title:
CYBORGS: Contrastively Bootstrapping Object Representations by Grounding
  in Segmentation

abstract:
  Many recent approaches in contrastive learning have worked to close the gap
between pretraining on iconic images like ImageNet and pretraining on complex
scenes like COCO. This gap exists largely because commonly used random crop
augmentations obtain semantically inconsistent content in crowded scene images
of diverse objects. Previous works use preprocessing pipelines to localize
salient objects for improved cropping, but an end-to-end solution is still
elusive. In this work, we propose a framework which accomplishes this goal via
joint learning of representations and segmentation. We leverage segmentation
masks to train a model with a mask-dependent contrastive loss, and use the
partially trained model to bootstrap better masks. By iterating between these
two components, we ground the contrastive updates in segmentation information,
and simultaneously improve segmentation throughout pretraining. Experiments
show our representations transfer robustly to downstream tasks in
classification, detection and segmentation.


title:
Confinement of passing and trapped runaway electrons in the simulation
  of an ITER current quench

abstract:
  Runaway electrons (REs) present a high-priority issue for ITER but little is
known about the extent to which RE generation is affected by the stochastic
field intrinsic to disrupting plasmas. RE generation can be modelled with
reduced kinetic models and there has been recent progress in involving losses
due to field stochasticity, either via a loss-time parameter or radial
transport coefficients which can be estimated by tracing test electrons in 3D
fields. We evaluate these terms in ITER using a recent JOREK 3D MHD simulation
of plasma disruption to provide the stochastic magnetic fields where RE markers
are traced with the built-in particle tracing module. While the MHD simulation
modelled only the current quench phase, the case is MHD unstable and exhibits
similar relaxation as would be expected during the thermal quench. Therefore,
the RE simulations can be considered beginning right after the thermal quench
but before the MHD relaxation is complete. The plasma is found to become fully
stochastic for 8 ms and the resulting transport is sufficient to overcome RE
avalanche before flux surfaces are reformed. We also study transport mechanisms
for trapped REs and find those to be deconfined as well during this phase.
While the results presented here are not sufficient to assess the magnitude of
the formed RE beam, we show that significant RE losses could be expected to
arise due to field stochasticity.


title: Operator Lie Algebras of Rotations and Transformations in White Noise

abstract:
  The infinitesimal generator of a one-parameter subgroup of the infinite
dimensional rotation group associated with the complex Gelfand triple $ (E)
\subset L^2(E^*, \mu) \subset (E)^* $ is of the form $$ R_\kappa =
\int_{T\times T} \kappa(s,t) (a_s^* a_t - a_t^* a_s) ds dt $$ where $\kappa \in
E \otimes E^*$ is a skew-symmetric distribution. Hence $R_\kappa$ is twice the
conservation operator associated with a skew-symmetric operator $S$. The Lie
algebra containing $R_\kappa$, identity operator, annihilation operator,
creation operator, number operator, (generalized) Gross Laplacian is discussed.
We show that this Lie algebra is associated with the orbit of the
skew-symmetric operator $S$.


title:
Error estimates for physics informed neural networks approximating the
  Navier-Stokes equations

abstract:
  We prove rigorous bounds on the errors resulting from the approximation of
the incompressible Navier-Stokes equations with (extended) physics informed
neural networks. We show that the underlying PDE residual can be made
arbitrarily small for tanh neural networks with two hidden layers. Moreover,
the total error can be estimated in terms of the training error, network size
and number of quadrature points. The theory is illustrated with numerical
experiments.


title: Dimensionality Reduction and Wasserstein Stability for Kernel Regression

abstract:
  In a high-dimensional regression framework, we study consequences of the
naive two-step procedure where first the dimension of the input variables is
reduced and second, the reduced input variables are used to predict the output
variable. More specifically we combine principal component analysis (PCA) with
kernel regression. In order to analyze the resulting regression errors, a novel
stability result of kernel regression with respect to the Wasserstein distance
is derived. This allows us to bound errors that occur when perturbed input data
is used to fit a kernel function. We combine the stability result with known
estimates from the literature on both principal component analysis and kernel
regression to obtain convergence rates for the two-step procedure.

categories: stat.ML cs.LG cs.NA math.NA math.ST stat.TH


title:
POSTER: Diagnosis of COVID-19 through Transfer Learning Techniques on CT
  Scans: A Comparison of Deep Learning Models

abstract:
  The novel coronavirus disease (COVID-19) constitutes a public health
emergency globally. It is a deadly disease which has infected more than 230
million people worldwide. Therefore, early and unswerving detection of COVID-19
is necessary. Evidence of this virus is most commonly being tested by RT-PCR
test. This test is not 100% reliable as it is known to give false positives and
false negatives. Other methods like X-Ray images or CT scans show the detailed
imaging of lungs and have been proven more reliable. This paper compares
different deep learning models used to detect COVID-19 through transfer
learning technique on CT scan dataset. VGG-16 outperforms all the other models
achieving an accuracy of 85.33% on the dataset.


title:
Single-shot quantitative aberration and scattering length measurements
  in mouse brain tissues using an extended-source Shack-Hartmann wavefront
  sensor

abstract:
  Deep fluorescence imaging in mammalian brain tissues remains challenging due
to scattering and optical aberration-induced loss in signal and resolution.
Correction of aberrations using adaptive optics (AO) requires their reliable
measurement in the tissues. Here, we show that an extended-source
Shack-Hartmann wavefront sensor (ESSH) allows quantitative aberration
measurements through fixed brain slices with a thickness up to four times their
scattering length. We demonstrate in particular that this wavefront measurement
method based on image correlation is more robust to scattering compared to the
standard centroid-based approach. Finally, we obtain a measurement of the
tissue scattering length taking advantage of the geometry of a Shack-Hartmann
sensor.


title:
Non-reciprocal charge transport in an intrinsic magnetic topological
  insulator MnBi2Te4

abstract:
  Symmetries, quantum geometries and electronic correlations are among the most
important ingredients of condensed matters, and lead to nontrivial phenomena in
experiments, for example, non-reciprocal charge transport. Here we report the
non-reciprocal charge transport in the intrinsic magnetic topological insulator
MnBi2Te4. The current direction relevant resistance is observed at chiral
edges, which is magnetically switchable, edge position sensitive and septuple
layer number controllable. Applying gate voltage can effectively tune the
non-reciprocal resistance. The observation and manipulation of non-reciprocal
charge transport indicate the fundamental role of chirality in charge transport
of MnBi2Te4, and pave ways to develop van der Waals spintronic devices by
chirality engineering.


title: Obstructions to countable saturation in corona algebras

abstract:
  We study the extent of countable saturation for coronas of abelian
C*-algebras. In particular, we show that the corona algebra of $C_0(\bbR^n)$ is
countably saturated if and only if $n=1$.


title: Appendix to Discrete localities II: p-local compact groups as localities

abstract:
  This appendix provides a connection between discrete localities and the
p-local compact groups of Broto, Levi, and Oliver.


title:
Batched matrix operations on distributed GPUs with application in
  theoretical physics

abstract:
  One of the most important and commonly used operations in many linear algebra
functions is matrix-matrix multiplication (GEMM), which is also a key component
in obtaining high performance of many scientific codes. It is a computationally
intensive function requiring $O(n^3)$ operations, and its high computational
intensity makes it well-suited to be significantly accelerated with GPUs.
Today, many research problems require solving a very large number of relatively
small GEMM operations that cannot utilise the entire GPU. To overcome this
bottleneck, special functions have been developed that pack several GEMM
operations into one and then compute them simultaneously on a GPU, which is
called a batch operation. In this research work, we have proposed a different
approach based on linking multiple GEMM operations to MPI ranks and then
binding multiple MPI ranks to a single GPU. To increase GPU utilisation, more
MPI ranks (i.e. GEMM operations) are added. We implement and test this approach
in the field of theoretical physics to compute entanglement properties through
simulated annealing Monte Carlo simulation of quantum spin chains. For the
specific use case, we were able to simulate a much larger spin system and
achieve a speed-up of up to $35\times$ compared to the parallel CPU-only
version.


title:
Context-Dependent Anomaly Detection with Knowledge Graph Embedding
  Models

abstract:
  Increasing the semantic understanding and contextual awareness of machine
learning models is important for improving robustness and reducing
susceptibility to data shifts. In this work, we leverage contextual awareness
for the anomaly detection problem. Although graphed-based anomaly detection has
been widely studied, context-dependent anomaly detection is an open problem and
without much current research. We develop a general framework for converting a
context-dependent anomaly detection problem to a link prediction problem,
allowing well-established techniques from this domain to be applied. We
implement a system based on our framework that utilizes knowledge graph
embedding models and demonstrates the ability to detect outliers using context
provided by a semantic knowledge base. We show that our method can detect
context-dependent anomalies with a high degree of accuracy and show that
current object detectors can detect enough classes to provide the needed
context for good performance within our example domain.


title: Eichler-Shimura Relation on Intersection Cohomology

abstract:
  We prove the Eichler-Shimura relation on intersection cohomolgoy of minimal
compactifications of Shimura Varieties of Hodge type.


title:
Network analyses reveal negative link between changes in adipose tissue
  GDF15 and BMI during dietary-induced weight loss

abstract:
  Adipose tissue (AT) transcriptome studies provide holistic pictures of
adaptation to weight and related bioclinical settings changes. Objective To
implement AT gene expression profiling and investigate the link between changes
in bioclinical parameters and AT gene expression during 3 steps of a 2-phase
dietary intervention (DI). Methods AT transcriptome profiling was obtained from
sequencing 1051 samples, corresponding to 556 distinct individuals enrolled in
a weight loss intervention (8-week low-calorie diet (LCD) at 800 kcal/day)
followed with a 6-month ad libitum randomized DI. Transcriptome profiles
obtained with QuantSeq sequencing were benchmarked against Illumina RNAseq.
Reverse transcription quantitative polymerase chain reaction was used to
further confirm associations. Cell specificity was assessed using freshly
isolated cells and THP-1 cell line. Results During LCD, 5 modules were found,
of which 3 included at least 1 bioclinical variable. Change in body mass index
(BMI) connected with changes in mRNA level of genes with inflammatory response
signature. In this module, change in BMI was negatively associated with changes
in expression of genes encoding secreted protein (GDF15, CCL3, and SPP1).
Through all phases of the DI, change in GDF15 was connected to changes in SPP1,
CCL3, LIPA and CD68. Further characterization showed that these genes were
specific to macrophages (with LIPA, CD68 and GDF15 expressed in
anti-inflammatory macrophages) and GDF15 also expressed in preadipocytes.
Conclusion Network analyses identified a novel AT feature with GDF15
upregulated with calorie restriction induced weight loss, concomitantly to
macrophage markers. In AT, GDF15 was expressed in preadipocytes and macrophages
where it was a hallmark of anti-inflammatory cells.


title:
Kochen and Specker's view on functional relations conflicts with the
  collapse postulate

abstract:
  A key ingredient of the Kochen-Specker theorem is the so-called functional
composition principle, which asserts that hidden states must ascribe values to
observables in a way that is consistent with all functional relations between
them. This principle is motivated by the assumption that, like functions of
observables in classical mechanics, a function $g(A)$ of an observable A in
quantum theory is simply a logically possible observable derived from $A$, and
that measuring $g(A)$ consists in measuring $A$ and post-processing the
resulting value via $g$. In this paper we show that, under suitable conditions,
this reasonable assumption is in conflict with the collapse postulate. We then
discuss possible solutions to this conflict, and we argue that the most
justifiable and less radical one consists in adapting the collapse postulate by
taking measurement contexts into account. This dependence on contexts arises
for precisely the same reason why noncontextual hidden-variable models are
ruled out by the Kochen-Specker theorem, namely that an observable A can
typically be written as a function $A=g(B)=h(C)$ of noncommuting observables
$B,C$.


title:
Low-rank Wasserstein polynomial chaos expansions in the framework of
  optimal transport

abstract:
  A unsupervised learning approach for the computation of an explicit
functional representation of a random vector $Y$ is presented, which only
relies on a finite set of samples with unknown distribution. Motivated by
recent advances with computational optimal transport for estimating Wasserstein
distances, we develop a new \textit{Wasserstein multi-element polynomial chaos
expansion} (WPCE). It relies on the minimization of a regularized empirical
Wasserstein metric known as debiased Sinkhorn divergence.
  As a requirement for an efficient polynomial basis expansion, a suitable
(minimal) stochastic coordinate system $X$ has to be determined with the aim to
identify ideally independent random variables. This approach generalizes
representations through diffeomorphic transport maps to the case of
non-continuous and non-injective model classes $\mathcal{M}$ with different
input and output dimension, yielding the relation $Y=\mathcal{M}(X)$ in
distribution. Moreover, since the used PCE grows exponentially in the number of
random coordinates of $X$, we introduce an appropriate low-rank format given as
stacks of tensor trains, which alleviates the curse of dimensionality, leading
to only linear dependence on the input dimension. By the choice of the model
class $\mathcal{M}$ and the smooth loss function, higher order optimization
schemes become possible. It is shown that the relaxation to a discontinuous
model class is necessary to explain multimodal distributions. Moreover, the
proposed framework is applied to a numerical upscaling task, considering a
computationally challenging microscopic random non-periodic composite material.
This leads to tractable effective macroscopic random field in adopted
stochastic coordinates.


title: Borel factors and embeddings of systems in subshifts

abstract:
  In this paper we study the combinatorics of free Borel actions of the group
$\mathbb Z^d$ on Polish spaces. Building upon recent work by Chandgotia and
Meyerovitch, we introduce property $F$ on $\mathbb Z^d$-shift spaces $X$ under
which there is an equivariant map from any free Borel action to the free part
of $X$. Under further entropic assumptions, we prove that any subshift $Y$
(modulo the periodic points) can be Borel embedded into $X$. Several examples
satisfy property $F$ including, but not limited to, the space of proper
$3$-colourings, tilings by rectangles (under a natural arithmetic condition),
proper $2d$-edge colourings of $\mathbb Z^d$ and the space of bi-infinite
Hamiltonian paths. This answers questions raised by Seward, and Gao-Jackson,
and recovers a result by Weilacher and some results announced by
Gao-Jackson-Krohne-Seward.


title: Behavior-aware Account De-anonymization on Ethereum Interaction Graph

abstract:
  Blockchain technology has the characteristics of decentralization,
traceability and tamper-proof, which creates a reliable decentralized trust
mechanism, further accelerating the development of blockchain finance. However,
the anonymization of blockchain hinders market regulation, resulting in
increasing illegal activities such as money laundering, gambling and phishing
fraud on blockchain financial platforms. Thus financial security has become a
top priority in the blockchain ecosystem, calling for effective market
regulation. In this paper, we consider identifying Ethereum accounts from a
graph classification perspective, and propose an end-to-end graph neural
network framework named Ethident, to characterize the behavior patterns of
accounts and further achieve account de-anonymization. Specifically, we first
construct an Account Interaction Graph (AIG) using raw Ethereum data. Then we
design a hierarchical graph attention encoder named HGATE as the backbone of
our framework, which can effectively characterize the node-level account
features and subgraph-level behavior patterns. For alleviating account label
sparsity, we further introduce contrastive self-supervision mechanism as
regularization to jointly train our framework. Comprehensive experiments on
Ethereum datasets demonstrate that our framework achieves superior performance
in account identification, yielding 1.13% - 4.93% relative improvement over
previous state-of-the-art. Furthermore, detailed analyses illustrate the
effectiveness of Ethident in identifying and understanding the behavior of
known participants in Ethereum (e.g. exchanges, miners, etc.), as well as that
of the lawbreakers (e.g. phishing scammers, hackers, etc.), which may aid in
risk assessment and market regulation.


title:
Expressivity of Planning with Horn Description Logic Ontologies
  (Technical Report)

abstract:
  State constraints in AI Planning globally restrict the legal environment
states. Standard planning languages make closed-domain and closed-world
assumptions. Here we address open-world state constraints formalized by
planning over a description logic (DL) ontology. Previously, this combination
of DL and planning has been investigated for the light-weight DL DL-Lite. Here
we propose a novel compilation scheme into standard PDDL with derived
predicates, which applies to more expressive DLs and is based on the
rewritability of DL queries into Datalog with stratified negation. We also
provide a new rewritability result for the DL Horn-ALCHOIQ, which allows us to
apply our compilation scheme to quite expressive ontologies. In contrast, we
show that in the slight extension Horn-SROIQ no such compilation is possible
unless the weak exponential hierarchy collapses. Finally, we show that our
approach can outperform previous work on existing benchmarks for planning with
DL ontologies, and is feasible on new benchmarks taking advantage of more
expressive ontologies. That is an extended version of a paper accepted at AAAI
22.


title: Fine Detailed Texture Learning for 3D Meshes with Generative Models

abstract:
  This paper presents a method to reconstruct high-quality textured 3D models
from both multi-view and single-view images. The reconstruction is posed as an
adaptation problem and is done progressively where in the first stage, we focus
on learning accurate geometry, whereas in the second stage, we focus on
learning the texture with a generative adversarial network. In the generative
learning pipeline, we propose two improvements. First, since the learned
textures should be spatially aligned, we propose an attention mechanism that
relies on the learnable positions of pixels. Secondly, since discriminator
receives aligned texture maps, we augment its input with a learnable embedding
which improves the feedback to the generator. We achieve significant
improvements on multi-view sequences from Tripod dataset as well as on
single-view image datasets, Pascal 3D+ and CUB. We demonstrate that our method
achieves superior 3D textured models compared to the previous works. Please
visit our web-page for 3D visuals.


title: Approximate localised dihedral patterns near a Turing instability

abstract:
  Fully localised patterns involving cellular hexagons or squares have been
found experimentally and numerically in various continuum models. However,
there is currently no mathematical theory for the emergence of these localised
cellular patterns from a quiescent state. A key issue is that standard
techniques for one-dimensional patterns have proven insufficient for
understanding localisation in higher dimensions. In this work, we present a
comprehensive approach to this problem by using techniques developed in the
study of radially-symmetric patterns. Our analysis covers localised planar
patterns equipped with a wide range of dihedral symmetries, thereby avoiding a
restriction to solutions on a predetermined lattice. The context in this paper
is a theory for the emergence of such patterns near a Turing instability for a
general class of planar reaction-diffusion equations. Posing the
reaction-diffusion system in polar coordinates, we carry out a finite-mode
Fourier decomposition in the angular variable to yield a large system of
coupled radial ordinary differential equations. We then perform radial
invariant-manifold and normal form analysis, leading to an algebraic matching
condition for localised patterns to exist in the finite-mode reduction. This
algebraic matching condition is nontrivial, and solving it reveals the
existence of a plethora of localised dihedral patterns. These results capture
the essence of the emergent localised hexagonal patterns witnessed in
experiments. Moreover, we combine rigorous numerics and a Newton-Kantorovich
procedure to prove the existence of localised patches with 6m-fold symmetry for
arbitrarily large Fourier decompositions. This includes the localised hexagon
patches that have been elusive to analytical treatment.


title: Interacting Attention Graph for Single Image Two-Hand Reconstruction

abstract:
  Graph convolutional network (GCN) has achieved great success in single hand
reconstruction task, while interacting two-hand reconstruction by GCN remains
unexplored. In this paper, we present Interacting Attention Graph Hand
(IntagHand), the first graph convolution based network that reconstructs two
interacting hands from a single RGB image. To solve occlusion and interaction
challenges of two-hand reconstruction, we introduce two novel attention based
modules in each upsampling step of the original GCN. The first module is the
pyramid image feature attention (PIFA) module, which utilizes multiresolution
features to implicitly obtain vertex-to-image alignment. The second module is
the cross hand attention (CHA) module that encodes the coherence of interacting
hands by building dense cross-attention between two hand vertices. As a result,
our model outperforms all existing two-hand reconstruction methods by a large
margin on InterHand2.6M benchmark. Moreover, ablation studies verify the
effectiveness of both PIFA and CHA modules for improving the reconstruction
accuracy. Results on in-the-wild images and live video streams further
demonstrate the generalization ability of our network. Our code is available at
https://github.com/Dw1010/IntagHand.


title: Semi-Markov Offline Reinforcement Learning for Healthcare

abstract:
  Reinforcement learning (RL) tasks are typically framed as Markov Decision
Processes (MDPs), assuming that decisions are made at fixed time intervals.
However, many applications of great importance, including healthcare, do not
satisfy this assumption, yet they are commonly modelled as MDPs after an
artificial reshaping of the data. In addition, most healthcare (and similar)
problems are offline by nature, allowing for only retrospective studies. To
address both challenges, we begin by discussing the Semi-MDP (SMDP) framework,
which formally handles actions of variable timings. We next present a formal
way to apply SMDP modifications to nearly any given value-based offline RL
method. We use this theory to introduce three SMDP-based offline RL algorithms,
namely, SDQN, SDDQN, and SBCQ. We then experimentally demonstrate that only
these SMDP-based algorithms learn the optimal policy in variable-time
environments, whereas their MDP counterparts do not. Finally, we apply our new
algorithms to a real-world offline dataset pertaining to warfarin dosing for
stroke prevention and demonstrate similar results.


title:
Asymptotic counting of minimal surfaces in hyperbolic manifolds
  [according to Calegari, Marques and Neves]

abstract:
  A report on an article of Calegari, Marques and Neves about counting minimal
surfaces. An idea of a proof is included using the concept of a laminar
measure.


title:
Admission Control and Resource Reservation for Prioritized Slice
  Requests with Guaranteed SLA under Uncertainties

abstract:
  Network slicing has emerged as a key concept in 5G systems, allowing Mobile
Network Operators (MNOs) to build isolated logical networks (slices) on top of
shared infrastructure networks managed by Infrastructure Providers (InP).
Network slicing requires the assignment of infrastructure network resources to
virtual network components at slice activation time and the adjustment of
resources for slices under operation. Performing these operations just-in-time,
on a best-effort basis, comes with no guarantee on the availability of enough
infrastructure resources to meet slice requirements.
  This paper proposes a prioritized admission control mechanism for concurrent
slices based on an infrastructure resource reservation approach. The
reservation accounts for the dynamic nature of slice requests while being
robust to uncertainties in slice resource demands. Adopting the perspective of
an InP, reservation schemes are proposed that maximize the number of slices for
which infrastructure resources can be granted while minimizing the costs
charged to the MNOs. This requires the solution of a max-min optimization
problem with a non-linear cost function and non-linear constraints induced by
the robustness to uncertainties of demands and the limitation of the impact of
reservation on background services. The cost and the constraints are linearized
and several reduced-complexity strategies are proposed to solve the slice
admission control and resource reservation problem. Simulations show that the
proportion of admitted slices of different priority levels can be adjusted by a
differentiated selection of the delay between the reception and the processing
instants of a slice resource request.


title:
Fundamental limit to the rectification of near-field heat flow: The
  potential of intrinsic semiconductor films

abstract:
  We derive the fundamental limit to near-field radiative thermal rectification
mediated by an intrinsic semiconductor film within the framework of
fluctuational electrodynamics. By leveraging the electromagnetic local density
of states, we identify {\epsilon}"_H/{\epsilon}"_L as an upper bound on the
rectification magnitude, where {\epsilon}"_H and {\epsilon}"_L are respectively
the imaginary parts of the film permittivity at high and low temperatures. This
bound is tight and can be approached regardless of whether the film is
suspended or supported. For intrinsic silicon the limit can in principle exceed
10^9. Our work highlights the possibility of controlling heat flow as
effectively as electric current, and offers guidelines to potentially achieve
this goal.


title: The nonequilibrium cost of accurate information processing

abstract:
  Accurate processing of information is crucial both in technology and in the
natural world. To achieve it, any information processing system needs an
initial supply of resources away from thermal equilibrium. Here we establish an
in-principle limit on the maximum accuracy achievable with a given amount of
nonequilibrium resources. The limit applies to arbitrary information processing
tasks and to arbitrary information processing systems subject to the laws of
quantum mechanics. It is expressed in terms of an entropic quantity, hereby
called the thermodynamic complexity, which characterises the thermodynamic
hardness of the given information processing task. When applied to the task of
replicating quantum information, the limit reveals a thermodynamic advantage of
copy machines that preserve quantum entanglement. Our results shed light on the
interplay between thermodynamic resources and information-theoretic tasks, and
in the long term can inform the design of quantum devices approaching the
ultimate efficiency limits.


title:
Production and backreaction of massive fermions during axion inflation
  with non-Abelian gauge fields

abstract:
  We study the production and backreaction of massive vector-like fermions in
the background of a classical SU(2) gauge field during axion-driven inflation.
We demonstrate all ultraviolet divergences due to the interactions with the
fermions can be absorbed by renormalization of the axion wavefunction and the
gauge coupling. The effects of the fermion-axion interaction vanish in the
massless limit as required by symmetry. For very massive fermions, contact
interactions are induced between the axion, the gauge field and the
gravitational field. In this massive limit, we find the usual axion-gauge field
interactions are induced, however, in addition we observe the appearance of
axion self-interactions, as well as kinetic braiding of the axion with the
Einstein tensor. These new axion derivative interactions present intriguing
opportunities for model building and phenomenology.


title: Transforming Gait: Video-Based Spatiotemporal Gait Analysis

abstract:
  Human pose estimation from monocular video is a rapidly advancing field that
offers great promise to human movement science and rehabilitation. This
potential is tempered by the smaller body of work ensuring the outputs are
clinically meaningful and properly calibrated. Gait analysis, typically
performed in a dedicated lab, produces precise measurements including
kinematics and step timing. Using over 7000 monocular video from an
instrumented gait analysis lab, we trained a neural network to map 3D joint
trajectories and the height of individuals onto interpretable biomechanical
outputs including gait cycle timing and sagittal plane joint kinematics and
spatiotemporal trajectories. This task specific layer produces accurate
estimates of the timing of foot contact and foot off events. After parsing the
kinematic outputs into individual gait cycles, it also enables accurate
cycle-by-cycle estimates of cadence, step time, double and single support time,
walking speed and step length.


title:
Using the Order of Tomographic Slices as a Prior for Neural Networks
  Pre-Training

abstract:
  The technical advances in Computed Tomography (CT) allow to obtain immense
amounts of 3D data. For such datasets it is very costly and time-consuming to
obtain the accurate 3D segmentation markup to train neural networks. The
annotation is typically done for a limited number of 2D slices, followed by an
interpolation. In this work, we propose a pre-training method SortingLoss. It
performs pre-training on slices instead of volumes, so that a model could be
fine-tuned on a sparse set of slices, without the interpolation step. Unlike
general methods (e.g. SimCLR or Barlow Twins), the task specific methods (e.g.
Transferable Visual Words) trade broad applicability for quality benefits by
imposing stronger assumptions on the input data. We propose a relatively mild
assumption -- if we take several slices along some axis of a volume, structure
of the sample presented on those slices, should give a strong clue to
reconstruct the correct order of those slices along the axis. Many biomedical
datasets fulfill this requirement due to the specific anatomy of a sample and
pre-defined alignment of the imaging setup. We examine the proposed method on
two datasets: medical CT of lungs affected by COVID-19 disease, and
high-resolution synchrotron-based full-body CT of model organisms (Medaka
fish). We show that the proposed method performs on par with SimCLR, while
working 2x faster and requiring 1.5x less memory. In addition, we present the
benefits in terms of practical scenarios, especially the applicability to the
pre-training of large models and the ability to localize samples within volumes
in an unsupervised setup.


title:
Graphene optical nonlinearity: From the third-order to the
  non-perturbative electrodynamic regime

abstract:
  A non-perturbative model for graphene optical nonlinearity is developed for
the study of ultrafast pulse propagation along a monolayer, as in the case of
graphene-comprising nanophotonic integrated waveguides. This graphene `hot
electron' model (GHEM) builds upon earlier work, based on the Fermi-Dirac
framework for 2D semiconductors, which was aimed mainly at steady-state
absorptive response of a monolayer under free-space laser-beam illumination.
Our extension adapts the GHEM to in-plane light-matter interaction along
graphene monolayers under intense ps-pulse excitation that leads to the
carrier-density saturation regime. We first provide a quantitative overview of
the `classic' perturbative third-order nonlinear response and then study the
static and transient response of graphene as a function of the GHEM parameters,
with focus on the monolayer quality and voltage-tunability. These results are
compared to phenomenological models for saturable absorption and nonlinear
refraction, showing good agreement with recent experimental works. In
conclusion, the GHEM unifies the experimentally observed absorptive and
refractive optical nonlinearities in a single multi-parametric framework
therefore enabling the evaluation of the voltage-tunable light-matter
interaction in structures such as diffraction-limited nanophotonic waveguides.
This formalism can be readily employed to THz frequencies, adapted to
multi-channel (e.g. pump-probe) nonlinear effects, or developed to include
carrier and lattice-temperature diffusion to extend its validity threshold.


title: A Systematic Study of Android Non-SDK (Hidden) Service API Security

abstract:
  Android allows apps to communicate with its system services via system
service helpers so that these apps can use various functions provided by the
system services. Meanwhile, the system services rely on their service helpers
to enforce security checks for protection. Unfortunately, the security checks
in the service helpers may be bypassed via directly exploiting the non-SDK
(hidden) APIs, degrading the stability and posing severe security threats such
as privilege escalation, automatic function execution without users'
interactions, crashes, and DoS attacks. Google has proposed various approaches
to address this problem, e.g., case-by-case fixing the bugs or even proposing a
blacklist to block all the non-SDK APIs. However, the developers can still
figure out new ways of exploiting these hidden APIs to evade the non-SDKs
restrictions.
  In this paper, we systematically study the vulnerabilities due to the hidden
API exploitation and analyze the effectiveness of Google's countermeasures. We
aim to answer if there are still vulnerable hidden APIs that can be exploited
in the newest Android 12. We develop a static analysis tool called ServiceAudit
to automatically mine the inconsistent security enforcement between service
helper classes and the hidden service APIs. We apply ServiceAudit to Android
6~12. Our tool discovers 112 vulnerabilities in Android 6 with higher precision
than existing approaches. Moreover, in Android 11 and 12, we identify more than
25 hidden APIs with inconsistent protections; however, only one of the
vulnerable APIs can lead to severe security problems in Android 11, and none of
them work on Android 12.


title:
Neural Part Priors: Learning to Optimize Part-Based Object Completion in
  RGB-D Scans

abstract:
  3D object recognition has seen significant advances in recent years, showing
impressive performance on real-world 3D scan benchmarks, but lacking in object
part reasoning, which is fundamental to higher-level scene understanding such
as inter-object similarities or object functionality. Thus, we propose to
leverage large-scale synthetic datasets of 3D shapes annotated with part
information to learn Neural Part Priors (NPPs), optimizable spaces
characterizing geometric part priors. Crucially, we can optimize over the
learned part priors in order to fit to real-world scanned 3D scenes at test
time, enabling robust part decomposition of the real objects in these scenes
that also estimates the complete geometry of the object while fitting
accurately to the observed real geometry. Moreover, this enables global
optimization over geometrically similar detected objects in a scene, which
often share strong geometric commonalities, enabling scene-consistent part
decompositions. Experiments on the ScanNet dataset demonstrate that NPPs
significantly outperforms state of the art in part decomposition and object
completion in real-world scenes.


title:
Gaussian initializations help deep variational quantum circuits escape
  from the barren plateau

abstract:
  Variational quantum circuits have been widely employed in quantum simulation
and quantum machine learning in recent years. However, quantum circuits with
random structures have poor trainability due to the exponentially vanishing
gradient with respect to the circuit depth and the qubit number. This result
leads to a general belief that deep quantum circuits will not be feasible for
practical tasks. In this work, we propose an initialization strategy with
theoretical guarantees for the vanishing gradient problem in general deep
circuits. Specifically, we prove that under proper Gaussian initialized
parameters, the norm of the gradient decays at most polynomially when the qubit
number and the circuit depth increase. Our theoretical results hold for both
the local and the global observable cases, where the latter was believed to
have vanishing gradients even for shallow circuits. Experimental results verify
our theoretical findings in the quantum simulation and quantum chemistry.


title:
Assessing bound states in a one-dimensional topological superconductor:
  Majorana versus Tamm

abstract:
  Majorana bound states in topological superconductors have attracted intense
research activity in view of applications in topological quantum computation.
However, they are not the only example of topological bound states that can
occur in such systems. We here study a model in which both Majorana and Tamm
bound states compete. We show both numerically and analytically that,
surprisingly, the Tamm state remains partially localized even when the spectrum
becomes gapless. Despite this fact, we demonstrate that the Majorana
polarization shows a clear transition between the two regimes.


title:
Revealing the determinants of gender inequality in urban cycling with
  large-scale data

abstract:
  Cycling is an outdoor activity with massive health benefits, and an effective
solution towards sustainable urban transport. Despite these benefits and the
recent rising popularity of cycling, most countries still have a negligible
uptake. This uptake is especially low for women: there is a largely
unexplained, persistent gender gap in cycling. To understand the determinants
of this gender gap in cycling at scale, here we use massive,
automatically-collected data from the tracking application Strava on outdoor
cycling for 61 cities across the United States, the United Kingdom, Italy and
the Benelux area. Leveraging the associated gender and usage information, we
first quantify the emerging gender gap in recreational cycling at city-level. A
comparison of cycling rates of women across cities within similar geographical
areas unveils a broad range of gender gaps. On a macroscopic level, we link
this heterogeneity to a variety of urban indicators and provide evidence for
traditional hypotheses on the determinants of the gender-cycling-gap. We find a
positive association between female cycling rate and urban road safety. On a
microscopic level, we identify female preferences for street-specific features
in the city of New York. Enhancing the quality of the dedicated cycling
infrastructure may be a way to make urban environments more accessible for
women, thereby making urban transport more sustainable for everyone.


title: Analysis of Arbitrary Content on Blockchain-Based Systems using BigQuery

abstract:
  Blockchain-based systems have gained immense popularity as enablers of
independent asset transfers and smart contract functionality. They have also,
since as early as the first Bitcoin blocks, been used for storing arbitrary
contents such as texts and images. On-chain data storage functionality is
useful for a variety of legitimate use cases. It does, however, also pose a
systematic risk. If abused, for example by posting illegal contents on a public
blockchain, data storage functionality can lead to legal consequences for
operators and users that need to store and distribute the blockchain, thereby
threatening the operational availability of entire blockchain ecosystems. In
this paper, we develop and apply a cloud-based approach for quickly discovering
and classifying content on public blockchains. Our method can be adapted to
different blockchain systems and offers insights into content-related usage
patterns and potential cases of abuse. We apply our method on the two most
prominent public blockchain systems - Bitcoin and Ethereum - and discuss our
results. To the best of our knowledge, the presented study is the first to
systematically analyze non-financial content stored on the Ethereum blockchain
and the first to present a side-by-side comparison between different
blockchains in terms of the quality and quantity of stored data.


title: Identifiability of Sparse Causal Effects using Instrumental Variables

abstract:
  Exogenous heterogeneity, for example, in the form of instrumental variables
can help us learn a system's underlying causal structure and predict the
outcome of unseen intervention experiments. In this paper, we consider linear
models in which the causal effect from covariates $X$ on a response $Y$ is
sparse. We provide conditions under which the causal coefficient becomes
identifiable from the observed distribution. These conditions can be satisfied
even if the number of instruments is as small as the number of causal parents.
We also develop graphical criteria under which identifiability holds with
probability one if the edge coefficients are sampled randomly from a
distribution that is absolutely continuous with respect to Lebesgue measure and
$Y$ is childless. As an estimator, we propose spaceIV and prove that it
consistently estimates the causal effect if the model is identifiable and
evaluate its performance on simulated data. If identifiability does not hold,
we show that it may still be possible to recover a subset of the causal
parents.


title:
Direct Gibbs posterior inference on risk minimizers: construction,
  concentration, and calibration

abstract:
  Real-world problems, often couched as machine learning applications, involve
quantities of interest that have real-world meaning, independent of any
statistical model. To avoid potential model misspecification bias or
over-complicating the problem formulation, a direct, model-free approach is
desired. The traditional Bayesian framework relies on a model for the
data-generating process so, apparently, the desired direct, model-free,
posterior-probabilistic inference is out of reach. Fortunately, likelihood
functions are not the only means of linking data and quantities of interest.
Loss functions provide an alternative link, where the quantity of interest is
defined, or at least could be defined, as a minimizer of the corresponding
risk, or expected loss. In this case, one can obtain what is commonly referred
to as a Gibbs posterior distribution by using the empirical risk function
directly. This manuscript explores the Gibbs posterior construction, its
asymptotic concentration properties, and the frequentist calibration of its
credible regions. By being free from the constraints of model specification,
Gibbs posteriors create new opportunities for probabilistic inference in modern
statistical learning problems.


title: Euler State Networks

abstract:
  Inspired by the numerical solution of ordinary differential equations, in
this paper we propose a novel Reservoir Computing (RC) model, called the Euler
State Network (EuSN). The introduced approach makes use of forward Euler
discretization and antisymmetric recurrent matrices to design reservoir
dynamics that are both stable and non-dissipative by construction.
  Our mathematical analysis shows that the resulting model is biased towards
unitary effective spectral radius and zero local Lyapunov exponents,
intrinsically operating at the edge of stability. Experiments on synthetic
tasks indicate the marked superiority of the proposed approach, compared to
standard RC models, in tasks requiring long-term memorization skills.
Furthermore, results on real-world time series classification benchmarks point
out that EuSN is capable of matching (or even surpassing) the level of accuracy
of trainable Recurrent Neural Networks, while allowing up to 100-fold savings
in computation time and energy consumption.


title:
Mass ejection and time variability in protostellar outflows: Cep E.
  SOLIS XVI

abstract:
  Protostellar jets are an important agent of star formation feedback, tightly
connected with the mass-accretion process. The history of jet formation and
mass-ejection provides constraints on the mass accretion history and the nature
of the driving source. We want to characterize the time-variability of the
mass-ejection phenomena at work in the Class 0 protostellar phase, in order to
better understand the dynamics of the outflowing gas and bring more constraints
on the origin of the jet chemical composition and the mass-accretion history.
We have observed the emission of the CO 2-1 and SO N_J=5_4-4_3 rotational
transitions with NOEMA, towards the intermediate-mass Class 0 protostellar
system Cep E. The CO high-velocity jet emission reveals a central component
associated with high-velocity molecular knots, also detected in SO, surrounded
by a collimated layer of entrained gas. The gas layer appears to accelerate
along the main axis over a length scale delta_0 ~700 au, while its diameter
gradually increases up to several 1000au at 2000au from the protostar. The jet
is fragmented into 18 knots of mass ~10^-3 Msun, unevenly distributed between
the northern and southern lobes, with velocity variations up to 15 km/s close
to the protostar, well below the jet terminal velocities. The knot interval
distribution is approximately bimodal with a scale of ~50-80yr close to the
protostar and ~150-200yr at larger distances >12". The mass-loss rates derived
from knot masses are overall steady, with values of 2.7x10^-5 Msun/yr
(8.9x10^-6 Msun/yr) in the northern (southern) lobe. The interaction of the
ambient protostellar material with high-velocity knots drives the formation of
a molecular layer around the jet, which accounts for the higher mass-loss rate
in the north. The jet dynamics are well accounted for by a simple precession
model with a period of 2000yr and a mass-ejection period of 55yr.


title:
Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast
  Fourier Transformation Library

abstract:
  In this paper, we present an early version of a SYCL-based FFT library,
capable of running on all major vendor hardware, including CPUs and GPUs from
AMD, ARM, Intel and NVIDIA. Although preliminary, the aim of this work is to
seed further developments for a rich set of features for calculating FFTs. It
has the advantage over existing portable FFT libraries in that it is
single-source, and therefore removes the complexities that arise due to
abundant use of pre-process macros and auto-generated kernels to target
different architectures. We exercise two SYCL-enabled compilers, Codeplay
ComputeCpp and Intel's open-source LLVM project, to evaluate performance
portability of our SYCL-based FFT on various heterogeneous architectures. The
current limitations of our library is it supports single-dimension FFTs up to
$2^{11}$ in length and base-2 input sequences. We compare our results with
highly optimized vendor specific FFT libraries and provide a detailed analysis
to demonstrate a fair level of performance, as well as potential sources of
performance bottlenecks.


title:
DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using
  Quality-aware Flow

abstract:
  Accurate LiDAR-camera extrinsic calibration is a precondition for many
multi-sensor systems in mobile robots. Most calibration methods rely on
laborious manual operations and calibration targets. While working online, the
calibration methods should be able to extract information from the environment
to construct the cross-modal data association. Convolutional neural networks
(CNNs) have powerful feature extraction ability and have been used for
calibration. However, most of the past methods solve the extrinsic as a
regression task, without considering the geometric constraints involved. In
this paper, we propose a novel end-to-end extrinsic calibration method named
DXQ-Net, using a differentiable pose estimation module for generalization. We
formulate a probabilistic model for LiDAR-camera calibration flow, yielding a
prediction of uncertainty to measure the quality of LiDAR-camera data
association. Testing experiments illustrate that our method achieves a
competitive with other methods for the translation component and
state-of-the-art performance for the rotation component. Generalization
experiments illustrate that the generalization performance of our method is
significantly better than other deep learning-based methods.


title:
Charge density wave order and fluctuations above T$_{CDW}$ and below
  superconducting T$_c$ in the kagome metal CsV$_{3}$Sb$_{5}$

abstract:
  The phase diagram of the kagome metal family \ch{AV3Sb5} (A = K, Rb, Cs)
features both superconductivity and charge density wave (CDW) instabilities,
which have generated tremendous recent attention. Nonetheless, significant
questions remain. In particular, the temperature evolution and demise of the
CDW state has not been extensively studied, and little is known about the
co-existence of the CDW with superconductivity at low temperatures. We report
an x-ray scattering study of \ch{CsV3Sb5} over a broad range of temperatures
from 300 K to $\sim$ 2 K, below the onset of its superconductivity at
$\textit{T}_\text{c}\sim$ 2.9 K. Order parameter measurements of the
$2\times2\times2$ CDW structure show an unusual and extended linear temperature
dependence onsetting at $T^*$ $\sim$ 160 K, much higher than the susceptibility
anomaly associated with CDW order at $\textit{T}_\text{CDW}=94$ K. This implies
strong CDW fluctuations exist to $\sim1.7\times\textit{T}_\text{CDW}$. The CDW
order parameter is observed to be constant from $T=16$ K to 2 K, implying that
the CDW and superconducting order co-exist below $\textit{T}_\text{c}$, and, at
ambient pressure, any possible competition between the two order parameters is
manifested at temperatures well below $\textit{T}_\text{c}$, if at all.
Anomalies in the temperature dependence in the lattice parameters coincide with
$\textit{T}_\text{CDW}$ for $\textit{c}(T)$ and with $T^*$ for $a(T)$.


title:
One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and
  Pixel-Level Imbalance Learning

abstract:
  Edge detection, a basic task in the field of computer vision, is an important
preprocessing operation for the recognition and understanding of a visual
scene. In conventional models, the edge image generated is ambiguous, and the
edge lines are also very thick, which typically necessitates the use of
non-maximum suppression (NMS) and morphological thinning operations to generate
clear and thin edge images. In this paper, we aim to propose a one-stage neural
network model that can generate high-quality edge images without
postprocessing. The proposed model adopts a classic encoder-decoder framework
in which a pre-trained neural model is used as the encoder and a
multi-feature-fusion mechanism that merges the features of each level with each
other functions as a learnable decoder. Further, we propose a new loss function
that addresses the pixel-level imbalance in the edge image by suppressing the
false positive (FP) edge information near the true positive (TP) edge and the
false negative (FN) non-edge. The results of experiments conducted on several
benchmark datasets indicate that the proposed method achieves state-of-the-art
results without using NMS and morphological thinning operations.


title:
A Text Attention Network for Spatial Deformation Robust Scene Text Image
  Super-resolution

abstract:
  Scene text image super-resolution aims to increase the resolution and
readability of the text in low-resolution images. Though significant
improvement has been achieved by deep convolutional neural networks (CNNs), it
remains difficult to reconstruct high-resolution images for spatially deformed
texts, especially rotated and curve-shaped ones. This is because the current
CNN-based methods adopt locality-based operations, which are not effective to
deal with the variation caused by deformations. In this paper, we propose a CNN
based Text ATTention network (TATT) to address this problem. The semantics of
the text are firstly extracted by a text recognition module as text prior
information. Then we design a novel transformer-based module, which leverages
global attention mechanism, to exert the semantic guidance of text prior to the
text reconstruction process. In addition, we propose a text structure
consistency loss to refine the visual appearance by imposing structural
consistency on the reconstructions of regular and deformed texts. Experiments
on the benchmark TextZoom dataset show that the proposed TATT not only achieves
state-of-the-art performance in terms of PSNR/SSIM metrics, but also
significantly improves the recognition accuracy in the downstream text
recognition task, particularly for text instances with multi-orientation and
curved shapes. Code is available at https://github.com/mjq11302010044/TATT.


title: Generalization of Bair spaces using diagonal

abstract:
  Generalizations of Baire spaces are constructed with the help of diagonal of
square.


title:
A Cube Algebra with Comparative Operations: Containment, Overlap,
  Distance and Usability

abstract:
  In this paper, we provide a comprehensive rigorous modeling for
multidimensional spaces with hierarchically structured dimensions in several
layers of abstractions and data cubes that live in such spaces. We model cube
queries and their semantics and define typical OLAP operators like Selections,
Roll-Up, Drill-Down, etc. The model serves as the basis to offer the main
contribution of this paper which includes theorems and algorithms for being
able to associate data cube queries via comparative operations that are
evaluated only on the syntax of the queries involved. Specifically, these
operations include: (a) foundational containment, referring to the coverage of
common parts of the most detailed level of aggregation of the multidimensional
space, (b/c) same-level containment and intersection, referring to the
inclusion/existence of common parts of the multidimensional space in two query
results of the same aggregation levels, (d) query distance, referring to being
able to assess the similarity of two queries in the same multidimensional
space, and, (e) cube usability, i.e., the possibility of computing a new cube
from a previous one, defined at a different level of abstraction.


title:
When Chosen Wisely, More Data Is What You Need: A Universal
  Sample-Efficient Strategy For Data Augmentation

abstract:
  Data Augmentation (DA) is known to improve the generalizability of deep
neural networks. Most existing DA techniques naively add a certain number of
augmented samples without considering the quality and the added computational
cost of these samples. To tackle this problem, a common strategy, adopted by
several state-of-the-art DA methods, is to adaptively generate or re-weight
augmented samples with respect to the task objective during training. However,
these adaptive DA methods: (1) are computationally expensive and not
sample-efficient, and (2) are designed merely for a specific setting. In this
work, we present a universal DA technique, called Glitter, to overcome both
issues. Glitter can be plugged into any DA method, making training
sample-efficient without sacrificing performance. From a pre-generated pool of
augmented samples, Glitter adaptively selects a subset of worst-case samples
with maximal loss, analogous to adversarial DA. Without altering the training
strategy, the task objective can be optimized on the selected subset. Our
thorough experiments on the GLUE benchmark, SQuAD, and HellaSwag in three
widely used training setups including consistency training, self-distillation
and knowledge distillation reveal that Glitter is substantially faster to train
and achieves a competitive performance, compared to strong baselines.


title: Quantum nonreciprocal interactions via dissipative gauge symmetry

abstract:
  One-way nonreciprocal interactions between two quantum systems are typically
described by a cascaded quantum master equation, and rely on an effective
breaking of time-reversal symmetry as well as the balancing of coherent and
dissipative interactions. Here, we present a new approach for obtaining
nonreciprocal quantum interactions that is completely distinct from cascaded
quantum systems, and that does not in general require broken TRS. Our method
relies on a local gauge symmetry present in any Markovian Lindblad master
equation. This new kind of quantum nonreciprocity has many implications,
including a new mechanism for performing dissipatively-stabilized gate
operations on a target quantum system. We also introduce a new, extremely
general quantum-information based metric for quantifying quantum
nonreciprocity.


title:
Interaction of coronal mass ejections and the solar wind. A force
  analysis

abstract:
  Aims. Our goal is to thoroughly analyse the dynamics of single and multiple
solar eruptions, as well as a stealth ejecta. The data were obtained through
self-consistent numerical simulations performed in a previous study. We also
assess the effect of a different background solar wind on the propagation of
these ejecta to Earth.
  Methods. We calculated all the components of the forces contributing to the
evolution of the numerically modelled consecutive coronal mass ejections (CMEs)
obtained with the 2.5D magnetohydrodynamics (MHD) module of the code
MPI-AMRVAC. We analysed the thermal and magnetic pressure gradients and the
magnetic tension dictating the formation of several flux ropes in different
locations in the aftermath of the eruptions. These three components were
tracked in the equatorial plane during the propagation of the CMEs to Earth.
Their interaction with other CMEs and with the background solar wind was also
studied.
  Results. We explain the formation of the stealth ejecta and the plasma blobs
(or plasmoids) occurring in the aftermath of solar eruptions. We also address
the faster eruption of a CME in one case with a different background wind, even
when the same triggering boundary motions were applied, and attribute this to
the slightly different magnetic configuration and the large neighbouring
arcade. The thermal pressure gradient revealed a shock in front of these slow
eruptions, formed during their propagation to 1 AU. The double-peaked magnetic
pressure gradient indicates that the triggering method affects the structure of
the CMEs and that a part of the adjacent streamer is ejected along with the
CME.


title: EUP-corrected thermodynamics of BTZ black hole

abstract:
  In this manuscript, we investigate the influence of the extended uncertainty
principle (EUP) on the thermodynamics of a charged rotating Ba\~{n}ados,
Teitelboim and Zanelli (BTZ) black hole in (2+1)-dimensional anti-de Sitter
(AdS) and de Sitter (dS) space-time, respectively. We find that EUP-corrected
Hawking temperature, entropy, volume, Gibbs free energy, pressure, and heat
capacity functions have different characteristic behaviors in (A)dS space-time.
During the detailed analysis, we obtain a critical horizon in the AdS
space-time where the pressure has a non-zero minimum value. However, in the dS
space-time, we cannot achieve such a minimum pressure value. Instead, there is
a "cut point" where the pressure becomes zero. We show that after that horizon
value the black hole becomes unstable, with negative pressure which leads to
the collapse of the black hole.


title: Zero-sum Partitions of Abelian Groups

abstract:
  The following problem has been known since the 80's. Let $\Gamma$ be an
Abelian group of order $m$ (denoted $|\Gamma|=m$), and let $t$ and
$\{m_i\}_{i=1}^{t}$, be positive integers such that $\sum_{i=1}^t m_i=m-1$.
Determine when $\Gamma^*=\Gamma\setminus\{0\}$, the set of non-zero elements of
$\Gamma$, can be partitioned into disjoint subsets $\{S_i\}_{i=1}^{t}$ such
that $|S_i|=m_i$ and $\sum_{s\in S_i}s=0$ for all $i$, $1 \leq i \leq t$. Such
subset partitions are called \textit{zero-sum partitions}. $|I(\Gamma)|\neq 1$,
where $I(\Gamma)$ is the set of involutions of $\Gamma$, is a necessary
condition for the existence of zero-sum partitions. In this paper we show that
the condition: $m_i\geq 4$ for all $i$, $1 \leq i \leq t$, is sufficient.
Moreover, we present some applications of zero-sum partitions to irregular,
magic- and anti-magic-type labelings of graphs.


title:
Cosmological structure formation in complex scalar field dark matter
  versus real ultralight axions: a comparative study using CLASS

abstract:
  (abridged) We continue the study of $\Lambda$SFDM cosmologies, which differ
from $\Lambda$CDM in that CDM is replaced by scalar field dark matter (SFDM) by
calculating the evolution of the background Universe, as well as linear
perturbations, focusing on scalar modes. We consider models with complex scalar
field with a repulsive, quartic self-interaction (SI), and models without SI,
referred to as fuzzy dark matter (FDM). To this end, we modify the Boltzmann
code CLASS, to incorporate the physics of complex SFDM which has as one of its
characteristics that its equation of state is maximally stiff in the very early
Universe, dominating then over all the other cosmic components, even over
radiation. We calculate CMB and matter power spectra as well as unconditional
Press-Schechter halo mass functions for various models, expanding previous
literature that were either limited to the background, or to a semi-analytical
approach to SFDM density perturbations neglecting the early stiff phase.
Comparing our results of each, SFDM and FDM, with real-field ultralight axion
models (ULAs) without SI, we characterize the differences between the
respective background evolution and linear structure growth. Our calculations
strengthen previous findings in recent literature, i.e. the suppression of
structure is higher than originally expected in SFDM, questioning its ability
to explain the small-scale problems on dwarf-galactic scales. Also we find that
the kinetic energy due to the phase of the complex field leads to marked
differences between SFDM/FDM versus ULAs. The mild falloff in the SFDM power
spectrum toward high $k$ is similar to that of CDM but based on different
effects, namely the rapidly shrinking Jeans mass for SFDM as opposed to the
Meszaros effect for CDM. In addition, we find that the sharp cutoff in the ULA
power spectrum is also followed by a mild falloff, albeit at very small power.


title:
Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive
  Bias to Sequence-to-sequence Models

abstract:
  Relations between words are governed by hierarchical structure rather than
linear ordering. Sequence-to-sequence (seq2seq) models, despite their success
in downstream NLP applications, often fail to generalize in a
hierarchy-sensitive manner when performing syntactic transformations - for
example, transforming declarative sentences into questions. However, syntactic
evaluations of seq2seq models have only observed models that were not
pre-trained on natural language data before being trained to perform syntactic
transformations, in spite of the fact that pre-training has been found to
induce hierarchical linguistic generalizations in language models; in other
words, the syntactic capabilities of seq2seq models may have been greatly
understated. We address this gap using the pre-trained seq2seq models T5 and
BART, as well as their multilingual variants mT5 and mBART. We evaluate whether
they generalize hierarchically on two transformations in two languages:
question formation and passivization in English and German. We find that
pre-trained seq2seq models generalize hierarchically when performing syntactic
transformations, whereas models trained from scratch on syntactic
transformations do not. This result presents evidence for the learnability of
hierarchical syntactic information from non-annotated natural language text
while also demonstrating that seq2seq models are capable of syntactic
generalization, though only after exposure to much more language data than
human learners receive.


title: A dilaton-axion model for string cosmology

abstract:
  The generic scale-invariant theory of an axion and a dilaton coupled to
gravity in $d$-dimensions is generalized to a `universal' one-axion model with
two dilatons that reproduces itself under consistent
dimensional-reduction/truncation. Flat FLRW cosmologies are shown to correspond
to trajectories of a three-dimensional autonomous dynamical system, which we
analyse with a focus on accelerated cosmic expansion, deriving the precise
swampland bounds that exclude eternal acceleration. We also show that for two
sets of values of its three independent parameters, the model is a consistent
truncation of maximal `massive' supergravity theories arising from
string/M-theory; for these maximal-supergravity parameter values the FLRW
cosmologies include some with a transient de Sitter-like phase, but not the
recurring de Sitter-like phase or eternal cosmic acceleration that is possible
for other parameter values.


title: Tilings and Twist at 1/N^4

abstract:
  We re-consider operator mixing in the so-called $SU(2)$ sector of ${\cal N}
\, = \, 4$ super Yang-Mills theory with gauge group $SU(N)$. Where possible,
single-trace operators of moderate length are completed by higher-trace
admixtures so as to yield large $N$ tree level eigenstates.
  We are particularly interested in parity pairs with three excitations. Since
parity is respected in the mixing, the odd single-trace operators at low length
cannot receive too many admixtures. We reproduce the tree-level norms of a set
of large $N$ eigenstates up to order $1/N^4$ by integrability methods. This
involves evaluating two-point functions on the sphere, the torus, and the
double-torus. A perfect match is found as long as descendents are absent from
the mixing.
  Using twist to make the descendents appear in the integrability picture
immediately leads to the question how to modify the entangled states occurring
in the hexagon tessellations. We take a closer look at the double-trace
admixtures to the parity even three-excitation operator at length seven, which
are both products of a primary state and a descendent. Their two-point
functions are sensitive to the twist introduced into the Bethe equations. For
transverse scalar excitations we succeed in recovering the corresponding field
theory results. For longitudinal magnons our methods fail, pointing at a
potential weakness of the formalism.


title:
Analysis of quantum correlations within the ground state of a three
  level Lipkin model

abstract:
  The performance of beyond mean field methods in solving the quantum many body
problem for fermions is usually characterized by the correlation energy
measured with respect to the underlying mean field value. In this paper we
address the issue of characterizing the amount of correlations associated to
different approximations from a quantum information perspective. With this goal
in mind, we analyze the traditional Hartree-Fock (HF) method with spontaneous
symmetry breaking, the HF with symmetry restoration and the generator
coordinate method (GCM) in a exactly solvable fermion model known as the
three-level Lipkin model. To characterize correlations including entanglement
and beyond we use the quantum discord between different partition orbitals. We
find that for physically motivated partitions, the quantum discord of the exact
ground state is reasonably well reproduced by the different approximations.
However, other partitions create "fake quantum correlations" in order to
capture quantum correlations corresponding to partitions for which the
Hartree-Fock solution fails. Those are removed and redistributed through a
symmetry restoration process.


title: Sterile neutrino and dark matter

abstract:
  We consider the equation that describes the dynamics of the sterile neutrino
density in primeval plasma. The analysis of this equation results in the 5%
contribution of the sterile neutrino with mixing parameters $\Delta m^2_{14} =
7.3\text{eV}^2$ and $\sin^2 2\theta_{14} = 0.33$ to the energy density of the
Universe. The considered parameters of the sterile neutrino correspond to the
warm dark matter.


title: Robust and Complex Approach of Pathological Speech Signal Analysis

abstract:
  This paper presents a study of the approaches in the state-of-the-art in the
field of pathological speech signal analysis with a special focus on
parametrization techniques. It provides a description of 92 speech features
where some of them are already widely used in this field of science and some of
them have not been tried yet (they come from different areas of speech signal
processing like speech recognition or coding). As an original contribution,
this work introduces 36 completely new pathological voice measures based on
modulation spectra, inferior colliculus coefficients, bicepstrum, sample and
approximate entropy and empirical mode decomposition. The significance of these
features was tested on 3 (English, Spanish and Czech) pathological voice
databases with respect to classification accuracy, sensitivity and specificity.


title: Improved Laplacian-level meta-GGA for the weakly-nonlocal solid metals

abstract:
  We derive and motivate a Laplacian-level meta-GGA (LL-MGGA) targeting
accurate ground-state properties of metallic solids. Our model for the
orbital-free kinetic energy density restores the fourth-order gradient
expansion for exchange to the r2SCAN meta-GGA [Furness et al, J. Phys. Chem.
Lett. 11, 8208 (2020)], yielding a LL-MGGA we call OFR2. OFR2 matches the
accuracy of SCAN for prediction of common lattice constants and improves the
equilibrium properties of alkali metals, transition metals, and intermetallics
that were poorly described by both SCAN and r2SCAN. We compare OFR2 to the
r2SCAN-L LL-MGGA [D. Mejia-Rodriguez and S.B. Trickey, Phys. Rev. B 102, 121109
(2020)] and show that OFR2 tends to outperform r2SCAN-L for the equilibrium
properties of solids, but r2SCAN-L much better describes the atomization
energies of molecules than OFR2. Numerical performance is discussed in detail,
and our work provides an outlook to machine learning.


title:
A probabilistic method for detecting solar-like oscillations using
  meaningful prior information

abstract:
  Current and future space-based observatories such as the Transiting Exoplanet
Survey Satellite (TESS) and PLATO are set to provide an enormous amount of new
data on oscillating stars, and in particular stars that oscillate similar to
the Sun. Solar-like oscillators constitute the majority of known oscillating
stars and so automated analysis methods are becoming an ever increasing
necessity to make as much use of these data as possible. Here we aim to
construct an algorithm that can automatically determine if a given time series
of photometric measurements shows evidence of solar-like oscillations. The
algorithm is aimed at analyzing data from the TESS mission and the future PLATO
mission, and in particular stars in the main-sequence and subgiant evolutionary
stages. The algorithm first tests the range of observable frequencies in the
power spectrum of a TESS light curve for an excess that is consistent with that
expected from solar-like oscillations. In addition, the algorithm tests if a
repeating pattern of oscillation frequencies is present in the time series, and
whether it is consistent with the large separation seen in solar-like
oscillators. Both methods use scaling relations and observations which were
established and obtained during the CoRoT, Kepler, and K2 missions. Using a set
of test data consisting of visually confirmed solar-like oscillators and
nonoscillators observed by TESS, we find that the proposed algorithm can attain
a $94.7\%$ true positive rate and a $8.2\%$ false positive rate at peak
accuracy. However, by applying stricter selection criteria, the false positive
rate can be reduced to $\approx2\%$, while retaining an $80\%$ true positive
rate.


title: How to write a contemporary scientific article

abstract:
  Today scientists are drowned in information and have no time for reading all
publications even in a specific area. The information is sifted and only a
small fraction of articles is being read. Under circumstances, scientific
articles have to be properly adjusted to pass through the superficial sifting.
Here I present written down instructions that I used to give to my students
with almost serious advises on how to write (and how not to write) a
contemporary scientific article. I argue that it should tell a story and should
answer on the three main questions: Why, What and So what?


title:
A study on some approximations on the average number of the LLL bases in
  higher dimensions

abstract:
  There is a result related to the average number of the $(\delta, \eta)$-LLL
bases in dimension $n$ in theoretical sense but the formula seems to be
complicated and computing in high dimension takes a long time. In practical
sense, we suggest some approximations which can be computed by just storing
some constants and computing relatively simple exponential functions.


title:
Universality aspects of quantum corrections to transverse momentum
  broadening in QCD media

abstract:
  We study non-linear quantum corrections to transverse momentum broadening
(TMB) of a fast parton propagating in dense QCD matter in the leading
logarithmic approximation. These non-local corrections yield an anomalous
super-diffusive behavior characterized by a heavy tailed distribution which is
associated with L\'{e}vy random walks. Using a formal analogy with the physics
of traveling waves, we show that at late times the transverse momentum
distribution tends to a universal scaling regime. We derive analytic solutions
in terms of an asymptotic expansion around the scaling limit for both fixed and
running coupling. We note that our analytic approach yields a good agreement
with the exact numerical solutions down to realistic values of medium length.
Finally, we discuss the interplay between system size and energy dependence of
the diffusion coefficient $\hat q$ and its connection with the gluon
distribution function that is manifest at large transverse momentum transfer.


title: Counterdiabatic driving for periodically-driven open quantum systems

abstract:
  We discuss dynamics of periodically-driven open quantum systems. Time
evolution of the quantum state is described by the quantum master equation and
the form of the dissipator is chosen so that the instantaneous stationary state
is given by the Gibbs distribution. By using the eigenstate basis of the
Hamiltonian, we write the quantum master equation in a vector form. Then, we
find that the correlation between the population part and the coherent part is
induced by adiabatic gauge potential. Although the introduction of the
counterdiabatic term cancels out the adiabatic gauge potential, additional
correlations prevent a convergence to the Gibbs distribution. We demonstrate
that the performance of the control is dependent on the scale of the original
Hamiltonian.


title: Cosmic Birefringence from Monodromic Axion Dark Energy

abstract:
  The recently reported non-zero isotropic birefringence angle in Planck 2018
polarization data provides a tantalizing hint for new physics of axions. In
this paper, we explain this by a string theory motivated axion with a monodromy
potential that plays the role of dark energy. Upon using the birefringence
measurement and the constraint on the equation of state for dark energy in this
scenario, we find an upper bound on the axion decay constant as $f_a \lesssim
10^{16}$ GeV. This naturally gives an energy scale of order GUT and can resolve
the theoretical issue of super-Planckian field range of the conventional axion
dark energy model. We further study the implications of cosmic birefringence
for the underlying theory and its consequences for the string swampland
conjectures. We finally discuss oscillatory features in the dark energy sector
and the expected cosmic birefringence tomography.


title: A Framework and Benchmark for Deep Batch Active Learning for Regression

abstract:
  We study the performance of different pool-based Batch Mode Deep Active
Learning (BMDAL) methods for regression on tabular data, focusing on methods
that do not require to modify the network architecture and training. Our
contributions are three-fold: First, we present a framework for constructing
BMDAL methods out of kernels, kernel transformations and selection methods,
showing that many of the most popular BMDAL methods fit into our framework.
Second, we propose new components, leading to a new BMDAL method. Third, we
introduce an open-source benchmark with 15 large tabular data sets, which we
use to compare different BMDAL methods. Our benchmark results show that a
combination of our novel components yields new state-of-the-art results in
terms of RMSE and is computationally efficient. We provide open-source code
that includes efficient implementations of all kernels, kernel transformations,
and selection methods, and can be used for reproducing our results.


title: Finite representation of commutator sequences

abstract:
  Several structural properties of a universal algebra can be seen from the
higher commutators of its congruences. Even on a finite algebra, the sequence
of higher commutator operations is an infinite object. In the present paper, we
exhibit finite representations of this sequence.


title:
Study on nuclear $\alpha$-decay energy by an artificial neural network
  with pairing and shell effects

abstract:
  We build and train the artificial neural network model (ANN) based on the
experimental $\alpha$-decay energy ($Q_{\alpha}$) data. Besides decays between
the ground states of parent and daughter nuclei, decays from the ground state
of parent nuclei to the excited state of daughter nuclei are also included. By
this way, the number of samples are increased dramatically. The results
calculated by ANN model reproduce the experimental data with a good accuracy.
The root-mean-square (rms) relative to the experiment data is 0.105 MeV. The
influence of different input is investigated. It is found that either the shell
effect or the pairing effect results in an obvious improvement of the
predictive power of ANN model, and the shell effect plays a more important
role. The optimal result can be obtained as both the shell and pairing effects
are considered simultaneously. Application of ANN model in prediction of the
$\alpha$-decay energy shows the neutron magic number at $N=184$, and a possible
sub-shell gap around $N=174$ or 176 in the superheavy nuclei region.


title: Stability and Risk Bounds of Iterative Hard Thresholding

abstract:
  In this paper, we analyze the generalization performance of the Iterative
Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The
parameter estimation and sparsity recovery consistency of IHT has long been
known in compressed sensing. From the perspective of statistical learning,
another fundamental question is how well the IHT estimation would predict on
unseen data. This paper makes progress towards answering this open question by
introducing a novel sparse generalization theory for IHT under the notion of
algorithmic stability. Our theory reveals that: 1) under natural conditions on
the empirical risk function over $n$ samples of dimension $p$, IHT with
sparsity level $k$ enjoys an $\mathcal{\tilde
O}(n^{-1/2}\sqrt{k\log(n)\log(p)})$ rate of convergence in sparse excess risk;
2) a tighter $\mathcal{\tilde O}(n^{-1/2}\sqrt{\log(n)})$ bound can be
established by imposing an additional iteration stability condition on a
hypothetical IHT procedure invoked to the population risk; and 3) a fast rate
of order $\mathcal{\tilde O}\left(n^{-1}k(\log^3(n)+\log(p))\right)$ can be
derived for strongly convex risk function under proper strong-signal
conditions. The results have been substantialized to sparse linear regression
and sparse logistic regression models to demonstrate the applicability of our
theory. Preliminary numerical evidence is provided to confirm our theoretical
predictions.


title:
Medium Transmission Map Matters for Learning to Restore Real-World
  Underwater Images

abstract:
  Underwater visual perception is essentially important for underwater
exploration, archeology, ecosystem and so on. The low illumination, light
reflections, scattering, absorption and suspended particles inevitably lead to
the critically degraded underwater image quality, which causes great challenges
on recognizing the objects from the underwater images. The existing underwater
enhancement methods that aim to promote the underwater visibility, heavily
suffer from the poor image restoration performance and generalization ability.
To reduce the difficulty of underwater image enhancement, we introduce the
media transmission map as guidance to assist in image enhancement. We formulate
the interaction between the underwater visual images and the transmission map
to obtain better enhancement results. Even with simple and lightweight network
configuration, the proposed method can achieve advanced results of 22.6 dB on
the challenging Test-R90 with an impressive 30 times faster than the existing
models. Comprehensive experimental results have demonstrated the superiority
and potential on underwater perception. Paper's code is offered on:
https://github.com/GroupG-yk/MTUR-Net.


title: What is Time? -- Thoughts of a Physicist

abstract:
  Discussion of some aspects of the concept of time for a general audience:
nature of time, A- and B-series, time in physics, the "now", measurement of
time, definition of time units, time's arrow, relativity of time, relativity of
simultaneity.


title:
Bi-directional Object-context Prioritization Learning for Saliency
  Ranking

abstract:
  The saliency ranking task is recently proposed to study the visual behavior
that humans would typically shift their attention over different objects of a
scene based on their degrees of saliency. Existing approaches focus on learning
either object-object or object-scene relations. Such a strategy follows the
idea of object-based attention in Psychology, but it tends to favor those
objects with strong semantics (e.g., humans), resulting in unrealistic saliency
ranking. We observe that spatial attention works concurrently with object-based
attention in the human visual recognition system. During the recognition
process, the human spatial attention mechanism would move, engage, and
disengage from region to region (i.e., context to context). This inspires us to
model the region-level interactions, in addition to the object-level reasoning,
for saliency ranking. To this end, we propose a novel bi-directional method to
unify spatial attention and object-based attention for saliency ranking. Our
model includes two novel modules: (1) a selective object saliency (SOS) module
that models objectbased attention via inferring the semantic representation of
the salient object, and (2) an object-context-object relation (OCOR) module
that allocates saliency ranks to objects by jointly modeling the object-context
and context-object interactions of the salient objects. Extensive experiments
show that our approach outperforms existing state-of-theart methods. Our code
and pretrained model are available at https://github.com/GrassBro/OCOR.


title: Superconductors and gravity

abstract:
  We review and discuss some recent developments on the unconventional
interaction between superconducting systems and the local gravitational field.
While it is known that gravitational perturbations (such as gravitational
waves) can affect supercondensates and supercurrents dynamics, here we want to
focus on the more subtle superfluid back-reaction acting on the surrounding
gravitational field, analysing some specific favourable situations. To this
end, we will consider suitable quantum macrosystems in a coherent state,
immersed in the static weak Earth's gravitational field, investigating possible
slight local alterations of the latter not explained in terms of classical
physics.

categories: gr-qc cond-mat.supr-con hep-ph hep-th quant-ph


title:
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose
  Estimation

abstract:
  Establishing correspondences from image to 3D has been a key task of 6DoF
object pose estimation for a long time. To predict pose more accurately, deeply
learned dense maps replaced sparse templates. Dense methods also improved pose
estimation in the presence of occlusion. More recently researchers have shown
improvements by learning object fragments as segmentation. In this work, we
present a discrete descriptor, which can represent the object surface densely.
By incorporating a hierarchical binary grouping, we can encode the object
surface very efficiently. Moreover, we propose a coarse to fine training
strategy, which enables fine-grained correspondence prediction. Finally, by
matching predicted codes with object surface and using a PnP solver, we
estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show
major improvement over the state of the art w.r.t. ADD(-S) metric, even
surpassing RGB-D based methods in some cases.


title:
Time-of-flight recoil detection in transmission using pulsed keV ion
  beams enables sensitive multi-element profiling with high depth resolution

abstract:
  The potential of time-of-flight recoil detection in transmission geometry
using pulsed keV ion beams for sensitive multi-element profiling of thin
membranes and quasi-2D systems is assessed. While the time-of-flight approach
allows for simultaneous detection of multiple elements, to the largest extent
irrespective of recoil charge states, the keV projectile energies guarantee
high recoil-cross sections yielding high sensitivity at low dose. We
demonstrate the capabilities of the approach using 22Ne and 40Ar as projectiles
transmitted through thin carbon foil featuring optional LiF-coatings and single
crystalline silicon membranes for different sample preparation routines and
crystal orientations. For a large position sensitive detector (0.13 sr), a
high-depth resolution below 6 nm and sensitivity below 1014 atoms/cm2 was
achieved. For crystalline targets, we show how the probability of creation and
detection of recoils and their observed angular distribution depend on sample
orientation.


title: Deep Unsupervised Hashing with Latent Semantic Components

abstract:
  Deep unsupervised hashing has been appreciated in the regime of image
retrieval. However, most prior arts failed to detect the semantic components
and their relationships behind the images, which makes them lack discriminative
power. To make up the defect, we propose a novel Deep Semantic Components
Hashing (DSCH), which involves a common sense that an image normally contains a
bunch of semantic components with homology and co-occurrence relationships.
Based on this prior, DSCH regards the semantic components as latent variables
under the Expectation-Maximization framework and designs a two-step iterative
algorithm with the objective of maximum likelihood of training data. Firstly,
DSCH constructs a semantic component structure by uncovering the fine-grained
semantics components of images with a Gaussian Mixture Modal~(GMM), where an
image is represented as a mixture of multiple components, and the semantics
co-occurrence are exploited. Besides, coarse-grained semantics components, are
discovered by considering the homology relationships between fine-grained
components, and the hierarchy organization is then constructed. Secondly, DSCH
makes the images close to their semantic component centers at both fine-grained
and coarse-grained levels, and also makes the images share similar semantic
components close to each other. Extensive experiments on three benchmark
datasets demonstrate that the proposed hierarchical semantic components indeed
facilitate the hashing model to achieve superior performance.


title:
Point Source Equilibrium Problems with Connections to Weighted
  Quadrature Domains

abstract:
  We explore the connection between supports of equilibrium measures and
quadrature identities, especially in the case of point sources added to the
external field $Q(z)=|z|^{2p}$ with $p \in \mathbb{N}$. Along the way, we
describe some quadrature domains with respect to weighted area measure
$|z|^{2p}dA_z$ and complex boundary measure $|z|^{-2p}dz$.


title: On measures strongly log-concave on a subspace

abstract:
  In this work we study the concentration properties of log-concave measures
that are curved only on a subspace of directions. Proofs uses an adapted
version of the stochastic localization process.


title:
Radio pulsations from a neutron star within the gamma-ray binary LS I
  +61$^{\circ}$ 303

abstract:
  LS I +61$^{\circ}$ 303 is one of the rare gamma-ray binaries, emitting most
of their luminosity in photons with energies beyond 100 MeV. The $\sim$26.5 d
orbital period is clearly detected at many wavelengths. Additional aspects of
its multi-frequency behavior make it the most interesting example of the class.
The morphology of high-resolution radio images changes with orbital phase
displaying a cometary tail pointing away from the high-mass star. LS I
+61$^{\circ}$ 303 also shows superorbital variability. A couple of energetic
($\sim 10^{37}$ erg s$^{-1}$), short, magnetar-like bursts have been plausibly
ascribed to it. LS I +61$^{\circ}$ 303's phenomenology has been put under
theoretical scrutiny for decades, but the lack of certainty regarding the
nature of the compact object in the binary has prevented advancing our
understanding of the source. Here, using observations done with the
Five-hundred-meter Aperture Spherical radio Telescope (FAST), we report on the
existence of transient radio pulsations from the direction of LS I
+61$^{\circ}$ 303. We find a period $P=269.15508 \pm 0.00016$ ms at a
significance of $> 20\sigma$. This is the first evidence for pulsations from
this source at any frequency, and strongly argues for the existence of a
rotating neutron star in LS I +61$^{\circ}$ 303.


title: elBERto: Self-supervised Commonsense Learning for Question Answering

abstract:
  Commonsense question answering requires reasoning about everyday situations
and causes and effects implicit in context. Typically, existing approaches
first retrieve external evidence and then perform commonsense reasoning using
these evidence. In this paper, we propose a Self-supervised Bidirectional
Encoder Representation Learning of Commonsense (elBERto) framework, which is
compatible with off-the-shelf QA model architectures. The framework comprises
five self-supervised tasks to force the model to fully exploit the additional
training signals from contexts containing rich commonsense. The tasks include a
novel Contrastive Relation Learning task to encourage the model to distinguish
between logically contrastive contexts, a new Jigsaw Puzzle task that requires
the model to infer logical chains in long contexts, and three classic SSL tasks
to maintain pre-trained models language encoding ability. On the representative
WIQA, CosmosQA, and ReClor datasets, elBERto outperforms all other methods,
including those utilizing explicit graph reasoning and external knowledge
retrieval. Moreover, elBERto achieves substantial improvements on
out-of-paragraph and no-effect questions where simple lexical similarity
comparison does not help, indicating that it successfully learns commonsense
and is able to leverage it when given dynamic context.


title: High Precision Higgs from High Energy Muon Colliders

abstract:
  Muon colliders are an exciting possibility for reaching the highest energies
possible on the shortest timescale. They potentially combine the greatest
strengths of $e^+e^-$ and $pp$ colliders by bridging the energy versus
precision dichotomy. In this paper we study the sensitivity of Higgs properties
that can be achieved with a future 3 or 10 TeV muon collider from single Higgs
production. The results presented here represent the first comprehensive
picture for the precision achievable including backgrounds and using fast
detector simulation with Delphes. Additionally, we compare the results of fast
detector simulation with available full simulation studies that include the
muon collider specific Beam Induced Background, and show the results are
largely unchanged. We comment on some of the strengths and weaknesses of a high
energy muon collider for Higgs physics alone, and demonstrate the
complementarity of such a collider with the LHC and $e^+e^-$ Higgs factories.
Furthermore, we discuss some of the exciting avenues for improving future
results from both theoretical and detector R&D that could be undertaken.


title:
D-brane masses at special fibres of hypergeometric families of
  Calabi-Yau threefolds, modular forms, and periods

abstract:
  We consider the fourteen families $W$ of Calabi-Yau threefolds with one
complex structure parameter and Picard-Fuchs equation of hypergeometric type,
like the mirror of the quintic in $\mathbb{P}^4$. Mirror symmetry identifies
the masses of even--dimensional D--branes of the mirror Calabi-Yau $M$ with
four periods of the holomorphic $(3,0)$-form over a symplectic basis of
$H_3(W,\mathbb{Z})$. It was discovered by Chad Schoen that the singular fiber
at the conifold of the quintic gives rise to a Hecke eigenform of weight four
under $\Gamma_0(25)$, whose Hecke eigenvalues are determined by the Hasse-Weil
zeta function which can be obtained by counting points of that fiber over
finite fields. Similar features are known for the thirteen other cases. In two
cases we further find special regular points, so called rank two attractor
points, where the Hasse-Weil zeta function gives rise to modular forms of
weight four and two. We numerically identify entries of the period matrix at
these special fibers as periods and quasiperiods of the associated modular
forms. In one case we prove this by constructing a correspondence between the
conifold fiber and a Kuga-Sato variety. We also comment on simpler applications
to local Calabi-Yau threefolds.


title: An inverse problem for a semi-linear wave equation: a numerical study

abstract:
  We consider an inverse problem of recovering a potential associated to a
semi-linear wave equation with a quadratic nonlinearity in $1 + 1$ dimensions.
We develop a numerical scheme to determine the potential from a noisy
Dirichlet-to-Neumann map on the lateral boundary. The scheme is based on the
recent higher order linearization method [20]. We also present an approach to
numerically estimating two-dimensional derivatives of noisy data via Tikhonov
regularization. The methods are tested using synthetic noisy measurements of
the Dirichlet-to-Neumann map. Various examples of reconstructions of the
potential functions are given.


title:
Applications of a Gaussian Process Framework for Modelling of
  High-Resolution Exoplanet Spectra

abstract:
  Observations of exoplanet atmospheres in high resolution have the potential
to resolve individual planetary absorption lines, despite the issues associated
with ground-based observations. The removal of contaminating stellar and
telluric absorption features is one of the most sensitive steps required to
reveal the planetary spectrum and, while many different detrending methods
exist, it remains difficult to directly compare the performance and efficacy of
these methods. Additionally, though the standard cross-correlation method
enables robust detection of specific atmospheric species, it only probes for
features that are expected a priori. Here we present a novel methodology using
Gaussian process (GP) regression to directly model the components of
high-resolution spectra, which partially addresses these issues. We use two
archival CRIRES/VLT data sets as test cases, observations of the hot Jupiters
HD 189733 b and 51 Pegasi b, recovering injected signals with average line
contrast ratios of $\sim 4.37 \times 10^{-3}$ and $\sim 1.39 \times 10^{-3}$,
and planet radial velocities $\Delta K_\mathrm{p} =1.45 \pm
1.53\,\mathrm{km\,s^{-1}}$ and $\Delta
K_\mathrm{p}=0.12\pm0.12\,\mathrm{km\,s^{-1}}$ from the injection velocities
respectively. In addition, we demonstrate an application of the GP method to
assess the impact of the detrending process on the planetary spectrum, by
implementing injection-recovery tests. We show that standard detrending methods
used in the literature negatively affect the amplitudes of absorption features
in particular, which has the potential to render retrieval analyses inaccurate.
Finally, we discuss possible limiting factors for the non-detections using this
method, likely to be remedied by higher signal-to-noise data.