title:
Predicting electron spin decoherence with a many-body first-principles
  approach

abstract:
  Developing a microscopic understanding of spin decoherence is essential to
advancing quantum technologies. Electron spin decoherence due to atomic
vibrations (phonons) plays a special role as it sets an intrinsic limit to the
performance of spin-based quantum devices. Two main sources of phonon-induced
spin decoherence - the Elliott-Yafet (EY) and Dyakonov-Perel (DP) mechanisms -
have distinct physical origins and theoretical treatments. Here we show
calculations that unify their modeling and enable accurate predictions of spin
relaxation and precession in semiconductors. We compute the phonon-dressed
vertex of the spin-spin correlation function, with a treatment analogous to the
calculation of the anomalous electron magnetic moment in QED. We find that the
vertex correction provides a giant renormalization of the electron spin
dynamics in solids, greater by many orders of magnitude than the corresponding
correction in vacuum. Our work demonstrates a general approach for quantitative
analysis of spin decoherence in materials, advancing the quest for spin-based
quantum technologies.


title:
Arbitrary high-order methods for one-sided direct event location in
  discontinuous differential problems with nonlinear event function

abstract:
  In this paper we are concerned with numerical methods for the one-sided event
location in discontinuous differential problems, whose event function is
nonlinear (in particular, of polynomial type). The original problem is
transformed into an equivalent Poisson problem, which is effectively solved by
suitably adapting a recently devised class of energy-conserving methods for
Poisson systems. The actual implementation of the methods is fully discussed,
with a particular emphasis to the problem at hand. Some numerical tests are
reported, to assess the theoretical findings.


title: Sum of Hamiltonian manifolds

abstract:
  In this paper, we study the operation of symplectic sum on two compact
symplectic manifolds with Hamiltonian group $G$-actions, which are simply
called Hamiltonian manifolds. Symplectic sum on two compact symplectic
manifolds without group action has been studied before by some mathematicians.
In many recent researches on symplectic geometry, some techniques from gauge
theory were introduced which give rise to some new invariants for Hamiltonian
manifolds. Then Hamiltonian manifolds become the important object for study. To
account the calculation of these invariants, one of effective ways is to study
the sum formula of these invariants under the operation of symplectic sum for
Hamiltonian manifolds. As a basic step for further study, we give two methods
of construction of symplectic sum and the derived moment map from two
Hamiltonian manifolds with common divisor. One method is based on the fiber
connected sum over divisor, the other is on contact hypersurface and its
symplectization.


title: A Proposal to Study "Is High Quality Data All We Need?"

abstract:
  Even though deep neural models have achieved superhuman performance on many
popular benchmarks, they have failed to generalize to OOD or adversarial
datasets. Conventional approaches aimed at increasing robustness include
developing increasingly large models and augmentation with large scale
datasets. However, orthogonal to these trends, we hypothesize that a smaller,
high quality dataset is what we need. Our hypothesis is based on the fact that
deep neural networks are data driven models, and data is what leads/misleads
models. In this work, we propose an empirical study that examines how to select
a subset of and/or create high quality benchmark data, for a model to learn
effectively. We seek to answer if big datasets are truly needed to learn a
task, and whether a smaller subset of high quality data can replace big
datasets. We plan to investigate both data pruning and data creation paradigms
to generate high quality datasets.


title:
Definite orthogonal modular forms: Computations, Excursions and
  Discoveries

abstract:
  We consider spaces of modular forms attached to definite orthogonal groups of
low even rank and nontrivial level, equipped with Hecke operators defined by
Kneser neighbours. After reviewing algorithms to compute with these spaces, we
investigate endoscopy using theta series and a theorem of Rallis. Along the
way, we exhibit many examples and pose several conjectures. As a first
application, we express counts of Kneser neighbours in terms of coefficients of
classical or Siegel modular forms, complementing work of Chenevier-Lannes. As a
second application, we prove new instances of Eisenstein congruences of
Ramanujan and Kurokawa-Mizumoto type.


title:
A numerical study of the side-wall effects on turbulent bands in channel
  flow at transitional Reynolds numbers

abstract:
  We investigated the side-wall effects on turbulent bands in channel flow at
transitional Reynolds numbers by direct numerical simulations using the open
source spectral-element code Nektar++. The width-to-height aspect ratio of 50:1
is considered for this study. Our study shows that turbulent bands can survive
the collision with the side wall above bulk Reynolds number of Re around 1000
but decay below Re around 975, i.e. the critical Reynolds number should be
approximately between the two Reynolds numbers. We also discussed about the
underlying mechanism for the decay of the band at low Reynolds numbers and
potential effects of larger spanwise channel widths than that considered in our
study. The results are informative for experimental studies of channel flow
turbulence at transitional Reynolds numbers.


title:
Transition Relation Aware Self-Attention for Session-based
  Recommendation

abstract:
  Session-based recommendation is a challenging problem in the real-world
scenes, e.g., ecommerce, short video platforms, and music platforms, which aims
to predict the next click action based on the anonymous session. Recently,
graph neural networks (GNNs) have emerged as the state-of-the-art methods for
session-based recommendation. However, we find that there exist two limitations
in these methods. One is the item transition relations are not fully exploited
since the relations are not explicitly modeled. Another is the long-range
dependencies between items can not be captured effectively due to the
limitation of GNNs. To solve the above problems, we propose a novel approach
for session-based recommendation, called Transition Relation Aware
Self-Attention (TRASA). Specifically, TRASA first converts the session to a
graph and then encodes the shortest path between items through the gated
recurrent unit as their transition relation. Then, to capture the long-range
dependencies, TRASA utilizes the self-attention mechanism to build the direct
connection between any two items without going through intermediate ones. Also,
the transition relations are incorporated explicitly when computing the
attention scores. Extensive experiments on three real-word datasets demonstrate
that TRASA outperforms the existing state-of-the-art methods consistently.


title:
Information retrieval for label noise document ranking by bag sampling
  and group-wise loss

abstract:
  Long Document retrieval (DR) has always been a tremendous challenge for
reading comprehension and information retrieval. The pre-training model has
achieved good results in the retrieval stage and Ranking for long documents in
recent years. However, there is still some crucial problem in long document
ranking, such as data label noises, long document representations, negative
data Unbalanced sampling, etc. To eliminate the noise of labeled data and to be
able to sample the long documents in the search reasonably negatively, we
propose the bag sampling method and the group-wise Localized Contrastive
Estimation(LCE) method. We use the head middle tail passage for the long
document to encode the long document, and in the retrieval, stage Use dense
retrieval to generate the candidate's data. The retrieval data is divided into
multiple bags at the ranking stage, and negative samples are selected in each
bag. After sampling, two losses are combined. The first loss is LCE. To fit bag
sampling well, after query and document are encoded, the global features of
each group are extracted by convolutional layer and max-pooling to improve the
model's resistance to the impact of labeling noise, finally, calculate the LCE
group-wise loss. Notably, our model shows excellent performance on the MS MARCO
Long document ranking leaderboard.


title:
Optimal Precoding Design for Monostatic ISAC Systems: MSE Lower Bound
  and DoF Completion

abstract:
  In this letter, we study the parameter estimation performance for monostatic
downlink integrated sensing and communications (ISAC) systems. In particular,
we analyze the mean squared error (MSE) lower bound for target sensing in the
downlink ISAC system that reveals the suboptimality in re-using the
conventional communication waveform for sensing. To realize a practical
dual-functional waveform, we propose a waveform augmentation strategy that
imposes an extra signal structure, namely the degrees-of-freedom (DoF)
completion method. The proposed approach is capable of improving the parameter
estimation performance of the ISAC system and achieving the derived MSE lower
bound. To improve the performance of the proposed strategy, we formulate an MSE
minimization problem to design the ISAC precoder, subject to the communication
users' signal-interference-plus-noise-ratio (SINR) constraints. Despite the
non-convexity of the waveform design problem, we obtain its globally optimal
solution via semi-definite relaxation (SDR) and the proposed constructive
method. Simulation results validate the proposed DoF completion technology
could achieve the derived MSE lower bound and the effectiveness of the
MSE-based ISAC waveform design.


title: Kernel Proposal Network for Arbitrary Shape Text Detection

abstract:
  Segmentation-based methods have achieved great success for arbitrary shape
text detection. However, separating neighboring text instances is still one of
the most challenging problems due to the complexity of texts in scene images.
In this paper, we propose an innovative Kernel Proposal Network (dubbed KPN)
for arbitrary shape text detection. The proposed KPN can separate neighboring
text instances by classifying different texts into instance-independent feature
maps, meanwhile avoiding the complex aggregation process existing in
segmentation-based arbitrary shape text detection methods. To be concrete, our
KPN will predict a Gaussian center map for each text image, which will be used
to extract a series of candidate kernel proposals (i.e., dynamic convolution
kernel) from the embedding feature maps according to their corresponding
keypoint positions. To enforce the independence between kernel proposals, we
propose a novel orthogonal learning loss (OLL) via orthogonal constraints.
Specifically, our kernel proposals contain important self-information learned
by network and location information by position embedding. Finally, kernel
proposals will individually convolve all embedding feature maps for generating
individual embedded maps of text instances. In this way, our KPN can
effectively separate neighboring text instances and improve the robustness
against unclear boundaries. To our knowledge, our work is the first to
introduce the dynamic convolution kernel strategy to efficiently and
effectively tackle the adhesion problem of neighboring text instances in text
detection. Experimental results on challenging datasets verify the impressive
performance and efficiency of our method. The code and model are available at
https://github.com/GXYM/KPN.


title:
Acousto-optic holography for micrometer-scale grid patterning of
  amplified laser pulses with single-pulse accuracy

abstract:
  Many optical systems use acousto-optics for fast steering of light. However,
acousto-optic (AO) light diffraction permits manipulation and shaping of light
fields in a much broader sense including holography. While acousto-optic
devices operate at incontestable speed, they are one dimensional (1D)
diffraction devices depending on analog serial data transfer and as such have a
mode of use distinguished from common 2D spatial light modulators. Combination
with an amplified laser source allows to clock the serial buildup of AO
diffraction patterns for avoidance of phase aberration in the output beam due
to acoustic wave progression. Pseudo 2D holography is established with two
crossed AO deflectors for biaxial spatial modulation of the input phase, and
optionally also of its amplitude. In this configuration, acousto-optics permits
light patterning into micrometer-scale 2D point grids with the possibility to
switch the pattern between individual laser pulses. We describe algorithms for
deriving AO holograms for either pure phase modulation or for a co-modulation
of amplitude. Computationally reconstructed phase holograms show focal patterns
in presence of side lobes. Side lobes are entirely suppressed by amplitude
modulation, though at the expense of reduced optical power transmission.


title: Nonlinear wave equations with slowly decaying initial data

abstract:
  New local smoothing estimates in Besov spaces adapted to the half-wave group
are proved via $\ell^2$-decoupling. We apply these estimates to obtain new
well-posedness results for the cubic nonlinear wave equation in two dimensions.
The results are compared to new well-posedness results in $L^p$-based Sobolev
spaces.


title:
Implicit LiDAR Network: LiDAR Super-Resolution via Interpolation Weight
  Prediction

abstract:
  Super-resolution of LiDAR range images is crucial to improving many
downstream tasks such as object detection, recognition, and tracking. While
deep learning has made a remarkable advances in super-resolution techniques,
typical convolutional architectures limit upscaling factors to specific output
resolutions in training. Recent work has shown that a continuous representation
of an image and learning its implicit function enable almost limitless
upscaling. However, the detailed approach, predicting values (depths) for
neighbor pixels in the input and then linearly interpolating them, does not
best fit the LiDAR range images since it does not fill the unmeasured details
but creates a new image with regression in a high-dimensional space. In
addition, the linear interpolation blurs sharp edges providing important
boundary information of objects in 3-D points. To handle these problems, we
propose a novel network, Implicit LiDAR Network (ILN), which learns not the
values per pixels but weights in the interpolation so that the superresolution
can be done by blending the input pixel depths but with non-linear weights.
Also, the weights can be considered as attentions from the query to the
neighbor pixels, and thus an attention module in the recent Transformer
architecture can be leveraged. Our experiments with a novel large-scale
synthetic dataset demonstrate that the proposed network reconstructs more
accurately than the state-of-the-art methods, achieving much faster convergence
in training.


title: A survey in Adversarial Defences and Robustness in NLP

abstract:
  In recent years, it has been seen that deep neural networks are lacking
robustness and are likely to break in case of adversarial perturbations in
input data. Strong adversarial attacks are proposed by various authors for
computer vision and Natural Language Processing (NLP). As a counter-effort,
several defense mechanisms are also proposed to save these networks from
failing. In contrast with image data, generating adversarial attacks and
defending these models is not easy in NLP because of the discrete nature of the
text data. However, numerous methods for adversarial defense are proposed of
late, for different NLP tasks such as text classification, named entity
recognition, natural language inferencing, etc. These methods are not just used
for defending neural networks from adversarial attacks, but also used as a
regularization mechanism during training, saving the model from overfitting.
The proposed survey is an attempt to review different methods proposed for
adversarial defenses in NLP in the recent past by proposing a novel taxonomy.
This survey also highlights the fragility of the advanced deep neural networks
in NLP and the challenges in defending them.


title:
Mixed dispersion nonlinear Schr\"odinger equation in higher dimensions:
  theoretical analysis and numerical computations

abstract:
  In the present work we provide a characterization of the ground states of a
higher-dimensional quadratic-quartic model of the nonlinear Schr{\"o}dinger
class with a combination of a focusing biharmonic operator with either an
isotropic or an anisotropic defocusing Laplacian operator (at the linear level)
and power-law nonlinearity. Examining principally the prototypical example of
dimension $d=2$, we find that instability arises beyond a certain threshold
coefficient of the Laplacian between the cubic and quintic cases, while all
solutions are stable for powers below the cubic. Above the quintic, and up to a
critical nonlinearity exponent $p$, there exists a progressively narrowing
range of stable frequencies. Finally, above the critical $p$ all solutions are
unstable. The picture is rather similar in the anisotropic case, with the
difference that even before the cubic case, the numerical computations suggest
an interval of unstable frequencies. Our analysis generalizes the relevant
observations for arbitrary combinations of Laplacian prefactor $b$ and
nonlinearity power $p$.


title:
Concentration Network for Reinforcement Learning of Large-Scale
  Multi-Agent Systems

abstract:
  When dealing with a series of imminent issues, humans can naturally
concentrate on a subset of these concerning issues by prioritizing them
according to their contributions to motivational indices, e.g., the probability
of winning a game. This idea of concentration offers insights into
reinforcement learning of sophisticated Large-scale Multi-Agent Systems (LMAS)
participated by hundreds of agents. In such an LMAS, each agent receives a long
series of entity observations at each step, which can overwhelm existing
aggregation networks such as graph attention networks and cause inefficiency.
In this paper, we propose a concentration network called ConcNet. First,
ConcNet scores the observed entities considering several motivational indices,
e.g., expected survival time and state value of the agents, and then ranks,
prunes, and aggregates the encodings of observed entities to extract features.
Second, distinct from the well-known attention mechanism, ConcNet has a unique
motivational subnetwork to explicitly consider the motivational indices when
scoring the observed entities. Furthermore, we present a concentration policy
gradient architecture that can learn effective policies in LMAS from scratch.
Extensive experiments demonstrate that the presented architecture has excellent
scalability and flexibility, and significantly outperforms existing methods on
LMAS benchmarks.


title:
Combinatorial results for order-preserving partial injective contraction
  mappings

abstract:
  Let $ \mathcal{I}_n$ be the symmetric inverse semigroup on $X_n = \{1, 2,
\ldots , n\}$. Let $\mathcal{OCI}_n$ be the subsemigroup of $\mathcal{I}_n$
consisting of all order-preserving injective partial contraction mappings, and
let $\mathcal{ODCI}_n$ be the subsemigroup of $\mathcal{I}_n$ consisting of all
order-preserving and order-decreasing injective partial contraction mappings of
$X_n$. In this paper, we investigate the cardinalities of some equivalences on
$\mathcal{OCI}_n$ and $\mathcal{ODCI}_n$ which lead naturally to obtaining the
order of these semigroups. Then, we relate the formulae obtained to Fibonacci
numbers. Similar results about $\mathcal{ORCI}_n$, the semigroup of
order-preserving or order-reversing injective partial contraction mappings, are
deduced.


title: Recurrence-in-Recurrence Networks for Video Deblurring

abstract:
  State-of-the-art video deblurring methods often adopt recurrent neural
networks to model the temporal dependency between the frames. While the hidden
states play key role in delivering information to the next frame, abrupt motion
blur tend to weaken the relevance in the neighbor frames. In this paper, we
propose recurrence-in-recurrence network architecture to cope with the
limitations of short-ranged memory. We employ additional recurrent units inside
the RNN cell. First, we employ inner-recurrence module (IRM) to manage the
long-ranged dependency in a sequence. IRM learns to keep track of the cell
memory and provides complementary information to find the deblurred frames.
Second, we adopt an attention-based temporal blending strategy to extract the
necessary part of the information in the local neighborhood. The adpative
temporal blending (ATB) can either attenuate or amplify the features by the
spatial attention. Our extensive experimental results and analysis validate the
effectiveness of IRM and ATB on various RNN architectures.


title:
When did you become so smart, oh wise one?! Sarcasm Explanation in
  Multi-modal Multi-party Dialogues

abstract:
  Indirect speech such as sarcasm achieves a constellation of discourse goals
in human communication. While the indirectness of figurative language warrants
speakers to achieve certain pragmatic goals, it is challenging for AI agents to
comprehend such idiosyncrasies of human communication. Though sarcasm
identification has been a well-explored topic in dialogue analysis, for
conversational systems to truly grasp a conversation's innate meaning and
generate appropriate responses, simply detecting sarcasm is not enough; it is
vital to explain its underlying sarcastic connotation to capture its true
essence. In this work, we study the discourse structure of sarcastic
conversations and propose a novel task - Sarcasm Explanation in Dialogue (SED).
Set in a multimodal and code-mixed setting, the task aims to generate natural
language explanations of satirical conversations. To this end, we curate WITS,
a new dataset to support our task. We propose MAF (Modality Aware Fusion), a
multimodal context-aware attention and global information fusion module to
capture multimodality and use it to benchmark WITS. The proposed attention
module surpasses the traditional multimodal fusion baselines and reports the
best performance on almost all metrics. Lastly, we carry out detailed analyses
both quantitatively and qualitatively.


title: Automatically Distilling Storyboard with Rich Features for Android Apps

abstract:
  Before developing a new mobile app, the development team usually endeavors
painstaking efforts to review many existing apps with similar purposes. The
review process is crucial in the sense that it reduces market risks and
provides inspirations for app development. However, manual exploration of
hundreds of existing apps by different roles (e.g., product manager, UI/UX
designer, developer, and tester) can be ineffective.
  Following the conception of storyboard in movie production, we propose a
system, named StoryDistiller, to automatically generate the storyboards for
Android apps with rich features through reverse engineering, and assist
different roles to review and analyze apps effectively and efficiently.
Specifically, we (1) propose a hybrid method to extract a relatively complete
Activity transition graph (ATG), that is, it first extracts the ATG of Android
apps through static analysis method first, and further leverages dynamic
component exploration to augment ATG; (2) extract the required inter-component
communication (ICC) data of each target Activity by leveraging static data-flow
analysis and renders UI pages dynamically by using app instrumentation together
with the extracted required ICC data; (3) obtain rich features including
comprehensive ATG with rendered UI pages, semantic activity names,
corresponding logic and layout code, etc. (4) implement the storyboard
visualization as a web service with the rendered UI pages and the corresponding
rich features. Our experiments unveil that StoryDistiller is effective and
indeed useful to assist app exploration and review. We also conduct a
comprehensive comparison study to demonstrate better performance over IC3,
Gator, Stoat, and StoryDroid.


title:
One-stage Video Instance Segmentation: From Frame-in Frame-out to
  Clip-in Clip-out

abstract:
  Many video instance segmentation (VIS) methods partition a video sequence
into individual frames to detect and segment objects frame by frame. However,
such a frame-in frame-out (FiFo) pipeline is ineffective to exploit the
temporal information. Based on the fact that adjacent frames in a short clip
are highly coherent in content, we propose to extend the one-stage FiFo
framework to a clip-in clip-out (CiCo) one, which performs VIS clip by clip.
Specifically, we stack FPN features of all frames in a short video clip to
build a spatio-temporal feature cube, and replace the 2D conv layers in the
prediction heads and the mask branch with 3D conv layers, forming clip-level
prediction heads (CPH) and clip-level mask heads (CMH). Then the clip-level
masks of an instance can be generated by feeding its box-level predictions from
CPH and clip-level features from CMH into a small fully convolutional network.
A clip-level segmentation loss is proposed to ensure that the generated
instance masks are temporally coherent in the clip. The proposed CiCo strategy
is free of inter-frame alignment, and can be easily embedded into existing FiFo
based VIS approaches. To validate the generality and effectiveness of our CiCo
strategy, we apply it to two representative FiFo methods, Yolact
\cite{bolya2019yolact} and CondInst \cite{tian2020conditional}, resulting in
two new one-stage VIS models, namely CiCo-Yolact and CiCo-CondInst, which
achieve 37.1/37.3\%, 35.2/35.4\% and 17.2/18.0\% mask AP using the ResNet50
backbone, and 41.8/41.4\%, 38.0/38.9\% and 18.0/18.2\% mask AP using the Swin
Transformer tiny backbone on YouTube-VIS 2019, 2021 and OVIS valid sets,
respectively, recording new state-of-the-arts. Code and video demos of CiCo can
be found at \url{https://github.com/MinghanLi/CiCo}.


title:
Accessible or Not? An Empirical Investigation of Android App
  Accessibility

abstract:
  Mobile apps provide new opportunities to people with disabilities to act
independently in the world. Motivated by this trend, researchers have conducted
empirical studies by using the inaccessibility issue rate of each page (i.e.,
screen level) to represent the characteristics of mobile app accessibility.
However, there still lacks an empirical investigation directly focusing on the
issues themselves (i.e., issue level) to unveil more fine-grained findings, due
to the lack of an effective issue detection method and a relatively
comprehensive dataset of issues.
  To fill in this literature gap, we first propose an automated app page
exploration tool, named Xbot, to facilitate app accessibility testing and
automatically collect accessibility issues by leveraging the instrumentation
technique and static program analysis. Owing to the relatively high activity
coverage (around 80%) achieved by Xbot when exploring apps, Xbot achieves
better performance on accessibility issue collection than existing testing
tools such as Google Monkey. With Xbot, we are able to collect a relatively
comprehensive accessibility issue dataset and finally collect 86,767 issues
from 2,270 unique apps including both closed-source and open-source apps, based
on which we further carry out an empirical study from the perspective of
accessibility issues themselves to investigate novel characteristics of
accessibility issues. Specifically, we extensively investigate these issues by
checking 1) the overall severity of issues with multiple criteria, 2) the
in-depth relation between issue types and app categories, GUI component types,
3) the frequent issue patterns quantitatively, and 4) the fixing status of
accessibility issues.


title: $\chi$-binding functions for some classes of $(P_3\cup P_2)$-free graphs

abstract:
  The class of $2K_2$-free graphs have been well studied in various contexts in
the past. It is known that the class of $\{2K_2,2K_1+K_p\}$-free graphs and
$\{2K_2,(K_1\cup K_2)+K_p\}$-free graphs admits a linear $\chi$-binding
function. In this paper, we study the classes of $(P_3\cup P_2)$-free graphs
which is a superclass of $2K_2$-free graphs. We show that $\{P_3\cup
P_2,2K_1+K_p\}$-free graphs and $\{P_3\cup P_2,(K_1\cup K_2)+K_p\}$-free graphs
also admits linear $\chi$-binding functions. In addition, we give tight
chromatic bounds for $\{P_3\cup P_2,HVN\}$-free graphs and $\{P_3\cup
P_2,diamond\}$-free graphs and it can be seen that the latter is an improvement
of the existing bound given by A. P. Bharathi and S. A. Choudum [Colouring of
$(P_3\cup P_2)$-free graphs, Graphs and Combinatorics 34 (2018), 97-107].


title:
VariabilityTrack:Multi-Object Tracking with Variable Speed Object
  Movement

abstract:
  Multi-object tracking (MOT) aims at estimating bounding boxes and identities
of objects in videos. Most methods can be roughly classified as
tracking-by-detection and joint-detection-association paradigms. Although the
latter has elicited more attention and demonstrates comparable performance
relative than the former, we claim that the tracking-by-detection paradigm is
still the optimal solution in terms of tracking accuracy,such as
ByteTrack,which achieves 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of
MOT17 with 30 FPS running speed on a single V100 GPU.However, under complex
perspectives such as vehicle and UAV acceleration, the performance of such a
tracker using uniform Kalman filter will be greatly affected, resulting in
tracking loss.In this paper, we propose a variable speed Kalman filter
algorithm based on environmental feedback and improve the matching process,
which can greatly improve the tracking effect in complex variable speed scenes
while maintaining high tracking accuracy in relatively static scenes.
Eventually, higher MOTA and IDF1 results can be achieved on MOT17 test set than
ByteTrack


title:
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal
  Artery/Vein Segmentation

abstract:
  Estimating clinically-relevant vascular features following vessel
segmentation is a standard pipeline for retinal vessel analysis, which provides
potential ocular biomarkers for both ophthalmic disease and systemic disease.
In this work, we integrate these clinical features into a novel vascular
feature optimised loss function (VAFO-Loss), in order to regularise networks to
produce segmentation maps, with which more accurate vascular features can be
derived. Two common vascular features, vessel density and fractal dimension,
are identified to be sensitive to intra-segment misclassification, which is a
well-recognised problem in multi-class artery/vein segmentation particularly
hindering the estimation of these vascular features. Thus we encode these two
features into VAFO-Loss. We first show that incorporating our end-to-end
VAFO-Loss in standard segmentation networks indeed improves vascular feature
estimation, yielding quantitative improvement in stroke incidence prediction, a
clinical downstream task. We also report a technically interesting finding that
the trained segmentation network, albeit biased by the feature optimised loss
VAFO-Loss, shows statistically significant improvement in segmentation metrics,
compared to those trained with other state-of-the-art segmentation losses.


title: T violation without complex entries in lepton mixing matrix

abstract:
  We demonstrate that T invariance can be violated even when
Pontecorvo--Maki--Nakagawa--Sakata lepton mixing matrix is real. We obtain a
sufficient condition of T violation in neutrino oscillations in matter and
electromagnetic field. In the two-flavor model we derive the T-violating
spin-flavor transition probabilities. Then we prove that the transition
probabilities for neutrino in moving medium in electromagnetic field differ
from those for antineutrino in matter composed of antiparticles and
electromagnetic field only in the sign of the T-violating term.


title:
Exploring the region encompassing gamma Cygni SNR and MAGIC J2019+408
  with the GMRT at 325 and 610 MHz

abstract:
  Gamma Cygni is a young supernova remnant located in the Cygnus region. MAGIC
(Major Atmospheric Gamma Imaging Cherenkov) telescopes detected TeV emission
(MAGIC J2019+408) to the north-west of this remnant, about 5 arcmin from its
border. We want to identify the radio sources within the region encompassing
gamma Cygni and MAGIC J2019+408 to shed light on their nature and investigate
if these radio sources could be potential contributors to gamma-ray emission.
We carried out a detailed study of the data we obtained with a survey of the
Cygnus region at 325 and 610 MHz with the Giant Metrewave Radio Telescope
(GMRT). We detected several radio sources in the region where the radio and the
TeV emission overlap, as well as several areas of enhanced radio emission. In
particular, two of these areas of diffuse enhanced emission may correspond to
the supernova remnant interacting with a high density region, which seems to be
the best candidate for the MAGIC source. Another two radio sources, which may
or may not contribute to the gamma rays, are also spatially coincident with the
emission peak of the MAGIC TeV source. One of them displays a rather peculiar
extended morphology whose nature is completely unknown. We have identified the
radio sources overlapping gamma Cygni and MAGIC J2019+408 and have shown that
their potential gamma-ray contribution is likely not dominant. In addition,
some of the studied sources show peculiar physical characteristics that deserve
deeper multi-wavelength observations.


title:
Radial oxygen and argon abundance gradients of the thin and thicker disc
  of Andromeda from planetary nebulae

abstract:
  Andromeda (M31) is the nearest giant spiral galaxy to our Milky Way (MW) and
the most massive member of our Local Group. We obtain a magnitude-limited
sample of M31 disc PNe with chemical abundance measurements through the direct
detection of the [OIII] 4363 \r{A} line. This leads to $205$ and $200$ PNe with
oxygen and argon abundance measurements respectively. We find that high- and
low-extinction M31 disc PNe have statistically distinct argon and oxygen
abundance distributions. In the radial range $2-30$ kpc, the older
low-extinction disc PNe are metal-poorer on average with a slightly positive
radial oxygen abundance gradient ($0.006 \pm 0.003$ dex/kpc) and slightly
negative radial argon abundance gradient ($-0.005 \pm 0.003$ dex/kpc), while
the younger high-extinction disc PNe are metal-richer on average with steeper
radial abundance gradients for both oxygen ($-0.013 \pm 0.006$ dex/kpc) and
argon ($-0.018 \pm 0.006$ dex/kpc), similar to the gradients measured for M31
HII regions. These abundance gradients are consistent with a major merger in
the M31 disc, with the majority of the low-extinction PNe being the older
pre-merger disc stars in the thicker disc, and the majority of the
high-extinction PNe being younger stars in the thin disc, formed during and
after the merger event. The chemical abundance of the M31 thicker disc has been
radially homogenized because of the major merger. Accounting for differences in
disc-scale lengths, we find the radial oxygen abundance gradient of the M31
thicker disc is amongst the most positive values observed till date in spiral
galaxies, flatter than that of the MW thick disc which has a negative radial
gradient. On the other hand, the thin discs of the MW and M31 have remarkably
similar negative oxygen abundance gradient values.


title: DFTR: Depth-supervised Fusion Transformer for Salient Object Detection

abstract:
  Automated salient object detection (SOD) plays an increasingly crucial role
in many computer vision applications. By reformulating the depth information as
supervision rather than as input, depth-supervised convolutional neural
networks (CNN) have achieved promising results on both RGB and RGB-D SOD
scenarios with the merits of no requirements for extra depth networks and depth
inputs in the inference stage. This paper, for the first time, seeks to expand
the applicability of depth supervision to the Transformer architecture.
Specifically, we develop a Depth-supervised Fusion TRansformer (DFTR), to
further improve the accuracy of both RGB and RGB-D SOD. The proposed DFTR
involves three primary features: 1) DFTR, to the best of our knowledge, is the
first pure Transformer-based model for depth-supervised SOD; 2) A multi-scale
feature aggregation (MFA) module is proposed to fully exploit the multi-scale
features encoded by the Swin Transformer in a coarse-to-fine manner; 3) To
enable bidirectional information flow across different streams of features, a
novel multi-stage feature fusion (MFF) module is further integrated into our
DFTR with the emphasis on salient regions at different network learning stages.
We extensively evaluate the proposed DFTR on ten benchmarking datasets.
Experimental results show that our DFTR consistently outperforms the existing
state-of-the-art methods for both RGB and RGB-D SOD tasks. The code and model
will be made publicly available.


title: Categories of Differentiable Polynomial Circuits for Machine Learning

abstract:
  Reverse derivative categories (RDCs) have recently been shown to be a
suitable semantic framework for studying machine learning algorithms. Whereas
emphasis has been put on training methodologies, less attention has been
devoted to particular \emph{model classes}: the concrete categories whose
morphisms represent machine learning models. In this paper we study
presentations by generators and equations of classes of RDCs. In particular, we
propose \emph{polynomial circuits} as a suitable machine learning model. We
give an axiomatisation for these circuits and prove a functional completeness
result. Finally, we discuss the use of polynomial circuits over specific
semirings to perform machine learning with discrete values.


title:
Deep learning-based conditional inpainting for restoration of
  artifact-affected 4D CT images

abstract:
  4D CT imaging is an essential component of radiotherapy of thoracic/abdominal
tumors. 4D CT images are, however, often affected by artifacts that compromise
treatment planning quality. In this work, deep learning (DL)-based conditional
inpainting is proposed to restore anatomically correct image information of
artifact-affected areas. The restoration approach consists of a two-stage
process: DL-based detection of common interpolation (INT) and double structure
(DS) artifacts, followed by conditional inpainting applied to the artifact
areas. In this context, conditional refers to a guidance of the inpainting
process by patient-specific image data to ensure anatomically reliable results.
Evaluation is based on 65 in-house 4D CT data sets of lung cancer patients (48
with only slight artifacts, 17 with pronounced artifacts) and the publicly
available DIRLab 4D CT data (independent external test set). Automated artifact
detection revealed a ROC-AUC of 0.99 for INT and 0.97 for DS artifacts
(in-house data). The proposed inpainting method decreased the average root mean
squared error (RMSE) by 60% (DS) and 42% (INT) for the in-house evaluation data
(simulated artifacts for the slight artifact data; original data were
considered as ground truth for RMSE computation). For the external DIR-Lab
data, the RMSE decreased by 65% and 36%, respectively. Applied to the
pronounced artifact data group, on average 68% of the detectable artifacts were
removed. The results highlight the potential of DL-based inpainting for the
restoration of artifact-affected 4D CT data. Improved performance of
conditional inpainting (compared to standard inpainting) illustrates the
benefits of exploiting patient-specific prior knowledge.


title: Kondo interaction in FeTe and its potential role in the magnetic order

abstract:
  Finding d-electron heavy fermion (HF) states has been an important topic as
the diversity in d-electron materials can lead to many exotic Kondo
effect-related phenomena or new states of matter such as correlation-driven
topological Kondo insulator or cooperation between long-range magnetism and
Kondo lattice behavior. Yet, obtaining direct spectroscopic evidence for a
d-electron HF system has been elusive to date. Here, we report the observation
of Kondo lattice behavior in an antiferromagnetic metal, FeTe, via
angle-resolved photoemission spectroscopy (ARPES) and transport properties
measurements. The Kondo lattice behavior is represented by the emergence of a
sharp quasiparticle at low temperatures. The transport property measurements
confirm the low-temperature Fermi liquid behavior and reveal successive
coherent-incoherent crossover upon increasing temperature. We interpret the
Kondo lattice behavior as a result of hybridization between localized Fe 3dxy
and itinerant Te 5pz orbitals. Our interpretation is further evidenced by
Fano-type tunneling spectra which accompany a hybridization gap. Our
observations strongly suggest unusual cooperation between Kondo lattice
behavior and long-range magnetic order.


title: DATR: Domain-adaptive transformer for multi-domain landmark detection

abstract:
  Accurate anatomical landmark detection plays an increasingly vital role in
medical image analysis. Although existing methods achieve satisfying
performance, they are mostly based on CNN and specialized for a single domain
say associated with a particular anatomical region. In this work, we propose a
universal model for multi-domain landmark detection by taking advantage of
transformer for modeling long dependencies and develop a domain-adaptive
transformer model, named as DATR, which is trained on multiple mixed datasets
from different anatomies and capable of detecting landmarks of any image from
those anatomies. The proposed DATR exhibits three primary features: (i) It is
the first universal model which introduces transformer as an encoder for
multi-anatomy landmark detection; (ii) We design a domain-adaptive transformer
for anatomy-aware landmark detection, which can be effectively extended to any
other transformer network; (iii) Following previous studies, we employ a
light-weighted guidance network, which encourages the transformer network to
detect more accurate landmarks. We carry out experiments on three widely used
X-ray datasets for landmark detection, which have 1,588 images and 62 landmarks
in total, including three different anatomies (head, hand, and chest).
Experimental results demonstrate that our proposed DATR achieves
state-of-the-art performances by most metrics and behaves much better than any
previous convolution-based models. The code will be released publicly.


title: Quantum Chaos and Unitary Black Hole Evaporation

abstract:
  The formation and evaporation of small AdS black holes in a theory with a
holographic dual is governed by the usual rules of quantum mechanics. The
eigenstate thermalization hypothesis explains the validity of semiclassical
gravity for local bulk observables and can be used to quantify the magnitude of
quantum corrections to the semiclassical approximation. The holographic dual
produces a basis of black hole states with finite energy width, and observables
that are smooth functions on the classical phase space will self-average over a
large number of energy eigenstates, exponential in the Bekenstein-Hawking
entropy S, leading to results that are consistent with semiclassical gravity up
to small corrections of order exp(-S/2). As expected, the semiclassical
description breaks down for transition amplitudes that contribute to the
unitary S matrix of the holographic theory.


title: Hochschild homology of reductive $p$-adic groups

abstract:
  Consider a reductive $p$-adic group $G$, its (complex-valued) Hecke algebra
$H(G)$ and the Harish-Chandra--Schwartz algebra $S(G)$. We compute the
Hochschild homology groups of $H(G)$ and of $S(G)$, and we describe the
outcomes in several ways.
  Our main tools are algebraic families of smooth $G$-representations. With
those we construct maps from $HH_n (H(G))$ and $HH_n (S(G))$ to modules of
differential $n$-forms on affine varieties. For $n = 0$ this provides a
description of the cocentres of these algebras in terms of nice linear
functions on the Grothendieck group of finite length (tempered)
$G$-representations.
  It is known from earlier work that every Bernstein ideal $H(G)^s$ of $H(G)$
is closely related to a crossed product algebra of the from $O(T) \rtimes W$.
Here $O(T)$ denotes the regular functions on the variety $T$ of unramified
characters of a Levi subgroup $L$ of $G$, and $W$ is a finite group acting on
$T$. We make this relation even stronger by establishing an isomorphism between
$HH_* (H(G)^s)$ and $HH_* (O(T) \rtimes W)$, although we have to say that in
some cases it is necessary to twist $C[W]$ by a 2-cocycle.
  Similarly we prove that the Hochschild homology of the two-sided ideal
$S(G)^s$ of $S(G)$ is isomorphic to $HH_* (C^\infty (T_u) \rtimes W)$, where
$T_u$ denotes the Lie group of unitary unramified characters of $L$. In these
pictures of $HH_* (H(G))$ and $HH_* (S(G))$ we also show how the Bernstein
centre of $H(G)$ acts.
  Finally, we derive similar expressions for the (periodic) cyclic homology
groups of $H(G)$ and of $S(G)$ and we relate that to topological K-theory.


title:
Maximization of Mathai's Entropy under the Constraints of Generalized
  Gini and Gini mean difference indices and its Applications in Insurance

abstract:
  Statistical Physics, Diffusion Entropy Analysis and Information Theory
commonly use Mathai's entropy which measures the randomness of probability
laws, whereas welfare economics and the Social Sciences commonly use Gini index
which measures the evenness of probability laws. Motivated by the principle of
maximal entropy, we explore the maximization of Mathai's entropy subject to the
conditions in the following scenarios: (i) the conditions of a density function
and fixed mean; (ii) the conditions of a density function and fixed Generalized
Gini index. We also maximizes the Mathai's entropy subject to the constraints
of a given Gini mean difference index and the conditions of a density function.
The obtained maximum entropy distribution is fitted to the loss ratios (yearly
data) for earthquake insurance in California from 1971 through 1994 and its
performance with some one-parameter distributions are compared.


title:
Beyond Gaussian processes: Flexible Bayesian modeling and inference for
  geostatistical processes

abstract:
  This work proposes a novel family of geostatistical models to account for
features that cannot be properly accommodated by traditional Gaussian
processes. The family is specified hierarchically and combines the infinite
dimensional dynamics of Gaussian processes to that of any multivariate
continuous distribution. This combination is stochastically defined through a
latent Poisson process and the new family is called the Poisson-Gaussian
Mixture Process - POGAMP. Whilst the attempt of defining a geostatistical
process by assigning some arbitrary continuous distributions to be the
finite-dimension distributions usually leads to non-valid processes, the POGAMP
can have its finite-dimensional distributions to be arbitrarily close to any
continuous distribution and still be a valid process. Formal results to
establish its existence and other important properties, such as absolute
continuity w.r.t. a Gaussian process measure, are provided. Also, a MCMC
algorithm is carefully devised to perform Bayesian inference when the POGAMP is
discretely observed in some space domain. Simulations are performed to
empirically investigate the modelling properties of the POGAMP and the
efficiency of the MCMC algorithm. Finally, some real datasets are analysed to
illustrate the applicability of the proposed methodology.


title:
Hierarchical Codebook based Multiuser Beam Training for Millimeter
  Massive MIMO

abstract:
  In this paper, multiuser beam training based on hierarchical codebook for
millimeter wave massive multi-input multi-output is investigated, where the
base station (BS) simultaneously performs beam training with multiple user
equipments (UEs). For the UEs, an alternative minimization method with a
closed-form expression (AMCF) is proposed to design the hierarchical codebook
under the constant modulus constraint. To speed up the convergence of the AMCF,
an initialization method based on Zadoff-Chu sequence is proposed. For the BS,
a simultaneous multiuser beam training scheme based on an adaptively designed
hierarchical codebook is proposed, where the codewords in the current layer of
the codebook are designed according to the beam training results of the
previous layer. The codewords at the BS are designed with multiple mainlobes,
each covering a spatial region for one or more UEs. Simulation results verify
the effectiveness of the proposed hierarchical codebook design schemes and show
that the proposed multiuser beam training scheme can approach the performance
of the beam sweeping but with significantly reduced beam training overhead.


title:
An end-user coding-based environment for programming an educational
  affective robot

abstract:
  In this paper we present an open source educational robot, designed both to
engage children in an affective and social interaction, and to be programmable
also in its social and affective behaviour. Indeed the robot, in addition to
classic programming tasks, can also be programmed as a social robot. In
addition to movements, the user can make the robot express emotions and make it
say things. The robot can also be left in autonomous mode, in which it is able
to carry out both biometric user's features and emotion recognition, and
greeting the user.


title:
Multi-step dual control for exploration and exploitation in autonomous
  search with convergence guarantee

abstract:
  Motivated by the recently proposed dual control for exploration and
exploitation (DCEE) concept, this paper presents a Multi-Step DCEE (MS-DCEE)
framework with guaranteed convergence for autonomous search of a source of
airborne dispersion. Different from the existing stochastic model predictive
control (SMPC) algorithm and informative path planning (IPP) approaches, the
proposed MS-DCEE approach uses the current and future input to not only drive
the agent towards the estimated source location (exploitation) but also reduce
its estimation uncertainty (exploration) by actively learning the operational
environment. Unknown source target position, together with unknown environment,
impose significant challenges in establishing the recursive feasibility and the
convergence of the proposed algorithm. To address them, with the help of the
property of Bayesian estimation, we develop a two-step approach where the
unbiasedness of the mean estimation is assumed first and then the randomness of
the mean estimate under each collected information sequence is accounted. Based
on that, we develop a MS-DCEE scheme with suitable terminal ingredients where
recursive feasibility and convergence are guaranteed. Two simulation scenarios
are conducted, which show that the proposed MS-DCEE algorithm outperforms the
SMPC, the IPP and the single-step DCEE approaches in terms of searching
successful rates and efficiency.


title: On noncompact warped product Ricci solitons

abstract:
  The goal of this article is to investigate complete noncompact warped product
gradient Ricci solitons. Nonexistence results, estimates for the warping
function and for its gradient are proven. When the soliton is steady or
expanding these nonexistence results generalize to a broader context certain
pde estimates and rigidity obtained when studying warped product Einstein
manifolds. When the soliton is shrinking, it is presented a nonexistence
theorem with no counterpart in the Einstein case, which is proved using
properties of the first eigenvalue of a weighted Laplacian.


title: Equivariant Graph Mechanics Networks with Constraints

abstract:
  Learning to reason about relations and dynamics over multiple interacting
objects is a challenging topic in machine learning. The challenges mainly stem
from that the interacting systems are exponentially-compositional, symmetrical,
and commonly geometrically-constrained. Current methods, particularly the ones
based on equivariant Graph Neural Networks (GNNs), have targeted on the first
two challenges but remain immature for constrained systems. In this paper, we
propose Graph Mechanics Network (GMN) which is combinatorially efficient,
equivariant and constraint-aware. The core of GMN is that it represents, by
generalized coordinates, the forward kinematics information (positions and
velocities) of a structural object. In this manner, the geometrical constraints
are implicitly and naturally encoded in the forward kinematics. Moreover, to
allow equivariant message passing in GMN, we have developed a general form of
orthogonality-equivariant functions, given that the dynamics of constrained
systems are more complicated than the unconstrained counterparts.
Theoretically, the proposed equivariant formulation is proved to be universally
expressive under certain conditions. Extensive experiments support the
advantages of GMN compared to the state-of-the-art GNNs in terms of prediction
accuracy, constraint satisfaction and data efficiency on the simulated systems
consisting of particles, sticks and hinges, as well as two real-world datasets
for molecular dynamics prediction and human motion capture.


title:
Dynamical symmetry algebra of two superintegrable two-dimensional
  systems

abstract:
  A complete classification of 2D superintegrable systems on two-dimensional
conformally flat spaces has been performed over the years and 58 models,
divided into 12 equivalence classes, have been obtained. We will re-examine two
pseudo-Hermitian quantum systems $E_{8}$ and $E_{10}$ from such a
classification by a new approach based on extra sets of ladder operators. Those
extra ladder operators are exploited to obtain the generating spectrum algebra
and the dynamical symmetry one. We will relate the generators of the dynamical
symmetry algebra to the Hamiltonian, thus demonstrating that the latter can be
written in an algebraic form. We will also link them to the integrals of motion
providing the superintegrability property. This demonstrates how the dynamical
symmetry algebra explains the symmetries. Furthermore, we will exploit those
algebraic constructions to generate extended sets of states and give the action
of the ladder operators on them. We will present polynomials of the Hamiltonian
and the integrals of motion that vanish on some of those states, then
demonstrating that the sets of states not only contain eigenstates, but also
generalized states. Our approach provides a natural framework for such states.


title:
GOLUM: A fast and precise methodology to search for, and analyze,
  strongly lensed gravitational-wave events

abstract:
  Like light, gravitational waves can be gravitationally lensed by massive
objects along their travel path. Strong lensing produces several images from
the same binary coalescence and is forecasted to have a promising rate in
ground-based gravitational detectors. To search for this effect in the data,
one would, in principle, have to analyze all the possible combinations of the
individual detected events, whose number will be ever-increasing. To keep up
with the rising computational cost, we propose a fast and precise methodology
to analyze strongly-lensed gravitational wave events. The method works by
effectively using the posterior of the first image as prior for the second
image. Thanks to its increased speed tractability, this method enables the
joint analysis of more than two images. In addition, it opens the door to new
strong lensing studies where a large number of injections is required.


title:
The mass-lumped midpoint scheme for computational micromagnetics: Newton
  linearization and application to magnetic skyrmion dynamics

abstract:
  We discuss a mass-lumped midpoint scheme for the numerical approximation of
the Landau-Lifshitz-Gilbert equation, which models the dynamics of the
magnetization in ferromagnetic materials. In addition to the classical
micromagnetic field contributions, our setting covers the non-standard
Dzyaloshinskii-Moriya interaction, which is the essential ingredient for the
enucleation and stabilization of magnetic skyrmions. Our analysis also includes
the inexact solution of the arising nonlinear systems, for which we discuss
both a constraint preserving fixed-point solver from the literature and a novel
approach based on the Newton method. We numerically compare the two
linearization techniques and show that the Newton solver leads to a
considerably lower number of nonlinear iterations. Moreover, in a numerical
study on magnetic skyrmions, we demonstrate that, for magnetization dynamics
that are very sensitive to energy perturbations, the midpoint scheme, due to
its conservation properties, is superior to the dissipative tangent plane
schemes from the literature.


title: A Duke's theorem for closed geodesics in the homology of modular curves

abstract:
  Given an element of the narrow class group of a real quadratic field one can
associate an oriented closed geodesic on the modular curve of level $p$
(assuming that $p$ splits in the quadratic field). In this paper we study the
distribution of the homology classes of these oriented closed geodesics as the
discriminant tends to infinity. We show that when averaged over subgroups (of
sufficiently small index) the homology classes in the non-compact homology
converge in a weak sense to the Eisenstein class. This fits into the framework
of Duke's theorem. We also consider the level aspect which leads to the problem
of bounding certain inner-products. This is resolved by comparing a geometric
and a spectral side of a homological incarnation of the amplified pre-trace
formula. This result might be of independent interest. Finally, we present
applications to group theory and modular forms.


title:
Analytical approximations for primordial power spectra in emergent
  universe

abstract:
  The emergent universe scenario was proposed to solve the big bang singularity
by suggesting that the universe originates from an Einstein static state and
then evolves into a subsequently inflationary era. Thus, to find the relic of
the existence of the Einstein static state becomes a crucial work. In this
paper, we derive analytical approximation of the primordial power spectra and
analyze the CMB TT-spectra in the closed emergent universe. After analyzing the
CMB TT-spectrum of the emergent universe scenario, we find that the CMB
TT-spectra of method I and method II are suppressed at $k<30$, and their
spectra are nearly identical. Additionally, by comparing the spectra of the
emergent universe scenario with the ones of the ultraslow-roll inflationary
model in the closed spacetime, we find that the CMB TT-spectrum of the emergent
universe is similar to the one of the inflationary model with the special case
$\eta_t = \eta_{max}$.


title:
Discrete, recurrent, and scalable patterns in human judgement underlie
  affective picture ratings

abstract:
  Operant keypress tasks, where each action has a consequence, have been
analogized to the construct of "wanting" and produce lawful relationships in
humans that quantify preferences for approach and avoidance behavior. It is
unknown if rating tasks without an operant framework, which can be analogized
to "liking", show similar lawful relationships. We studied three independent
cohorts of participants (N = 501, 506, and 4,019 participants) collected by two
distinct organizations, using the same 7-point Likert scale to rate negative to
positive preferences for pictures from the International Affective Picture Set.
Picture ratings without an operant framework produced similar value functions,
limit functions, and trade-off functions to those reported in the literature
for operant keypress tasks, all with goodness of fits above 0.75. These value,
limit, and trade-off functions were discrete in their mathematical formulation,
recurrent across all three independent cohorts, and demonstrated scaling
between individual and group curves. In all three experiments, the computation
of loss aversion showed 95% confidence intervals below the value of 2, arguing
against a strong overweighting of losses relative to gains, as has previously
been reported for keypress tasks or games of chance with calibrated
uncertainty. Graphed features from the three cohorts were similar and argue
that preference assessments meet three of four criteria for lawfulness,
providing a simple, short, and low-cost method for the quantitative assessment
of preference without forced choice decisions, games of chance, or operant
keypressing. This approach can easily be implemented on any digital device with
a screen (e.g., cellphones).


title: Investigation of radiation-enhanced diffusion using first-passage time

abstract:
  The approach of the first-passage time a certain level by a random process is
applied to the description of radiation-enhanced diffusion. This is an integral
approach to describing the problem of radiation-enhanced diffusion, which does
not specify the details of the process, but takes into account the overall
change in entropy during the course of the process. Two models are considered:
the drift-diffusion process and the Feller process. In the first case,
arbitrary influences on the system, including radiation, only reduce the time
of the first reaching the boundary, accelerating the diffusion process. In the
second case, influences are possible under which the diffusion process slows
down. It is noted that there are a large number of diffusion models. The task
of comparing mathematical and physical models is topical.


title: Low-Complexity Multicast Beamforming for Millimeter Wave Communications

abstract:
  To develop a low-complexity multicast beamforming method for millimeter wave
communications, we first propose a channel gain estimation method in this
article. We use the beam sweeping to find the best codeword and its two
neighboring codewords to form a composite beam. We then estimate the channel
gain based on the composite beam, which is computed off-line by minimizing the
variance of beam gain within beam coverage. With the estimated channel gain, we
propose a multicast beamforming design method under the max-min fair (MMF)
criterion. To reduce the computational complexity, we divide the large antenna
array into several small-size sub-arrays, where the size of each sub-array is
determined by the estimated channel gain. In particular, we introduce a phase
factor for each sub-array to explore additional degree of freedom for the
considered problem. Simulation results show that the proposed multicast
beamforming design method can substantially reduce the computational complexity
with little performance sacrifice compared to the existing methods.


title: Bringing Rolling Shutter Images Alive with Dual Reversed Distortion

abstract:
  Rolling shutter (RS) distortion can be interpreted as the result of picking a
row of pixels from instant global shutter (GS) frames over time during the
exposure of the RS camera. This means that the information of each instant GS
frame is partially, yet sequentially, embedded into the row-dependent
distortion. Inspired by this fact, we address the challenging task of reversing
this process, i.e., extracting undistorted GS frames from images suffering from
RS distortion. However, since RS distortion is coupled with other factors such
as readout settings and the relative velocity of scene elements to the camera,
models that only exploit the geometric correlation between temporally adjacent
images suffer from poor generality in processing data with different readout
settings and dynamic scenes with both camera motion and object motion. In this
paper, instead of two consecutive frames, we propose to exploit a pair of
images captured by dual RS cameras with reversed RS directions for this highly
challenging task. Grounded on the symmetric and complementary nature of dual
reversed distortion, we develop a novel end-to-end model, IFED, to generate
dual optical flow sequence through iterative learning of the velocity field
during the RS time. Extensive experimental results demonstrate that IFED is
superior to naive cascade schemes, as well as the state-of-the-art which
utilizes adjacent RS images. Most importantly, although it is trained on a
synthetic dataset, IFED is shown to be effective at retrieving GS frame
sequences from real-world RS distorted images of dynamic scenes.


title:
Lasing versus lasing without inversion in an optically thin gain medium
  near a metal surface

abstract:
  A theory of lasing in an optically thin layer of active centers disposed at a
metal surface is developed from first principles. The approach is based on a
rigorous account of the local field in a close vicinity of a reflective surface
which provides a feedback for dipole oscillations in active centers. It is
demonstrated that the gain medium thickness plays a crucial role in the lasing
condition and controls a switching from conventional lasing to lasing without
inversion. The numerical calculations are carried out for erbium doped glass
bordering a gold surface where radiation at telecom wavelength (1532 nm) can be
generated.


title: Staircase Patterns in Hirzebruch Surfaces

abstract:
  This paper gives an almost complete description of the staircases in the
ellipsoidal capacity functions of the symplectic Hirzebruch surfaces $H_b$
formed by blowing up the projective plane with weight $b$. We describe an
interweaving, recursively defined, family of obstructions to symplectic
embeddings of ellipsoids that show there is an open dense set of shape
parameters $b$ that are blocked, i.e. have no staircase, and an uncountable
number of other values of $b$ that do admit staircases. The remaining
$b$-values form a countable sequence of special rational numbers that are
closely related to the symmetries discussed in Magill--McDuff
(arXiv:2106.09143). We show that none of them admit ascending staircases.
Conjecturally, none admit descending staircases. Finally, we show that, as long
as $b$ is not one of these special rational values, any staircase in $H_b$ has
irrational accumulation point. A crucial ingredient of our proofs is the new,
more indirect approach to using almost toric fibrations in the analysis of
staircases recently introduced by Magill. In particular, the structure of the
relevant mutations of the set of almost toric fibrations on $H_b$ is echoed in
the structure of the set of blocked $b$-intervals.


title:
Laser plasma accelerated ultra-intense electron beam for efficiently
  exciting nuclear isomers

abstract:
  Utilizing laser plasma wakefield to accelerate ultra-high charge electron
beam is critical for many pioneering applications, for example to efficiently
produce nuclear isomers with short lifetimes which may be widely used. However,
because of the beam loading effect, electron charge in a single plasma bubble
is limited in level of hundreds picocoulomb. Here, we experimentally present
that a hundred kilo-ampere, twenty nanocoulomb, tens of MeV collimated electron
beam is produced from a chain of wakefield acceleration, via a tightly focused
intense laser pulse transversely matched in dense plasma. This ultra-intense
electron beam ascribes to a novel efficient injection that the nitrogen atom
inner shell electrons are ionized and continuously injected into multiple
plasma bubbles. This intense electron beam has been utilized to exciting
nuclear isomers with an ultra-high peak efficiency of $1.76\times10^{15}$
particles/s via photonuclear reactions. This efficient production method of
isomers can be widely used for pumping isotopes with excited state lifetimes
down to picosecond, which is benefit for deep understanding nuclear transition
mechanisms and stimulating gamma-ray lasers.


title: Learning Phase Transitions from Regression Uncertainty

abstract:
  For performing regression tasks involved in various physics problems,
enhancing the precision, or equivalently reducing the uncertainty of the
regression results is undoubtedly one of the central goals. Here, somewhat
surprisingly, we find that the unfavorable regression uncertainty in performing
regression tasks of inverse statistical problems actually contains "hidden"
information concerning phase transitions of the system under consideration. By
utilizing this "hidden" information, we develop a new unsupervised machine
learning approach dubbed "learning from regression uncertainty" for automated
detection of phases of matter, with the core working horse being a neural
network performing regression tasks instead of classification tasks as in
various related machine learning approaches developed so far. This is achieved
by revealing an intrinsic connection between regression uncertainty and
response properties of the system under consideration, thus making the results
of this machine learning approach directly interpretable. We demonstrate the
approach by identifying the critical point of the ferromagnetic Ising model and
revealing the existence of the intermediate phase in the six-state and
seven-state clock models. Our approach paves the way towards intriguing
possibilities for unveiling new physics via machine learning in an
interpretable manner.


title:
Energy networks for state estimation with random sensors using sparse
  labels

abstract:
  State estimation is required whenever we deal with high-dimensional dynamical
systems, as the complete measurement is often unavailable. It is key to gaining
insight, performing control or optimizing design tasks. Most deep
learning-based approaches require high-resolution labels and work with fixed
sensor locations, thus being restrictive in their scope. Also, doing Proper
orthogonal decomposition (POD) on sparse data is nontrivial. To tackle these
problems, we propose a technique with an implicit optimization layer and a
physics-based loss function that can learn from sparse labels. It works by
minimizing the energy of the neural network prediction, enabling it to work
with a varying number of sensors at different locations. Based on this
technique we present two models for discrete and continuous prediction in
space. We demonstrate the performance using two high-dimensional fluid problems
of Burgers' equation and Flow Past Cylinder for discrete model and using Allen
Cahn equation and Convection-diffusion equations for continuous model. We show
the models are also robust to noise in measurements.


title:
3D-GIF: 3D-Controllable Object Generation via Implicit Factorized
  Representations

abstract:
  While NeRF-based 3D-aware image generation methods enable viewpoint control,
limitations still remain to be adopted to various 3D applications. Due to their
view-dependent and light-entangled volume representation, the 3D geometry
presents unrealistic quality and the color should be re-rendered for every
desired viewpoint. To broaden the 3D applicability from 3D-aware image
generation to 3D-controllable object generation, we propose the factorized
representations which are view-independent and light-disentangled, and training
schemes with randomly sampled light conditions. We demonstrate the superiority
of our method by visualizing factorized representations, re-lighted images, and
albedo-textured meshes. In addition, we show that our approach improves the
quality of the generated geometry via visualization and quantitative
comparison. To the best of our knowledge, this is the first work that extracts
albedo-textured meshes with unposed 2D images without any additional labels or
assumptions.


title:
Factored Attention and Embedding for Unstructured-view Topic-related
  Ultrasound Report Generation

abstract:
  Echocardiography is widely used to clinical practice for diagnosis and
treatment, e.g., on the common congenital heart defects. The traditional manual
manipulation is error-prone due to the staff shortage, excess workload, and
less experience, leading to the urgent requirement of an automated
computer-aided reporting system to lighten the workload of ultrasonologists
considerably and assist them in decision making. Despite some recent successful
attempts in automatical medical report generation, they are trapped in the
ultrasound report generation, which involves unstructured-view images and
topic-related descriptions. To this end, we investigate the task of the
unstructured-view topic-related ultrasound report generation, and propose a
novel factored attention and embedding model (termed FAE-Gen). The proposed
FAE-Gen mainly consists of two modules, i.e., view-guided factored attention
and topic-oriented factored embedding, which 1) capture the homogeneous and
heterogeneous morphological characteristic across different views, and 2)
generate the descriptions with different syntactic patterns and different
emphatic contents for different topics. Experimental evaluations are conducted
on a to-be-released large-scale clinical cardiovascular ultrasound dataset
(CardUltData). Both quantitative comparisons and qualitative analysis
demonstrate the effectiveness and the superiority of FAE-Gen over seven
commonly-used metrics.


title: An edge centrality measure based on the Kemeny constant

abstract:
  A new measure $c(e)$ of the centrality of an edge $e$ in an undirected graph
$G$ is introduced. It is based on the variation of the Kemeny constant of the
graph after removing the edge $e$. The new measure is designed in such a way
that the Braess paradox is avoided. A numerical method for computing $c(e)$ is
introduced and a regularization technique is designed in order to deal with
cut-edges and disconnected graphs. Numerical experiments performed on synthetic
tests and on real road networks show that this measure is particularly
effective in revealing bottleneck roads whose removal would greatly reduce the
connectivity of the network.


title: Classification and quantification of incompatibility

abstract:
  For two orthonormal bases of a $d$-dimensional Hilbert space, the notion of
complete incompatibility was introduced recently by De Bi\`{e}vre [S. De
Bi\`{e}vre, Phys. Rev. Lett. 127, 190404 (2021)]. In this work, we introduce
the notion of $s$-order incompatibility with positive integer $s$ satisfying
$2\leq s\leq d+1.$ In particular, $(d+1)$-order incompatibility just coincides
with the complete incompatibility. We find some relationships between $s$-order
incompatibility, minimal support uncertainty and $t$-order rank deficiency of
the transition matrix. We also propose a framework to quantify incompatibility.


title: Gravitational Redshift for Wide Binaries in Gaia eDR3

abstract:
  The Doppler effect is commonly used to infer the velocity difference between
stars based on the relative shifts in the rest-frame wavelengths of their
spectral features. In wide binaries, the difference in gravitational redshift
from the surfaces of the constituent stars with distinct compactness dominates
at separations above 0.01 pc. I suggest that this effect became apparent for
wide pairs in the Gaia eDR3 catalogue but incorrectly interpreted as a possible
modification of Newtonian gravity in the internal kinematics of very wide
binaries.


title:
Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in
  Practice

abstract:
  Classifiers in natural language processing (NLP) often have a large number of
output classes. For example, neural language models (LMs) and machine
translation (MT) models both predict tokens from a vocabulary of thousands. The
Softmax output layer of these models typically receives as input a dense
feature representation, which has much lower dimensionality than the output. In
theory, the result is some words may be impossible to be predicted via argmax,
irrespective of input features, and empirically, there is evidence this happens
in small language models. In this paper we ask whether it can happen in
practical large language models and translation models. To do so, we develop
algorithms to detect such \emph{unargmaxable} tokens in public models. We find
that 13 out of 150 models do indeed have such tokens; however, they are very
infrequent and unlikely to impact model quality. We release our code so that
others can inspect their models.


title:
A Systematic Review on Computer Vision-Based Parking Lot Management
  Applied on Public Datasets

abstract:
  Computer vision-based parking lot management methods have been extensively
researched upon owing to their flexibility and cost-effectiveness. To evaluate
such methods authors often employ publicly available parking lot image
datasets. In this study, we surveyed and compared robust publicly available
image datasets specifically crafted to test computer vision-based methods for
parking lot management approaches and consequently present a systematic and
comprehensive review of existing works that employ such datasets. The
literature review identified relevant gaps that require further research, such
as the requirement of dataset-independent approaches and methods suitable for
autonomous detection of position of parking spaces. In addition, we have
noticed that several important factors such as the presence of the same cars
across consecutive images, have been neglected in most studies, thereby
rendering unrealistic assessment protocols. Furthermore, the analysis of the
datasets also revealed that certain features that should be present when
developing new benchmarks, such as the availability of video sequences and
images taken in more diverse conditions, including nighttime and snow, have not
been incorporated.


title:
A Deep Reinforcement Learning Environment for Particle Robot Navigation
  and Object Manipulation

abstract:
  Particle robots are novel biologically-inspired robotic systems where
locomotion can be achieved collectively and robustly, but not independently.
While its control is currently limited to a hand-crafted policy for basic
locomotion tasks, such a multi-robot system could be potentially controlled via
Deep Reinforcement Learning (DRL) for different tasks more efficiently.
However, the particle robot system presents a new set of challenges for DRL
differing from existing swarm robotics systems: the low degrees of freedom of
each robot and the increased necessity of coordination between robots. We
present a 2D particle robot simulator using the OpenAI Gym interface and Pymunk
as the physics engine, and introduce new tasks and challenges to research the
underexplored applications of DRL in the particle robot system. Moreover, we
use Stable-baselines3 to provide a set of benchmarks for the tasks. Current
baseline DRL algorithms show signs of achieving the tasks but are yet unable to
reach the performance of the hand-crafted policy. Further development of DRL
algorithms is necessary in order to accomplish the proposed tasks.


title:
Towards On-Device AI and Blockchain for 6G enabled Agricultural
  Supply-chain Management

abstract:
  6G envisions artificial intelligence (AI) powered solutions for enhancing the
quality-of-service (QoS) in the network and to ensure optimal utilization of
resources. In this work, we propose an architecture based on the combination of
unmanned aerial vehicles (UAVs), AI and blockchain for agricultural
supply-chain management with the purpose of ensuring traceability,
transparency, tracking inventories and contracts. We propose a solution to
facilitate on-device AI by generating a roadmap of models with various
resource-accuracy trade-offs. A fully convolutional neural network (FCN) model
is used for biomass estimation through images captured by the UAV. Instead of a
single compressed FCN model for deployment on UAV, we motivate the idea of
iterative pruning to provide multiple task-specific models with various
complexities and accuracy. To alleviate the impact of flight failure in a 6G
enabled dynamic UAV network, the proposed model selection strategy will assist
UAVs to update the model based on the runtime resource requirements.


title:
Partitioning planar graphs without 4-cycles and 6-cycles into a forest
  and a disjoint union of paths

abstract:
  In this paper, we show that every planar graph without $4$-cycles and
$6$-cycles has a partition of its vertex set into two sets, where one set
induces a forest, and the other induces a forest with maximum degree at most
$2$ (equivalently, a disjoint union of paths).
  Note that we can partition the vertex set of a forest into two independent
sets. However, a pair of independent sets combined may not induce a forest.
Thus our result extends the result of Wang and Xu (2013) stating that the
vertex set of every planar graph without $4$-cycles and $6$-cycles can be
partitioned into three sets, where one induces a graph with maximum degree two,
and the remaining two are independent sets.


title: G$^3$SR: Global Graph Guided Session-based Recommendation

abstract:
  Session-based recommendation tries to make use of anonymous session data to
deliver high-quality recommendation under the condition that user-profiles and
the complete historical behavioral data of a target user are unavailable.
Previous works consider each session individually and try to capture user
interests within a session. Despite their encouraging results, these models can
only perceive intra-session items and cannot draw upon the massive historical
relational information. To solve this problem, we propose a novel method named
G$^3$SR (Global Graph Guided Session-based Recommendation). G$^3$SR decomposes
the session-based recommendation workflow into two steps. First, a global graph
is built upon all session data, from which the global item representations are
learned in an unsupervised manner. Then, these representations are refined on
session graphs under the graph networks, and a readout function is used to
generate session representations for each session. Extensive experiments on two
real-world benchmark datasets show remarkable and consistent improvements of
the G$^3$SR method over the state-of-the-art methods, especially for cold
items.


title: Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal

abstract:
  Existing unsupervised person re-identification (ReID) methods focus on
adapting a model trained on a source domain to a fixed target domain. However,
an adapted ReID model usually only works well on a certain target domain, but
can hardly memorize the source domain knowledge and generalize to upcoming
unseen data. In this paper, we propose unsupervised lifelong person ReID, which
focuses on continuously conducting unsupervised domain adaptation on new
domains without forgetting the knowledge learnt from old domains. To tackle
unsupervised lifelong ReID, we conduct a contrastive rehearsal on a small
number of stored old samples while sequentially adapting to new domains. We
further set an image-to-image similarity constraint between old and new models
to regularize the model updates in a way that suits old knowledge. We
sequentially train our model on several large-scale datasets in an unsupervised
manner and test it on all seen domains as well as several unseen domains to
validate the generalizability of our method. Our proposed unsupervised lifelong
method achieves strong generalizability, which significantly outperforms
previous lifelong methods on both seen and unseen domains. Code will be made
available at https://github.com/chenhao2345/UCR.


title: Semiparametric doubly robust targeted double machine learning: a review

abstract:
  In this review we cover the basics of efficient nonparametric parameter
estimation (also called functional estimation), with a focus on parameters that
arise in causal inference problems. We review both efficiency bounds (i.e.,
what is the best possible performance for estimating a given parameter?) and
the analysis of particular estimators (i.e., what is this estimator's error,
and does it attain the efficiency bound?) under weak assumptions. We emphasize
minimax-style efficiency bounds, worked examples, and practical shortcuts for
easing derivations. We gloss over most technical details, in the interest of
highlighting important concepts and providing intuition for main ideas.


title: A Simple Setup for the Determination of the Cosmic Muon Magnetic Moment

abstract:
  We present a simple setup for the measurement of the magnetic moment of
cosmic muons convenient for a student laboratory experiment. A significant
simplification is made in the detector system compared to previous experiments
of this kind, yet it retains all the necessary functionality of the system.
This simplification additionally allows for the use of low-cost custom-made
electronics for data readout and storage. These improvements considerably
reduce the cost and provide a more accessible setup for a student laboratory
experiment.


title: Critical growth fractional Kirchhoff elliptic problems

abstract:
  This article is concerned with the existence and multiplicity of positive
weak solutions for the following fractional Kirchhoff-Choquard problem:
  \begin{equation*}
  \begin{array}{cc}
  \displaystyle M\left( \|u\|^2\right) (-\Delta)^s u = \ds\lambda
f(x)|u|^{q-2}u + \left( \int\limits_{\Omega} \frac{|u(y)|^{2^{*}_{\mu
,s}}}{|x-y|^ \mu}\, dy\right) |u|^{2^{*}_{\mu ,s}-2}u \;\text{in} \; \Omega,
  u > 0\quad \text{in} \; \Omega, \,\,
  u = 0\quad \text{in} \; \mathbb{R}^{N}\backslash\Omega,
  \end{array}
  \end{equation*}
  where $\Omega$ is open bounded domain of $\mathbb{R}^{N}$ with $C^2$
boundary, $N > 2s$ and $s \in (0,1)$, here $M$ models Kirchhoff-type
coefficient of the form $M(t) = a + bt^{\te-1}$, where $a, b > 0$ are given
constants.
  $(-\Delta)^s$ is fractional Laplace operator, $\lambda > 0$ is a real
parameter. We explore using the variational methods, the existence of solution
for ${q} \in (1,2^*_s)$ and $\te \geq 1$.
  % and we also consider the case when $\te > 2^*_{\mu,s}$ for $2< q <
2^*_{s}$.
  Here $2^*_s = \frac{2N}{N-2s}$ and $2^{*}_{\mu ,s} = \frac{2N-\mu}{N-2s}$ is
the critical exponent in the sense of Hardy-Littlewood-Sobolev inequality.


title: The K-theory of perfectoid rings

abstract:
  We establish various properties of the p-adic algebraic K-theory of smooth
algebras over perfectoid rings living over perfectoid valuation rings. In
particular, the p-adic K-theory of such rings is homotopy invariant, and
coincides with the p-adic K-theory of the p-adic generic fibre in high degrees.
In the case of smooth algebras over perfectoid valuation rings of mixed
characteristic the latter isomorphism holds in all degrees and generalises a
result of Nizio{\l}.


title:
On Some Inequalities-Equalities Concerning the continuous generalized
  Fusion Frame in Hilbert spaces

abstract:
  Continuous generalized fusion frame theory was recently introduced by Rahimi
and al. Several equalities and inequalities have been obtained for frame,
fusion generalized fusion frame, among others. In the present paper, we
continue and extend these results to obtain some important identities and
inequalities in the case of continuous generalized fusion frame, Parceval
continuous generalized fusion frame, $ \lambda$-tight continuous generalized
fusion frame. Moreover, we obtain some new inequalities for the alternate dual
continuous generalized fusion frame. Finally, we obtain frame operator of a
pair of Bessel continuous generalized fusion mapping and we derive some results
about resolution of identity.


title: Optimizer Amalgamation

abstract:
  Selecting an appropriate optimizer for a given problem is of major interest
for researchers and practitioners. Many analytical optimizers have been
proposed using a variety of theoretical and empirical approaches; however, none
can offer a universal advantage over other competitive optimizers. We are thus
motivated to study a new problem named Optimizer Amalgamation: how can we best
combine a pool of "teacher" optimizers into a single "student" optimizer that
can have stronger problem-specific performance? In this paper, we draw
inspiration from the field of "learning to optimize" to use a learnable
amalgamation target. First, we define three differentiable amalgamation
mechanisms to amalgamate a pool of analytical optimizers by gradient descent.
Then, in order to reduce variance of the amalgamation process, we also explore
methods to stabilize the amalgamation process by perturbing the amalgamation
target. Finally, we present experiments showing the superiority of our
amalgamated optimizer compared to its amalgamated components and learning to
optimize baselines, and the efficacy of our variance reducing perturbations.
Our code and pre-trained models are publicly available at
http://github.com/VITA-Group/OptimizerAmalgamation.


title: Quantifying active and resistive stresses in adherent cells

abstract:
  To understand cell migration, it is crucial to gain knowledge on how cells
exert and integrate forces on/from their environment. A quantity of prime
interest for biophysicists interested in cell movements modeling is the
intracellular stresses. Up to now, three different methods have been proposed
to calculate it, they are all in the regime of the thin plate approximation.
Two are based on solving the mechanical equilibrium equation inside the cell
material (Monolayer Stress Microscopy, and Bayesian Inference Stress
Microscopy) and one is based on the continuity of displacement at the
cell/substrate interface (Intracellular Stress Microscopy). We show here using
3D FEM modeling that these techniques do not calculate the same quantities (as
was previously assumed), the first techniques calculate the sum of the active
and resistive stresses within the cell, whereas the last one only calculate the
resistive component. Combining these techniques should in principle permit to
get access to the active stress alone.


title: The ReLiC: Recycling Linear $e^+e^-$ Collider

abstract:
  In this white paper we describe concept of $e^+e^-$ linear collider recycling
both the used particles and the used beam energy.


title: Homoclinic and heteroclinic intersections for lemon billiards

abstract:
  In this paper we study the dynamical billiards on a symmetric lemon table
$\mathcal{Q}(b)$, where $\mathcal{Q}(b)$ is the intersection of two unit disks
with center distance $b$. We show that there exists $\delta_0>0$ such that for
all $b\in(1.5, 1.5+\delta_0)$ (except possibly a discrete subset), the billiard
map $F_b$ on the lemon table $\mathcal{Q}(b)$ admits crossing homoclinic and
heteroclinic intersections. In particular, such lemon billiards have positive
topological entropy.


title:
Simulating neutron star mergers with the Lagrangian Numerical Relativity
  code SPHINCS_BSSN

abstract:
  We present the first neutron star merger simulations performed with the newly
developed Numerical Relativity code SPHINCS_BSSN. This code evolves the
spacetime on a mesh using the BSSN formulation, but matter is evolved via
Lagrangian particles according to a high-accuracy version of
general-relativistic Smooth Particle Hydrodynamics (SPH). Our code contains a
number of new methodological elements compared to other Numerical Relativity
codes. The main focus here is on the new elements that were introduced to model
neutron star mergers. These include a) a refinement (fixed in time) of the
spacetime-mesh, b) corresponding changes in the particle--mesh mapping
algorithm and c) a novel way to construct SPH initial data for binary systems
via the recently developed "Artificial Pressure Method." This latter method
makes use of the spectral initial data produced by the library LORENE, and is
implemented in a new code called SPHINCS_ID. While our main focus is on
introducing these new methodological elements and documenting the current
status of SPHINCS_BSSN, we also show as a first application a set of neutron
star merger simulations employing "soft" ($\Gamma=2.00$) and "stiff"
($\Gamma=2.75$) polytropic equations of state.


title:
The rigidity dependence of galactic cosmic-ray fluxes and its connection
  with the diffusion coefficient

abstract:
  Thanks to tremendous experimental efforts, galactic cosmic-ray fluxes are
being measured up to the unprecedented per cent precision level. The
logarithmic slope of these fluxes is a crucial quantity that promises us
information on the diffusion properties and the primary or secondary nature of
the different species. However, these measured slopes are sometimes interpreted
in the pure diffusive regime, guiding to misleading conclusions. In this paper,
we have studied the propagation of galactic cosmic rays by computing the fluxes
of species between H and Fe using the USINE code and considering all the
relevant physical processes and an updated set of cross-section data. We show
that the slope of the well-studied secondary-to-primary B/C ratio is distinctly
different from the diffusion coefficient slope, by an offset of about 0.2 in
the rigidity range in which the AMS-02 data reach their best precision (several
tens of GV).
  Furthermore, we have demonstrated that none of the species from H to Fe
follows the expectations of the pure-diffusive regime. We argue that these
differences arise from propagation processes such as fragmentation, convection,
and reacceleration, which cannot be neglected. On this basis, we also provide
predictions for the spectral slope of elemental fluxes not yet analysed by the
AMS collaboration.


title: Fabrication of quantum Hall p-n junction checkerboards

abstract:
  Measurements of fractional multiples of the {\nu}=2 plateau quantized Hall
resistance (R_H {\approx} 12906 {\Omega}) were enabled by the utilization of
multiple current terminals on millimetre-scale graphene p-n junction devices
fabricated with interfaces along both lateral directions. These quantum Hall
resistance checkerboard devices have been demonstrated to match quantized
resistance outputs numerically calculated with the LTspice circuit simulator.
From the devices' functionality, more complex embodiments of the quantum Hall
resistance checkerboard were simulated to highlight the parameter space within
which these devices could operate. Moreover, these measurements suggest that
the scalability of p-n junction fabrication on millimetre or centimetre scales
is feasible with regards to graphene device manufacturing by using the far more
efficient process of standard ultraviolet lithography.


title: GATSBI: Generative Adversarial Training for Simulation-Based Inference

abstract:
  Simulation-based inference (SBI) refers to statistical inference on
stochastic models for which we can generate samples, but not compute
likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not
require explicit likelihoods. We study the relationship between SBI and GANs,
and introduce GATSBI, an adversarial approach to SBI. GATSBI reformulates the
variational objective in an adversarial setting to learn implicit posterior
distributions. Inference with GATSBI is amortised across observations, works in
high-dimensional posterior spaces and supports implicit priors. We evaluate
GATSBI on two SBI benchmark problems and on two high-dimensional simulators. On
a model for wave propagation on the surface of a shallow water body, we show
that GATSBI can return well-calibrated posterior estimates even in high
dimensions. On a model of camera optics, it infers a high-dimensional posterior
given an implicit prior, and performs better than a state-of-the-art SBI
approach. We also show how GATSBI can be extended to perform sequential
posterior estimation to focus on individual observations. Overall, GATSBI opens
up opportunities for leveraging advances in GANs to perform Bayesian inference
on high-dimensional simulation-based models.


title: FiNER: Financial Numeric Entity Recognition for XBRL Tagging

abstract:
  Publicly traded companies are required to submit periodic reports with
eXtensive Business Reporting Language (XBRL) word-level tags. Manually tagging
the reports is tedious and costly. We, therefore, introduce XBRL tagging as a
new entity extraction task for the financial domain and release FiNER-139, a
dataset of 1.1M sentences with gold XBRL tags. Unlike typical entity extraction
datasets, FiNER-139 uses a much larger label set of 139 entity types. Most
annotated tokens are numeric, with the correct tag per token depending mostly
on context, rather than the token itself. We show that subword fragmentation of
numeric expressions harms BERT's performance, allowing word-level BILSTMs to
perform better. To improve BERT's performance, we propose two simple and
effective solutions that replace numeric expressions with pseudo-tokens
reflecting original token shapes and numeric magnitudes. We also experiment
with FIN-BERT, an existing BERT model for the financial domain, and release our
own BERT (SEC-BERT), pre-trained on financial filings, which performs best.
Through data and error analysis, we finally identify possible limitations to
inspire future work on XBRL tagging.


title:
Algorithms for determining resistances in quantum Hall annuli with p-n
  junctions

abstract:
  Just a few of the promising applications of graphene Corbino pnJ devices
include two-dimensional Dirac fermion microscopes, custom programmable
quantized resistors, and mesoscopic valley filters. In some cases, device
scalability is crucial, as seen in fields like resistance metrology, where
graphene devices are required to accommodate currents of the order 100 {\mu}A
to be compatible with existing infrastructure. However, fabrication of these
devices still poses many difficulties. In this work, unusual quantized
resistances are observed in epitaxial graphene Corbino p-n junction devices
held at the i=2 plateau and agree with numerical simulations performed with the
LTspice circuit simulator. The formulae describing experimental and simulated
data are empirically derived for generalized placement of up to three current
terminals and accurately reflects observed partial edge channel cancellation.
These results support the use of ultraviolet lithography as a way to scale up
graphene-based devices with suitably narrow junctions that could be applied in
a variety of subfields