title:
Effects of the COVID-19 pandemic in higher education: a particular case
  from the perspective of complex systems

abstract:
  The COVID-19 pandemic abruptly changed the classroom context, understood as
the meeting space between teachers and students where a fundamental part of the
construction of new knowledge takes place. This presented enormous challenges
for all actors in the educational process, who had to overcome multiple
difficulties and adapt to a new daily life in which the incorporation of new
strategies and tools was essential. In this work we study the knowledge
acquisition process in two different contexts: face-to-face (before the onset
of the pandemic) and virtual (during confinement), for a particular case in
higher education in Argentina. We developed an analytical model for the
knowledge acquisition process, based on a series of surveys and information on
academic performance. We analyzed the significance of the model by means of
Artificial Neural Networks and a Multiple Linear Regression Method. We found
that the virtual context produced a decrease in motivation to learn. We also
found that the structure of the emerging contact network from peer interaction
presents very different characteristics in both contexts. Finally, we show that
in all cases, interaction with teachers is of utmost importance in the process
of acquiring knowledge.


title: Electronic structure of the highly conductive perovskite oxide SrMoO$_3$

abstract:
  We use angle-resolved photoemission to map the Fermi surface and
quasiparticle dispersion of bulk-like thin films of SrMoO$_3$ grown by pulsed
laser deposition. The electronic self-energy deduced from our data reveals weak
to moderate correlations in SrMoO$_3$, consistent with our observation of
well-defined electronic states over the entire occupied band width. We further
introduce spectral function calculations that combine dynamical mean-field
theory with an unfolding procedure of density functional calculations and
demonstrate good agreement of this approach with our experiments.


title: Hofstadter Butterfly in Graphene

abstract:
  This is a short review of the recent progresses on Hofstadter butterfly in
graphene, organized in the following. We first briefly introduce the Hofstadter
butterfly, including the theoretic toy model of Bloch electrons in the magnetic
field, the resulting Harper's equation and its solution, and realizations in
non-graphene systems. Then, we discuss the ways to realize the fractal
Hofstadter spectra in graphene, and introduce three types of graphene
superlattice structure, including graphene-hBN, twisted graphene layers,
nanofabricated graphene superlattice. In particular, details about the fractal
Hofstadter spectrum in graphene will be focused and discussed in the
graphene-hBN superlattice and the twisted bilayer graphene.


title:
aiWave: Volumetric Image Compression with 3-D Trained Affine
  Wavelet-like Transform

abstract:
  Volumetric image compression has become an urgent task to effectively
transmit and store images produced in biological research and clinical
practice. At present, the most commonly used volumetric image compression
methods are based on wavelet transform, such as JP3D. However, JP3D employs an
ideal, separable, global, and fixed wavelet basis to convert input images from
pixel domain to frequency domain, which seriously limits its performance. In
this paper, we first design a 3-D trained wavelet-like transform to enable
signal-dependent and non-separable transform. Then, an affine wavelet basis is
introduced to capture the various local correlations in different regions of
volumetric images. Furthermore, we embed the proposed wavelet-like transform to
an end-to-end compression framework called aiWave to enable an adaptive
compression scheme for various datasets. Last but not least, we introduce the
weight sharing strategies of the affine wavelet-like transform according to the
volumetric data characteristics in the axial direction to reduce the amount of
parameters. The experimental results show that: 1) when cooperating our trained
3-D affine wavelet-like transform with a simple factorized entropy module,
aiWave performs better than JP3D and is comparable in terms of encoding and
decoding complexities; 2) when adding a context module to further remove signal
redundancy, aiWave can achieve a much better performance than HEVC.


title: Towards Open Intent Detection

abstract:
  The open intent detection problem is presented in this paper, which aims to
identify known intents and detect open intent in natural language
understanding. Current methods have two core challenges. On the one hand, the
existing methods have limitations in learning robust representations to detect
the open intent without any prior knowledge. On the other hand, there lacks an
effective approach to learning the specific and compact decision boundary to
distinguish the known intents and the open intent. This paper introduces an
original pipeline framework, DA-ADB, to address these issues, which
successively learns discriminative intent features with distance-aware strategy
and appropriate decision boundaries adaptive to the feature space for open
intent detection. The proposed method first leverages distance information to
enhance the distinguishing capability of the intent representations. Then, it
obtains discriminative decision boundaries adaptive to the known intent feature
space by balancing both the empirical and open space risks. Extensive
experiments show the effectiveness of distance-aware and boundary learning
strategies. Compared with the state-of-the-art methods, our method achieves
substantial improvements on three benchmark intent datasets. It also yields
robust performance with different proportions of labeled data and known
categories.


title:
Towards Analyzing the Bias of News Recommender Systems Using Sentiment
  and Stance Detection

abstract:
  News recommender systems are used by online news providers to alleviate
information overload and to provide personalized content to users. However,
algorithmic news curation has been hypothesized to create filter bubbles and to
intensify users' selective exposure, potentially increasing their vulnerability
to polarized opinions and fake news. In this paper, we show how information on
news items' stance and sentiment can be utilized to analyze and quantify the
extent to which recommender systems suffer from biases. To that end, we have
annotated a German news corpus on the topic of migration using stance detection
and sentiment analysis. In an experimental evaluation with four different
recommender systems, our results show a slight tendency of all four models for
recommending articles with negative sentiments and stances against the topic of
refugees and migration. Moreover, we observed a positive correlation between
the sentiment and stance bias of the text-based recommenders and the
preexisting user bias, which indicates that these systems amplify users'
opinions and decrease the diversity of recommended news. The knowledge-aware
model appears to be the least prone to such biases, at the cost of predictive
accuracy.


title: WiCV 2021: The Eighth Women In Computer Vision Workshop

abstract:
  In this paper, we present the details of Women in Computer Vision Workshop -
WiCV 2021, organized alongside the virtual CVPR 2021. It provides a voice to a
minority (female) group in the computer vision community and focuses on
increasing the visibility of these researchers, both in academia and industry.
WiCV believes that such an event can play an important role in lowering the
gender imbalance in the field of computer vision. WiCV is organized each year
where it provides a)~opportunity for collaboration between researchers from
minority groups, b)~mentorship to female junior researchers, c)~financial
support to presenters to overcome monetary burden and d)~large and diverse
choice of role models, who can serve as examples to younger researchers at the
beginning of their careers. In this paper, we present a report on the workshop
program, trends over the past years, a summary of statistics regarding
presenters, attendees, and sponsorship for the WiCV 2021 workshop.


title:
Absence of Walker breakdown in the dynamics of chiral Neel domain walls
  driven by in-plane strain gradients

abstract:
  We investigate theoretically the motion of chiral N\'eel domain walls in
perpendicularly magnetized systems driven by in-plane strain gradients. We show
that such strain drives domain walls efficiently towards increasing tensile
(compressive) strain for positive (negative) magnetostrictive materials. During
their motion a local damping torque that opposes the precessional torque due to
the strain gradient arises. This torque prevents the onset of turbulent
dynamics, and steady domain wall motion with constant velocity is
asymptotically reached for any arbitrary large strain gradient. Withal,
velocities in the range of 500 m/s can be obtained using voltage-induced strain
under realistic conditions.


title:
The Development of a Chemical Kinetic Mechanism for Combustion in
  Supercritical Carbon Dioxide

abstract:
  Direct fired supercritical CO2 (sCO2) power cycles allow for the combustion
of gaseous fuels under oxyfuel conditions with inherent carbon capture. As the
CO2 is captured intrinsically, the efficiency penalty of capture on the overall
plant is small, meaning that power plants achieve a similar efficiency to
traditional fossil fuel power plants without carbon capture and storage.
However, at high pressures and in large dilutions of CO2, combustion mechanisms
are poorly understood. Therefore, in this paper sensitivity and quantitative
analysis of four established chemical kinetic mechanisms have been employed to
determine the most important reactions and the best performing mechanisms over
a range of different conditions. CH3O2 chemistry was identified as a pivotal
mechanism component for modelling methane combustion above 200 atm. The
University of Sheffield (UoS) sCO2 mechanism created in the present work better
models the ignition delay time (IDT) of high-pressure combustion in a large
dilution of CO2. Quantitative analysis showed that the UoS sCO2 mechanism was
the best fit to the greatest number of IDT datasets and had the lowest average
absolute error value, thus indicating a superior performance compared to the
four existing chemical kinetic mechanisms, well-validated for lower pressure
conditions.


title: Four-point semidefinite bound for equiangular lines

abstract:
  A set of lines in $\mathbb{R}^d$ passing through the origin is called
equiangular if any two lines in the set form the same angle. We proved an
alternative version of the three-point semidefinite constraints developed by
Bachoc and Vallentin, and the multi-point semidefinite constraints developed by
Musin for spherical codes. The alternative semidefinite constraints are simpler
when the concerned object is a spherical $s$-distance set. Using the
alternative four-point semidefinite constraints, we found the four-point
semidefinite bound for equiangular lines. This result improves the upper bounds
for infinitely many dimensions $d$ with prescribed angles. As a corollary of
the bound, we proved the uniqueness of the maximum construction of equiangular
lines in $\mathbb{R}^d$ for $7 \leq d \leq 14$ with inner product $\alpha =
1/3$, and for $23 \leq d \leq 64$ with $\alpha = 1/5$.


title:
Quality of service based radar resource management for interference
  mitigation

abstract:
  An intelligent radar resource management is an essential building block of
any modern radar system. The quality of service based resource allocation model
(Q-RAM) provides a framework for profound and quantifiable decision making but
lacks the flexibility necessary for optimal mitigation strategies in the
presence of interference. We define an extension of the Q-RAM based radar
resource management framework with an intelligent interference handling
capability using various mitigation methods. The approach incorporates virtual
time resources and alternative task configurations to compute near-optimal
solutions in the presence of interference. The provided experimental results
demonstrate a significant improvement over traditional strategies.


title:
Analysing Ultra-Wide Band Positioning for Geofencing in a Safety
  Assurance Context

abstract:
  There is a desire to move towards more flexible and automated factories. To
enable this, we need to assure the safety of these dynamic factories. This
safety assurance must be achieved in a manner that does not unnecessarily
constrain the systems and thus negate the benefits of flexibility and
automation. We previously developed a modular safety assurance approach, using
safety contracts, as a way to achieve this. In this case study we show how this
approach can be applied to Autonomous Guided Vehicles (AGV) operating as part
of a dynamic factory and why it is necessary. We empirically evaluate
commercial, indoor fog/edge localisation technology to provide geofencing for
hazardous areas in a laboratory. The experiments determine how factors such as
AGV speeds, tag transmission timings, control software and AGV capabilities
affect the ability of the AGV to stop outside the hazardous areas. We describe
how this approach could be used to create a safety case for the AGV operation.


title: Combining Multiple Testing with Multivariate Singular Spectrum Analysis

abstract:
  Appropriate preprocessing is a fundamental prerequisite for analyzing a noisy
dataset. The purpose of this paper is to apply a nonparametric preprocessing
method, called Singular Spectrum Analysis (SSA), to a variety of datasets which
are subsequently analyzed by means of multiple statistical hypothesis tests.
SSA is a nonparametric preprocessing method which has recently been utilized in
the context of many life science problems. In the present work, SSA is compared
with three other state-of-the-art preprocessing methods in terms of goodness of
denoising and in terms of the statistical power of the subsequent multiple
test. These other methods are either parametric or nonparametric. Our findings
demonstrate that (multivariate) SSA can be taken into account as a promising
method to reduce noise, to extract the main signal from noisy data, and to
detect statistically significant signal components.


title: Homogenization of one-dimensional layered and graded structures

abstract:
  The homogenization of one-dimensional acoustic or elastic structures of
finite extent is considered. A new homogenization method based on transfer
matrices is derived. The new homogenization method may account for variable
cross sectional area and for Willis coupling, which couples the stress-strain
and momentum-velocity constitutive relations. The homogenization method is then
demonstrated by considering acoustic waves normally incident upon a
rigidly-backed double-layered wall and plane waves propagating in a duct with a
section of exponentially-growing cross-sectional area.


title:
Nonlinear Model Order Reduction using Diffeomorphic Transformations of a
  Space-Time Domain

abstract:
  In many applications, for instance when describing dynamics of fluids or
gases, hyperbolic conservation laws arise naturally in the modeling of
conserved quantities of a system, like mass or energy. These types of equations
exhibit highly nonlinear behaviors like shock formation or shock interaction.
In the case of parametrized hyperbolic equations, where, for instance, varying
transport velocities are considered, these nonlinearities and strong transport
effects result in a highly nonlinear solution manifold. This solution manifold
cannot be approximated properly by linear subspaces. To this end, nonlinear
approaches for model order reduction of hyperbolic conservation laws are
required. We propose a new method for nonlinear model order reduction that is
especially well-suited for hyperbolic equations with discontinuous solutions.
The approach is based on a space-time discretization and employs diffeomorphic
transformations of the underlying space-time domain to align the
discontinuities. To derive a reduced model for the diffeomorphisms, the Lie
group structure of the diffeomorphism group is used to associate
diffeomorphisms with corresponding velocity fields via the exponential map. In
the linear space of velocity fields, standard model order reduction techniques,
such as proper orthogonal decomposition, can be applied to extract a reduced
subspace. For a parametrized Burgers' equation with two merging shocks,
numerical experiments show the potential of the approach.


title: XMM-Newton study of the Sculptor dwarf spheroidal galaxy

abstract:
  We analysed observations with XMM-Newton in the field of the Sculptor dwarf
spheroidal galaxy (dSph). The aim of the study was the classification of X-ray
binaries and accreting white dwarfs, which belong to the Sculptor dSph. Using
different methods of X-ray timing and spectral analyses, together with an
extensive multi-wavelength study of the optical and infrared counterparts of
the X-ray sources, we classified the sources detected with XMM-Newton in the
field of Sculptor dSph. The long term variability of the sources has been
studied over two \xmm\, observations. None of the members of Sculptor dSph show
a significant long-term variability over these two observations. We also
searched for periodicity and pulsation using the Lomb-Scargle and Rayleigh's
Z_n^2 techniques. No signal of pulsation or periodicity have been found for the
X-ray sources. The results show the presence of a noticeable number of
background X-ray sources in the field of this galaxy. We classified 43 sources
as active galactic nuclei (AGNs), galaxies, and galaxy candidates. Three
Galactic foreground stars have been identified in the field of Sculptor dSph,
and one of them is an M-dwarf candidate. Moreover, we classified four symbiotic
star candidates and three quiescent low-mass X-ray binary candidates in
Sculptor dSph. The luminosity of these X-ray sources is 10^{33-35} erg/s.


title:
MLRM: A Multiple Linear Regression based Model for Average Temperature
  Prediction of A Day

abstract:
  Weather is a phenomenon that affects everything and everyone around us on a
daily basis. Weather prediction has been an important point of study for
decades as researchers have tried to predict the weather and climatic changes
using traditional meteorological techniques. With the advent of modern
technologies and computing power, we can do so with the help of machine
learning techniques. We aim to predict the weather of an area using past
meteorological data and features using the Multiple Linear Regression Model.
The performance of the model is evaluated and a conclusion is drawn. The model
is successfully able to predict the average temperature of a day with an error
of 2.8 degrees Celsius.


title: Efficient and Robust Semantic Mapping for Indoor Environments

abstract:
  A key proficiency an autonomous mobile robot must have to perform high-level
tasks is a strong understanding of its environment. This involves information
about what types of objects are present, where they are, what their spatial
extend is, and how they can be reached, i.e., information about free space is
also crucial. Semantic maps are a powerful instrument providing such
information. However, applying semantic segmentation and building 3D maps with
high spatial resolution is challenging given limited resources on mobile
robots. In this paper, we incorporate semantic information into efficient
occupancy normal distribution transform (NDT) maps to enable real-time semantic
mapping on mobile robots. On the publicly available dataset Hypersim, we show
that, due to their sub-voxel accuracy, semantic NDT maps are superior to other
approaches. We compare them to the recent state-of-the-art approach based on
voxels and semantic Bayesian spatial kernel inference~(S-BKI) and to an
optimized version of it derived in this paper. The proposed semantic NDT maps
can represent semantics to the same level of detail, while mapping is 2.7 to
17.5 times faster. For the same grid resolution, they perform significantly
better, while mapping is up to more than 5 times faster. Finally, we prove the
real-world applicability of semantic NDT maps with qualitative results in a
domestic application.


title: Some patterned matrices with independent entries

abstract:
  Patterned random matrices such as the reverse circulant, the symmetric
circulant, the Toeplitz and the Hankel matrices and their almost sure limiting
spectral distribution (LSD), have attracted much attention. Under the
assumption that the entries are taken from an i.i.d. sequence with finite
variance, the LSD are tied together by a common thread -- the $2k$th moment of
the limit equals a weighted sum over different types of pair-partitions of the
set $\{1, 2, \ldots, 2k\}$ and are universal. Some results are also known for
the sparse case. In this paper we generalise these results by relaxing
significantly the i.i.d. assumption. For our models, the limits are defined via
a larger class of partitions and are also not universal. Several existing and
new results for patterned matrices, their band and sparse versions, as well as
for matrices with continuous and discrete variance profile follow as special
cases.


title: Staking Pools on Blockchains

abstract:
  Several proof-of-stake blockchains allow for "staking pools", i.e. agents
interested in validating transactions can open a pool to which others can
delegate their stake. We develop a game-theoretic model of staking pool
formation in the presence of malicious agents who want to disrupt the
blockchain. We establish the existence and uniqueness of equilibria. Moreover,
we identify the potential and risk of staking pools. First, staking pools can
never increase current blockchain security over a system in which such pools
are not allowed. Yet, by optimally selecting the distribution of the validation
returns, honest stakeholders obtain higher returns, which may be beneficial for
future blockchain operations. Second, by choosing welfare optimal distribution
rewards, staking pools prevent from allocating large rewards to malicious
agents. Third, when pool owners can freely distribute the returns from
validation to delegators, staking pools disrupt blockchain operations, since
malicious agents attract all delegators by distributing most of the returns to
them.


title:
Inter-Calibration of Atmospheric Cherenkov Telescopes with UAV-based
  Airborne Calibration System

abstract:
  The recent advances in the flight capability of remotely piloted aerial
vehicles (here after referred to as UAVs) have afforded the astronomical
community the possibility of a new telescope calibration technique: UAV-based
calibration. Building upon a feasibility study which characterised the
potential that a UAV-based calibration system has for the future Cherenkov
Telescope Array, we created a first-generation UAV-calibration prototype and
undertook a field-campaign of inter-calibrating the sensitivity of the H.E.S.S.
telescope array with two successful calibration flights. In this paper we
report the key results of our first test campaign: firstly, by comparing the
intensity of the UAV-calibration events, as recorded by the individual HESS-I
cameras, we find that a UAV-based inter-calibration is consistent with the
standard muon inter-calibration technique at the level of \SI{5.4}{\%} and
\SI{5.8}{\%} for the two individual UAV-calibration runs. Secondly, by
comparing the position of the UAV-calibration signal on the camera focal plane,
for a variety of telescope pointing models, we were able to constrain the
pointing accuracy of the HESS-I telescopes at the tens of arc-second accuracy
level. This is consistent with the pointing accuracy derived from other
pointing calibration methods. Importantly both the inter-calibration and
pointing accuracy results were achieved with a first-generation UAV-calibration
prototype, which eludes to the potential of the technique and highlights that a
UAV-based system is a viable calibration technique for current and future
ground-based $\gamma$-ray telescope arrays.


title: Automatic Identification and Classification of Bragging in Social Media

abstract:
  Bragging is a speech act employed with the goal of constructing a favorable
self-image through positive statements about oneself. It is widespread in daily
communication and especially popular in social media, where users aim to build
a positive image of their persona directly or indirectly. In this paper, we
present the first large scale study of bragging in computational linguistics,
building on previous research in linguistics and pragmatics. To facilitate
this, we introduce a new publicly available data set of tweets annotated for
bragging and their types. We empirically evaluate different transformer-based
models injected with linguistic information in (a) binary bragging
classification, i.e., if tweets contain bragging statements or not; and (b)
multi-class bragging type prediction including not bragging. Our results show
that our models can predict bragging with macro F1 up to 72.42 and 35.95 in the
binary and multi-class classification tasks respectively. Finally, we present
an extensive linguistic and error analysis of bragging prediction to guide
future research on this topic.


title: Interactions of strings on a T-fold

abstract:
  We consider the interactions of strings on T-folds from the world-sheet point
of view which are exact in $\alpha'$. As a concrete example, we take a model
where the internal torus at the so(8) enhancement point is twisted by the
T-duality (T-folded), and compute the scattering amplitudes of a class of
massless strings. The four-point amplitudes involving both twisted and
untwisted strings are obtained in a closed form in terms of the hypergeometric
function. By their factorization, the three-point coupling of the twisted and
untwisted strings is found to be suppressed by the chiral momenta along the
internal torus, and quantized in integer powers of 1/4. The asymptotic forms of
the four-point amplitudes in high-energy limits are also obtained. Our results
rely only on general properties of the asymmetric orbifold by the T-duality
twist and of the Lie algebra lattice from the symmetry enhancement, and thus
may be extended qualitatively to more general T-folds.


title: Multiple Inputs Neural Networks for Medicare fraud Detection

abstract:
  Medicare fraud results in considerable losses for governments and insurance
companies and results in higher premiums from clients. Medicare fraud costs
around 13 billion euros in Europe and between 21 billion and 71 billion US
dollars per year in the United States. This study aims to use artificial neural
network based classifiers to predict medicare fraud. The main difficulty using
machine learning techniques in fraud detection or more generally anomaly
detection is that the data sets are highly imbalanced. To detect medicare
frauds, we propose a multiple inputs deep neural network based classifier with
a Long-short Term Memory (LSTM) autoencoder component. This architecture makes
it possible to take into account many sources of data without mixing them and
makes the classification task easier for the final model. The latent features
extracted from the LSTM autoencoder have a strong discriminating power and
separate the providers into homogeneous clusters. We use the data sets from the
Centers for Medicaid and Medicare Services (CMS) of the US federal government.
The CMS provides publicly available data that brings together all of the cost
price requests sent by American hospitals to medicare companies. Our results
show that although baseline artificial neural network give good performances,
they are outperformed by our multiple inputs neural networks. We have shown
that using a LSTM autoencoder to embed the provider behavior gives better
results and makes the classifiers more robust to class imbalance.


title:
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented
  Dialogue Generation

abstract:
  We study the interpretability issue of task-oriented dialogue systems in this
paper. Previously, most neural-based task-oriented dialogue systems employ an
implicit reasoning strategy that makes the model predictions uninterpretable to
humans. To obtain a transparent reasoning process, we introduce neuro-symbolic
to perform explicit reasoning that justifies model decisions by reasoning
chains. Since deriving reasoning chains requires multi-hop reasoning for
task-oriented dialogues, existing neuro-symbolic approaches would induce error
propagation due to the one-phase design. To overcome this, we propose a
two-phase approach that consists of a hypothesis generator and a reasoner. We
first obtain multiple hypotheses, i.e., potential operations to perform the
desired task, through the hypothesis generator. Each hypothesis is then
verified by the reasoner, and the valid one is selected to conduct the final
prediction. The whole system is trained by exploiting raw textual dialogues
without using any reasoning chain annotations. Experimental studies on two
public benchmark datasets demonstrate that the proposed approach not only
achieves better results, but also introduces an interpretable decision process.


title:
Resource Allocation in Quantum Networks for Distributed Quantum
  Computing

abstract:
  The evolution of quantum computing technologies has been advancing at a
steady pace in the recent years, and the current trend suggests that it will
become available at scale for commercial purposes in the near future. The
acceleration can be boosted by pooling compute infrastructures to either
parallelize algorithm execution or solve bigger instances that are not feasible
on a single quantum computer, which requires an underlying Quantum Internet:
the interconnection of quantum computers by quantum links and repeaters to
exchange entangled quantum bits. However, Quantum Internet research so far has
been focused on provisioning point-to-point flows only, which is suitable for
(e.g.) quantum sensing and metrology, but not for distributed quantum
computing. In this paper, after a primer on quantum computing and networking,
we investigate the requirements and objectives of smart computing on
distributed nodes from the perspective of quantum network provisioning. We then
design a resource allocation strategy that is evaluated through a comprehensive
simulation campaign, whose results highlight the key features and performance
issues, and lead the way to further investigation in this direction.


title: Flexible Amortized Variational Inference in qBOLD MRI

abstract:
  Streamlined qBOLD acquisitions enable experimentally straightforward
observations of brain oxygen metabolism. $R_2^\prime$ maps are easily inferred;
however, the Oxygen extraction fraction (OEF) and deoxygenated blood volume
(DBV) are more ambiguously determined from the data. As such, existing
inference methods tend to yield very noisy and underestimated OEF maps, while
overestimating DBV.
  This work describes a novel probabilistic machine learning approach that can
infer plausible distributions of OEF and DBV. Initially, we create a model that
produces informative voxelwise prior distribution based on synthetic training
data. Contrary to prior work, we model the joint distribution of OEF and DBV
through a scaled multivariate logit-Normal distribution, which enables the
values to be constrained within a plausible range. The prior distribution model
is used to train an efficient amortized variational Bayesian inference model.
This model learns to infer OEF and DBV by predicting real image data, with few
training data required, using the signal equations as a forward model.
  We demonstrate that our approach enables the inference of smooth OEF and DBV
maps, with a physiologically plausible distribution that can be adapted through
specification of an informative prior distribution. Other benefits include
model comparison (via the evidence lower bound) and uncertainty quantification
for identifying image artefacts. Results are demonstrated on a small study
comparing subjects undergoing hyperventilation and at rest. We illustrate that
the proposed approach allows measurement of gray matter differences in OEF and
DBV and enables voxelwise comparison between conditions, where we observe
significant increases in OEF and $R_2^\prime$ during hyperventilation.


title: Signatures of gluon saturation from structure-function measurements

abstract:
  We study experimentally observable signals for nonlinear QCD dynamics in deep
inelastic scattering (DIS) at small Bjorken variable $x$ and moderate
virtuality $Q^2$, by quantifying differences between the linear
Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) evolution and nonlinear
evolution with the Balitsky-Kovchegov (BK) equation. To remove the effect of
the parametrization freedom in the initial conditions of both equations, we
first match the predictions for the DIS structure functions $F_2$ and $F_{\rm
L}$ from both frameworks in a region in $x,Q^2$ where both frameworks should
provide an accurate description of the relevant physics. The differences in the
dynamics are then quantified by the deviations when one moves away from this
matching region. For free protons we find that the differences in $F_2$ remain
at a few-percent level, while in $F_{\rm L}$ the deviations are larger, up to
$10\,\%$ at the EIC and $40\,\%$ at the LHeC kinematics. With a heavy nucleus
the differences are up to $10\,\%$ in $F_2$, and can reach $20\,\%$ and
$60\,\%$ in $F_{\rm L}$ for the EIC and the LHeC, respectively.


title: Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology

abstract:
  Recognition of glomeruli lesions is the key for diagnosis and treatment
planning in kidney pathology; however, the coexisting glomerular structures
such as mesangial regions exacerbate the difficulties of this task. In this
paper, we introduce a scheme to recognize fine-grained glomeruli lesions from
whole slide images. First, a focal instance structural similarity loss is
proposed to drive the model to locate all types of glomeruli precisely. Then an
Uncertainty Aided Apportionment Network is designed to carry out the
fine-grained visual classification without bounding-box annotations. This
double branch-shaped structure extracts common features of the child class from
the parent class and produces the uncertainty factor for reconstituting the
training dataset. Results of slide-wise evaluation illustrate the effectiveness
of the entire scheme, with an 8-22% improvement of the mean Average Precision
compared with remarkable detection methods. The comprehensive results clearly
demonstrate the effectiveness of the proposed method.


title:
Pardon? An Overview of the Current State and Requirements of Voice User
  Interfaces for Blind and Visually Impaired Users

abstract:
  People with special needs like blind and visually impaired (BVI) people can
particularly benefit from using voice assistants providing spoken information
input and output in everyday life. However, it is crucial to understand their
needs and to include these in the development of accessible and useful
assistance systems. By conducting an online survey with 145 BVI people, this
paper revealed that common voice assistants like Apple's Siri or Amazon's Alexa
are used by a majority of BVI people and are also considered helpful. In
particular, features in the context of audio entertainment, internet access and
everyday life practical things like weather queries, time-related information
(e.g. setting an alarm clock), checking calendar entries and taking notes are
particularly often used and appreciated. The participants also indicated that
the integration of smart home devices, the optimization of existing
functionalities and voice input are important, but also potentially negative
aspects such as data privacy and data security are relevant. Therefore, it
seems particularly interesting to implement an online data processing as far as
possible. Our results contribute to this development by providing an overview
of empirically collected requirements for functions and implementation-related
aspects.


title: A Neural Network Assisted $^{171}$Yb$^{+}$ Quantum Magnetometer

abstract:
  A versatile magnetometer must deliver a readable response when exposed to
targets fields in a wide range of parameters. In this work, we experimentally
demonstrate that the combination of a $^{171}$Yb$^{+}$ atomic sensor with
adequately trained neural networks enables the characterisation of target
fields in distinct challenging scenarios. In particular, we estimate parameters
of radio frequency (RF) drivings with large shot noise, including the limiting
case of continuous data acquisition with single-shot measurements, and in
regimes where the magnetometer delivers a response that significantly departs
from the standard harmonic behavior.


title:
Modelling the formation of the first two neutron star-black hole
  mergers, GW200105 and GW200115: metallicity, chirp masses and merger remnant
  spins

abstract:
  The two neutron star-black hole mergers (GW200105 and GW200115) observed in
gravitational waves by advanced LIGO and Virgo, mark the first ever discovery
of such binaries in nature. We study these two neutron star-black hole systems
through isolated binary evolution, using a grid of population synthesis models.
Using both mass and spin observations (chirp mass, effective spin and remnant
spin) of the binaries, we probe their different possible formation channels in
different metallicity environments. Our models only support LIGO data when
assuming the black hole is non spinning. Our results show a strong preference
that GW200105 and GW200115 formed from stars with sub-solar metallicities
$Z\lesssim 0.005$. Only two metal-rich ($Z=0.02$) models are in agreement with
GW200115. We also find that chirp mass and remnant spins jointly aid in
constraining the models, whilst the effective spin parameter does not add any
further information. We also present the observable (i.e. post selection
effects) median values of spin and mass distribution from all our models, which
maybe used as a reference for future mergers. Further, we show that the remnant
spin parameter distribution exhibits distinguishable features in different
neutron star-black hole sub-populations. We find that non-spinning, first born
black holes dominate significantly the merging neutron star-black hole
population, ensuring electromagnetic counterparts to such mergers a rare
affair.


title:
APEX and NOEMA observations of H$_{2}$S in nearby luminous galaxies and
  the ULIRG Mrk~231 -- Is there a relation between dense gas properties and
  molecular outflows?

abstract:
  In order to understand the evolution and feedback of Active Galactic Nuclei
(AGN) and star formation it is important to use molecular lines as probes of
physical conditions and chemistry. We use H$_{2}$S to investigate the impact of
starburst and AGN activity on the chemistry of the molecular interstellar
medium in luminous infrared galaxies. Using the APEX single dish telescope, we
have observed the $1_{10}$--$1_{01}$ transition of ortho-H$_{2}$S at 168 GHz
towards the centres of twelve nearby luminous infrared galaxies. We have also
observed the same line towards the ultra luminous infrared galaxy (ULIRG)
Mrk~231 with the NOEMA interferometer. We have detected H$_{2}$S towards
NGC~253, NGC~1068, NGC~3256, NGC~4418, NGC~4826, NGC~4945, Circinus, M~83 and
Mrk~231. Four galaxies show elevated H$_{2}$S emission relative to HCN. We
suggest that the high line ratios are caused by elevated H$_{2}$S abundances in
the dense gas. However, we do not find any clear connection between the
H$_{2}$S/HCN line intensity ratio, and the presence (or speed) of molecular
outflows in the sample galaxies. Therefore H$_{2}$S abundances do not seem to
be globally affected by the large-scale outflows. We discuss possible
mechanisms behind the suggested H$_{2}$S abundance enhancements in NGC~4418,
Circinus, NGC~3256 and NGC~4826. These include radiative processes (for example
X-rays or cosmic-rays) or smaller scale shocks. We suggest that
$L_{\mathrm{H_{2}S}}$ serves as a tracer of the dense gas content, similar to
$L_{\mathrm{HCN}}$, and that the correlation between $L_{\mathrm{H_{2}S}}$ and
$M_{\rm outflow}$(H$_2$) implies a relation between the dense gas reservoir and
the properties and evolution of the molecular feedback. This potential link
requires further study since it holds important keys to our understanding of
how the properties of molecular outflows relate to that of their host galaxies.


title: De Finetti Theorems for the unitary dual group

abstract:
  We prove several de Finetti Theorems for the unitary dual group, also called
the Brown algebra.
  Firstly, we provide a finite de Finetti Theorem characterizing $R$-diagonal
elements with identical distribution. This is surprising, since it applies to
finite sequences in contrast to the de Finetti Theorems for classical and
quantum groups; also, it does not involve any known independence notion.
Secondly, considering infinite sequences in $W^*$-probability spaces, our
characterization boils down to operator-valued free centered circular elements,
as in the case of the unitary quantum group $U_n^+$.
  Thirdly, the above de Finetti Theorems build on dual group actions, the
natural action when viewing the Brown algebra as a dual group. However, we may
also equip the Brown algebra with a bialgebra action, which is closer to the
quantum group setting in a way. But then, we obtain a no-go de Finetti Theorem:
invariance under the bialgebra action of the Brown algebra yields zero
sequences, in $W^*$-probability spaces. On the other hand, if we drop the
assumption of faithful states in $W^*$-probability spaces, we obtain a
non-trivial half a de Finetti Theorem similar to the case of the dual group
action.


title: Finding Weakly Simple Closed Quasigeodesics on Polyhedral Spheres

abstract:
  A closed quasigeodesic on a convex polyhedron is a closed curve that is
locally straight outside of the vertices, where it forms an angle at most $\pi$
on both sides. While the existence of a simple closed quasigeodesic on a convex
polyhedron has been proved by Pogorelov in 1949, finding a polynomial-time
algorithm to compute such a simple closed quasigeodesic has been repeatedly
posed as an open problem. Our first contribution is to propose an extended
definition of quasigeodesics in the intrinsic setting of (not necessarily
convex) polyhedral spheres, and to prove the existence of a weakly simple
closed quasigeodesic in such a setting. Our proof does not proceed via an
approximation by smooth surfaces, but relies on an adapation of the disk flow
of Hass and Scott to the context of polyhedral surfaces. Our second result is
to leverage this existence theorem to provide a finite algorithm to compute a
weakly simple closed quasigeodesic on a polyhedral sphere. On a convex
polyhedron, our algorithm computes a simple closed quasigeodesic, solving an
open problem of Demaine, Hersterberg and Ku.


title: Bogoliubov's causal perturbative QED and white noise. Interacting fields

abstract:
  We present the Bogoliubov's causal perturbative QFT, which includes only one
refinement: the creation-annihilation operators at a point, i.e. for a specific
momentum, are mathematically interpreted as the Hida operators from the white
noise analysis. We leave the rest of the theory completely unchanged. This
allows avoiding infrared -- and ultraviolet -- divergences in the transition to
the adiabatic limit for interacting fields. We present here existence proof of
the adiabatic limit for interacting fields in causal QED with Hida operators.
This limit exists if and only if the normalization in the Epstein-Glaser
splitting of the causal distributions, in the construction of the scattering
operator, is "natural", and thus eliminates arbitrariness in the choice of the
splitting making the theory definite, with its predictive power considerably
strengthened. For example, we present a charge-mass relation which can be
proved within this theory, and which is confirmed experimentally.


title:
Accessing the in-medium effects on nucleon-nucleon elastic cross section
  with collective flows and nuclear stopping

abstract:
  A systematic study of the in-medium correction factor ($F$) on
nucleon-nucleon elastic cross section is performed within the
Ultra-relativistic Quantum Molecular Dynamics (UrQMD) model. The effects of the
beam energy dependence of $F$ on the directed, elliptic flow and nuclear
stopping in $^{197}$Au+$^{197}$Au collisions with energy ranging from $0.09$ to
$0.8A$ GeV are explored. It is found that the directed, elliptic flow and
nuclear stopping at relatively low energies are very sensitive to $F$, and the
sensitivity gradually weakens with increasing beam energy. The beam energy
dependent in-medium correction factor $F$ is deduced from the comparison of the
excitation functions of the directed, elliptic flow and nuclear stopping
between the calculated results and the FOPI experimental data.


title:
Exponential convergence in Wasserstein metric for distribution dependent
  SDEs

abstract:
  The existence and uniqueness of stationary distributions and the exponential
convergence in $L^p$-Wasserstein distance are derived for distribution
dependent SDEs from associated decoupled equations. To establish the
exponential convergence, we introduce a twinned Talagrand inequality of the
original SDE and the associated decoupled equation, and explicit convergence
rate is obtained. Our results can be applied to SDEs without uniformly
dissipative drift and distribution dependent diffusion term, which cover the
Curie-Weiss model and the granular media model in double-well landscape with
quadratic interaction as examples.


title: Stability of Two-dimensional Potential Flows Using Bicomplex Numbers

abstract:
  The use of the complex velocity potential and the complex velocity is widely
disseminated in the study of two-dimensional incompressible potential flows.
The advantages of working with complex analytical functions made this
representation of the flow ubiquitous in the field of theoretical aerodynamics.
However, this representation is not usually employed in linear stability
studies, where the representation of the velocity as real vectors is preferred
by most authors, in order to allow the representation of the perturbation as
the complex exponential function. Some of the classical attempts to use the
complex velocity potential in stability studies suffer from formal errors. In
this work, we present a framework that reconciles these two complex
representations using bicomplex numbers. This framework is applied to the
stability of the von K\'arm\'an vortex street and a generalized formula is
found. It is shown that the classical results of the symmetric and staggered
von K\'arm\'an vortex streets are just particular cases of the generalized
dynamical system in bicomplex formulation.


title:
Deep Learning-Based Blind Multiple User Detection for Grant-free SCMA
  and MUSA Systems

abstract:
  Grant-free random access and uplink non-orthogonal multiple access (NOMA) are
techniques to increase the overload factor and reduce transmission latency with
signaling overhead in massive machine-type communications (mMTC). Sparse code
multiple access (SCMA) and Multi-user shared access (MUSA) are introduced as
advanced code domain NOMA schemes. In grant-free NOMA, machine-type devices
(MTD) transmit information to the base station (BS) without a grant, creating a
challenging task for the BS to identify the active MTD among all potential
active devices. In this paper, a novel deep neural network (DNN)-based
multi-user detection (MUD) scheme for the grant-free SCMA and MUSA system in an
mMTC uplink framework is proposed to jointly identify the received signal's
sparsity and the active MTDs in the absence of channel state information. The
proposed scheme learns the correlation between the received signal and the
multi-dimensional codebook for SCMA and spreading sequences for MUSA schemes
and is able to identify the active MTDs from the received signal without any
prior knowledge of the device sparsity level. The application of the proposed
MUD scheme is further investigated in an indoor factory setting using four
different mmWave channels. Numerical results show that when the number of
active MTDs in the system is large, DNN-MUD has a significantly higher
probability of detection compared to existing approaches over the
signal-to-noise ratio range of interest.


title: New, spherical solutions of non-relativistic, dissipative hydrodynamics

abstract:
  We present a new family of exact solutions of dissipative fireball
hydrodynamics for arbitrary bulk and shear viscosities. The main property of
these solutions is a spherically symmetric, Hubble flow field. The motivation
of this paper is mostly academic: we apply non-relativistic kinematics for
simplicity and clarity. In this limiting case, the theory is particularly
clear: the non-relativistic Navier-Stokes equations describe the dissipation in
a well-understood manner. From the asymptotic analysis of our new exact
solutions of dissipative fireball hydrodynamics, we could draw a surprising
conclusion: this new class of exact solutions of non-relativistic dissipative
hydrodynamics is asymptotically perfect.


title: Modelling non-stationarity in asymptotically independent extremes

abstract:
  In many practical applications, evaluating the joint impact of combinations
of environmental variables is important for risk management and structural
design analysis. When such variables are considered simultaneously,
non-stationarity can exist within both the marginal distributions and
dependence structure, resulting in complex data structures. In the context of
extremes, few methods have been proposed for modelling trends in extremal
dependence, even though capturing this feature is important for quantifying
joint impact. Motivated by the increasing dependence of data from the UK
Climate Projections, we propose a novel semi-parametric modelling framework for
bivariate extremal dependence structures. This framework allows us to capture a
wide variety of dependence trends for data exhibiting asymptotic independence.
When applied to the climate projection dataset, our model is able to capture
observed dependence trends and, in combination with models for marginal
non-stationarity, can be used to produce estimates of bivariate risk measures
at future time points.


title: Quantum gravity as a communication resource

abstract:
  It has been theorized that when a quantum communication protocol takes place
near a black hole, the spacetime structure induced by the black hole causes an
inescapable and fundamental degradation in the protocol's performance compared
to if the protocol took place in flat spacetime. This is due to quantum
information beyond the event horizon being inaccessible, introducing noise and
degrading the entanglement resources of the protocol. However, despite black
holes being a place where we expect quantum gravity to be integral, it has been
assumed in these results that the black hole is a classical object with a
classical spacetime. We show that when the quantum nature of a black hole and
its spacetime are taken into account, their quantum properties can be used as
resources to allay the degradation of entanglement caused by the event horizon,
and thus improve the performance of quantum communication protocols near black
holes. Investigating the resourceful nature of quantum gravity could be useful
in better understanding the fundamental features of quantum gravity, just as
the resourcefulness of quantum theory has revealed new insights into its
foundations.


title: Thermodynamics of Charge Regulation Near Surface Neutrality

abstract:
  The interaction between two adjacent charged surfaces immersed in aqueous
solution is known to be affected by charge regulation - the modulation of
surface charge as two charged surfaces approach each other. This phenomenon is
particularly important near surface neutrality where the stability of objects
such as colloids or biomolecules is jeopardized. Focusing on this ubiquitous
case, we elucidate the underlying thermodynamics and show that charge
regulation is governed in this case by surface entropy. We derive explicit
expressions for charge regulation and formulate a new universal limiting law
for the free energy of ion adsorption to the surfaces. The latter turns out to
be proportional to $k_{B}T$, and independent of the association energy of ions
to surface groups. These new results are applied to the analysis of unipolar as
well as amphoteric surfaces such as oxides near their point of zero charge or
proteins near their isoelectric point.


title:
Accurate and efficient computation of mean ionization states with an
  average-atom Kubo-Greenwood approach

abstract:
  The mean ionization state (MIS) is a critical property in dense plasma and
warm dense matter research. Unfortunately however, the best way to compute the
MIS remains an open question. Average-atom (AA) models are widely-used due to
their computational efficiency, but we show that the canonical approach for
calculating the MIS in AA models is typically insufficient. However, we
demonstrate that an approach which uses the Kubo-Greenwood conductivity formula
in an AA model yields excellent agreement with both higher fidelity simulations
and experimental data.


title: Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals

abstract:
  The increasing availability of wireless access points (APs) is leading
towards human sensing applications based on Wi-Fi signals as support or
alternative tools to the widespread visual sensors, where the signals enable to
address well-known vision-related problems such as illumination changes or
occlusions. Indeed, using image synthesis techniques to translate radio
frequencies to the visible spectrum can become essential to obtain otherwise
unavailable visual data. This domain-to-domain translation is feasible because
both objects and people affect electromagnetic waves, causing radio and optical
frequencies variations. In literature, models capable of inferring
radio-to-visual features mappings have gained momentum in the last few years
since frequency changes can be observed in the radio domain through the channel
state information (CSI) of Wi-Fi APs, enabling signal-based feature extraction,
e.g., amplitude. On this account, this paper presents a novel two-branch
generative neural network that effectively maps radio data into visual
features, following a teacher-student design that exploits a cross-modality
supervision strategy. The latter conditions signal-based features in the visual
domain to completely replace visual data. Once trained, the proposed method
synthesizes human silhouette and skeleton videos using exclusively Wi-Fi
signals. The approach is evaluated on publicly available data, where it obtains
remarkable results for both silhouette and skeleton videos generation,
demonstrating the effectiveness of the proposed cross-modality supervision
strategy.


title: Dangling bonds on the Cl- and Br-terminated Si(100) surfaces

abstract:
  Halogen monolayer on a silicon surface is attracting active attention for
applications in electronic device fabrication with individual impurities. To
create a halogen mask for the impurities incorporation, it is desirable to be
able to remove a single halogen atom from the surface. We report the desorption
of individual halogen atoms from the Si(100)-2x1-Cl and -Br surfaces in a
scanning tunneling microscope (STM). Silicon dangling bonds (DBs) formed on the
Si surface after halogen desorption were investigated using STM and the density
functional theory. Three charge states: positive, neutral, and negative were
identified. Our results show that the charge states of DBs can be manipulated,
which will allow to locally tune the reactivity of the Cl- and Br-terminated
Si(100) surfaces.


title: Uncloneable Decryptors from Quantum Copy-Protection

abstract:
  Uncloneable decryptors are encryption schemes (with classical plaintexts and
ciphertexts) with the added functionality of deriving uncloneable quantum
states, called decryptors, which could be used to decrypt ciphers without
knowledge of the secret key (Georgiou and Zhandry, IACR'20). We study
uncloneable decryptors in the computational setting and provide increasingly
strong security notions which extend the various indistinguishable security
notions of symmetric encryption.
  We show that CPA secure uncloneable bit decryptors could be instantiated from
a copy protection scheme (Aaronson, CCC'09) for any balanced binary function.
We introduce a new notion of flip detection security for copy protection
schemes inspired by the notions of left or right security for encryption
schemes, and show that it could be used to instantiate CPA secure uncloneable
decryptors for messages of unrestricted length.
  We then show how to strengthen the CPA security of uncloneable decryptors to
CCA2 security using strong EUF-CMA secure digital signatures. We show that our
constructions could be instantiated relative to either the quantum oracle used
in [Aar09] or the classical oracle used in (Aaronson et al., CRYPTO'21) to
instantiate copy protection schemes. Our constructions are the first to achieve
CPA or CCA2 security in the symmetric setting.


title:
Tunable superconductivity at the oxide-insulator/KTaO$_3$ interface and
  its origin

abstract:
  Superconductivity forms out of the condensation of Cooper pairs of electrons.
The mechanism by which Cooper pairs are created in non-conventional
superconductors is often elusive because experimental signatures that connect a
specific pairing mechanism to the properties of superconducting state are rare.
The recently discovered superconducting oxide-insulator/KTaO$_3$ interface may
offer clues about its origins. Here we observe distinct dependences of the
superconducting transition temperature Tc on carrier density n$_{2D}$ for
electron gases formed at KTaO$_3$ (111), (001) and (110) interfaces. For the
KTaO$_3$ (111) interface, a remarkable linear dependence of Tc on n$_{2D}$ is
observed over a range of nearly one order of magnitude. Further, our study of
the dependence of superconductivity on gate electric fields reveals the role of
the interface in mediating superconductivity, which also allows for a
reversible electric switching of superconductivity at T = 2 K. We found that
the extreme sensitivity of superconductivity to crystallographic orientation
can be explained by Cooper pairing via inter-orbital interactions induced by
the inversion-breaking transverse optical (TO1) phonons and quantum
confinement. This mechanism is also consistent with the dependence of Tc on
n$_{2D}$ at the KTaO$_3$ (111) interface. Our study may shed light on the
pairing mechanism in other superconducting quantum-paraelectrics.


title: A well-balanced scheme for Euler equations with singular sources

abstract:
  Numerical methods for the Euler equations with a singular source are
discussed in this paper.
  The stationary discontinuity induced by the singular source and its coupling
with the convection of fluid presents challenges to numerical methods.
  We show that the splitting scheme is not well-balanced and leads to incorrect
results; in addition, some popular well-balanced schemes also give incorrect
solutions in extreme cases due to the singularity of source.
  To fix such difficulties, we propose a solution-structure based approximate
Riemann solver, in which the structure of Riemann solution is first predicted
and then its corresponding approximate solver is given.
  The proposed solver can be applied to the calculation of numerical fluxes in
a general finite volume method, which can lead to a new well-balanced scheme.
  Numerical tests show that the discontinuous Galerkin method based on the
present approximate Riemann solver has the ability to capture each wave
accurately.


title: Thermodynamically consistent coarse-graining of polar active fluids

abstract:
  We introduce a closure model for coarse-grained kinetic theories of polar
active fluids. Based on a thermodynamically consistent, quasi-equilibrium
approximation of the particle distribution function, the model closely captures
important analytical properties of the kinetic theory, including its linear
stability and the balance of entropy production and dissipation. Nonlinear
simulations show the model reproduces the qualitative behavior and
nonequilibrium statistics of the kinetic theory, unlike commonly used closure
models. We use the closure model to simulate highly turbulent suspensions in
both two and three dimensions in which we observe complex multiscale dynamics,
including large concentration fluctuations and a proliferation of polar and
nematic defects.


title: Channel Tracking and Prediction for IRS-aided Wireless Communications

abstract:
  For intelligent reflecting surface (IRS)-aided wireless communications,
channel estimation is essential and usually requires excessive channel training
overhead when the number of IRS reflecting elements is large. The acquisition
of accurate channel state information (CSI) becomes more challenging when the
channel is not quasi-static due to the mobility of the transmitter and/or
receiver. In this work, we study an IRS-aided wireless communication system
with a time-varying channel model and propose an innovative two-stage
transmission protocol. In the first stage, we send pilot symbols and track the
direct/reflected channels based on the received signal, and then data signals
are transmitted. In the second stage, instead of sending pilot symbols first,
we directly predict the direct/reflected channels and all the time slots are
used for data transmission. Based on the proposed transmission protocol, we
propose a two-stage channel tracking and prediction (2SCTP) scheme to obtain
the direct and reflected channels with low channel training overhead, which is
achieved by exploiting the temporal correlation of the time-varying channels.
Specifically, we first consider a special case where the IRS-access point (AP)
channel is assumed to be static, for which a Kalman filter (KF)-based algorithm
and a long short-term memory (LSTM)-based neural network are proposed for
channel tracking and prediction, respectively. Then, for the more general case
where the IRS-AP, user-IRS and user-AP channels are all assumed to be
time-varying, we present a generalized KF (GKF)-based channel tracking
algorithm, where proper approximations are employed to handle the underlying
non-Gaussian random variables. Numerical simulations are provided to verify the
effectiveness of our proposed transmission protocol and channel
tracking/prediction algorithms as compared to existing ones.


title:
Nonlocal correlations in noisy multiqubit systems simulated using matrix
  product operators

abstract:
  We introduce an open source solver for the Lindblad master equation, based on
matrix product states and matrix product operators. Using this solver we study
the dynamics of tens of interacting qubits with different connectivities when
an edge qubit is being continuously driven on resonance -- a fundamental
operation in quantum devices. Because of the driving, induced quasiparticles
propagate through the qubits until the system reaches a steady state due to the
incoherent terms. We find that with alternating-frequency qubits whose
interactions with their off-resonant neighbors appear weak, the tunneling
quasiparticles lead to large correlations between distant qubits in the system.
Some two-qubit correlation functions are found to increase as a function of
distance in the system (in contrast to the typical decay with distance),
peaking on the two edge qubits farthest apart from each other.


title: Irradiated but not eclipsed, the case of PSR J0610-2100

abstract:
  We report on radio timing observations of the black widow binary pulsar
J0610$-$2100 and optical observations of its binary companion. The radio timing
observations extend the timing baseline to 16yr and reveal a marginal detection
of the orbital period derivative, but they show no significant evidence of
orbital variations such as those seen in other black widow pulsars.
Furthermore, no eclipses are seen in the observations at observing frequencies
ranging from 310 to 2700MHz. The optical VRI light curves were modulated with
the orbital period, reaching maximum brightness of $V=26.8$, $R=25.4$, and
$I=23.8$ at superior conjunction of the companion, confirming irradiation of
the companion by the pulsar. Modelling the light curves indicates that the
companion is likely not filling its Roche lobe, while having a moderate
inclination ($i > 54\degr$). We find an unusually low temperature and a low
irradiation for the irradiated hemisphere of the companion. We investigate the
absence of radio eclipses in PSR J0610-2100 and in other black widow systems in
relation to their binary, pulsar, and companion properties. We also discuss the
suitability of PSR J0610-2100 for pulsar timing array observations aimed at
detecting nano-Hertz gravitational waves.


title:
A geometrical viewpoint on the benign overfitting property of the
  minimum $l_2$-norm interpolant estimator

abstract:
  Practitioners have observed that some deep learning models generalize well
even with a perfect fit to noisy training data [5,45,44]. Since then many
theoretical works have revealed some facets of this phenomenon [4,2,1,8] known
as benign overfitting. In particular, in the linear regression model, the
minimum $l_2$-norm interpolant estimator $\hat\beta$ has received a lot of
attention [1,39] since it was proved to be consistent even though it perfectly
fits noisy data under some condition on the covariance matrix $\Sigma$ of the
input vector. Motivated by this phenomenon, we study the generalization
property of this estimator from a geometrical viewpoint. Our main results
extend and improve the convergence rates as well as the deviation probability
from [39]. Our proof differs from the classical bias/variance analysis and is
based on the self-induced regularization property introduced in [2]:
$\hat\beta$ can be written as a sum of a ridge estimator $\hat\beta_{1:k}$ and
an overfitting component $\hat\beta_{k+1:p}$ which follows a decomposition of
the features space $\mathbb{R}^p=V_{1:k}\oplus^\perp V_{k+1:p}$ into the space
$V_{1:k}$ spanned by the top $k$ eigenvectors of $\Sigma$ and the ones
$V_{k+1:p}$ spanned by the $p-k$ last ones. We also prove a matching lower
bound for the expected prediction risk. The two geometrical properties of
random Gaussian matrices at the heart of our analysis are the Dvoretsky-Milman
theorem and isomorphic and restricted isomorphic properties. In particular, the
Dvoretsky dimension appearing naturally in our geometrical viewpoint coincides
with the effective rank from [1,39] and is the key tool to handle the behavior
of the design matrix restricted to the sub-space $V_{k+1:p}$ where overfitting
happens.


title: Deep Learning for Wireless Dynamics

abstract:
  This paper aims to predict radio channel variations over time by deep
learning from channel observations without knowledge of the underlying channel
dynamics. In next-generation wideband cellular systems, multicarrier
transmission for higher data rate leads to the high-resolution predicting
problem. By leveraging recent advances of deep learning in high-resolution
image processing, we propose a purely data-driven deep learning (DL) approach
to predicting high-resolution temporal evolution of wideband radio channels. In
order to investigate the effect of architectural design choices, we develop and
study three deep learning prediction models, namely, baseline, image
completion, and next-frame prediction models using UNet. Numerical results show
that the proposed DL approach achieves a 52% lower prediction error than the
traditional approach based on the Kalman filter (KF) in mean absolute errors.
To quantify impact of channel aging and prediction on precoding performance, we
also evaluate the performance degradation due to outdated and predicted channel
state information (CSI) compared to perfect CSI. Our simulations show that the
proposed DL approach can reduce the performance loss due to channel aging by
71% through adapting precoding vector to changes in radio channel while the
traditional KF approach only shows a 27% reduction.


title: Using Word Embeddings to Analyze Protests News

abstract:
  The first two tasks of the CLEF 2019 ProtestNews events focused on
distinguishing between protest and non-protest related news articles and
sentences in a binary classification task. Among the submissions, two well
performing models have been chosen in order to replace the existing word
embeddings word2vec and FastTest with ELMo and DistilBERT. Unlike bag of words
or earlier vector approaches, ELMo and DistilBERT represent words as a sequence
of vectors by capturing the meaning based on contextual information in the
text. Without changing the architecture of the original models other than the
word embeddings, the implementation of DistilBERT improved the performance
measured on the F1-Score of 0.66 compared to the FastText implementation.
DistilBERT also outperformed ELMo in both tasks and models. Cleaning the
datasets by removing stopwords and lemmatizing the words has been shown to make
the models more generalizable across different contexts when training on a
dataset with Indian news articles and evaluating the models on a dataset with
news articles from China.


title: Global Gravity Field Model from Taiji-1 Observations

abstract:
  Taiji-1 is the first technology demonstration satellite of the Taiji program
of China's space-borne gravitational wave antenna. After the demonstration of
the key individual technologies, Taiji-1 continues collecting the data of the
precision orbit determinations, the satellite attitudes, and the
non-conservative forces exerted on the S/C. Therefore, during its free-fall,
Taiji-1 can be viewed as operating in the high-low satellite-to-satellite
tracking mode of a gravity recovery mission. In this work, one month data from
Taiji-1's observations have been selected and the techniques are developed to
resolve the long term interruptions and disturbances in the measurement data
due to the scheduled technology demonstration experiments. The first global
gravity model TJGM-r1911, that independently derived from China's own satellite
mission, is successfully built from Taiji-1's observations. Compared with
gravity models from CHAMP and other satellite gravity missions, accuracy
discrepancies exist, which is mainly caused by the data discontinuity problem.
As the approved extended free-falling phase with minimal disruptions and
disturbances, Taiji-1 will serve as the first satellite gravity mission for
China since 2022 and will provide us the independent measurement of both the
static and the monthly time-variable global gravity field.

categories: gr-qc astro-ph.EP astro-ph.IM physics.geo-ph


title:
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive
  Learning Framework for Sentence Embeddings

abstract:
  Contrastive learning has shown great potential in unsupervised sentence
embedding tasks, e.g., SimCSE. However, We find that these existing solutions
are heavily affected by superficial features like the length of sentences or
syntactic structures. In this paper, we propose a semantics-aware contrastive
learning framework for sentence embeddings, termed Pseudo-Token BERT (PT-BERT),
which is able to exploit the pseudo-token space (i.e., latent semantic space)
representation of a sentence while eliminating the impact of superficial
features such as sentence length and syntax. Specifically, we introduce an
additional pseudo token embedding layer independent of the BERT encoder to map
each sentence into a sequence of pseudo tokens in a fixed length. Leveraging
these pseudo sequences, we are able to construct same-length positive and
negative pairs based on the attention mechanism to perform contrastive
learning. In addition, we utilize both the gradient-updating and
momentum-updating encoders to encode instances while dynamically maintaining an
additional queue to store the representation of sentence embeddings, enhancing
the encoder's learning performance for negative examples. Experiments show that
our model outperforms the state-of-the-art baselines on six standard semantic
textual similarity (STS) tasks. Furthermore, experiments on alignments and
uniformity losses, as well as hard examples with different sentence lengths and
syntax, consistently verify the effectiveness of our method.


title:
Wireless Quantized Federated Learning: A Joint Computation and
  Communication Design

abstract:
  Recently, federated learning (FL) has sparked widespread attention as a
promising decentralized machine learning approach which provides privacy and
low delay. However, communication bottleneck still constitutes an issue, that
needs to be resolved for an efficient deployment of FL over wireless networks.
In this paper, we aim to minimize the total convergence time of FL, by
quantizing the local model parameters prior to uplink transmission. More
specifically, the convergence analysis of the FL algorithm with stochastic
quantization is firstly presented, which reveals the impact of the quantization
error on the convergence rate. Following that, we jointly optimize the
computing, communication resources and number of quantization bits, in order to
guarantee minimized convergence time across all global rounds, subject to
energy and quantization error requirements, which stem from the convergence
analysis. The impact of the quantization error on the convergence time is
evaluated and the trade-off among model accuracy and timely execution is
revealed. Moreover, the proposed method is shown to result in faster
convergence in comparison with baseline schemes. Finally, useful insights for
the selection of the quantization error tolerance are provided.


title:
Complex electronic structure evolution of NdSb across the magnetic
  transition

abstract:
  The rare-earth monopnictide (REM) family, which hosts magnetic ground states
with extreme magnetoresistance, has established itself as a fruitful playground
for the discovery of interesting topological phases. Here, by using
high-resolution angle-resolved photoemission spectroscopy complemented by
first-principles density functional-theory based modeling, we examine the
evolution of the electronic structure of the candidate REM Dirac semimetal NdSb
across the magnetic transition. A complex angel-wing-like band structure near
the zone center and three arc-like features at the zone corner have been
observed. This dramatic reconstruction of the itinerant bands around the zone
center is shown to be driven by the magnetic transition: Specifically,, the Nd
5d electron band backfolds at the Gamma point and hybridizes with the Sb 5p
hole bands in the antiferromagnetic phase. Our study indicates that
antiferromagnetism plays an intricate role in the electronic structure of the
REM family.


title: Multi-modal Graph Learning for Disease Prediction

abstract:
  Benefiting from the powerful expressive capability of graphs, graph-based
approaches have been popularly applied to handle multi-modal medical data and
achieved impressive performance in various biomedical applications. For disease
prediction tasks, most existing graph-based methods tend to define the graph
manually based on specified modality (e.g., demographic information), and then
integrated other modalities to obtain the patient representation by Graph
Representation Learning (GRL). However, constructing an appropriate graph in
advance is not a simple matter for these methods. Meanwhile, the complex
correlation between modalities is ignored. These factors inevitably yield the
inadequacy of providing sufficient information about the patient's condition
for a reliable diagnosis. To this end, we propose an end-to-end Multi-modal
Graph Learning framework (MMGL) for disease prediction with multi-modality. To
effectively exploit the rich information across multi-modality associated with
the disease, modality-aware representation learning is proposed to aggregate
the features of each modality by leveraging the correlation and complementarity
between the modalities. Furthermore, instead of defining the graph manually,
the latent graph structure is captured through an effective way of adaptive
graph learning. It could be jointly optimized with the prediction model, thus
revealing the intrinsic connections among samples. Our model is also applicable
to the scenario of inductive learning for those unseen data. An extensive group
of experiments on two disease prediction tasks demonstrates that the proposed
MMGL achieves more favorable performance. The code of MMGL is available at
\url{https://github.com/SsGood/MMGL}.


title:
High Mixing Entropy Enhanced Hydrogen Evolution Reaction in AlMnYNiCoAu
  Catalysts

abstract:
  An effective method for increasing the electrocatalytic activity of metallic
glasses (MGs) in hydrogen evolution reaction (HER) is reported. This method
applies a noble metal hybridization strategy to design a highly reactive
catalyst for alkaline HER based on MGs with a nano-porous structure. The porous
structure provides an abundance of active sites and exposes a large specific
surface area to the electrolyte, thus effectively improving the activity of the
HER catalysts. The doping of Au element can create electronic defects, adjust
the electronic structure, change the electronic interaction, introduce lattice
distortion, regulate mixing entropy, and improve the electrocatalytic
performance of the catalyst. All these properties make np-AlMnYNiCoAu catalyst
a potential electrode for alkaline HER. Significantly, we find that the high
mixing entropy can enhance the HER performances. The present work could lead to
a new approach to further develop environmentally friendly amorphous
electrocatalyst with high efficiency and excellent stability for HER in
alkaline solution.


title: Improving the transferability of speech separation by meta-learning

abstract:
  Speech separation aims to separate multiple speech sources from a speech
mixture. Although speech separation is well-solved on some existing English
speech separation benchmarks, it is worthy of more investigation on the
generalizability of speech separation models on the accents or languages unseen
during training. This paper adopts meta-learning based methods to improve the
transferability of speech separation models. With the meta-learning based
methods, we discovered that only using speech data with one accent, the native
English accent, as our training data, the models still can be adapted to new
unseen accents on the Speech Accent Archive. We compared the results with a
human-rated native-likeness of accents, showing that the transferability of
MAML methods has less relation to the similarity of data between the training
and testing phase compared to the typical transfer learning methods.
Furthermore, we found that models can deal with different language data from
the CommonVoice corpus during the testing phase. Most of all, the MAML methods
outperform typical transfer learning methods when it comes to new accents, new
speakers, new languages, and noisy environments.


title: Representations on the cohomology of $\overline{\mathcal{M}}_{0,n}$

abstract:
  The moduli space $\overline{\mathcal{M}}_{0,n}$ of $n$ pointed stable curves
of genus $0$ admits an action of the symmetric group $S_n$ by permuting the
marked points. We provide a closed formula for the character of the
$S_n$-action on the cohomology of $\overline{\mathcal{M}}_{0,n}$. This is
achieved by studying wall crossings of the moduli spaces of quasimaps which
provide us with a new inductive construction of $\overline{\mathcal{M}}_{0,n}$,
equivariant with respect to the symmetric group action. Moreover we prove that
$H^{2k}(\overline{\mathcal{M}}_{0,n})$ for $k\le 3$ and
$H^{2k}(\overline{\mathcal{M}}_{0,n})\oplus
H^{2k-2}(\overline{\mathcal{M}}_{0,n})$ for any $k$ are permutation
representations. Our method works for related moduli spaces as well and we
provide a closed formula for the character of the $S_n$-representation on the
cohomology of the Fulton-MacPherson compactification $\mathbb{P}^1[n]$ of the
configuration space of $n$ points on $\mathbb{P}^1$ and more generally on the
cohomology of the moduli space
$\overline{\mathcal{M}}_{0,n}(\mathbb{P}^{m-1},1)$ of stable maps.


title: Causal perturbative QED and white noise

abstract:
  We present the Bogoliubov's causal perturbative QFT, which includes only one
refinement: the creation-annihilation operators at a point, \emph{i.e.} for a
specific momentum, are mathematically interpreted as the Hida operators from
the white noise analysis. We leave the rest of the theory completely unchanged.
This allows avoiding infrared -- and ultraviolet -- divergences in the
transition to the adiabatic limit for interacting fields. We present here the
analysis of the causal axioms for the scattering operator with the Hida
operators as the creation-annihilation operators.


title: Electrochemical 3D Printing of Ni-Mn and Ni-Co alloy with FluidFM

abstract:
  Additive manufacturing can realize almost any designed geometry, enabling the
fabrication of innovative products for advanced applications. Local
electrochemical plating is a powerful approach for additive manufacturing of
metal microstructures; however, previously reported data have been mostly
obtained with copper, and only a few cases have been reported with other
elements. In this study, we assessed the ability of fluidic force microscopy
(FluidFM) to produce Ni-Mn and Ni-Co alloy structures. Once the optimal
deposition potential window was determined, pillars with relatively smooth
surfaces were obtained. The printing process was characterized by printing
rates in the range of 50-60 nm/s. Cross-sections exposed by focused ion beam
showed highly dense microstructures, while the corresponding face scan with
energy-dispersive X-ray spectroscopy (EDX) spectra revealed a uniform
distribution of alloy components.


title:
Improved uniform error bounds on time-splitting methods for the
  long-time dynamics of the weakly nonlinear Dirac equation

abstract:
  Improved uniform error bounds on time-splitting methods are rigorously proven
for the long-time dynamics of the weakly nonlinear Dirac equation (NLDE), where
the nonlinearity strength is characterized by a dimensionless parameter
$\varepsilon \in (0, 1]$ . We adopt a second order Strang splitting method to
discretize the NLDE in time and combine the Fourier pseudospectral method in
space for the full-discretization. By employing the {\sl regularity
compensation oscillation} (RCO) technique where the high frequency modes are
controlled by the regularity of the exact solution and the low frequency modes
are analyzed by phase cancellation and energy method, we establish improved
uniform error bounds at $O(\varepsilon^2\tau^2)$ and $O(h^{m-1}+
\varepsilon^2\tau^2)$ for the second-order Strang splitting semi-discretizaion
and full-discretization up to the long-time $T_{\varepsilon} = T/\varepsilon^2$
with $T>0$ fixed, respectively. Furthermore, the numerical scheme and error
estimates are extended to an oscillatory NLDE which propagates waves with
$O(\varepsilon^2)$ wavelength in time. Finally, numerical examples verifying
our analytical results are given.


title:
Vertex Cover and Feedback Vertex Set Above and Below Structural
  Guarantees

abstract:
  Vertex Cover parameterized by the solution size k is the quintessential
fixed-parameter tractable problem. FPT algorithms are most interesting when the
parameter is small. Several lower bounds on k are well-known, such as the
maximum size of a matching. This has led to a line of research on
parameterizations of Vertex Cover by the difference of the solution size k and
a lower bound. The most prominent cases for such lower bounds for which the
problem is FPT are the matching number or the optimal fractional LP solution.
We investigate parameterizations by the difference between k and other graph
parameters including the feedback vertex number, the degeneracy, cluster
deletion number, and treewidth with the goal of finding the border of
fixed-parameter tractability for said difference parameterizations. We also
consider similar parameterizations of the Feedback Vertex Set problem.


title: Moser-Tardos Algorithm with small number of random bits

abstract:
  We study a variant of the parallel Moser-Tardos Algorithm. We prove that if
we restrict attention to a class of problems whose dependency graphs have some
fixed subexponential growth, then the expected total number of random bits used
by the algorithm is constant; in particular, it is independent from the number
of variables. This is achieved by using the same random bits to resample
variables which are far enough in the dependency graph.
  There are two colloraries. First, we obtain a deterministic algorithm for
finding a satisfying assignment, which in any class of problems as in the
previous paragraph runs in time $O(n)$, where $n$ is the number of variables.
Second, we present a Borel version of the Lov\'asz Local Lemma.


title: Addressing the Gravitational Wave - Collider Inverse Problem

abstract:
  We provide a roadmap for analyzing the interplay between hypothetical future
collider observations and the detection of a gravitational wave signal produced
by a strong first order electroweak phase transition in beyond the Standard
Model theories. We rely on a combination of a dimensionally reduced,
three-dimensional effective field theory and results of both perturbation
theory and non-perturbative lattice simulations. We apply these
state-of-the-art methods to the real scalar triplet extension of the Standard
Model, which admits a possible two-step electroweak symmetry-breaking thermal
history. We find that (1) a first order transition during the second step could
generate a signal accessible to LISA generation detectors and (2) the
gravitational wave signal displays a strong sensitivity to the portal coupling
between the new scalar and the Higgs boson. We illustrate how a combination of
direct and indirect measurements of the new scalar properties, in combination
with the presence or absence of a gravitational wave detection, could test the
model and identify the values of the model parameters.


title:
Video Coding for Machines with Feature-Based Rate-Distortion
  Optimization

abstract:
  Common state-of-the-art video codecs are optimized to deliver a low bitrate
by providing a certain quality for the final human observer, which is achieved
by rate-distortion optimization (RDO). But, with the steady improvement of
neural networks solving computer vision tasks, more and more multimedia data is
not observed by humans anymore, but directly analyzed by neural networks. In
this paper, we propose a standard-compliant feature-based RDO (FRDO) that is
designed to increase the coding performance, when the decoded frame is analyzed
by a neural network in a video coding for machine scenario. To that extent, we
replace the pixel-based distortion metrics in conventional RDO of VTM-8.0 with
distortion metrics calculated in the feature space created by the first layers
of a neural network. Throughout several tests with the segmentation network
Mask R-CNN and single images from the Cityscapes dataset, we compare the
proposed FRDO and its hybrid version HFRDO with different distortion measures
in the feature space against the conventional RDO. With HFRDO, up to 5.49 %
bitrate can be saved compared to the VTM-8.0 implementation in terms of
Bj{\o}ntegaard Delta Rate and using the weighted average precision as quality
metric. Additionally, allowing the encoder to vary the quantization parameter
results in coding gains for the proposed HFRDO of up 9.95 % compared to
conventional VTM.


title:
A Deep Learning Model with Radiomics Analysis Integration for
  Glioblastoma Post-Resection Survival Prediction

abstract:
  Purpose: To develop a novel deep-learning model that integrates radiomics
analysis in a multi-dimensional feature fusion workflow for glioblastoma (GBM)
post-resection survival prediction. Methods: A cohort of 235 GBM patients with
complete surgical resection was divided into short-term/long-term survival
groups with 1-yr survival time threshold. Each patient received a pre-surgery
multi-parametric MRI exam, and three tumor subregions were segmented by
neuroradiologists. The developed model comprises three data source branches: in
the 1st radiomics branch, 456 radiomics features (RF) were from each patient;
in the 2nd deep learning branch, an encoding neural network architecture was
trained for survival group prediction using each single MR modality, and
high-dimensional parameters of the last two network layers were extracted as
deep features (DF). The extracted radiomics features and deep features were
processed by a feature selection procedure to reduce dimension size of each
feature space. In the 3rd branch, non-image-based patient-specific clinical
features (PSCF) were collected. Finally, data sources from all three branches
were fused as an integrated input for a supporting vector machine (SVM)
execution for survival group prediction. Different strategies of model design,
including 1) 2D/3D-based image analysis, and 2) different data source
combinations in SVM input design, were investigated in comparison studies.
Results: The model achieved 0.638 prediction accuracy when using PSCF only,
which was higher than the results using RF or DF only in both 2D and 3D
analysis. The joint use of RF/PSCF improved accuracy results to 0.681 in 3D
analysis. The most accurate models in 2D/3D analysis reached the highest
accuracy 0.745 with different combinations of RF/DF/ PSCF, and the
corresponding ROC AUC results were 0.69(2D) and 0.71(3D), respectively.


title:
Construction of multivariate polynomial approximation kernels via
  semidefinite programming

abstract:
  In this paper we construct a hierarchy of multivariate polynomial
approximation kernels via semidefinite programming. We give details on the
implementation of the semidefinite programs defining the kernels. Finally, we
show how a symmetry reduction may be performed to increase numerical
tractability.


title: DRTAM: Dual Rank-1 Tensor Attention Module

abstract:
  Recently, attention mechanisms have been extensively investigated in computer
vision, but few of them show excellent performance on both large and mobile
networks. This paper proposes Dual Rank-1 Tensor Attention Module (DRTAM), a
novel residual-attention-learning-guided attention module for feed-forward
convolutional neural networks. Given a 3D feature tensor map, DRTAM firstly
generates three 2D feature descriptors along three axes. Then, using three
descriptors, DRTAM sequentially infers two rank-1 tensor attention maps, the
initial attention map and the complement attention map, combines and multiplied
them to the input feature map for adaptive feature refinement(see Fig.1(c)). To
generate two attention maps, DRTAM introduces rank-1 tensor attention module
(RTAM) and residual descriptors extraction module (RDEM): RTAM divides each 2D
feature descriptors into several chunks, and generate three factor vectors of a
rank-1 tensor attention map by employing strip pooling on each chunk so that
local and long-range contextual information can be captured along three
dimension respectively; RDEM generates three 2D feature descriptors of the
residual feature to produce the complement attention map, using three factor
vectors of the initial attention map and three descriptors of the input
feature. Extensive experimental results on ImageNet-1K, MS COCO and PASCAL VOC
demonstrate that DRTAM achieves competitive performance on both large and
mobile networks compare with other state-of-the-art attention modules.


title:
Crossed Andreev reflection in spin-polarized chiral edge states due to
  Meissner effect

abstract:
  We consider a hybrid quantum Hall-superconductor system, where a
superconducting finger with oblique profile is wedged into a two-dimensional
electron gas in the presence of a perpendicular magnetic field, as considered
by Lee $\textit{et al.}$, Nat. Phys. 13, 693 (2017). The electron gas is in the
quantum Hall regime at filling factor $\nu=1$. Due to the Meissner effect, the
perpendicular magnetic field close to the quantum Hall-superconductor boundary
is distorted and gives rise to an in-plane component of the magnetic field.
This component enables non-local crossed Andreev reflection between the
spin-polarized chiral edge states running on opposite sides of the
superconducting finger, thus opening a gap in the spectrum of the edge states
without the need of spin-orbit interaction or non-trivial magnetic textures. We
compute numerically the transport properties of this setup and show that a
negative resistance exists as consequence of non-local Andreev processes. We
also obtain numerically the zero-energy local density of states, which
systematically shows peaks stable to disorder. The latter result is compatible
with the emergence of Majorana bound states.


title: Quantum Coherence in Loopless Superconductive Networks

abstract:
  Measurements indicating that planar networks of superconductive islands
connected by Josephson junctions display long range quantum coherence are
reported. The networks consist of superconducting islands connected by
Josephson junctions and have a tree-like topological structure containing no
loops. Enhancements of superconductive gap over specific branches of the
networks and sharp increases of pair currents are the main signatures of the
coherent states and, in order to unambiguously attribute the observed effects
to branches being embedded in the networks, comparisons with geometrically
equivalent, but isolated, counterparts are reported. Tuning the Josephson
coupling energy by an external magnetic field generates increases of the
Josephson currents, along the above mentioned specific branches, which follow a
functional dependence typical of phase transitions. Results are presented for
double comb and star geometry networks and in both cases the observed effects
provide positive quantitative evidence of the predictions of existing
theoretical models.


title: Unimon qubit

abstract:
  Superconducting qubits are one of the most promising candidates to implement
quantum computers. The superiority of superconducting quantum computers over
any classical device in simulating random but well-determined quantum circuits
has already been shown in two independent experiments and important steps have
been taken in quantum error correction. However, the currently wide-spread
qubit designs do not yet provide high enough performance to enable practical
applications or efficient scaling of logical qubits owing to one or several
following issues: sensitivity to charge or flux noise leading to decoherence,
too weak non-linearity preventing fast operations, undesirably dense excitation
spectrum, or complicated design vulnerable to parasitic capacitance. Here, we
introduce and demonstrate a superconducting-qubit type, the unimon, which
combines the desired properties of high non-linearity, full insensitivity to dc
charge noise, insensitivity to flux noise, and a simple structure consisting
only of a single Josephson junction in a resonator. We measure the qubit
frequency, $\omega_{01}/(2\pi)$, and anharmonicity $\alpha$ over the full
dc-flux range and observe, in agreement with our quantum models, that the qubit
anharmonicity is greatly enhanced at the optimal operation point, yielding, for
example, 99.9% and 99.8% fidelity for 13-ns single-qubit gates on two qubits
with $(\omega_{01},\alpha)=(4.49~\mathrm{GHz}, 434~\mathrm{ MHz})\times 2\pi$
and $(3.55~\mathrm{GHz}, 744~\mathrm{ MHz})\times 2\pi$, respectively. The
energy relaxation time $T_1\lesssim 10~\mu\mathrm{s}$ is stable for hours and
seems to be limited by dielectric losses. Thus, future improvements of the
design, materials, and gate time may promote the unimon to break the 99.99%
fidelity target for efficient quantum error correction and possible quantum
advantage with noisy systems.


title:
Enhanced spin hall effect of reflected light due to Optical Tamm states
  with Dirac semimetal at the terahertz range

abstract:
  The enhanced photonic spin hall effect (PSHE) plays a positive role in the
flexible manipulation of photons. Here, by combining Dirac semimetals with
Bragg reflector constructed by one-dimensional photonic crystal, we
theoretically design a simple multilayer structure to enhance and manipulate
the PSHE. Through the detailed and optimal design of the conductivity
characteristics of Dirac semimetals and the structural parameters of the whole
model, we realize the excitation of Optical Tamm states (OTSs) in the
multilayer structure, so that the PSHE can be enhanced and controllable. The
theoretical results show that by optimizing the Fermi energy and the thickness
of Dirac semimetal, the reflection coefficient ratio can be increased under
p-polarization and s-polarization, thus creating conditions for enhanced PSHE.
In addition, the effects of the incident angle and the parameters of the spacer
layer on the PSHE are also clarified. We believe above results can provide a
new paradigm for the construction of controllable spin devices.


title: Hyperbolic Image Segmentation

abstract:
  For image segmentation, the current standard is to perform pixel-level
optimization and inference in Euclidean output embedding spaces through linear
hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable
alternative for image segmentation and propose a tractable formulation of
hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image
Segmentation opens up new possibilities and practical benefits for
segmentation, such as uncertainty estimation and boundary information for free,
zero-label generalization, and increased performance in low-dimensional output
embeddings.