
  transparency

abstract:
  We report a new measurement of the transition amplitude ratio $\chi$ of
electric quadrupole ($E2$) to magnetic dipole ($M1$) of the
$6P_{1/2}\rightarrow6P_{3/2}$ transition in atomic thallium. We utilized the
electromagnetically induced transparency (EIT) mechanism and the
sideband-bridging technique to resolve the isotopic transitions and their
hyperfine manifold. Our measurement gives $\chi_{205}~=~0.2550(20)(2)$ for
$^{205}$Tl, and $\chi_{203}=0.2532(73)(2)$ for $^{203}$Tl, which was measured
for the first time. These results are in good agreement with the theoretical
value but have a 3$\sigma$ discrepancy with the previous measurements. Our
result provides a new input for the long dispute on the atomic thallium PNC
measurements.


title:
On the influence of the nonlinear term in the numerical approximation of
  Incompressible Flows by means of proper orthogonal decomposition methods

abstract:
  We consider proper orthogonal decomposition (POD) methods to approximate the
incompressible Navier-Stokes equations. We study the case in which one
discretization for the nonlinear term is used in the snapshots (that are
computed with a full order method (FOM)) and a different discretization of the
nonlinear term is applied in the POD method. We prove that an additional error
term appears in this case, compared with the case in which the same
discretization of the nonlinear term is applied for both the FOM and the POD
methods. However, the added term has the same size as the error coming from the
FOM so that the rate of convergence of the POD method is barely affected. We
analyze the case in which we add grad-div stabilization to both the FOM and the
POD methods because it allows to get error bounds with constants independent of
inverse powers of the viscosity. We also study the case in which no
stabilization is added. Some numerical experiments support the theoretical
analysis.


title: Nested Collaborative Learning for Long-Tailed Visual Recognition

abstract:
  The networks trained on the long-tailed dataset vary remarkably, despite the
same training settings, which shows the great uncertainty in long-tailed
learning. To alleviate the uncertainty, we propose a Nested Collaborative
Learning (NCL), which tackles the problem by collaboratively learning multiple
experts together. NCL consists of two core components, namely Nested Individual
Learning (NIL) and Nested Balanced Online Distillation (NBOD), which focus on
the individual supervised learning for each single expert and the knowledge
transferring among multiple experts, respectively. To learn representations
more thoroughly, both NIL and NBOD are formulated in a nested way, in which the
learning is conducted on not just all categories from a full perspective but
some hard categories from a partial perspective. Regarding the learning in the
partial perspective, we specifically select the negative categories with high
predicted scores as the hard categories by using a proposed Hard Category
Mining (HCM). In the NCL, the learning from two perspectives is nested, highly
related and complementary, and helps the network to capture not only global and
robust features but also meticulous distinguishing ability. Moreover,
self-supervision is further utilized for feature enhancement. Extensive
experiments manifest the superiority of our method with outperforming the
state-of-the-art whether by using a single model or an ensemble.


title: Time-optimal control of cranes subject to container height constraints

abstract:
  The productivity and efficiency of port operations strongly depend on how
fast a ship can be unloaded and loaded again. With this in mind, ship-to-shore
cranes perform the critical task of transporting containers into and onto a
ship and must do so as fast as possible. Though the problem of minimizing the
time spent in moving the payload has been addressed in previous studies, the
different heights of the container stacks have not been the focus. In this
paper, we perform a change of variable and reformulate the optimization problem
to deal with the constraints on the stack heights. As consequence, these
constraints become trivial and easy to represent since they turn into bound
constraints when the problem is discretized for the numerical solver. To
validate the idea, we simulate a small-scale scenario where different stack
heights are used. The results confirm our idea and the representation of the
stack constraints become indeed trivial. This approach is promising to be
applied in real crane operations and has the potential to enhance their
automation.


title:
Self-Supervised Image Representation Learning with Geometric Set
  Consistency

abstract:
  We propose a method for self-supervised image representation learning under
the guidance of 3D geometric consistency. Our intuition is that 3D geometric
consistency priors such as smooth regions and surface discontinuities may imply
consistent semantics or object boundaries, and can act as strong cues to guide
the learning of 2D image representations without semantic labels. Specifically,
we introduce 3D geometric consistency into a contrastive learning framework to
enforce the feature consistency within image views. We propose to use geometric
consistency sets as constraints and adapt the InfoNCE loss accordingly. We show
that our learned image representations are general. By fine-tuning our
pre-trained representations for various 2D image-based downstream tasks,
including semantic segmentation, object detection, and instance segmentation on
real-world indoor scene datasets, we achieve superior performance compared with
state-of-the-art methods.


title:
Domain Invariant Siamese Attention Mask for Small Object Change
  Detection via Everyday Indoor Robot Navigation

abstract:
  The problem of image change detection via everyday indoor robot navigation is
explored from a novel perspective of the self-attention technique. Detecting
semantically non-distinctive and visually small changes remains a key challenge
in the robotics community. Intuitively, these small non-distinctive changes may
be better handled by the recent paradigm of the attention mechanism, which is
the basic idea of this work. However, existing self-attention models require
significant retraining cost per domain, so it is not directly applicable to
robotics applications. We propose a new self-attention technique with an
ability of unsupervised on-the-fly domain adaptation, which introduces an
attention mask into the intermediate layer of an image change detection model,
without modifying the input and output layers of the model. Experiments, in
which an indoor robot aims to detect visually small changes in everyday
navigation, demonstrate that our attention technique significantly boosts the
state-of-the-art image change detection model.


title:
Nonlinear dynamics of topological helicity wave in a frustrated skyrmion
  string

abstract:
  A skyrmion in frustrated magnetic system has the helicity degree of freedom.
A skyrmion string is formed in a frustrated layered system, which is well
described by the $XY$ model owing to the exchange coupling between adjacent
layers. We consider a system where the interlayer exchange couplings are
alternating, where the dimerized $XY$ model is materialized, whose linear limit
is the Su-Schrieffer-Heeger model. We argue that it is a nonlinear topological
system. We study the quench dynamics of the helicity wave under the initial
condition that the helicity of the skyrmion in the bottommost layer is rotated.
It yields a good signal to detect whether the system is topological or trivial.
Our results show that the helicity dynamics of the skyrmion string have a rich
physics in the modulated exchange-coupled system.


title:
The Inefficiency of Language Models in Scholarly Retrieval: An
  Experimental Walk-through

abstract:
  Language models are increasingly becoming popular in AI-powered scientific IR
systems. This paper evaluates popular scientific language models in handling
(i) short-query texts and (ii) textual neighbors. Our experiments showcase the
inability to retrieve relevant documents for a short-query text even under the
most relaxed conditions. Additionally, we leverage textual neighbors, generated
by small perturbations to the original text, to demonstrate that not all
perturbations lead to close neighbors in the embedding space. Further, an
exhaustive categorization yields several classes of orthographically and
semantically related, partially related, and completely unrelated neighbors.
Retrieval performance turns out to be more influenced by the surface form
rather than the semantics of the text.


title: High-mass pulsators in eclipsing binaries observed using TESS

abstract:
  Pulsations and binarity are both common features of massive stars. The study
of pulsating massive stars in eclipsing binary systems hold great potential for
constraining stellar structure and evolution theory. However, prior to the
all-sky Transiting Exoplanet Survey Satellite (TESS) mission, few such systems
had been discovered or studied in detail. We have inspected the TESS light
curves of a large number of eclipsing binaries known to contain high-mass
stars, and compiled a list of 18 objects which show intrinsic variability. The
light curves were modelled both to determine the physical properties of the
systems, and to remove the effects of binarity in order to leave residual light
curves suitable for asteroseismic analysis. Precise mass and radius
measurements were obtained for delta Cir, CC Cas, SZ Cam, V436 Per and V539
Ara. We searched the residual light curves for pulsation signatures and, within
our sample of 18 objects, we find six definite and eight possible cases of beta
Cephei pulsation, seven cases of stochastic low-frequency (SLF) variability,
and eight instances of possible slowly pulsating B (SPB) star pulsation. The
large number of pulsating eclipsing systems we have identified makes
asteroseismology of high-mass stars in eclipsing binaries a feasible avenue to
constrain the interior physics of a large sample of massive stars for the first
time.


title: Face segmentation: A comparison between visible and thermal images

abstract:
  Face segmentation is a first step for face biometric systems. In this paper
we present a face segmentation algorithm for thermographic images. This
algorithm is compared with the classic Viola and Jones algorithm used for
visible images. Experimental results reveal that, when segmenting a
multispectral (visible and thermal) face database, the proposed algorithm is
more than 10 times faster, while the accuracy of face segmentation in thermal
images is higher than in case of Viola-Jones


title: On factorising twists in AdS_3 and AdS_2

abstract:
  In this paper we study factorising twists of the massless AdS_3 and AdS_2
integrable R-matrices, and explore the programme of analysis of form factors
which Maillet et al developed for ordinary spin-chains. We derive the
factorising twists from the universal R-matrix of the gl(1|1) Yangian double,
and discuss the RTT relations for the two- and three-site monodromy matrix. We
show how the twist can be used to compute a simple scalar product. We then
implement our construction in the language of free fermions. Finally, we show
how to obtain the massless AdS_2 quantum R-matrix from the Yangian universal
R-matrix, and compute a peculiar factorising twist for this case as well.


title:
Multiclass classification using quantum convolutional neural networks
  with hybrid quantum-classical learning

abstract:
  Multiclass classification is of great interest for various machine learning
applications, for example, it is a common task in computer vision, where one
needs to categorize an image into three or more classes. Here we propose a
quantum machine learning approach based on quantum convolutional neural
networks for solving this problem. The corresponding learning procedure is
implemented via TensorFlowQuantum as a hybrid quantum-classical (variational)
model, where quantum output results are fed to softmax cost function with
subsequent minimization of it via optimization of parameters of quantum
circuit. Our conceptional improvements include a new model for quantum
perceptron and optimized structure of the quantum circuit. We use the proposed
approach to demonstrate the 4-class classification for the case of the MNIST
dataset using eight qubits for data encoding and four acnilla qubits. Our
results demonstrate comparable accuracy of our solution with classical
convolutional neural networks with comparable numbers of trainable parameters.
We expect that our finding provide a new step towards the use of quantum
machine learning for solving practically relevant problems in the NISQ era and
beyond.


title: Geographic Diversity in Public Code Contributions

abstract:
  We conduct an exploratory, large-scale, longitudinal study of 50 years of
commits to publicly available version control system repositories, in order to
characterize the geographic diversity of contributors to public code and its
evolution over time. We analyze in total 2.2 billion commits collected by
Software Heritage from 160 million projects and authored by 43 million authors
during the 1971-2021 time period. We geolocate developers to 12 world regions
derived from the United Nation geoscheme, using as signals email top-level
domains, author names compared with names distributions around the world, and
UTC offsets mined from commit metadata.We find evidence of the early dominance
of North America in open source software, later joined by Europe. After that
period, the geographic diversity in public code has been constantly increasing.
We also identify relevant historical shifts related to the UNIX wars, the
increase of coding literacy in Central and South Asia, and broader phenomena
like colonialism and people movement across countries (immigration/emigration).


title:
A Principle-based Ethical Assurance Argument for AI and Autonomous
  Systems

abstract:
  An assurance case presents a clear and defensible argument, supported by
evidence, that a system will operate as intended in a particular context.
Assurance cases often inform third party certification of a system. One
emerging proposal within the trustworthy AI and Autonomous Systems (AS)
research community is to extend and apply the assurance case methodology to
achieve justified confidence that a system will be ethically acceptable when
used in a particular context. In this paper, we develop and further advance
this proposal, in order to bring the idea of ethical assurance cases to life.
First, we discuss the assurance case methodology and the Goal Structuring
Notation (GSN), which is a graphical notation that is widely used to record and
present assurance cases. Second, we describe four core ethical principles to
guide the design and deployment of AI/AS: justice; beneficence;
non-maleficence; and respect for personal autonomy. Third, we bring these two
components together and structure an ethical assurance argument pattern - a
reusable template for ethical assurance cases - on the basis of the four
ethical principles. We call this a Principle-based Ethical Assurance Argument
pattern. Throughout, we connect stages of the argument to examples of AI/AS
applications and contexts. This helps to show the initial plausibility of the
proposed methodology.


title: mc-BEiT: Multi-choice Discretization for Image BERT Pre-training

abstract:
  Image BERT pre-training with masked image modeling (MIM) becomes a popular
practice to cope with self-supervised representation learning. A seminal work,
BEiT, casts MIM as a classification task with a visual vocabulary, tokenizing
the continuous visual signals into discrete vision tokens using a pre-learned
dVAE. Despite a feasible solution, the improper discretization hinders further
improvements of image pre-training. Since image discretization has no
ground-truth answers, we believe that the masked patch should not be assigned
with a unique token id even if a better tokenizer can be obtained. In this
work, we introduce an improved BERT-style image pre-training method, namely
mc-BEiT, which performs MIM proxy tasks towards eased and refined multi-choice
training objectives. Specifically, the multi-choice supervision for the masked
image patches is formed by the soft probability vectors of the discrete token
ids, which are predicted by the off-the-shelf image tokenizer and further
refined by high-level inter-patch perceptions resorting to the observation that
similar patches should share their choices. Extensive experiments on
classification, segmentation, and detection tasks demonstrate the superiority
of our method, e.g., the pre-trained ViT-B achieves 84.1% top-1 fine-tuning
accuracy on ImageNet-1K classification, 50.8% mIOU on ADE20K semantic
segmentation, 51.2% AP^b and 44.3% AP^m of object detection and instance
segmentation on COCO, outperforming the competitive counterparts.


title:
Schur-Nevanlinna parameters, Riesz bases, and compact Hankel operators
  on the model space

abstract:
  We study Riesz bases/Riesz sequences of reproducing kernels in the model
space $K_\theta$ in connection with the corresponding Schur--Nevanlinna
parameters and functions. In particular, we construct inner functions with
given Schur--Nevanlinna parameters at a given sequence $\Lambda$ such that the
corresponding systems of projections of reproducing kernels in the model space
are complete/non complete. Furthermore, we give a compactness criterion for
Hankel operators with symbol $\theta\overline B$, where $\theta$ is an inner
function and $B$ is an interpolating Blaschke product and use this criterion to
describe Riesz bases $\mathcal K_{\Lambda, \theta}$, with $\lim_{\lambda \in
\Lambda, |\lambda| \to 1} \theta(\lambda) =0$.


title:
Photon-pair blockade in a Josephson-photonics circuit with two
  nondegenerate microwave resonators

abstract:
  We propose to generate photon-pair blockade in a Josephson-photonics circuit
that consists of a dc voltage-biased Josephson junction in series with a
superconducting charge qubit and two nondegenerate microwave resonators. The
two-level charge qubit is utilized to create anticorrelations of the charge
transport; that is, the simultaneous Cooper pair tunneling events are
inhibited. When the Josephson frequency matches the sum of resonance
frequencies of the charge qubit and the two resonators, we demonstrate that the
two resonators can release their energies in the form of antibunched pairs of
two strongly correlated photons. The present work provides a practical way for
producing a bright microwave source of antibunched photon pairs, with potential
applications ranging from spectroscopy and metrology to quantum information
processing.


title: A detailed study on novel metamaterial absorber at WR3 band

abstract:
  Frequency band of 30 to 300 GHz, which is termed as mm wave band offers 100
times bandwidth as compared to sub 6 GHz band. In this study, we propose a
novel design which acts as a near unity absorber at 258 GHz or 0.258 THz in Y
polarization and 292 GHz or 0.292 THz in X polarization. The study also
includes effect of change in parameters on resonance frequency in both X and Y
polarization which allows us to use same design with modified parameters to get
near unity absorptivity in frequency of 205 to 345 GHz. Further, we also
discuss on higher frequency mode which particularly appears in Y polarization
and on changing the inner square dimension of the design.


title: A Style-aware Discriminator for Controllable Image Translation

abstract:
  Current image-to-image translations do not control the output domain beyond
the classes used during training, nor do they interpolate between different
domains well, leading to implausible results. This limitation largely arises
because labels do not consider the semantic distance. To mitigate such
problems, we propose a style-aware discriminator that acts as a critic as well
as a style encoder to provide conditions. The style-aware discriminator learns
a controllable style space using prototype-based self-supervised learning and
simultaneously guides the generator. Experiments on multiple datasets verify
that the proposed model outperforms current state-of-the-art image-to-image
translation methods. In contrast with current methods, the proposed approach
supports various applications, including style interpolation, content
transplantation, and local image translation.


title: Variational methods and deep Ritz method for active elastic solids

abstract:
  Variational methods have been widely used in soft matter physics for both
static and dynamic problems. These methods are mostly based on two variational
principles: the variational principle of minimum free energy (MFEVP) and
Onsager's variational principle (OVP). Our interests lie in the applications of
these variational methods to active matter physics. In our former work [Soft
Matter, 2021, 17, 3634], we have explored the applications of OVP-based
variational methods for the modeling of active matter dynamics. In the present
work, we explore variational (or energy) methods that are based on MFEVP for
static problems in active elastic solids. We show that MFEVP can be used not
only to derive equilibrium equations, but also to develop approximate solution
methods, such as Ritz method, for active solid statics. Moreover, the power of
Ritz-type method can be further enhanced using deep learning methods if we use
deep neural networks to construct the trial solutions of the variational
problems. We then apply these variational methods and the deep Ritz method to
study the spontaneous bending and contraction of a thin active circular plate
that is induced by internal asymmetric active contraction. The circular plate
is found to be bent towards its bending side. The study of such a simple toy
system gives implications for understanding the morphogenesis of solid-like
confluent cell monolayers. In addition, we introduce a so-called activogravity
length to characterize the importance of gravitational forces relative to
internal active contraction in driving the bending of the active plate. When
the lateral plate dimension is larger than the activogravity length (about 100
micron), gravitational forces become important. Such gravitaxis behaviors at
multicellular scales may play significant roles in the morphogenesis and in the
up-down symmetry broken during tissue development.


title: Spoofing-Aware Speaker Verification by Multi-Level Fusion

abstract:
  Recently, many novel techniques have been introduced to deal with spoofing
attacks, and achieve promising countermeasure (CM) performances. However, these
works only take the stand-alone CM models into account. Nowadays, a spoofing
aware speaker verification (SASV) challenge which aims to facilitate the
research of integrated CM and ASV models, arguing that jointly optimizing CM
and ASV models will lead to better performance, is taking place. In this paper,
we propose a novel multi-model and multi-level fusion strategy to tackle the
SASV task. Compared with purely scoring fusion and embedding fusion methods,
this framework first utilizes embeddings from CM models, propagating CM
embeddings into a CM block to obtain a CM score. In the second-level fusion,
the CM score and ASV scores directly from ASV systems will be concatenated into
a prediction block for the final decision. As a result, the best single fusion
system has achieved the SASV-EER of 0.97% on the evaluation set. Then by
ensembling the top-5 fusion systems, the final SASV-EER reached 0.89%.


title: 2-colored Rogers-Ramanujan partition identities

abstract:
  In this paper, we combined two types of partitions and introduced 2-colored
Rogers-Ramanujan partitions. By finding some functional equations and using a
constructive method, some identities have been found. Some Overpartition
identities coincide with our findings. A correspondence between colored
partitions and overpartitions is provided.


title: VoiceMe: Personalized voice generation in TTS

abstract:
  Novel text-to-speech systems can generate entirely new voices that were not
seen during training. However, it remains a difficult task to efficiently
create personalized voices from a high dimensional speaker space. In this work,
we use speaker embeddings from a state-of-the-art speaker verification model
(SpeakerNet) trained on thousands of speakers to condition a TTS model. We
employ a human sampling paradigm to explore this speaker latent space. We show
that users can create voices that fit well to photos of faces, art portraits,
and cartoons. We recruit online participants to collectively manipulate the
voice of a speaking face. We show that (1) a separate group of human raters
confirms that the created voices match the faces, (2) speaker gender apparent
from the face is well-recovered in the voice, and (3) people are consistently
moving towards the real voice prototype for the given face. Our results
demonstrate that this technology can be applied in a wide number of
applications including character voice development in audiobooks and games,
personalized speech assistants, and individual voices for people with speech
impairment.


title: SepViT: Separable Vision Transformer

abstract:
  Vision Transformers have witnessed prevailing success in a series of vision
tasks. However, they often require enormous amount of computations to achieve
high performance, which is burdensome to deploy on resource-constrained
devices. To address these issues, we draw lessons from depthwise separable
convolution and imitate its ideology to design the Separable Vision
Transformer, abbreviated as SepViT. SepViT helps to carry out the information
interaction within and among the windows via a depthwise separable
self-attention. The novel window token embedding and grouped self-attention are
employed to model the attention relationship among windows with negligible
computational cost and capture a long-range visual dependencies of multiple
windows, respectively. Extensive experiments on various benchmark tasks
demonstrate SepViT can achieve state-of-the-art results in terms of trade-off
between accuracy and latency. Among them, SepViT achieves 84.0% top-1 accuracy
on ImageNet-1K classification while decreasing the latency by 40%, compared to
the ones with similar accuracy (e.g., CSWin, PVTV2). As for the downstream
vision tasks, SepViT with fewer FLOPs can achieve 50.4% mIoU on ADE20K semantic
segmentation task, 47.5 AP on the RetinaNet-based COCO detection task, 48.7 box
AP and 43.9 mask AP on Mask R-CNN-based COCO detection and segmentation tasks.


title:
Alignment-Uniformity aware Representation Learning for Zero-shot Video
  Classification

abstract:
  Most methods tackle zero-shot video classification by aligning
visual-semantic representations within seen classes, which limits
generalization to unseen classes. To enhance model generalizability, this paper
presents an end-to-end framework that preserves alignment and uniformity
properties for representations on both seen and unseen classes. Specifically,
we formulate a supervised contrastive loss to simultaneously align
visual-semantic features (i.e., alignment) and encourage the learned features
to distribute uniformly (i.e., uniformity). Unlike existing methods that only
consider the alignment, we propose uniformity to preserve maximal-info of
existing features, which improves the probability that unobserved features fall
around observed data. Further, we synthesize features of unseen classes by
proposing a class generator that interpolates and extrapolates the features of
seen classes. Besides, we introduce two metrics, closeness and dispersion, to
quantify the two properties and serve as new measurements of model
generalizability. Experiments show that our method significantly outperforms
SoTA by relative improvements of 28.1% on UCF101 and 27.0% on HMDB51. Code is
available.


title:
Modified Poisson-Boltzmann equations and macroscopic forces in
  inhomogeneous ionic fluids

abstract:
  We propose a field-theoretical approach based on the thermodynamic
perturbation theory and within it derive a grand thermodynamic potential of the
inhomogeneous ionic fluid as a functional of electrostatic potential for an
arbitrary reference fluid system. We obtain a modified Poisson-Boltzmann
equation as the Euler-Lagrange equation for the obtained functional. Applying
Noether's theorem to this functional, we derive a general mean-field expression
for the stress tensor consistent with the respective modified Poisson-Boltzmann
equation. We derive a general expression for the macroscopic force acting on
the dielectric or conductive body immersed in an ionic fluid. In particular, we
derive a general mean-field expression for the disjoining pressure of an ionic
fluid in a slit pore. We apply the developed formalism to describe three ionic
fluid models of practical importance: nonpolarizable models (including the
well-known Poisson-Boltzmann and Poisson-Fermi equations), polarizable models
(ions carry nonzero permanent dipole or static polarizability), and models of
ion-dipole mixtures (including the well-known Poisson-Boltzmann-Langevin
equation). For these models, we obtain modified Poisson-Boltzmann equations and
respective stress tensors, which could be valuable for different applications,
where it is necessary to estimate the macroscopic forces acting on the
dielectric or conductive bodies (electrodes, colloids, membranes, {\it etc}.)
together with the local electrostatic potential (field) and ionic
concentrations.


title: Category Guided Attention Network for Brain Tumor Segmentation in MRI

abstract:
  Objective: Magnetic resonance imaging (MRI) has been widely used for the
analysis and diagnosis of brain diseases. Accurate and automatic brain tumor
segmentation is of paramount importance for radiation treatment. However, low
tissue contrast in tumor regions makes it a challenging task.Approach: We
propose a novel segmentation network named Category Guided Attention U-Net (CGA
U-Net). In this model, we design a Supervised Attention Module (SAM) based on
the attention mechanism, which can capture more accurate and stable long-range
dependency in feature maps without introducing much computational cost.
Moreover, we propose an intra-class update approach to reconstruct feature maps
by aggregating pixels of the same category. Main results: Experimental results
on the BraTS 2019 datasets show that the proposed method outperformers the
state-of-the-art algorithms in both segmentation performance and computational
complexity. Significance: The CGA U-Net can effectively capture the global
semantic information in the MRI image by using the SAM module, while
significantly reducing the computational cost. Code is available at
https://github.com/delugewalker/CGA-U-Net.


title:
Doping effect and Li-ion conduction mechanism of ALi6XO6 (A = K or Rb,
  and X = pentavalent): A first-principles study

abstract:
  Recent theoretical and experimental evaluations demonstrated that KLi6TaO6 is
a good Li-ion conductor. In this study, the energetics and detailed mechanism
of Li-ion migration, relevant to the point defects of KLi6TaO6, were analyzed
by performing first-principles calculations. The defect formation energy for a
Li vacancy was found to be lower than that for a substitutional defect, which
could hinder the achievement of a Li-excess condition, and the interactions
between substitutional defects and interstitial Li could raise the migration
energy barrier of interstitial Li. These results indicate the limiting factors
for achieving high Li-ion conductivity in this material. Based on the insights
gained from the investigations on KLi6TaO6, isostructural materials of the form
ALi6XO6, with various combinations of constituent elements A and X, were each
screened on the basis of high stability and low Li-ion migration energy. Twelve
structures of the form (A = K or Rb)Li6XO6 were suggested, of which X was
pentavalent. Among them, (K or Rb)Li6VO6 and (K or Rb)Li6BiO6 exhibited low
energies for the formation of substitutional defects, which was sufficient to
achieve Li-excess. Sn-doped KLi6VO6 and KLi6BiO6 were evaluated using
first-principles molecular dynamics, and their Li-ion conductivities at room
temperature were comparable with those of garnet-type Li-ion conductors.


title: Geometry of cotangent bundle of Heisenberg group

abstract:
  In this paper the classification of left-invariant Riemannian metrics, up to
the action of the automorphism group, on cotangent bundle of (2n+1)-dimensional
Heisenberg group is presented. Also, it is proved that the complex structure on
that group is unique and the corresponding pseudoK\"ahler metrics are described
and shown to be Ricci flat. It is well known that this algebra admits an
ad-invariant metric of neutral signature. Here, the uniqueness of such metric
is proved


title:
Pareto Set Learning for Neural Multi-objective Combinatorial
  Optimization

abstract:
  Multiobjective combinatorial optimization (MOCO) problems can be found in
many real-world applications. However, exactly solving these problems would be
very challenging, particularly when they are NP-hard. Many handcrafted
heuristic methods have been proposed to tackle different MOCO problems over the
past decades. In this work, we generalize the idea of neural combinatorial
optimization, and develop a learning-based approach to approximate the whole
Pareto set for a given MOCO problem without further search procedure. We
propose a single preference-conditioned model to directly generate approximate
Pareto solutions for any trade-off preference, and design an efficient
multiobjective reinforcement learning algorithm to train this model. Our
proposed method can be treated as a learning-based extension for the
widely-used decomposition-based multiobjective evolutionary algorithm (MOEA/D).
It uses a single model to accommodate all the possible preferences, whereas
other methods use a finite number of solution to approximate the Pareto set.
Experimental results show that our proposed method significantly outperforms
some other methods on the multiobjective traveling salesman problem,
multiobjective vehicle routing problem and multiobjective knapsack problem in
terms of solution quality, speed, and model efficiency.


title:
Commande hybride d' un drone convertible pour des d\'eplacements sous
  optimaux

abstract:
  Whether for their maneuverability, autonomy or ergonomics, convertible UAVs,
meaning those with the ability to take off or land vertically and fly like an
airplane, present many interests. However, their dynamics are complicated by
various phenomena such as non-linearities, aerodynamic couplings or the large
number of degrees of freedom, and it is necessary to use new control tools.
Thus, hybrid control becomes crucial, useful when making binary choices. Since
the drone is able to move in two ways, in pseudo-stationary flight and in
horizontal flight, the question of the relevance of a command with respect to
the other, according to the operational context, arises. This problem is
answered by the design of two nonlinear control laws, based on Lyapunov
functions, which allow to validate the requirements. Therefore, depending on
the configuration, the hybrid mechanism will have to select the appropriate
flight mode. Finally, with the objective of a flight in real conditions, an
implementation has been initiated.


title:
Federated Learning-Based Localization with Heterogeneous Fingerprint
  Database

abstract:
  Fingerprint-based localization plays an important role in indoor
location-based services, where the position information is usually collected in
distributed clients and gathered in a centralized server. However, the
overloaded transmission as well as the potential risk of divulging private
information burdens the application.Owning the ability to address these
challenges, federated learning (FL)-based fingerprinting localization comes
into people's sights, which aims to train a global model while keeping raw data
locally. However, in distributed machine learning (ML) scenarios, the
unavoidable database heterogeneity usually degrades the performance of existing
FL-based localization algorithm (FedLoc). In this paper, we first characterize
the database heterogeneity with a computable metric, i.e., the area of convex
hull, and verify it by experimental results. Then, a novel heterogeneous
FL-based localization algorithm with the area of convex hull-based aggregation
(FedLoc-AC) is proposed. Extensive experimental results, including real-word
cases are conducted. We can conclude that the proposed FedLoc-AC can achieve an
obvious prediction gain compared to FedLoc in heterogeneous scenarios and has
almost the same prediction error with it in homogeneous scenarios. Moreover,
the extension of FedLoc-AC in multi-floor cases is proposed and verified.


title:
Sharp local well-posedness of the two-dimensional periodic nonlinear
  Schr\"odinger equation with a quadratic nonlinearity $|u|^2$

abstract:
  We study the nonlinear Schr\"odinger equation (NLS) with the quadratic
nonlinearity $|u|^2$, posed on the two-dimensional torus $\mathbb{T}^2$. While
the relevant $L^3$-Strichartz estimate is known only with a derivative loss, we
prove local well-posedness of the quadratic NLS in $L^2(\mathbb{T}^2)$, thus
resolving an open problem of thirty years since Bourgain (1993). In view of
ill-posedness in negative Sobolev spaces, this result is sharp. We establish a
crucial bilinear estimate by separately studying the non-resonant and nearly
resonant cases. As a corollary, we obtain a tri-linear version of the
$L^3$-Strichartz estimate without any derivative loss.


title: ReIL: A Framework for Reinforced Intervention-based Imitation Learning

abstract:
  Compared to traditional imitation learning methods such as DAgger and DART,
intervention-based imitation offers a more convenient and sample efficient data
collection process to users. In this paper, we introduce Reinforced
Intervention-based Learning (ReIL), a framework consisting of a general
intervention-based learning algorithm and a multi-task imitation learning model
aimed at enabling non-expert users to train agents in real environments with
little supervision or fine tuning. ReIL achieves this with an algorithm that
combines the advantages of imitation learning and reinforcement learning and a
model capable of concurrently processing demonstrations, past experience, and
current observations. Experimental results from real world mobile robot
navigation challenges indicate that ReIL learns rapidly from sparse supervisor
corrections without suffering deterioration in performance that is
characteristic of supervised learning-based methods such as HG-Dagger and IWR.
The results also demonstrate that in contrast to other intervention-based
methods such as IARL and EGPO, ReIL can utilize an arbitrary reward function
for training without any additional heuristics.


title:
Adaptive state observer for linear time-varying system with partially
  unknown parameters of the state matrix and the input vector

abstract:
  The article deals with the problem of synthesis of an adaptive observer of
state variables of a linear time-varying SISO dynamic system. It is assumed
that the control signal and the output variable are measurable. It is assumed
that the state matrix of the plant contains known variables and unknown
constant parameters, and the control matrix (vector) is unknown. The synthesis
of the observer is based on the GPEBO method (generalized parameter based
observer) proposed in $[1]$. Synthesis of adaptive provides for preliminary
parametrization of the initial system and its transformation to a linear
regression model with further identification of unknown parameters. To identify
unknown constant parameters a classical estimation algorithm was used (the
least squares method with a forgetting factor). This approach has proven itself
well in cases where the known regressor is frequency poor (that is, the
spectral composition of the regressor contains less than $r/2$ harmonics, where
r is the number of unknown parameters) or does not satisfy the so-called
undamped excitation condition. To illustrate the efficiency of the proposed
method an example is presented in the article. A time-varying second-order
object with four unknown parameters was considered. Parameterization of the
initial dynamic model was performed and a linear static regression containing
six unknowns parameters was obtained (including the vector of unknown initial
conditions of system state variables). An adaptive observer was synthesized and
the results of computer modeling illustrating the achievement of a given goal
were presented. The main difference from the results published earlier in $[2]$
is the new assumption that the linear a time-varing system contains not only
unknown parameters in the state matrix, but also the matrix (vector) for
control contains unknown constant coefficients.


title: Efficient Hybrid Network: Inducting Scattering Features

abstract:
  Recent work showed that hybrid networks, which combine predefined and learnt
filters within a single architecture, are more amenable to theoretical analysis
and less prone to overfitting in data-limited scenarios. However, their
performance has yet to prove competitive against the conventional counterparts
when sufficient amounts of training data are available. In an attempt to
address this core limitation of current hybrid networks, we introduce an
Efficient Hybrid Network (E-HybridNet). We show that it is the first scattering
based approach that consistently outperforms its conventional counterparts on a
diverse range of datasets. It is achieved with a novel inductive architecture
that embeds scattering features into the network flow using Hybrid Fusion
Blocks. We also demonstrate that the proposed design inherits the key property
of prior hybrid networks -- an effective generalisation in data-limited
scenarios. Our approach successfully combines the best of the two worlds:
flexibility and power of learnt features and stability and predictability of
scattering representations.