title:
Stochastic exciton-scattering theory of optical lineshapes: Renormalized
  many-body contributions

abstract:
  Spectral line-shapes provide a window into the local environment coupled to a
quantum transition in the condensed phase. In this paper, we build upon a
stochastic model to account for non-stationary background processes produced by
broad-band pulsed laser stimulation. In particular, we consider the
contribution of pair-fluctuations arising from the full bosonic many-body
Hamiltonian within a mean-field approximation, treating the coupling to the
system as a stochastic noise term. Using the It{\^o} transformation, we
consider two limiting cases for our model which lead to a connection between
the observed spectral fluctuations and the spectral density of the environment.
In the first case, we consider a Brownian environment and show that this
produces spectral dynamics that relax to form dressed excitonic states and
recover an Anderson-Kubo-like form for the spectral correlations. In the second
case, we assume that the spectrum is Anderson-Kubo like, and invert to
determine the corresponding background. Using the Jensen inequality, we obtain
an upper limit for the spectral density for the background. The results
presented here provide the technical tools for applying the stochastic model to
a broad range of problems.


title:
drsphelps at SemEval-2022 Task 2: Learning idiom representations using
  BERTRAM

abstract:
  This paper describes our system for SemEval-2022 Task 2 Multilingual
Idiomaticity Detection and Sentence Embedding sub-task B. We modify a standard
BERT sentence transformer by adding embeddings for each idioms, which are
created using BERTRAM and a small number of contexts. We show that this
technique increases the quality of idiom representations and leads to better
performance on the task. We also perform analysis on our final results and show
that the quality of the produced idiom embeddings is highly sensitive to the
quality of the input contexts.


title:
Language Resources and Technologies for Non-Scheduled and Endangered
  Indian Languages

abstract:
  In the present paper, we will present a survey of the language resources and
technologies available for the non-scheduled and endangered languages of India.
While there have been different estimates from different sources about the
number of languages in India, it could be assumed that there are more than
1,000 languages currently being spoken in India. However barring some of the 22
languages included in the 8th Schedule of the Indian Constitution (called the
scheduled languages), there is hardly any substantial resource or technology
available for the rest of the languages. Nonetheless there have been some
individual attempts at developing resources and technologies for the different
languages across the country. Of late, some financial support has also become
available for the endangered languages. In this paper, we give a summary of the
resources and technologies for those Indian languages which are not included in
the 8th schedule of the Indian Constitution and/or which are endangered.


title:
"Merging Results Is No Easy Task": An International Survey Study of
  Collaborative Data Analysis Practices Among UX Practitioners

abstract:
  Analysis is a key part of usability testing where UX practitioners seek to
identify usability problems and generate redesign suggestions. Although
previous research reported how analysis was conducted, the findings were
typically focused on individual analysis or based on a small number of
professionals in specific geographic regions. We conducted an online
international survey of 279 UX practitioners on their practices and challenges
while collaborating during data analysis. We found that UX practitioners were
often under time pressure to conduct analysis and adopted three modes of
collaboration: independently analyze different portions of the data and then
collaborate, collaboratively analyze the session with little or no independent
analysis, and independently analyze the same set of data and then collaborate.
Moreover, most encountered challenges related to lack of resources,
disagreements with colleagues regarding usability problems, and difficulty
merging analysis from multiple practitioners. We discuss design implications to
better support collaborative data analysis.


title:
ShowFace: Coordinated Face Inpainting with Memory-Disentangled
  Refinement Networks

abstract:
  Face inpainting aims to complete the corrupted regions of the face images,
which requires coordination between the completed areas and the non-corrupted
areas. Recently, memory-oriented methods illustrate great prospects in the
generation related tasks by introducing an external memory module to improve
image coordination. However, such methods still have limitations in restoring
the consistency and continuity for specificfacial semantic parts. In this
paper, we propose the coarse-to-fine Memory-Disentangled Refinement Networks
(MDRNets) for coordinated face inpainting, in which two collaborative modules
are integrated, Disentangled Memory Module (DMM) and Mask-Region Enhanced
Module (MREM). Specifically, the DMM establishes a group of disentangled memory
blocks to store the semantic-decoupled face representations, which could
provide the most relevant information to refine the semantic-level
coordination. The MREM involves a masked correlation mining mechanism to
enhance the feature relationships into the corrupted regions, which could also
make up for the correlation loss caused by memory disentanglement. Furthermore,
to better improve the inter-coordination between the corrupted and
non-corrupted regions and enhance the intra-coordination in corrupted regions,
we design InCo2 Loss, a pair of similarity based losses to constrain the
feature consistency. Eventually, extensive experiments conducted on CelebA-HQ
and FFHQ datasets demonstrate the superiority of our MDRNets compared with
previous State-Of-The-Art methods.


title: An Empirical Study of Remote Sensing Pretraining

abstract:
  Deep learning has largely reshaped remote sensing research for aerial image
understanding. Nevertheless, most of existing deep models are initialized with
ImageNet pretrained weights, where the natural images inevitably presents a
large domain gap relative to the aerial images, probably limiting the
finetuning performance on downstream aerial scene tasks. This issue motivates
us to conduct an empirical study of remote sensing pretraining (RSP). To this
end, we train different networks from scratch with the help of the largest
remote sensing scene recognition dataset up to now-MillionAID, to obtain the
remote sensing pretrained backbones, including both convolutional neural
networks (CNN) and vision transformers such as Swin and ViTAE, which have shown
promising performance on computer vision tasks. Then, we investigate the impact
of ImageNet pretraining (IMP) and RSP on a series of downstream tasks including
scene recognition, semantic segmentation, object detection, and change
detection using the CNN and vision transformers backbones. We have some
empirical findings as follows. First, vision transformers generally outperforms
CNN backbones, where ViTAE achieves the best performance, owing to its strong
representation capacity by introducing intrinsic inductive bias from
convolutions to transformers. Second, both IMP and RSP help deliver better
performance, where IMP enjoys a versatility by learning more universal
representations from diverse images belonging to much more categories while RSP
is distinctive in perceiving remote sensing related semantics. Third, RSP
mitigates the data discrepancy of IMP for remote sensing but may still suffer
from the task discrepancy, where downstream tasks require different
representations from the scene recognition task. These findings call for
further research efforts on both large-scale pretraining datasets and effective
pretraining methods.


title:
Precision measurements of the Lepton-Charge and Forward-Backward
  Drell-Yan Asymmetries to Enhance the Sensitivity to Broad Resonances of New
  Gauge Sectors

abstract:
  We study the impact of future measurements of lepton-charge and
forward-backward asymmetries in Drell-Yan processes in regions of transverse
and invariant masses near the Standard Model gauge bosons peaks to improve the
Parton Distribution Functions uncertainties. We study the implications on
$W^{'}$ and $Z^{'}$ searches following the reduction of these uncertainties. We
find that the sensitivity to the Beyond the Standard Model states is greatly
increased with respect to the case of base Parton Distribution Functions sets,
thereby enabling one to set more stringent limits on (or indeed discover) such
new particles.


title: On the meeting of random walks on random DFA

abstract:
  We consider two random walks evolving synchronously on a random out-regular
graph of $n$ vertices with bounded out-degree $r\ge 2$, also known as a random
Deterministic Finite Automaton (DFA). We show that, with high probability with
respect to the generation of the graph, the meeting time of the two walks is
stochastically dominated by a geometric random variable of rate
$(1+o(1))n^{-1}$, uniformly over their starting locations. Further, we prove
that this upper bound is typically tight, i.e., it is also a lower bound when
the locations of the two walks are selected uniformly at random. Our work takes
inspiration from a recent conjecture by Fish and Reyzin in the context of
computational learning, the connection with which is discussed.


title:
WeChat uptake of Chinese scholarly journals: an analysis of
  CSSCI-indexed journals

abstract:
  The study of how science is discussed and how scholarly actors interact on
social media has increasingly become popular in the field of scientometrics in
recent years. While most prior studies focused on research outputs discussed on
global platforms, such as Twitter or Facebook, the presence of scholarly
journals on local platforms was seldom studied, especially in the Chinese
social media context. To fill this gap, this study investigates the uptake of
WeChat (a Chinese social network app) by the Chinese scholarly journals indexed
by the Chinese Social Sciences Citation Index (CSSCI). The results show that
65.3% of CSSCI-indexed journals have created WeChat public accounts and posted
over 193 thousand WeChat posts in total. At the journal level, bibliometric
indicators (e.g., citations, downloads, and journal impact factors) and WeChat
indicators (e.g., clicks, likes, replies, and recommendations) are weakly
correlated with each other, reinforcing the idea of fundamentally
differentiated dimensions of indicators between bibliometrics and social media
metrics. Results also show that journals with WeChat public accounts slightly
outperform those without WeChat public accounts in terms of citation impact,
suggesting that the WeChat presence of scientific journals is mostly positively
associated with their citation impact.


title: On integral local Shimura varieties

abstract:
  We give a construction of "integral local Shimura varieties" which are formal
schemes that generalize the well-known integral models of the Drinfeld $p$-adic
upper half spaces. The construction applies to all classical groups, at least
for odd $p$. These formal schemes also generalize the formal schemes defined by
Rapoport-Zink via moduli of $p$-divisible groups, and are characterized purely
in group-theoretic terms. More precisely, for a local $p$-adic Shimura datum
$(G, b, \mu)$ and a quasi-parahoric group scheme $\mathcal G$ for $G$, Scholze
has defined a functor on perfectoid spaces which parametrizes $p$-adic shtukas.
He conjectured that this functor is representable by a normal formal scheme
which is locally formally of finite type and flat over $O_{\breve E}$.
Scholze-Weinstein proved this conjecture when $(G, b, \mu)$ is of (P)EL type by
using Rapoport-Zink formal schemes. We prove this conjecture for any $(G, \mu)$
of abelian type when $p\neq 2$, and when $p=2$ and $G$ is of type $A$ or $C$.
We also relate the generic fiber of this formal scheme to the local Shimura
variety, a rigid-analytic space attached by Scholze to $(G, b, \mu, {\mathcal
G})$.


title:
Free-Space Quantum Key Distribution with Single Photons from Defects in
  Hexagonal Boron Nitride

abstract:
  We present a proof-of-concept demonstration of free-space quantum key
distribution (QKD) with single photons generated from an isolated defect in
hexagonal boron nitride (hBN). The source, operating at room temperature with a
10% brightness, is integrated into a B92 protocol and a secure key rate (SKR)
of 238 bps and a quantum bit error rate (QBER) of 8.95% are achieved with 1 MHz
clock rate. The effect of temporal filtering of detected photons on the
performance of QKD parameters is also studied. We believe that our results will
accelerate the work on improving the performance of optically active defects in
hBN and their use in high-performance practical QKD systems.


title:
The Dirichlet and Neumann problems in Lipschitz and in $\mathscr{C}^{1,
  1}$ domains. Abstract

abstract:
  The main purpose of this paper is to address some questions concerning
boundary value problems related to the Laplacian and bi-Laplacian operators,
set in the framework of classical $H^s$ Sobolev spaces on a bounded Lipschitz
domain of R^N. These questions are not new and a lot of work has been done in
this direction by many authors using various techniques since the 80's. If for
regular domains almost every thing is elucidated, it is not the case for
Lipschitz ones and for $s$ of the form
  $s = k + 1/2$, with $k$ integer. It is well known that this framework is
delicate. Even in these cases many results are well established but sometimes
not satisfactory. Several questions remain posed. Our main goal through this
work is on one hand to give some improvements to the theory and on another one
by using techniques which do not require too intricate calculations. We also
tried to obtain maximal regularity for the solutions and as far as we can
optimality of the results.


title:
Eppur si muove... On the Origin of Long Secondary Periods in Red Giant
  Stars

abstract:
  Long secondary periods (LSPs), observed in a third of pulsating red giant and
supergiant stars, are the only unexplained type of large-amplitude stellar
variability known at this time. Numerous authors have explored various
scenarios for the origin of LSPs, but were unable to give a final solution to
this problem. We present known properties of LSP variables and show new results
proving that the physical mechanism responsible for LSPs is binarity. Namely,
the LSP light changes are due to the presence of a dusty cloud orbiting the red
giant together with a brown-dwarf companion and obscuring the star once per
orbit. In this scenario, the low-mass companion is a former planet that
accreted a significant amount of mass from the envelope of its host star and
grew into a brown dwarf.


title:
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad
  Stepsize

abstract:
  In this paper, we propose a new, simplified high probability analysis of
AdaGrad for smooth, non-convex problems. More specifically, we focus on a
particular accelerated gradient (AGD) template (Lan, 2020), through which we
recover the original AdaGrad and its variant with averaging, and prove a
convergence rate of $\mathcal O (1/ \sqrt{T})$ with high probability without
the knowledge of smoothness and variance. We use a particular version of
Freedman's concentration bound for martingale difference sequences (Kakade &
Tewari, 2008) which enables us to achieve the best-known dependence of $\log (1
/ \delta )$ on the probability margin $\delta$. We present our analysis in a
modular way and obtain a complementary $\mathcal O (1 / T)$ convergence rate in
the deterministic setting. To the best of our knowledge, this is the first high
probability result for AdaGrad with a truly adaptive scheme, i.e., completely
oblivious to the knowledge of smoothness and uniform variance bound, which
simultaneously has best-known dependence of $\log( 1/ \delta)$. We further
prove noise adaptation property of AdaGrad under additional noise assumptions.


title: Long-term relaxation of ${1D}$ self-gravitating systems

abstract:
  We investigate the long-term relaxation of one-dimensional (${1D}$)
self-gravitating systems, using both kinetic theory and $N$-body simulations.
We consider thermal and Plummer equilibria, with and without collective
effects. All combinations are found to be in clear agreement with respect to
the Balescu-Lenard and Landau predictions for the diffusion coefficients.
Interestingly, collective effects reduce the diffusion by a factor ${\sim 10}$.
The predicted flux for Plummer equilibrium matches the measured one, which is a
remarkable validation of kinetic theory. We also report on a situation of quasi
kinetic blocking for the same equilibrium.


title:
Visibility, invisibility and unique recovery of inverse electromagnetic
  problems with conical singularities

abstract:
  In this paper, we study time-harmonic electromagnetic scattering in two
scenarios, where the anomalous scatterer is either a pair of electromagnetic
sources or an inhomogeneous medium, both with compact supports. We are mainly
concerned with the geometrical inverse scattering problem of recovering the
support of the scatterer, independent of its physical contents, by a single
far-field measurement. It is assumed that the support of the scatterer
(locally) possesses a conical singularity. We establish a local
characterisation of the scatterer when invisibility/transparency occurs,
showing that its characteristic parameters must vanish locally around the
conical point. Using this characterisation, we establish several local and
global uniqueness results for the aforementioned inverse scattering problems,
showing that visibility must imply unique recovery. In the process, we also
establish the local vanishing property of the electromagnetic transmission
eigenfunctions around a conical point under the H\"older regularity or a
regularity condition in terms of Herglotz approximation.


title:
A note on the van der Waerden conjecture on random polynomials with
  symmetric Galois group for function fields

abstract:
  Let f(x) = x^n + (a[n-1] t + b[n-1]) x^(n-1) + ... + (a[0] t + b[0]) be of
constant degree n in x and degree <= 1 in t, where all a[i],b[i] are randomly
and uniformly selected from a finite field GF(q) of q elements. Then the
probability that the Galois group of f over the rational function field
GF(q)(t) is the symmetric group S(n) on n elements is 1 - O(1/q). Furthermore,
the probability that the Galois group of f(x) over GF(q)(t) is not S(n) is >=
1/q.


title:
Online Feedback Droop Scheduling in Distribution Grids for Frequency and
  Local Voltage Control

abstract:
  This paper presents a novel framework for collective control of Distributed
Energy Resources (DERs) in active Distribution Networks (DNs). The proposed
approach unifies the commonly employed local (i.e., decentralized) voltage and
frequency droop control schemes into a transfer matrix relating frequency and
voltage magnitude measurements to active and reactive power injection
adjustments. Furthermore, the transfer matrices of individual DER units are
adaptively tuned in real-time via slow communication links using an online gain
scheduling approach, with the objective to enable frequency support provision
to the transmission system and ensure that the DN voltages are kept within the
allowable limits. A global asymptomatic stability condition of the analyzed
droop-controlled DN is analytically established. The considered gain scheduling
problem is solved by leveraging an online primal-dual gradient-based method and
a suitable linearized power flow model. Additional ancillary service providers
can be trivially incorporated into the proposed framework in a plug-and-play
fashion. Numerical simulations of the 37-bus IEEE test system confirm the
validity of the approach and demonstrate numerous advantages of the proposed
scheme over the state-of-the-art.


title:
Critical behavior of isotropic systems with strong dipole-dipole
  interaction: three-loop study

abstract:
  We analyze the critical behavior of isotropic systems with dipole-dipole
interaction by renormalization-group methods in fixed space-time dimensions.
Working in three-dimensional theory we analytically find three-loop expressions
for critical exponents in the limit of dominating dipole-dipole forces.
Resummation of the series obtained provides numerical values close to
$O(3)$-theory predictions, justifying the applicability of such a simplified
model to systems with strong dipole-dipole interaction.


title:
CCAT-NET: A Novel Transformer Based Semi-supervised Framework for
  Covid-19 Lung Lesion Segmentation

abstract:
  The spread of the novel coronavirus disease 2019 (COVID-19) has claimed
millions of lives. Automatic segmentation of lesions from CT images can assist
doctors with screening, treatment, and monitoring. However, accurate
segmentation of lesions from CT images can be very challenging due to data and
model limitations. Recently, Transformer-based networks have attracted a lot of
attention in the area of computer vision, as Transformer outperforms CNN at a
bunch of tasks. In this work, we propose a novel network structure that
combines CNN and Transformer for the segmentation of COVID-19 lesions. We
further propose an efficient semi-supervised learning framework to address the
shortage of labeled data. Extensive experiments showed that our proposed
network outperforms most existing networks and the semi-supervised learning
framework can outperform the base network by 3.0% and 8.2% in terms of Dice
coefficient and sensitivity.


title:
Asymptotics of the meta-atom: plane wave scattering by a single
  Helmholtz resonator

abstract:
  Using a combination of multipole methods and the method of matched
asymptotics, we present a solution procedure for acoustic plane wave scattering
by a single Helmholtz resonator in two dimensions. Closed-form representations
for the multipole scattering coefficients of the resonator are derived, valid
at low frequencies, with three fundamental configurations examined in detail:
the thin-walled, moderately thick-walled, and very thick-walled limits.
Additionally, we examine the impact of dissipation for very thick-walled
resonators, and also numerically evaluate the scattering, absorption, and
extinction cross sections (efficiencies) for representative resonators in all
three wall thickness regimes. In general, we observe strong enhancement in both
the scattered fields and cross sections at the Helmholtz resonance frequencies.
As expected, dissipation is shown to shift the resonance frequency, reduce the
amplitude of the field, and reduce the extinction efficiency at the fundamental
Helmholtz resonance. Finally, we confirm results in the literature on
Willis-like coupling effects for this resonator design, and crucially, connect
these findings to earlier works by the authors on two-dimensional arrays of
resonators, deducing that depolarisability effects (off-diagonal terms) for a
single resonator do not ensure the existence of Willis coupling effects
(bianisotropy) in bulk.


title: Spectral Denoising for Microphone Classification

abstract:
  In this paper, we propose the use of denoising for microphone classification,
to enable its usage for several key application domains that involve noisy
conditions. We describe the proposed analysis pipeline and the baseline
algorithm for microphone classification, and discuss various denoising
approaches which can be applied to it within the time or spectral domain;
finally, we determine the best-performing denoising procedure, and evaluate the
performance of the overall, integrated approach with several SNR levels of
additive input noise. As a result, the proposed method achieves an average
accuracy increase of about 25% on denoised content over the reference baseline.


title:
Open-Source Tools for Behavioral Video Analysis: Setup, Methods, and
  Development

abstract:
  Recently developed methods for video analysis, especially models for pose
estimation and behavior classification, are transforming behavioral
quantification to be more precise, scalable, and reproducible in fields such as
neuroscience and ethology. These tools overcome long-standing limitations of
manual scoring of video frames and traditional "center of mass" tracking
algorithms to enable video analysis at scale. The expansion of open-source
tools for video acquisition and analysis has led to new experimental approaches
to understand behavior. Here, we review currently available open source tools
for video analysis, how to set them up in a lab that is new to video recording
methods, and some issues that should be addressed by developers and advanced
users, including the need to openly share datasets and code, how to compare
algorithms and their parameters, and the need for documentation and
community-wide standards. We hope to encourage more widespread use and
continued development of the tools. They have tremendous potential for
accelerating scientific progress for understanding the brain and behavior.


title:
Tensor train based isogeometric analysis for PDE approximation on
  parameter dependent geometries

abstract:
  This work develops a numerical solver based on the combination of
isogeometric analysis (IGA) and the tensor train (TT) decomposition for the
approximation of partial differential equations (PDEs) on parameter-dependent
geometries. First, the discrete Galerkin operator as well as the solution for a
fixed geometry configuration are represented as tensors and the TT format is
employed to reduce their computational complexity. Parametric dependencies are
included by considering the parameters that control the geometry configuration
as additional dimensions next to the physical space coordinates. The parameters
are easily incorporated within the TT-IGA solution framework by introducing a
tensor product basis expansion in the parameter space. The discrete Galerkin
operators are accordingly extended to accommodate the parameter dependence,
thus obtaining a single system that includes the parameter dependency. The
system is solved directly in the TT format and a low-rank representation of the
parameter-dependent solution is obtained. The proposed TT-IGA solver is applied
to several test cases which showcase its high computational efficiency and
tremendous compression ratios achieved for representing the parameter-dependent
IGA operators and solutions.


title:
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware
  Adversarial Training

abstract:
  Existing deep learning real denoising methods require a large amount of
noisy-clean image pairs for supervision. Nonetheless, capturing a real
noisy-clean dataset is an unacceptable expensive and cumbersome procedure. To
alleviate this problem, this work investigates how to generate realistic noisy
images. Firstly, we formulate a simple yet reasonable noise model that treats
each real noisy pixel as a random variable. This model splits the noisy image
generation problem into two sub-problems: image domain alignment and noise
domain alignment. Subsequently, we propose a novel framework, namely
Pixel-level Noise-aware Generative Adversarial Network (PNGAN). PNGAN employs a
pre-trained real denoiser to map the fake and real noisy images into a nearly
noise-free solution space to perform image domain alignment. Simultaneously,
PNGAN establishes a pixel-level adversarial training to conduct noise domain
alignment. Additionally, for better noise fitting, we present an efficient
architecture Simple Multi-scale Network (SMNet) as the generator. Qualitative
validation shows that noise generated by PNGAN is highly similar to real noise
in terms of intensity and distribution. Quantitative experiments demonstrate
that a series of denoisers trained with the generated noisy images achieve
state-of-the-art (SOTA) results on four real denoising benchmarks.


title: Light-by-light scattering cross-section measurements at LHC

abstract:
  This note represents an attempt to gather the input related to light-by-light
scattering ($\gamma\gamma$) cross-section measurements at LHC with the aim of
checking the consistency with different standard model predictions. For the
first time, we also consider the contribution from the $\eta_b(1S)$ meson
production to the diphoton invariant mass distribution, by calculating its
inclusive photoproduction cross-section. Using a simplified set of assumptions,
we find a result of $115\pm 19\,\,\text{nb}$, consistent with standard model
predictions within two standard deviations. Although an improved determination
of the integrated fiducial $\textrm{PbPb}\,(\gamma\gamma)\to
\textrm{Pb}^{(\ast)}\textrm{+}\textrm{Pb}^{(\ast)}\,\gamma\gamma$ cross-section
by approximately 10\% could be potentially achieved relative to current
measurements, further improvements are expected with the inclusion of existing
or forthcoming LHC nuclear data.


title:
Evidence for positive long- and short-term effects of vaccinations
  against COVID-19 in wearable sensor metrics -- Insights from the German
  Corona Data Donation Project

abstract:
  Vaccines are among the most powerful tools used to combat the COVID-19
pandemic. They are highly effective against infection and substantially reduce
the risk of severe disease, hospitalization, ICU admission, and death. However,
their potential for attenuating long-term effects of a SARS-CoV-2 infection,
commonly denoted as Long COVID, remains elusive and is still subject of debate.
Such long-term effects can be effectively monitored at the individual level by
analyzing physiological data collected by consumer-grade wearable sensors.
Here, we investigate changes in resting heart rate, daily physical activity,
and sleep duration in response to a SARS-CoV-2 infection stratified by
vaccination status. Data was collected over a period of two years in the
context of the German Corona Data Donation Project with currently around
190,000 monthly active donors. Compared to their unvaccinated counterparts, we
find that vaccinated individuals on average experience smaller changes in their
vital data that also return to normal levels more quickly. Likewise, extreme
changes in vitals during the acute phase of the disease occur less frequently
in vaccinated individuals. Our results solidify evidence that vaccines can
mitigate long-term detrimental effects of SARS-CoV-2 infections both in terms
of duration and magnitude. Furthermore, they demonstrate the value of large
scale, high-resolution wearable sensor data in public health research.


title: On algebras of finite general representation type

abstract:
  We introduce the notion of "finite general representation type" for a
finite-dimensional algebra, a property related to the "dense orbit property"
introduced by Chindris-Kinser-Weyman. We use an interplay of geometric,
combinatorial, and algebraic methods to produce a family of algebras of wild
representation type but finite general representation type. For completeness,
we also give a short proof that the only local algebras of finite general
representation type are already of finite representation type. We end with a
Brauer-Thrall style conjecture for general representations of algebras.


title:
Berry curvature dipole senses topological transition in a moir\'e
  superlattice

abstract:
  Topological aspects of electron wavefunction play a crucial role in
determining the physical properties of materials. Berry curvature and Chern
number are used to define the topological structure of electronic bands. While
Berry curvature and its effects in materials have been studied, detecting
changes in the topological invariant, Chern number, is challenging. In this
regard, twisted double bilayer graphene (TDBG) has emerged as a promising
platform to gain electrical control over the Berry curvature hotspots and the
valley Chern numbers of its flat bands. In addition, strain induced breaking of
the three-fold rotation (C3) symmetry in TDBG, leads to a non-zero first moment
of Berry curvature called the Berry curvature dipole (BCD), which can be sensed
using nonlinear Hall (NLH) effect. We reveal, using TDBG, that the BCD detects
topological transitions in the bands and changes its sign. In TDBG, the
perpendicular electric field tunes the valley Chern number and the BCD
simultaneously allowing us a tunable system to probe the physics of topological
transitions. Furthermore, we find hysteresis of longitudinal and NLH responses
with electric field that can be attributed to switching of electric
polarization in moir\'e systems. Such a hysteretic response holds promise for
next-generation Berry curvature-based memory devices. Probing topological
transitions, as we show, can be emulated in other 3D topological systems.


title: KNN-Diffusion: Image Generation via Large-Scale Retrieval

abstract:
  While the availability of massive Text-Image datasets is shown to be
extremely useful in training large-scale generative models (e.g. DDPMs,
Transformers), their output typically depends on the quality of both the input
text, as well as the training dataset. In this work, we show how large-scale
retrieval methods, in particular efficient K-Nearest-Neighbors (KNN) search,
can be used in order to train a model to adapt to new samples. Learning to
adapt enables several new capabilities. Sifting through billions of records at
inference time is extremely efficient and can alleviate the need to train or
memorize an adequately large generative model. Additionally, fine-tuning
trained models to new samples can be achieved by simply adding them to the
table. Rare concepts, even without any presence in the training set, can be
then leveraged during test time without any modification to the generative
model. Our diffusion-based model trains on images only, by leveraging a joint
Text-Image multi-modal metric. Compared to baseline methods, our generations
achieve state of the art results both in human evaluations as well as with
perceptual scores when tested on a public multimodal dataset of natural images,
as well as on a collected dataset of 400 million Stickers.


title: Influence of Color Spaces for Deep Learning Image Colorization

abstract:
  Colorization is a process that converts a grayscale image into a color one
that looks as natural as possible. Over the years this task has received a lot
of attention. Existing colorization methods rely on different color spaces:
RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the
results obtained by training a deep neural network, to answer the question: "Is
it crucial to correctly choose the right color space in deep-learning based
colorization?". First, we briefly summarize the literature and, in particular,
deep learning-based methods. We then compare the results obtained with the same
deep neural network architecture with RGB, YUV and Lab color spaces.
Qualitative and quantitative analysis do not conclude similarly on which color
space is better. We then show the importance of carefully designing the
architecture and evaluation protocols depending on the types of images that are
being processed and their specificities: strong/small contours, few/many
objects, recent/archive images.


title:
Feller and ergodic properties of jump-move processes with applications
  to interacting particle systems

abstract:
  We consider Markov processes that alternate continuous motions and jumps in a
general locally compact polish space. Starting from a mechanistic construction,
a first contribution of this article is to provide conditions on the dynamics
so that the associated transition kernel forms a Feller semigroup, and to
deduce the corresponding infinitesimal generator. In a second contribution, we
investigate the ergodic properties in the special case where the jumps consist
of births and deaths, a situation observed in several applications including
epidemiology, ecology and microbiology. Based on a coupling argument, we obtain
conditions for the convergence to a stationary measure with a geometric rate of
convergence. Throughout the article, we illustrate our results by general
examples of systems of interacting particles in R d with births and deaths. We
show that in some cases the stationary measure can be made explicit and
corresponds to a Gibbs measure on a compact subset of R d. Our examples include
in particular Gibbs measure associated to repulsive Lennard-Jones potentials
and to Riesz potentials.


title: Muon spin rotation and relaxation study on Nb$_{1-y}$Fe$_{2+y}$

abstract:
  We present a detailed study of the magnetic properties of weakly
ferromagnetic/quantum critical Nb$_{1-y}$Fe$_{2+y}$ using muon spin rotation
and relaxation ($\mu$SR). By means of an angular dependent study of the muon
spin rotation signal in applied magnetic fields on a single crystal in the
paramagnetic state we establish the muon stopping site in the crystallographic
lattice of NbFe$_2$. With this knowledge we develop models to describe the muon
spin rotation and relaxation signals in the weakly ferromagnetic, spin density
wave and quantum critical phases of Nb$_{1-y}$Fe$_{2+y}$ and fit the
corresponding experimental data. In particular, we quantify the $\mu$SR
response for quantum critical behavior in Nb$_{1.0117}$Fe$_{1.9883}$ and
extract the influence of residual weak structural disorder. From our analysis,
Nb$_{1-y}$Fe$_{2+y}$ emerges to be uniquely suited to study quantum criticality
close to weak itinerant ferromagnetic order.


title: Systematic investigation of critical charge limits in Thick GEMs

abstract:
  We present discharge probability studies performed with a single Thick Gas
Electron Multiplier (THGEM) irradiated with alpha particles in Ar-CO$_2$ and
Ne-CO$_2$ mixtures. We observe a clear dependency of the discharge stability on
the noble gas and quencher content pointing to lighter gases to be more stable
against the development of streamer discharges. A detailed comparison of the
measurements with Geant4 simulations allowed us to extract the critical charge
value leading to the formation of a spark in a THGEM hole, which is found to be
within the range of 3-7$\times10^6$ electrons, depending on the gas mixture.
  Our experimental findings are compared to previous GEM results. We show that
the discharge probability of THGEMs exceeds the one measured with GEMs by
orders of magnitude. This can be explained with simple geometrical
considerations, where primary ionization is collected by a lower number of
holes available in a THGEM structure, reaching higher primary charge densities
and thus increasing the probability of a spark occurrence. However, we show
that the critical charge limits are similar for both amplification structures.


title:
Retrieval-based Spatially Adaptive Normalization for Semantic Image
  Synthesis

abstract:
  Semantic image synthesis is a challenging task with many practical
applications. Albeit remarkable progress has been made in semantic image
synthesis with spatially-adaptive normalization and existing methods normalize
the feature activations under the coarse-level guidance (e.g., semantic class).
However, different parts of a semantic object (e.g., wheel and window of car)
are quite different in structures and textures, making blurry synthesis results
usually inevitable due to the missing of fine-grained guidance. In this paper,
we propose a novel normalization module, termed as REtrieval-based Spatially
AdaptIve normaLization (RESAIL), for introducing pixel level fine-grained
guidance to the normalization architecture. Specifically, we first present a
retrieval paradigm by finding a content patch of the same semantic class from
training set with the most similar shape to each test semantic mask. Then,
RESAIL is presented to use the retrieved patch for guiding the feature
normalization of corresponding region, and can provide pixel level fine-grained
guidance, thereby greatly mitigating blurry synthesis results. Moreover,
distorted ground-truth images are also utilized as alternatives of
retrieval-based guidance for feature normalization, further benefiting model
training and improving visual quality of generated images. Experiments on
several challenging datasets show that our RESAIL performs favorably against
state-of-the-arts in terms of quantitative metrics, visual quality, and
subjective evaluation. The source code and pre-trained models will be publicly
available.


title:
SPIDER-WEB enables stable, repairable, and encryptible algorithms under
  arbitrary local biochemical constraints in DNA-based storage

abstract:
  DNA has been considered as a promising medium for storing digital
information. Despite the biochemical progress in DNA synthesis and sequencing,
novel coding algorithms need to be constructed under the specific constraints
in DNA-based storage. Many functional operations and storage carriers were
introduced in recent years, bringing in various biochemical constraints
including but not confined to long single-nucleotide repeats and abnormal GC
content. Existing coding algorithms are not applicable or unstable due to more
local biochemical constraints and their combinations. In this paper, we design
a graph-based architecture, named SPIDER-WEB, to generate corresponding
graph-based algorithms under arbitrary local biochemical constraints. These
generated coding algorithms could be used to encode arbitrary digital data as
DNA sequences directly or served as a benchmark for the follow-up construction
of coding algorithms. To further consider recovery and security issues existing
in the storage field, it also provides pluggable algorithmic patches based on
the generated coding algorithms: path-based correcting and mapping shuffling.
They provide approaches for probabilistic error correction and symmetric
encryption respectively.


title:
Equilibrium states of endomorphisms of $\mathbb{P}^k$ II: spectral
  stability and limit theorems

abstract:
  We establish the existence of a spectral gap for the transfer operator
induced on $\mathbb P^k = \mathbb P^k (\mathbb C)$ by a generic holomorphic
endomorphism and a suitable continuous weight and its perturbations on various
functional spaces, which is new even in dimension one. The main issue to
overcome is the rigidity of the complex objects, since the transfer operator is
a non-holomorphic perturbation of the operator $f_*$. The system is moreover
non-uniformly hyperbolic and one may have critical points on the Julia set. The
construction of our norm requires the introduction and study of several
intermediate new norms, and a careful combination of ideas from pluripotential
and interpolation theory. As far as we know, this is the first time that
pluripotential methods have been applied to solve a mixed real-complex problem.
Thanks to the spectral gap, we establish an exponential speed of convergence
for the equidistribution of the backward orbits of points towards the conformal
measure. Moreover, we obtain a full list of statistical properties for the
equilibrium states: exponential mixing, CLT, Berry-Esseen theorem, local CLT,
ASIP, LIL, LDP, almost sure CLT. Many of these properties are new even in
dimension one, some even in the case of zero weight function (i.e., for the
measure of maximal entropy).


title:
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon
  Estimation Method with Feasibility and Near-optimality Guarantees

abstract:
  This paper proposes a primal-dual framework to learn a stable estimator for
linear constrained estimation problems leveraging the moving horizon approach.
To avoid the online computational burden in most existing methods, we learn a
parameterized function offline to approximate the primal estimate. Meanwhile, a
dual estimator is trained to check the suboptimality of the primal estimator
during execution time. Both the primal and dual estimators are learned from
data using supervised learning techniques, and the explicit sample size is
provided, which enables us to guarantee the quality of each learned estimator
in terms of feasibility and optimality. This in turn allows us to bound the
probability of the learned estimator being infeasible or suboptimal.
Furthermore, we analyze the stability of the resulting estimator with a bounded
error in the minimization of the cost function. Since our algorithm does not
require the solution of an optimization problem during runtime, state estimates
can be generated online almost instantly. Simulation results are presented to
show the accuracy and time efficiency of the proposed framework compared to
online optimization of moving horizon estimation and Kalman filter. To the best
of our knowledge, this is the first learning-based state estimator with
feasibility and near-optimality guarantees for linear constrained systems.


title: A characterisation of 3-colourable 3-dimensional triangulations

abstract:
  We extend Heawood's theorem on the colourability of plane triangulations to
triangulations of 3-space. This proves the 3-dimensional case from a conjecture
by Robert D Edwards, Jacob Eli Goodman, and Hironori Onishi.


title:
The equivalence of the Szemer\'edi and Petruska conjecture and the
  maximum order of $3$-uniform $\tau$-critical hypergraphs

abstract:
  Recently we asymptotically resolved the long-standing Szemer\'edi and
Petruska conjecture. Several decades ago Gy\'arf\'as et al. observed, via a
straightforward but unpublished argument, that this conjecture is equivalent to
the problem of determining the maximum order of a $3$-uniform $\tau$-critical
hypergraph. Consequently, an asymptotically tight upper bound for the maximum
order of a $3$-uniform $\tau$-critical hypergraph follows from our recent work,
reawakening interest in this equivalence. In this companion paper we supply a
simple proof of this equivalence. We also present related background with open
problems, and mention combinatorial geometry applications of the Szemer\'edi
and Petruska conjecture.


title: On Cohen and Prikry Forcing Notions

abstract:
  We show that it is possible to add $\kappa^+-$Cohen subsets to $\kappa$ with
a Prikry forcing over $\kappa$. This answers a question from
\cite{HayutBenhanouGitik}. A strengthening of non-Galvin property is
introduced. It is shown to be consistent using a single measurable cardinal
which improves a previous result by S. Garti, S. Shelah, and the first author
\cite{BenhamouGartieShelah}. A situation with extender-based Prikry forcings is
examined. This relates to a question of H. Woodin.


title: Transport Layer Networking

abstract:
  In this paper we focus on the invention of new network forwarding behaviors
between network Layers 4 and Layer 7 in the OSI network model. Our design goal
is to propose no changes to L3 - The IP network layer, thus maintaining 100%
compatibility with the existing internet. Small changes are made to L4 the
transport layer, and a new design for a session ( L5 ) is proposed. This new
capability is intended to have minimal or no impact on the application layer,
except for exposing the ability for L7 to select this new mode of data transfer
or not. The invention of new networking technologies is frequently done in an
academic setting, however the design needs to be constrained by practical
considerations for cost, operational feasibility, robustness and scale. Our
goal is to improve the production data infrastructure for HEP 24/7 on a global
scale.


title:
Tailored nano-electronics and photonics with two-dimensional materials
  at terahertz frequencies

abstract:
  The discovery of graphene and its fascinating capabilities have triggered an
unprecedented interest in inorganic two-dimensional (2D) materials. Van der
Waals (vdW) layered materials as graphene, hexagonal boron nitride (hBN),
transition metal dichalcogenides (TMDs), and the more recently re-discovered
black phosphorus (BP) indeed display an exceptional technological potential for
engineering nano-electronic and nano-photonic devices and components by design,
offering a unique platform for developing new devices with a variety of ad-hoc
properties. In this perspective article, we provide a vision on the key
transformative applications of 2D nanomaterials for the developments of
nanoelectronic, nanophotonic, optical and plasmonic devices, at terahertz
frequencies, highlighting how the rich physical phenomena enabled by their
unique band-structure engineering can allow those devices to boost the vibrant
field of quantum science and quantum technologies.


title:
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing
  for One-Shot Imitation Learning

abstract:
  We present DOME, a novel method for one-shot imitation learning, where a task
can be learned from just a single demonstration and then be deployed
immediately, without any further data collection or training. DOME does not
require prior task or object knowledge, and can perform the task in novel
object configurations and with distractors. At its core, DOME uses an
image-conditioned object segmentation network followed by a learned visual
servoing network, to move the robot's end-effector to the same relative pose to
the object as during the demonstration, after which the task can be completed
by replaying the demonstration's end-effector velocities. We show that DOME
achieves near 100% success rate on 7 real-world everyday tasks, and we perform
several studies to thoroughly understand each individual component of DOME.


title: Free-space quantum walk of neutral atoms

abstract:
  We demonstrate that a coherent propagating light causes quantum walk of a
neutral atom with tunable step length in free-space without optical lattice.
This effect can be seen as an extension of electromagnetically induced walking
to the superposition states of atoms. The superposition of atom internal states
leads to atom wave packets propagate in opposite directions. It can be
explained by the spin-orbit coupling in free-space which is described by
analytical solutions of Schr\"{o}dinger equation here. These results should be
important for the development of quantum information processing and topological
effects with atomic gas in continuum.


title: How to build the optical inverse of a multimode fibre

abstract:
  When light propagates through a multimode optical fibre (MMF), the spatial
information it carries is scrambled. Wavefront shaping can undo this
scrambling, typically one spatial mode at a time - enabling deployment of MMFs
as ultra-thin micro-endoscopes. In this work we go beyond serial wavefront
shaping by showing how to simultaneously unscramble all spatial modes emerging
from an MMF in parallel. We introduce a passive multiple-scattering element -
crafted through the process of inverse design - that is complementary to an MMF
and undoes its optical effects. This optical inverter makes possible both
single-shot wide-field imaging and super-resolution imaging through MMFs. Our
design consists of a cascade of diffractive elements, and can be understood
from the perspective of both multi-plane light conversion, and as a physically
inspired deep diffractive neural network. This physical architecture can
outperform state-of-the-art electronic neural networks tasked with unscrambling
light, as it preserves the phase and coherence information of the optical
signals flowing through it. Here we demonstrate our MMF inversion concept
through numerical simulations, and efficiently sort and unscramble up to ~400
step-index fibre modes, reforming incoherent images of scenes at arbitrary
distances from the distal fibre facet. We also describe how our optical
inverter can dynamically adapt to see through flexible fibres with a range of
experimentally realistic TMs - made possible by moulding optical memory effects
into the structure of our design. Although complex, our inversion scheme is
based on current fabrication technology so could be realised in the near
future. Beyond imaging through scattering media, these concepts open up a range
of new avenues for optical multiplexing, communications and computation in the
realms of classical and quantum photonics.


title: Light Response of Poly(ethylene 2,6-napthalate) to Neutrons

abstract:
  There is increasing necessity for low background active materials as
ton-scale, rare-event and cryogenic detectors are developed.
Poly(ethylene-2,6-naphthalate) (PEN) has been considered for these applications
because of its robust structural characteristics, and its scintillation light
in the blue wavelength region. Radioluminescent properties of PEN have been
measured to aid in the evaluation of this material. In this article we present
a measurement of PEN's quenching factor using three different neutron sources;
neutrons emitted from spontaneous fission in \cf, neutrons generated from a DD
generator, and neutrons emitted from the \Can and the \Lipn nuclear reactions.
The fission source used time-of-flight to determine the neutron energy, and the
neutron energy from the nuclear reactions was defined using thin targets and
reaction kinematics. The Birk's factor and scintillation efficiency were found
to be $kB = 0.12 \pm 0.01$ mm MeV$^{-1}$ and $S = 1.31\pm0.09$ MeV$_{ee}$
MeV$^{-1}$ from a simultaneous analysis of the data obtained from the three
different sources. With these parameters, it is possible to evaluate PEN as a
viable material for large-scale, low background physics experiments.


title:
Optical Purification of Materials via Electromagnetically Induced
  Walking

abstract:
  Natural materials are always mixed with foreign matters which affect their
physical or chemical properties. Here, we give an optical method for the
precise purification of gaseous state materials. Principle of this method is
originated from spin-orbit coupling in electromagnetically induced walking
where fingerprints of atoms such as masses and optical transition wavelengths
determine walking speed of the atoms. This method could be used for some hardly
separable atoms and isotopes depending on the condition of atom coherent time.
The present work opens a platform for applications of cold atom technology in
the purification of atoms and molecules.


title:
Solving Severely Ill-Posed Linear Systems with Time Discretization Based
  Iterative Regularization Methods

abstract:
  Recently, inverse problems have attracted more and more attention in
computational mathematics and become increasingly important in engineering
applications. After the discretization, many of inverse problems are reduced to
linear systems. Due to the typical ill-posedness of inverse problems, the
reduced linear systems are often ill-posed, especially when their scales are
large. This brings great computational difficulty. Particularly, a small
perturbation in the right side of an ill-posed linear system may cause a
dramatical change in the solution. Therefore, regularization methods should be
adopted for stable solutions. In this paper, a new class of accelerated
iterative regularization methods is applied to solve this kind of large-scale
ill-posed linear systems. An iterative scheme becomes a regularization method
only when the iteration is early terminated. And a Morozov's discrepancy
principle is applied for the stop criterion. Compared with the conventional
Landweber iteration, the new methods have acceleration effect, and can be
compared to the well-known accelerated v-method and Nesterov method. From the
numerical results, it is observed that using appropriate discretization
schemes, the proposed methods even have better behavior when comparing with
v-method and Nesterov method.


title:
Characterization of different user behaviors for demand response in data
  centers

abstract:
  Digital technologies are becoming ubiquitous while their impact increases. A
growing part of this impact happens far away from the end users, in networks or
data centers, contributing to a rebound effect. A solution for a more
responsible use is therefore to involve the user. As a first step in this
quest, this work considers the users of a data center and characterizes their
contribution to curtail the computing load for a short period of time by solely
changing their job submission behavior.The contributions are: (i) an
open-source plugin for the simulator Batsim to simulate users based on real
data; (ii) the exploration of four types of user behaviors to curtail the load
during a time window namely delaying, degrading, reconfiguring or renouncing to
their job submissions. We study the impact of these behaviors on four different
metrics: the energy consumed during and after the time window, the mean waiting
time and the mean slowdown. We also characterize the conditions under which the
involvement of users is the most beneficial.


title:
Deep Learning-based Small Magnitude Earthquake Detection and Seismic
  Phase Classification

abstract:
  Reliable earthquake detection and seismic phase classification is often
challenging especially in the circumstances of low magnitude events or poor
signal-to-noise ratio. With improved seismometers and better global coverage, a
sharp increase in the volume of recorded seismic data is witnessed. This makes
the handling of the seismic data rather daunting based on traditional
approaches and therefore fuels the need for a more robust and reliable method.
In this study, we investigate two deep learningbased models, termed 1D
ResidualNeuralNetwork (ResNet) and multi-branch ResNet, for tackling the
problem of seismic signal detection and phase identification, especially the
later can be used in the case where multiple classes is organized in the
hierarchical format. These methods are trained and tested on the dataset of the
Southern California Seismic Network. Results demonstrate that the proposed
methods can achieve robust performance for the detection of seismic signals,
and the identification of seismic phases, even when the seismic events are of
small magnitude and are masked by noise. Compared with previously proposed deep
learning methods, the introduced frameworks achieve 4% improvement in
earthquake monitoring, and a slight enhancement in seismic phase
classification.


title: Freeness criteria via vanishing of $Tor$

abstract:
  We investigate some aspects of the module $L$ equipped with the property that
$Tor_1^R(L, F) =0$ implies that $F$ is free. This has some applications.


title:
Analyzing cluster randomized trials designed to support generalizable
  inferences

abstract:
  Background: When planning a cluster randomized trial, evaluators often have
access to an enumerated cohort representing the target population of clusters.
Practicalities of conducting the trial, such as the need to oversample clusters
with certain characteristics to improve trial economy or to support inference
about subgroups of clusters, may preclude simple random sampling from the
cohort into the trial, and thus interfere with the goal of producing
generalizable inferences about the target population.
  Methods: We describe a nested trial design where the randomized clusters are
embedded within a cohort of trial-eligible clusters from the target population
and where clusters are selected for inclusion in the trial with known sampling
probabilities that may depend on cluster characteristics (e.g., allowing
clusters to be chosen to facilitate trial conduct or to examine hypotheses
related to their characteristics). We develop and evaluate methods for
analyzing data from this design to generalize causal inferences to the target
population underlying the cohort.
  Results: We present identification and estimation results for the expectation
of the average potential outcome and for the average treatment effect, in the
entire target population of clusters and in its non-randomized subset. In
simulation studies we show that different estimators have low bias but markedly
different precision.
  Conclusions: Cluster randomized trials where clusters are selected for
inclusion with known sampling probabilities that depend on cluster
characteristics, combined with efficient estimation methods, can precisely
quantify treatment effects in the target population, while addressing
objectives of trial conduct that require oversampling clusters on the basis of
their characteristics.


title:
Multi-task Unscented Kalman Inversion for joint inversion of receiver
  function and surface wave dispersion

abstract:
  Based on the recently developed theory of Unscented Kalman Inversion in
computational mathematics, we proposed a Bayesian joint inversion framework,
i.e., Multi-task Unscented Kalman Inversion (MTUKI), and apply it to the joint
inversion of receiver function (RF) and surface wave dispersion (SWD). This
method can share information between different observations in a
derivative-free way and provide an efficient Gaussian approximation to the
posterior distribution of model parameters (thickness and S-wave velocity in
each layer of media). The theory and experiments show that our proposed
framework demonstrates superior performance in terms of robustness, accuracy,
and high efficiency.


title: ECLIPSE: Efficient Long-range Video Retrieval using Sight and Sound

abstract:
  We introduce an audiovisual method for long-range text-to-video retrieval.
Unlike previous approaches designed for short video retrieval (e.g., 5-15
seconds in duration), our approach aims to retrieve minute-long videos that
capture complex human actions. One challenge of standard video-only approaches
is the large computational cost associated with processing hundreds of densely
extracted frames from such long videos. To address this issue, we propose to
replace parts of the video with compact audio cues that succinctly summarize
dynamic audio events and are cheap to process. Our method, named ECLIPSE
(Efficient CLIP with Sound Encoding), adapts the popular CLIP model to an
audiovisual video setting, by adding a unified audiovisual transformer block
that captures complementary cues from the video and audio streams. In addition
to being 2.92x faster and 2.34x memory-efficient than long-range video-only
approaches, our method also achieves better text-to-video retrieval accuracy on
several diverse long-range video datasets such as ActivityNet, QVHighlights,
YouCook2, DiDeMo and Charades.


title: Phase transitions in de Sitter: The stochastic formalism

abstract:
  The stochastic spectral expansion method offers a simple framework for
calculations in de Sitter spacetimes. We show how to extend its reach to
metastable vacuum states, both in the case when the potential is bounded from
below, and when it is unbounded from below and therefore no stable vacuum state
exists. In both cases, the decay rate of the metastable vacuum is given by the
lowest non-zero eigenvalue associated to the Fokker-Planck equation. We show
how the corresponding eigenfunction determines the field probability
distribution which can be used to compute correlation functions and other
observables in the metastable vacuum state.


title: Designing globally optimal entangling gates using geometric space curves

abstract:
  High-fidelity entangling gates are essential for quantum computation.
Currently, most approaches to designing such gates are based either on simple,
analytical pulse waveforms or on ones obtained from numerical optimization
techniques. In both cases, it is typically not possible to obtain a global
understanding of the space of waveforms that generate a target gate operation,
making it challenging to design globally optimal gates. Here, we show that in
the case of weakly coupled qubits, it is possible to find all pulses that
implement a target entangling gate. We do this by mapping quantum evolution
onto geometric space curves. We derive the minimal conditions these curves must
satisfy in order to guarantee a gate with a desired entangling power is
implemented. Pulse waveforms are extracted from the curvatures of these curves.
We illustrate our method by designing fast, CNOT-equivalent entangling gates
for silicon quantum dot spin qubits with fidelities exceeding 99%. We show that
fidelities can be further improved while maintaining low bandwidth requirements
by using geometrically derived pulses as initial guesses in numerical
optimization routines.


title:
PAnDR: Fast Adaptation to New Environments from Offline Experiences via
  Decoupling Policy and Environment Representations

abstract:
  Deep Reinforcement Learning (DRL) has been a promising solution to many
complex decision-making problems. Nevertheless, the notorious weakness in
generalization among environments prevent widespread application of DRL agents
in real-world scenarios. Although advances have been made recently, most prior
works assume sufficient online interaction on training environments, which can
be costly in practical cases. To this end, we focus on an
\textit{offline-training-online-adaptation} setting, in which the agent first
learns from offline experiences collected in environments with different
dynamics and then performs online policy adaptation in environments with new
dynamics. In this paper, we propose Policy Adaptation with Decoupled
Representations (PAnDR) for fast policy adaptation. In offline training phase,
the environment representation and policy representation are learned through
contrastive learning and policy recovery, respectively. The representations are
further refined by mutual information optimization to make them more decoupled
and complete. With learned representations, a Policy-Dynamics Value Function
(PDVF) (Raileanu et al., 2020) network is trained to approximate the values for
different combinations of policies and environments. In online adaptation
phase, with the environment context inferred from few experiences collected in
new environments, the policy is optimized by gradient ascent with respect to
the PDVF. Our experiments show that PAnDR outperforms existing algorithms in
several representative policy adaptation problems.


title: Thermal activation of low-density Ga implanted in Ge

abstract:
  The nuclear spins of low-density implanted Ga atoms in Ge are interesting
candidates for solid state-based qubits. To date, activation studies of
implanted Ga in Ge have focused on high densities. Here we extend activation
studies into the low-density regime. We use spreading resistance profiling and
secondary ion mass spectrometry to derive electrical activation of Ga ions
implanted into Ge as a function of rapid thermal anneal temperature and implant
density. We show that for our implant conditions the activation is best for
anneal temperatures between 400 and 650 $^\circ$C, with a maximum activation of
64% at the highest fluence. Below 400 $^\circ$C, remaining implant damage
results in defects that act as superfluous carriers, and above 650 $^\circ$C,
surface roughening and loss of Ga ions are observed. The activation increased
monotonically from 10% to 64% as the implant fluence increased from
$6\times10^{10}$ to $6\times10^{12}$ cm$^{-2}$. The results provide thermal
anneal conditions to be used for initial studies of using low-density Ga atoms
in Ge as nuclear spin qubits.


title: Combinatorics of Integer Partitions With Prescribed Perimeter

abstract:
  We prove that the number of even parts and the number of times that parts are
repeated have the same distribution over integer partitions with a fixed
perimeter. This refines Straub's analog of Euler's Odd-Distinct partition
theorem. We generalize the two concerned statistics to these of the
part-difference less than $d$ and the parts not congruent to $1$ modulo $d+1$
and prove a distribution inequality, that has a similar flavor as Alder's
ex-conjecture, over partitions with a prescribed perimeter. Both of our results
are proved analytically and combinatorially.


title:
Leading superconducting instabilities in three-dimensional models for
  Sr2RuO4

abstract:
  The superconductor Sr2RuO4 has been the subject of enormous interest over
more than two decades, but until now the form of its order parameter has not
been determined. Since groundbreaking NMR experiments revealed that the pairs
are of dominant spin-singlet character, attention has focused on time-reversal
symmetry breaking linear combinations of $s$-, $d$- and $g$-wave
one-dimensional (1D) irreducible representations. However, a state of the form
$d_{xz}+id_{yz}$ has also been proposed. We present a systematic study of the
stability of various superconducting candidate states, assuming that pairing is
driven by the fluctuation exchange mechanism, including a realistic
three-dimensional Fermi surface, full treatment of both local and non-local
spin-orbit couplings, and a wide range of interaction parameters $U,J,U',J'$.
The leading superconducting instabilities are found to exhibit nodal
even-parity $A_{1g} (s')$ or $B_{1g} (d_{x^2-y^2})$ symmetries, similar to the
findings in two-dimensional models without longer-range Coulomb interaction
which tends to favor $d_{xy}$ over $d_{x^2-y^2}$. Within the so-called Hund's
coupling mean-field pairing scenario, the $E_g (d_{xz}/d_{yz})$ solution can be
stabilized for large $J$ and specific forms of the spin-orbit coupling, but for
all cases studied here the eigenvalues of other superconducting solutions are
significantly larger when the full fluctuation exchange vertex is included in
the pairing kernel. Additionally, we compute the spin susceptibility in
relevant superconducting candidate phases and compare to recent neutron
scattering and NMR Knight shift measurements.


title: FAIR Data will Fuel a Revolution in Materials Research

abstract:
  Data - arguably the most important product of worldwide materials research
investment - are rarely shared. The small and biased proportion of results
published are buried in plots and text licensed by journals. This situation
wastes resources, hinders innovation, and, in the current era of data-driven
discovery, is no longer tenable. In this comment, we identify opportunities for
synergistic, collaborative, and global actions to assemble large quantities of
FAIR (Findable, Accessible, Interoperable, Reusable) (1) materials data.


title: Meager composants of tree-like continua

abstract:
  A subset $M$ of a continuum $X$ is called a meager composant if $M$ is
maximal with respect to the property that every two of its points are contained
in a nowhere dense subcontinuum of $X$. Motivated by questions of Bellamy,
Mouron and Ordo\~{n}ez, we show that no tree-like continuum has a proper open
meager composant, and that every tree-like continuum has either $1$ or
$2^{\aleph_0}$ meager composants. We also prove a decomposition theorem: If $X$
is tree-like and every indecomposable subcontinuum of $X$ is nowhere dense,
then the partition of $X$ into meager composants is upper semi-continuous, and
the space of meager composants is a dendrite.


title:
Data-driven Robust LQR with Multiplicative Noise via System Level
  Synthesis

abstract:
  This paper aims to develop a data-driven method for solving the closed-loop
state-feedback control of a discrete-time LQR problem for systems affected by
multiplicative norm bounded model uncertainty. To synthesize a tractable robust
state feedback policy, first, we adopt the recently developed system-level
synthesis (SLS) framework to reformulate the LQR control design closed-loop
system responses rather than the control gain. In many situations, however, the
solution to this worst-case optimization problem may be too conservative since
it sets out to enforce the design constraints for every possible value of the
uncertainty. To deal with this issue, we reformulate this optimization problem
as a chance-constrained program (CCP), where the guarantees are not expressed
as deterministic satisfaction against all possible uncertainty outcomes but
rather expressed as guarantees against uncertainty outcomes. To approximately
solve the CCP without requiring prior knowledge of how the uncertainties in the
system matrices are described, we employ the so-called scenario approach, which
provides probabilistic guarantees based on a finite number of samples and
results in a convex optimization program with moderate computational
complexity. Finally, numerical simulations are presented to illustrate the
theoretical findings.


title:
Speed limits of the trace distance in the interaction picture for open
  quantum system

abstract:
  We investigate the speed limit of the state transformation in open quantum
systems described by the Lindblad type quantum master equation. We obtain
universal bounds of the total entropy production described by the trace
distance between the initial and final states in the interaction picture.
Beyond a previous study by Vu and Saito [Phys. Rev. Lett. 128, 010602 (2022)]
applicable only to the completely mixed initial state, our results are
applicable to an arbitrary initial state. Our bounds can be tighter than the
bound of Vu and Hasegawa [Phys. Rev. Lett. 126, 010601 (2021)] which measures
the distance by the modified Wasserstein distance $d_T$: For a system of which
Hilbert space is two-dimensional, the trace distance is greater than or equal
to $d_T$. For this reason, our results can significantly improve Vu-Hasegawa's
bound.


title:
Model for two-body collisions between ultracold dipolar molecules around
  a F\"orster resonance in an electric field

abstract:
  We propose a one-channel, simple model to describe the dynamics of ultracold
dipolar molecules around a F\"orster resonance. Slightly above a specific
electric field, a collisional shielding can take place, suppressing the
molecular losses in a gas. The overall description of the quantum physical
mechanism comes back to the dynamics on a unique energy surface, which depends
on the relative distance and angular approach of the molecules. This surface
enables to interpret how the dipole moments of the molecules are induced and
interlocked by the electric field and the dipole-dipole interaction during the
process, especially when the shielding is triggered. Averaging the relative
angular motion over a unique partial wave (the lowest one when the ultracold
regime is reached), the model reproduces well the behaviour of the rate
coefficients observed experimentally and predicted theoretically [Matsuda et
al., Science 370, 1324 (2020); Li et al., Nat. Phys. 17, 1144 (2021)]. This
economic model encapsulates the main physics of the quantum process. Therefore,
it can be used as an alternative to a full quantum dynamical treatment and is
promising for future studies of collisions involving more bodies.


title: Towards interferometry of neutrino electromagnetism

abstract:
  It is predicted within the Standard Model of elementary particles that
asymmetric neutrino environments cause rotation of linear polarization of
electromagnetic wave -- the birefringence. We demonstrate that this effect is
strongly enhanced if additionally the photon is propagating through refractive
medium, which effectively increases the photon exposure to the neutrino medium.
Our estimate for infrared laser beam in $1\,\mathrm{m}$ long optical fiber
exposed to reactor anti-neutrino flux results in linear polarization rotation
by the angle $\sim4.6\times10^{-39}\,\mathrm{rad}$. We also derive the proper
dependence of the effect on the angle between the directions of photon and
neutrino propagation in the laboratory frame. For that purpose we derive the
correct form of the basis of polarization four-vectors, which differs from the
one widely used in literature. We also estimate the sub-leading optical effect
of the neutrino medium due to the neutrino dipole magnetic moment, in terms of
a variation of the refractive index and its angular dependence. A rough
monochromatic approximation points towards the existence of a resonant
enhancement of the effect.


title:
Sampling-based Fast Gradient Rescaling Method for Highly Transferable
  Adversarial Attacks

abstract:
  Deep neural networks have shown to be very vulnerable to adversarial examples
crafted by adding human-imperceptible perturbations to benign inputs. After
achieving impressive attack success rates in the white-box setting, more focus
is shifted to black-box attacks. In either case, the common gradient-based
approaches generally use the $sign$ function to generate perturbations at the
end of the process. However, only a few works pay attention to the limitation
of the $sign$ function. Deviation between the original gradient and the
generated noises may lead to inaccurate gradient update estimation and
suboptimal solutions for adversarial transferability, which is crucial for
black-box attacks. To address this issue, we propose a Sampling-based Fast
Gradient Rescaling Method (S-FGRM) to improve the transferability of the
crafted adversarial examples. Specifically, we use data rescaling to substitute
the inefficient $sign$ function in gradient-based attacks without extra
computational cost. We also propose a Depth First Sampling method to eliminate
the fluctuation of rescaling and stabilize the gradient update. Our method can
be used in any gradient-based optimizations and is extensible to be integrated
with various input transformation or ensemble methods for further improving the
adversarial transferability. Extensive experiments on the standard ImageNet
dataset show that our S-FGRM could significantly boost the transferability of
gradient-based attacks and outperform the state-of-the-art baselines.


title: Towards a controllable SQUID

abstract:
  Josephson junctions and superconducting quantum interference devices (SQUID)
are important electronic elements, which are based on normal conductor
sandwiched between two superconductors. These junctions are produced by
evaporation techniques, and once they are embedded in an electronic circuit,
their properties are fixed. Using SQUIDs as a tunable component requires the
ability to generate Josephson junctions in situ in a reversible controllable
manner. In this work we demonstrated how a normal (metallic) region along a
line traversing a superconductor can be turned on and off externally thus
potentially generating a controllable Josephson junction or a SQUID. The
concept is based on a long, current-carrying excitation coil, piercing a ring
shaped superconductor with nucleation points. The vector potential produced by
this coil generates a circular current that destroys superconductivity along a
radial line starting at the nucleation point. Unlike the destruction of
superconductivity with magnetic field, the vector potential method is
reversible and reproducible; full superconductivity is recovered upon removing
the current from the coil and different cool-downs yield the same normal lines.


title:
A Cognitive Framework for Delegation Between Error-Prone AI and Human
  Agents

abstract:
  With humans interacting with AI-based systems at an increasing rate, it is
necessary to ensure the artificial systems are acting in a manner which
reflects understanding of the human. In the case of humans and artificial AI
agents operating in the same environment, we note the significance of
comprehension and response to the actions or capabilities of a human from an
agent's perspective, as well as the possibility to delegate decisions either to
humans or to agents, depending on who is deemed more suitable at a certain
point in time. Such capabilities will ensure an improved responsiveness and
utility of the entire human-AI system. To that end, we investigate the use of
cognitively inspired models of behavior to predict the behavior of both human
and AI agents. The predicted behavior, and associated performance with respect
to a certain goal, is used to delegate control between humans and AI agents
through the use of an intermediary entity. As we demonstrate, this allows
overcoming potential shortcomings of either humans or agents in the pursuit of
a goal.


title: DBF: Dynamic Belief Fusion for Combining Multiple Object Detectors

abstract:
  In this paper, we propose a novel and highly practical score-level fusion
approach called dynamic belief fusion (DBF) that directly integrates inference
scores of individual detections from multiple object detection methods. To
effectively integrate the individual outputs of multiple detectors, the level
of ambiguity in each detection score is estimated using a confidence model
built on a precision-recall relationship of the corresponding detector. For
each detector output, DBF then calculates the probabilities of three hypotheses
(target, non-target, and intermediate state (target or non-target)) based on
the confidence level of the detection score conditioned on the prior confidence
model of individual detectors, which is referred to as basic probability
assignment. The probability distributions over three hypotheses of all the
detectors are optimally fused via the Dempster's combination rule. Experiments
on the ARL, PASCAL VOC 07, and 12 datasets show that the detection accuracy of
the DBF is significantly higher than any of the baseline fusion approaches as
well as individual detectors used for the fusion.


title:
Extraction of deterministic components for high frequency stochastic
  process -- an application from CSI 300 index

abstract:
  This paper models stochastic process of price time series of CSI 300 index in
Chinese financial market, analyzes volatility characteristics of intraday
high-frequency price data. In the new generalized Barndorff-Nielsen and
Shephard model, the lag caused by asynchrony of market information is
considered, and the problem of lack of long-term dependence is solved. To speed
up the valuation process, several machine learning and deep learning algorithms
are used to estimate parameter and evaluate forecast results. Tracking
historical jumps of different magnitudes offers promising avenues for
simulating dynamic price processes and predicting future jumps. Numerical
results show that the deterministic component of stochastic volatility
processes would always be captured over short and longer-term windows. Research
finding could be suitable for influence investors and regulators interested in
predicting market dynamics based on realized volatility.


title: Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks

abstract:
  The field of Natural Language Processing (NLP) has experienced a dramatic
leap in capabilities with the recent introduction of huge Language Models
(LMs). Despite this success, natural language problems that involve several
compounded steps are still practically unlearnable, even by the largest LMs.
This complies with experimental failures for end-to-end learning of composite
problems that were demonstrated in a variety of domains. A known mitigation is
to introduce intermediate supervision for solving sub-tasks of the compounded
problem. Recently, several works have demonstrated high gains by taking a
straightforward approach for incorporating intermediate supervision in
compounded natural language problems: the sequence-to-sequence LM is fed with
an augmented input, in which the decomposed tasks' labels are simply
concatenated to the original input. In this paper, we prove a positive learning
result that motivates these recent efforts. We show that when concatenating
intermediate supervision to the input and training a sequence-to-sequence model
on this modified input, an unlearnable composite problem becomes learnable. We
prove this for the notoriously unlearnable composite task of bit-subset parity,
with the intermediate supervision being parity results of increasingly large
bit-subsets. Beyond motivating contemporary empirical efforts for incorporating
intermediate supervision in sequence-to-sequence language models, our positive
theoretical result is the first of its kind in the landscape of results on the
benefits of intermediate supervision: Until now, all theoretical results on the
subject are negative, i.e., show cases where learning is impossible without
intermediate supervision, while our result is positive, showing a case where
learning is facilitated in the presence of intermediate supervision.


title: Quantization of the dissipative oscillator

abstract:
  Nowadays, two of the most prospering fields of physics are quantum computing
and spintronics. In both, the loss of information and dissipation plays a
crucial role. In the present work we formulate the quantization of the
dissipative oscillator, which aids understanding of the above mentioned, and
creates a theoretical frame to overcome these issues in the future. Based on
the Lagrangian framework of the damped spring system, the canonically
conjugated pairs and the Hamiltonian of the system are obtained, by which the
quantization procedure can be started and consistently applied. As a result,
the damping quantum wave equation of the dissipative oscillator is deduced, by
which an exact damping wave solution of this equation is obtained.
Consequently, we arrive at such an irreversible quantum theory by which the
quantum losses can be described.


title: Incompressible limit of a compressible Oldroyd-B model

abstract:
  In this paper, we consider the Cauchy problem for a compressible Oldroyd-B
model in three dimensions. Under some smallness assumptions on the initial
data, we obtain the global wellposedness of strong solution with uniform
regularity. Moreover, we provide a rigorous justification for the link between
the compressible model and the corresponding incompressible model via
incompressible limits.


title:
Eliminating orbital selectivity from the metal-insulator transition by
  strong magnetic fluctuations

abstract:
  The orbital-selective electronic behavior is one of the most remarkable
manifestations of strong electronic correlations in multi-orbitals systems. A
prominent example is the orbital-selective Mott transition (OSMT), which is
characterized by the coexistence of localized electrons in some orbitals, and
itinerant electrons in other orbitals. The state-of-the-art theoretical
description of the OSMT is based on local approximations to electronic
correlations provided by dynamical mean-field theory or slave spin method. In
this work we go beyond the local picture and focus on the effect of spatial
collective electronic fluctuations on the OSMT. To this aim, we consider a
half-filled Hubbard-Kanamori model on a cubic lattice with two orbitals that
have different bandwidths. We show that strong magnetic fluctuations that are
inherent in this system prevent the OSMT and favor the N\'eel transition that
occurs at the same critical temperature for both orbitals.


title: Scalar Induced Gravitational Waves from Warm Inflation

abstract:
  Stochastic gravitational waves can be induced from the primordial curvature
perturbations generated during inflation, through scalar-tensor mode coupling
at the second order of cosmological perturbation theory. Here we discuss a
model of warm inflation in which large curvature perturbations are generated at
the small scales because of inflaton dissipation. These overdense perturbations
then collapse at later epoch to form primordial black holes, as was studied in
our earlier work (Ref. \cite{Arya:2019wck}), and therefore may also act as a
source to the second-order tensor perturbations. In this study, we calculate
the spectrum of these secondary gravitational waves from our warm inflationary
model. We find that our model leads to a generation of scalar induced
gravitational waves (SIGW) over a frequency range ($1-10^6$) Hz. Further, we
discuss the detection possibilities of these SIGWs, taking in account the
sensitivity of different ongoing and future gravitational wave experiments.


title:
Generalized model for steady-state bifurcations without parameters in
  memristor-based oscillators with lines of equilibria

abstract:
  We demonstrate how the pitchfork, transcritical and saddle-node bifurcations
of steady states observed in dynamical systems with a finite number of isolated
equilibrium points occur in systems with lines of equilibria. The exploration
is carried out by using the numerical simulation and linear stability analysis
applied to a model of a memristor-based oscillator. First, all the discussed
bifurcation scenarios are considered in the context of systems including Chua's
memristor with a piecewise-smooth characteristic. Then the memristor
characteristic is changed to a function that is smooth everywhere. Finally, the
action of the memristor forgetting effect is taken into consideration. The
presented results are obtained for electronic circuit models, but the
considered bifurcation phenomena can be exhibited by systems with a line of
equilibria of any nature.


title: End-to-End Instance Edge Detection

abstract:
  Edge detection has long been an important problem in the field of computer
vision. Previous works have explored category-agnostic or category-aware edge
detection. In this paper, we explore edge detection in the context of object
instances. Although object boundaries could be easily derived from segmentation
masks, in practice, instance segmentation models are trained to maximize IoU to
the ground-truth mask, which means that segmentation boundaries are not
enforced to precisely align with ground-truth edge boundaries. Thus, the task
of instance edge detection itself is different and critical. Since precise edge
detection requires high resolution feature maps, we design a novel transformer
architecture that efficiently combines a FPN and a transformer decoder to
enable cross attention on multi-scale high resolution feature maps within a
reasonable computation budget. Further, we propose a light weight dense
prediction head that is applicable to both instance edge and mask detection.
Finally, we use a penalty reduced focal loss to effectively train the model
with point supervision on instance edges, which can reduce annotation costs. We
demonstrate highly competitive instance edge detection performance compared to
state-of-the-art baselines, and also show that the proposed task and loss are
complementary to instance segmentation and object detection.


title: Optimal steering of matrix product states and quantum many-body scars

abstract:
  Ongoing development of quantum simulators allows for a progressively finer
degree of control of quantum many-body systems. This motivates the development
of efficient approaches to facilitate the control of such systems and enable
the preparation of non-trivial quantum states. Here we formulate an approach to
control quantum systems based on matrix product states (MPS). We compare
counter-diabatic and leakage minimization approaches to the so-called local
steering problem, that consists in finding the best value of the control
parameters for generating a unitary evolution of the specific MPS state in a
given direction. In order to benchmark the different approaches, we apply them
to the generalization of the PXP model known to exhibit coherent quantum
dynamics due to quantum many-body scars. Finally, we perform the first steps
towards global trajectory optimization and demonstrate entanglement steering
capabilities in the generalized PXP model.

categories: quant-ph cond-mat.quant-gas cond-mat.stat-mech cond-mat.str-el


title:
Partial $*$-algebraic quantum groups and Drinfeld doubles of partial
  compact quantum groups

abstract:
  We introduce a notion of partial algebraic quantum group. This is an
important special case of a weak multiplier Hopf algebra with integrals, as
introduced in the work of Van Daele and Wang. At the same time, it generalizes
the notion of partial compact quantum group as introduced by De Commer and
Timmermann. As an application, we show that the Drinfeld double of a partial
compact quantum group can be defined as a partial $*$-algebraic quantum group.


title: Visualizing Multidimensional Linear Programming Problems

abstract:
  The article proposes an n-dimensional mathematical model of the visual
representation of a linear programming problem. This model makes it possible to
use artificial neural networks to solve multidimensional linear optimization
problems, the feasible region of which is a bounded non-empty set. To visualize
the linear programming problem, an objective hyperplane is introduced, the
orientation of which is determined by the gradient of the linear objective
function: the gradient is the normal to the objective hyperplane. In the case
of searching a maximum, the objective hyperplane is positioned in such a way
that the value of the objective function at all its points exceeds the value of
the objective function at all points of the feasible region, which is a bounded
convex polytope. For an arbitrary point of the objective hyperplane, the
objective projection onto the polytope is determined: the closer the objective
projection point is to the objective hyperplane, the greater the value of the
objective function at this point. Based on the objective hyperplane, a finite
regular set of points is constructed, called the receptive field. Using
objective projections, an image of a polytope is constructed. This image
includes the distances from the receptive points to the corresponding points of
the polytope surface. Based on the proposed model, parallel algorithms for
visualizing a linear programming problem are constructed. An analytical
estimation of its scalability is performed. Information about the software
implementation and the results of large-scale computational experiments
confirming the efficiency of the proposed approaches are presented.


title:
Efficient Bayesian Network Structure Learning via Parameterized Local
  Search on Topological Orderings

abstract:
  In Bayesian Network Structure Learning (BNSL), one is given a variable set
and parent scores for each variable and aims to compute a DAG, called Bayesian
network, that maximizes the sum of parent scores, possibly under some
structural constraints. Even very restricted special cases of BNSL are
computationally hard, and, thus, in practice heuristics such as local search
are used. A natural approach for a local search algorithm is a hill climbing
strategy, where one replaces a given BNSL solution by a better solution within
some pre-defined neighborhood as long as this is possible. We study
ordering-based local search, where a solution is described via a topological
ordering of the variables. We show that given such a topological ordering, one
can compute an optimal DAG whose ordering is within inversion distance $r$ in
subexponential FPT time; the parameter $r$ allows to balance between solution
quality and running time of the local search algorithm. This running time bound
can be achieved for BNSL without structural constraints and for all structural
constraints that can be expressed via a sum of weights that are associated with
each parent set. We also introduce a related distance called `window inversions
distance' and show that the corresponding local search problem can also be
solved in subexponential FPT time for the parameter $r$. For two further
natural modification operations on the variable orderings, we show that
algorithms with an FPT time for $r$ are unlikely. We also outline the limits of
ordering-based local search by showing that it cannot be used for common
structural constraints on the moralized graph of the network.


title: In Situ Plasma Processing of Superconducting Cavities at Jefferson Lab

abstract:
  Jefferson Lab began a plasma processing program start-ing in the spring of
2019. Plasma processing is a common technique for removing hydrocarbons from
surfaces, which increases the work function and reduces the sec-ondary emission
coefficient. Unlike helium processing which relies on ion bombardment of the
field emitters, plasma processing uses free oxygen produced in the plasma to
break down the hydrocarbons on the surface of the cavity. The residuals of the
hydrocarbons in the form of water, carbon monoxide and carbon dioxide are
re-moved from the cryomodule as part of the process gas flow. The initial focus
of the effort is processing C100 cavities by injecting RF power into the HOM
coupler ports. We will then start investigating processing of C50 cavities by
introducing RF into the fundamental power coupler. The plan is to start
processing cryomodules in the CEBAF tunnel in the mid-term future, with a goal
of improving the operational gradients and the energy mar-gin of the linacs.
This work will describe the systems and methods used at JLAB for processing
cavities using an argon oxygen gas mixture. Before and after plasma pro-cessing
results will also be presented.


title: Vecchia-approximated Deep Gaussian Processes for Computer Experiments

abstract:
  Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional
composition, in which intermediate GP layers warp the original inputs,
providing flexibility to model non-stationary dynamics. Two DGP regimes have
emerged in recent literature. A "big data" regime, prevalent in machine
learning, favors approximate, optimization-based inference for fast,
high-fidelity prediction. A "small data" regime, preferred for computer
surrogate modeling, deploys posterior integration for enhanced uncertainty
quantification (UQ). We aim to bridge this gap by expanding the capabilities of
Bayesian DGP posterior inference through the incorporation of the Vecchia
approximation, allowing linear computational scaling without compromising
accuracy or UQ. We are motivated by surrogate modeling of simulation campaigns
with upwards of 100,000 runs -- a size too large for previous fully-Bayesian
implementations -- and demonstrate prediction and UQ superior to that of "big
data" competitors. All methods are implemented in the "deepgp" package on CRAN.


title:
EMMT: A simultaneous eye-tracking, 4-electrode EEG and audio corpus for
  multi-modal reading and translation scenarios

abstract:
  We present the Eyetracked Multi-Modal Translation (EMMT) corpus, a dataset
containing monocular eye movement recordings, audio and 4-electrode
electroencephalogram (EEG) data of 43 participants. The objective was to
collect cognitive signals as responses of participants engaged in a number of
language intensive tasks involving different text-image stimuli settings when
translating from English to Czech.
  Each participant was exposed to 32 text-image stimuli pairs and asked to (1)
read the English sentence, (2) translate it into Czech, (3) consult the image,
(4) translate again, either updating or repeating the previous translation. The
text stimuli consisted of 200 unique sentences with 616 unique words coupled
with 200 unique images as the visual stimuli.
  The recordings were collected over a two week period and all the participants
included in the study were Czech natives with strong English skills. Due to the
nature of the tasks involved in the study and the relatively large number of
participants involved, the corpus is well suited for research in Translation
Process Studies, Cognitive Sciences among other disciplines.