title:
Federated Learning: Balancing the Thin Line Between Data Intelligence
  and Privacy

abstract:
  Federated learning holds great promise in learning from fragmented sensitive
data and has revolutionized how machine learning models are trained. This
article provides a systematic overview and detailed taxonomy of federated
learning. We investigate the existing security challenges in federated learning
and provide a comprehensive overview of established defense techniques for data
poisoning, inference attacks, and model poisoning attacks. The work also
presents an overview of current training challenges for federated learning,
focusing on handling non-i.i.d. data, high dimensionality issues, and
heterogeneous architecture, and discusses several solutions for the associated
challenges. Finally, we discuss the remaining challenges in managing federated
learning training and suggest focused research directions to address the open
questions. Potential candidate areas for federated learning, including IoT
ecosystem, healthcare applications, are discussed with a particular focus on
banking and financial domains.


title: How Much is Performance Worth to Users? A Quantitative Approach

abstract:
  Architects and systems designers artfully balance multiple competing design
constraints during the design process but are unable to translate between
system metrics and end user experience. This work presents three methodologies
to fill in this gap. The first is an incentive-compatible methodology that
determines a "ground truth" measurement of users' value of speed in terms of US
dollars, and find that users would accept a performance losses of 10%, 20%, and
30% to their personal computer in exchange for \$2.27, \$4.07, and \$4.43 per
day, respectively. However, while highly accurate the methodology is a
painstaking process and does not scale with large numbers of participants. To
allow for scalability, we introduce a second methodology -- a lab-based
simulation experiment -- which finds that users would accept a permanent
performance loss of 10%, 20%, and 30% to their personal computer in exchange
for \$127, \$169, and \$823, respectively. Finally, to allow for even greater
scalability, we introduce a third methodology -- a survey -- and observe that
the lack of incentive compatibility and the lack of hands-on experience with
throttled device performance skews the results significantly, thus
demonstrating the need for lab-based or incentive compatible study designs. By
quantifying the tradeoff between user satisfaction and performance, we enable
architects and systems designers to make more nuanced tradeoffs between design
requirements.


title:
Channel Pruned YOLOv5-based Deep Learning Approach for Rapid and
  Accurate Outdoor Obstacles Detection

abstract:
  One-stage algorithm have been widely used in target detection systems that
need to be trained with massive data. Most of them perform well both in
real-time and accuracy. However, due to their convolutional structure, they
need more computing power and greater memory consumption. Hence, we applied
pruning strategy to target detection networks to reduce the number of
parameters and the size of model. To demonstrate the practicality of the
pruning method, we select the YOLOv5 model for experiments and provide a data
set of outdoor obstacles to show the effect of model. In this specific data
set, in the best circumstances, the volume of the network model is reduced by
49.7% compared with the original model, and the reasoning time is reduced by
52.5%. Meanwhile, it also uses data processing methods to compensate for the
drop in accuracy caused by pruning.


title: Identifying Critical LMS Features for Predicting At-risk Students

abstract:
  Learning management systems (LMSs) have become essential in higher education
and play an important role in helping educational institutions to promote
student success. Traditionally, LMSs have been used by postsecondary
institutions in administration, reporting, and delivery of educational content.
In this paper, we present an additional use of LMS by using its data logs to
perform data-analytics and identify academically at-risk students. The
data-driven insights would allow educational institutions and educators to
develop and implement pedagogical interventions targeting academically at-risk
students. We used anonymized data logs created by Brightspace LMS during fall
2019, spring 2020, and fall 2020 semesters at our college. Supervised machine
learning algorithms were used to predict the final course performance of
students, and several algorithms were found to perform well with accuracy above
90%. SHAP value method was used to assess the relative importance of features
used in the predictive models. Unsupervised learning was also used to group
students into different clusters based on the similarities in their
interaction/involvement with LMS. In both of supervised and unsupervised
learning, we identified two most-important features
(Number_Of_Assignment_Submissions and Content_Completed). More importantly, our
study lays a foundation and provides a framework for developing a real-time
data analytics metric that may be incorporated into a LMS.


title:
Comparative Electron Irradiations of Amorphous and Crystalline
  Astrophysical Ice Analogues

abstract:
  Laboratory studies of the radiation chemistry occurring in astrophysical ices
have demonstrated the dependence of this chemistry on a number of experimental
parameters. One experimental parameter which has received significantly less
attention is that of the phase of the solid ice under investigation. In this
present study, we have performed systematic 2 keV electron irradiations of the
amorphous and crystalline phases of pure CH3OH and N2O astrophysical ice
analogues. Radiation-induced decay of these ices and the concomitant formation
of products were monitored in situ using FT-IR spectroscopy. A direct
comparison between the irradiated amorphous and crystalline CH3OH ices revealed
a more rapid decay of the former compared to the latter. Interestingly, a
significantly lesser difference was observed when comparing the decay rates of
the amorphous and crystalline N2O ices. These observations have been
rationalised in terms of the strength and extent of the intermolecular forces
present in each ice. The strong and extensive hydrogen-bonding network that
exists in crystalline CH3OH (but not in the amorphous phase) is suggested to
significantly stabilise this phase against radiation-induced decay. Conversely,
although alignment of the dipole moment of N2O is anticipated to be more
extensive in the crystalline structure, its weak attractive potential does not
significantly stabilise the crystalline phase against radiation-induced decay,
hence explaining the smaller difference in decay rates between the amorphous
and crystalline phases of N2O compared to those of CH3OH. Our results are
relevant to the astrochemistry of interstellar ices and icy Solar System
objects, which may experience phase changes due to thermally-induced
crystallisation or space radiation-induced amorphisation.

categories: astro-ph.GA astro-ph.HE astro-ph.IM cond-mat.other


title:
Neighbor-Based Optimized Logistic Regression Machine Learning Model For
  Electric Vehicle Occupancy Detection

abstract:
  This paper presents an optimized logistic regression machine learning model
that predicts the occupancy of an Electric Vehicle (EV) charging station given
the occupancy of neighboring stations. The model was optimized for the time of
day. Trained on data from 57 EV charging stations around the University of
California San Diego campus, the model achieved an 88.43% average accuracy and
92.23% maximum accuracy in predicting occupancy, outperforming a persistence
model benchmark.


title:
Evidence for a neutral near-threshold structure in the $K^{0}_{\rm S}$
  recoil-mass spectra in $e^+e^-\rightarrow K^{0}_{\rm
  S}(D_s^-D^{*+}$+$D_s^{*-}D^{+})$

abstract:
  We study the processes $e^+e^-\rightarrow K^{0}_{\rm S}
(D_s^-D^{*+}+D_s^{*-}D^{+})$ at five center-of-mass energies between 4.628~GeV
and 4.699~GeV, using data samples corresponding to an integrated luminosity of
$3.8~\rm{fb^{-1}}$ collected by the BESIII detector at the BEPCII storage ring.
Based on a partial reconstruction technique, we find evidence of a structure
near the thresholds for $D_s^-D^{*+}$ and $D_s^{*-}D^{+}$ production in the
$K^{0}_{\rm S}$ recoil-mass spectrum, which we refer to as the $
Z_{cs}(3985)^0$. Fitting with a Breit-Wigner line shape, we find the mass of
the structure to be $(3992.2\pm{1.7}\pm{1.6})~\rm{MeV}/c^2$ and the width to be
$(7.7_{-3.8}^{+4.1}\pm{4.3})~\rm{MeV}$, where the first uncertainties are
statistical and the second are systematic. The significance of the $
Z_{cs}(3985)^0$ signal is found to be $4.6\sigma$ including both the
statistical and systematic uncertainty. We report the Born cross section
multiplied by the branching fraction at different energy points. The mass of
the $Z_{cs}(3985)^0$ is close to that of the $Z_{cs}(3985)^+$. Assuming SU(3)
symmetry, the cross section of the neutral channel is consistent with that of
the charged one. Hence, we conclude that the $Z_{cs}(3985)^0$ is the isospin
partner of the $Z_{cs}(3985)^+$.


title:
Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction
  in Low Dimensions

abstract:
  Knowledge graph embeddings (KGE) have been validated as powerful methods for
inferring missing links in knowledge graphs (KGs) since they map entities into
Euclidean space and treat relations as transformations of entities. Currently,
some Euclidean KGE methods model semantic hierarchies prevalent in KGs and
promote the performance of link prediction. For hierarchical data, instead of
traditional Euclidean space, hyperbolic space as an embedding space has shown
the promise of high fidelity and low memory consumption; however, existing
hyperbolic KGE methods neglect to model them. To address this issue, we propose
a novel KGE model -- hyperbolic hierarchical KGE (HypHKGE). To be specific, we
first design the attention-based learnable curvatures for hyperbolic space to
preserve rich semantic hierarchies. Moreover, we define the hyperbolic
hierarchical transformations based on the theory of hyperbolic geometry, which
utilize hierarchies that we preserved to infer the links. Experiments show that
HypHKGE can effectively model semantic hierarchies in hyperbolic space and
outperforms the state-of-the-art hyperbolic methods, especially in low
dimensions.


title:
Coupling Deep Imputation with Multitask Learning for Downstream Tasks on
  Genomics Data

abstract:
  Genomics data such as RNA gene expression, methylation and micro RNA
expression are valuable sources of information for various clinical predictive
tasks. For example, predicting survival outcomes, cancer histology type and
other patients' related information is possible using not only clinical data
but molecular data as well. Moreover, using these data sources together, for
example in multitask learning, can boost the performance. However, in practice,
there are many missing data points which leads to significantly lower patient
numbers when analysing full cases, which in our setting refers to all
modalities being present.
  In this paper we investigate how imputing data with missing values using deep
learning coupled with multitask learning can help to reach state-of-the-art
performance results using combined genomics modalities, RNA, micro RNA and
methylation. We propose a generalised deep imputation method to impute values
where a patient has all modalities present except one. Interestingly enough,
deep imputation alone outperforms multitask learning alone for the
classification and regression tasks across most combinations of modalities. In
contrast, when using all modalities for survival prediction we observe that
multitask learning alone outperforms deep imputation alone with statistical
significance (adjusted p-value 0.03). Thus, both approaches are complementary
when optimising performance for downstream predictive tasks.


title: The mu Arae planetary system: Radial velocities and astrometry

abstract:
  With Hubble Space Telescope Fine Guidance Sensor astrometry and published and
previously unpublished radial velocity measures we explore the exoplanetary
system mu Arae. Our modeling of the radial velocities results in improved
orbital elements for the four previously known components. Our astrometry
contains no evidence for any known companion, but provides upper limits for
three companion masses. A final summary of all past Fine Guidance Sensor
exoplanet astrometry results uncover a bias towards small inclinations (more
face-on than edge-on). This bias remains unexplained by either small number
statistics, modeling technique, Fine Guidance Sensor mechanical issues, or
orbit modeling of noise-dominated data. A numerical analysis using our refined
orbital elements suggests that planet d renders the mu Arae system dynamically
unstable on a timescale of 10^5 years, in broad agreement with previous work.


title:
Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing
  Modalities

abstract:
  Multimodal sentiment analysis has been studied under the assumption that all
modalities are available. However, such a strong assumption does not always
hold in practice, and most of multimodal fusion models may fail when partial
modalities are missing. Several works have addressed the missing modality
problem; but most of them only considered the single modality missing case, and
ignored the practically more general cases of multiple modalities missing. To
this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE)
network to handle the problem of missing uncertain modalities. Specifically, we
design a tag encoding module to cover both the single modality and multiple
modalities missing cases, so as to guide the network's attention to those
missing modalities. Besides, we adopt a new space projection pattern to align
common vectors. Then, a Transformer encoder-decoder network is utilized to
learn the missing modality features. At last, the outputs of the Transformer
encoder are used for the final sentiment classification. Extensive experiments
are conducted on CMU-MOSI and IEMOCAP datasets, showing that our method can
achieve significant improvements compared with several baselines.


title: Orbifolds by 2-groups and decomposition

abstract:
  In this paper we study three-dimensional orbifolds by 2-groups with a
trivially-acting one-form symmetry group BK. These orbifolds have a global
two-form symmetry, and so one expects that they decompose into (are equivalent
to) a disjoint union of other three-dimensional theories, which we demonstrate.
These theories can be interpreted as sigma models on 2-gerbes, whose formal
structures reflect properties of the orbifold construction.


title: Stabilizing Gauge Theories in Quantum Simulators: A Brief Review

abstract:
  Quantum simulation is at the heart of the ongoing "second" quantum
revolution, with various synthetic quantum matter platforms realizing evermore
exotic condensed matter and particle physics phenomena at high levels of
precision and control. The implementation of gauge theories on modern quantum
simulators is especially appealing due to three main reasons: (i) it offers a
new probe of high-energy physics on low-energy tabletop devices, (ii) it allows
exploring condensed matter phenomena that are prominent in gauge theories even
without a direct connection to high-energy physics, and (iii) it serves as a
banner of experimental benchmarking given the plethora of local constraints
arising from the gauge symmetry that need to be programmed and controlled. In
order to faithfully model gauge-theory phenomena on a quantum simulator,
stabilizing the underlying gauge symmetry is essential. In this brief review,
we outline recently developed experimentally feasible methods introduced by us
that have shown, in numerical and experimental benchmarks, reliable
stabilization of quantum-simulator implementations of gauge theories. We
explain the mechanism behind these \textit{linear gauge protection} schemes,
and illustrate their power in protecting salient features such as gauge
invariance, disorder-free localization, quantum many-body scars, and other
phenomena of topical interest. We then discuss their application in experiments
based on Rydberg atoms, superconducting qubits, and in particular ultracold
neutral atoms in optical superlattices. We hope this review will illustrate
some facets of the exciting progress in stabilization of gauge symmetry and in
gauge-theory quantum simulation in general.

categories: cond-mat.quant-gas cond-mat.str-el hep-lat quant-ph


title:
A Unified and Modular Model Predictive Control Framework for Soft
  Continuum Manipulators under Internal and External Constraints

abstract:
  The emerging field of fluidically actuated soft robotic control has promising
capabilities such as inherent compliance and user safety. However, these are
counterbalanced by issues not common to rigid robots, like nonlinear actuation
dynamics, motion constraints, workspace limitations, and variable shape
stiffness. In this work, we have adapted Model Predictive Control (MPC), that
has recently seen an exponential rise in popularity and fields of applications,
to a soft robotic arm called SoPrA. We have addressed the problems that current
control methods are facing, trying to propose a unique environment to handle
them in a modular way. This work shows, both with simulation and experimental
results, that Task-Space MPC can be successfully implemented for dynamic soft
robotic control, while past research has usually focused on Joint-Space
references. We have provided a way to couple the Piece-wise Constant Curvature
and the Augmented Rigid Body Model assumptions with internal and external
constraints and actuation dynamics, delivering an algorithm that can manage all
these information and optimize over them. We believe that an MPC implementation
based on our approach could be the way to address most of model-based soft
robotics control issues within a unified and modular framework, while allowing
to include improvements that usually belong to other control domains such as
learning techniques.


title: Radio sky reveals primordial electron-proton interactions

abstract:
  For several decades, astronomers have measured the electromagnetic emission
in the universe from the lowest to the highest energies with incredible
precision. The lowest end of the spectrum, corresponding to radio waves, is
fairly well studied and understood. Yet there is a long standing discrepancy
between measurements and predictions, which has prompted the construction of
many new models of radio emitters. Here we show that remnant electron-proton
interactions, leading to photon production in the early universe, also referred
to as cosmic free-free emission, solves the discrepancy between theory and
observations. While the possibility of cosmic free-free emission has been
postulated for several decades, this is the first time that the amplitude and
shape of the signal has been computed and its existence demonstrated. Using
current measurements we estimate this emission to become important from around
a redshift of $z \simeq 2150$. This contribution from fundamental particles and
interactions represents the lowest energy test from the early universe of one
of the pillars of modern physics, Quantum Electrodynamics. The next generation
of deep radio surveys will be able to measure primordial signals from this
cosmic era with greater precision and further solidify our understanding of the
radio sky.


title:
Cosmological simulations predict that AGN preferentially live in
  gas-rich, star-forming galaxies despite effective feedback

abstract:
  Negative feedback from active galactic nuclei (AGN) is the leading mechanism
for the quenching of massive galaxies in the vast majority of modern galaxy
evolution models. However, direct observational evidence that AGN feedback
causes quenching on a population scale is lacking. Studies have shown that
luminous AGN are preferentially located in gas-rich and star-forming galaxies,
an observation that has sometimes been suggested to be in tension with a
negative AGN feedback picture. We investigate three of the current cosmological
simulations (IllustrisTNG, EAGLE and SIMBA) along with post-processed models
for molecular hydrogen gas masses and perform similar tests to those used by
observers. We find that the simulations predict: (i) no strong negative trends
between AGN luminosity and molecular gas fraction or sSFR; (ii) both
high-luminosity ($L_{bol}>10^{44}$ erg/s) and high-Eddington ratio (>1%) AGN
are preferentially located in galaxies with high molecular gas fractions and
sSFR; and (iii) that the gas-depleted and quenched fractions of AGN host
galaxies are lower than a control sample of non-active galaxies. These three
findings are in qualitative agreement with observational samples at $z=0$ and
$z=2$ and show that such results are not in tension with the presence of strong
AGN feedback, which all simulations we employ require to produce realistic
massive galaxies. However, we also find quantifiable differences between
predictions from the simulations, which could allow us to observationally test
the different subgrid feedback models.


title: Learning cosmology and clustering with cosmic graphs

abstract:
  We train deep learning models on thousands of galaxy catalogues from the
state-of-the-art hydrodynamic simulations of the CAMELS project to perform
regression and inference. We employ Graph Neural Networks (GNNs), architectures
designed to work with irregular and sparse data, like the distribution of
galaxies in the Universe. We first show that GNNs can learn to compute the
power spectrum of galaxy catalogues with a few percent accuracy. We then train
GNNs to perform likelihood-free inference at the galaxy-field level. Our models
are able to infer the value of $\Omega_{\rm m}$ with a $\sim12\%-13\%$ accuracy
just from the positions of $\sim1000$ galaxies in a volume of $(25~h^{-1}{\rm
Mpc})^3$ at $z=0$ while accounting for astrophysical uncertainties as modelled
in CAMELS. Incorporating information from galaxy properties, such as stellar
mass, stellar metallicity, and stellar radius, increases the accuracy to
$4\%-8\%$. Our models are built to be translational and rotational invariant,
and they can extract information from any scale larger than the minimum
distance between two galaxies. However, our models are not completely robust:
testing on simulations run with a different subgrid physics than the ones used
for training does not yield as accurate results.

categories: astro-ph.CO astro-ph.GA astro-ph.IM cs.CV cs.LG


title:
Interpreting the atmospheric composition of exoplanets: sensitivity to
  planet formation assumptions

abstract:
  Constraining planet formation based on the atmospheric composition of
exoplanets is a fundamental goal of the exoplanet community. Existing studies
commonly try to constrain atmospheric abundances, or to analyze what abundance
patterns a given description of planet formation predicts. However, there is
also a pressing need to develop methodologies that investigate how to transform
atmospheric compositions into planetary formation inferences. In this study we
summarize the complexities and uncertainties of state-of-the-art planet
formation models and how they influence planetary atmospheric compositions. We
introduce a methodology that explores the effect of different formation model
assumptions when interpreting atmospheric compositions. We apply this framework
to the directly imaged planet HR 8799e. Based on its atmospheric composition,
this planet may have migrated significantly during its formation. We show that
including the chemical evolution of the protoplanetary disk leads to a reduced
need for migration. Moreover, we find that pebble accretion can reproduce the
planet's composition, but some of our tested setups lead to too low atmospheric
metallicities, even when considering that evaporating pebbles may enrich the
disk gas. We conclude that the definitive inversion from atmospheric abundances
to planet formation for a given planet may be challenging, but a qualitative
understanding of the effects of different formation models is possible, opening
up pathways for new investigations.


title:
Close detached white dwarf + brown dwarf binaries: further evidence for
  low values of the common envelope efficiency

abstract:
  Common envelope evolution is a fundamental ingredient in our understanding of
the formation of close binary stars containing compact objects which includes
the progenitors of type Ia supernovae, short gamma ray bursts and most stellar
gravitational wave sources. To predict the outcome of common envelope evolution
we still rely to a large degree on a simplified energy conservation equation.
Unfortunately, this equation contains a theoretically rather poorly constrained
efficiency parameter ($\alpha_{\mathrm{CE}}$) and, even worse, it is unclear if
energy sources in addition to orbital energy (such as recombination energy)
contribute to the envelope ejection process. In previous works we reconstructed
the evolution of observed populations of post common envelope binaries (PCEBs)
consisting of white dwarfs with main sequence star companions and found
indications that the efficiency is rather small
($\alpha_{\mathrm{CE}}\simeq0.2-0.3$) and that extra energy sources are only
required in very few cases. Here we used the same reconstruction tool to
investigate the evolutionary history of a sample of observed PCEBs with brown
dwarf companions. In contrast to previous works, we found that the evolution of
observationally well characterized PCEBs with brown dwarf companions can be
understood assuming a low common envelope efficiency
($\alpha_{\mathrm{CE}}=0.24-0.41$), similar to that required to understand
PCEBs with main sequence star companions, and that contributions from
recombination energy are not required. We conclude that the vast majority of
PCEBs form from common envelope evolution that can be parameterized with a
small efficiency and without taking into account additional energy sources.


title: SpeX near-infrared spectroscopic extinction curves in the Milky Way

abstract:
  Interstellar dust extinction curves provide valuable information about dust
properties, including the composition and size of the dust grains, and are
essential to correct observations for the effects of interstellar dust. In this
work, we measure a representative sample of near-infrared (NIR; 0.8-5.5 $\mu$m)
spectroscopic extinction curves for the first time, enabling us to investigate
the extinction at wavelengths where it is usually only measured in broad
photometric bands. We use IRTF/SpeX spectra of a sample of reddened and
comparison stars to measure 15 extinction curves with the pair method. Our
sample spans A(V) values from 0.78 to 5.65 and R(V) values from 2.43 to 5.33.
We confirm that the NIR extinction curves are well fit by a power law, with
indices and amplitudes differing from sight line to sight line. Our average
diffuse NIR extinction curve can be represented by a single power law with
index $\alpha = 1.7$, but because of the sight line-to-sight line variations,
the shape of any average curve will depend on the parental sample. We find that
most of the variation in our sample can be linked to the ratio of
total-to-selective extinction R(V), a rough measurement of the average dust
grain size. Two sight lines in our sample clearly show the ice extinction
feature at 3 $\mu$m, which can be fitted by a modified Drude profile. We find
tentative ice detections with slightly over 3$\sigma$ significance in two other
sight lines. In our average diffuse extinction curve, we measure a 3$\sigma$
upper limit of A(ice)/A(V) = 0.0021 for this ice feature.


title:
Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with
  Wavelet Scattering Transforms

abstract:
  We perform the first application of the wavelet scattering transform (WST) on
actual galaxy observations, through a WST analysis of the BOSS DR12 CMASS
dataset. We lay out the detailed procedure on how to capture all necessary
layers of realism for an application on data obtained from a spectroscopic
survey, including the effects of redshift-space anisotropy, non-trivial survey
geometry, the shortcomings of the dataset through a set of systematic weights
and the Alcock-Paczynski distortion effect. In order to capture the
cosmological dependence of the WST, we use galaxy mocks obtained from the
state-of-the-art ABACUSSUMMIT simulations, tuned to match the anisotropic
correlation function of the BOSS CMASS sample in the redshift range
$0.46<z<0.60$. Using our theory model for the WST coefficients, as well as for
the first 2 multipoles of the galaxy power spectrum, that we use as reference,
we perform a likelihood analysis of the CMASS data and obtain the posterior
probability distributions of 4 cosmological parameters,
$\{\omega_b,\omega_c,n_s,\sigma_8\}$, as well as the Hubble constant, derived
from a fixed value of the angular size of the sound horizon at last scattering
measured by the Planck satellite, all of which are marginalized over the 7
nuisance parameters of the Halo Occupation Distribution model. The WST is found
to deliver a substantial improvement in the values of the predicted $1\sigma$
errors compared to the regular power spectrum, which are tighter by a factor in
the range $3-6$ in the case of flat and uninformative priors and by a factor of
$4-28$, when a Big Bang Nucleosynthesis prior is applied on the value of
$\omega_b$. Furthermore, in the latter case, we obtain a 0.6% measurement of
the Hubble constant. Our results are investigative and subject to certain
approximations in our analysis, that we discuss in the text.

categories: astro-ph.CO astro-ph.GA astro-ph.IM hep-ph


title: Driving quantum many-body scars

abstract:
  Periodic driving has been established as a powerful technique for engineering
novel phases of matter and intrinsically out-of-equilibrium phenomena such as
time crystals. Recent work by Bluvstein et al. [Science 371, 1355 (2021)] has
demonstrated that periodic driving can also lead to a significant enhancement
of quantum many-body scarring, whereby certain non-integrable systems can
display persistent quantum revivals from special initial states. Nevertheless,
the mechanisms behind driving-induced scar enhancement remain poorly
understood. Here we report a detailed study of the effect of periodic driving
on the PXP model describing Rydberg atoms in the presence of a strong Rydberg
blockade - the canonical static model of quantum many-body scarring. We show
that periodic modulation of the chemical potential gives rise to a rich phase
diagram, with at least two distinct types of scarring regimes that we
distinguish by examining their Floquet spectra. We formulate a toy model, based
on a sequence of square pulses, that accurately captures the details of the
scarred dynamics and allows for analytical treatment in the large-amplitude and
high-frequency driving regimes. Finally, we point out that driving with a
spatially inhomogeneous chemical potential allows to stabilize quantum revivals
from arbitrary initial states in the PXP model, via a mechanism similar to
prethermalization.


title:
MQT Bench: Benchmarking Software and Design Automation Tools for Quantum
  Computing

abstract:
  Quantum software tools for a wide variety of design tasks on and across
different levels of abstraction are crucial in order to eventually realize
useful quantum applications. This requires practical and relevant benchmarks
for new software tools to be empirically evaluated and compared to the current
state of the art. While benchmarks for specific design tasks are commonly
available, the demand for an overarching cross-level benchmark suite is not
fully met yet and there is no mutual consolidation in how quantum software
tools are evaluated thus far. In this work, we propose the MQT Bench benchmark
suite (as part of the Munich Quantum Toolkit (MQT)) based on four core traits:
(1) cross-level support for different abstraction levels, (2) accessibility via
an easy to use web interface (https://www.cda.cit.tum.de/mqtbench), (3)
provision of a broad selection of benchmarks to facilitate generalizability, as
well as (4) extendability to future algorithms, gate-sets, and hardware
architectures. By comprising more than 30.000 benchmark circuits ranging from 2
up to 130 qubits on four abstraction levels, MQT Bench presents a first step
towards benchmarking different abstraction levels with a single benchmark
suite.


title: Nonadiabatic transition at a band-touching point

abstract:
  Low-energy Hamiltonians with a linear crossing in their energy dispersion
(dubbed Dirac Hamiltonians) have recently been the subject of intense
investigations. The linear dispersion is often the result of an approximation
in the energy dispersion at the band-touching point in which higher order terms
are discarded. In this paper, we show that, in terms of nonadiabatic
transitions, by passing through a touching point, certain types of quadratic
terms could not be omitted, even in the arbitrary vicinity of it, ie, quadratic
terms could significantly affect the transition probability, hence the
Hamiltonian is not reducible to a linear one. We further show that the presence
of terms with exponents larger than two only affects the transition probability
away from the touching point. In the end, we discuss conditions that may lead
to the appearance of oscillations in the transition probability profile.


title: Accurate CMB covariance matrices: exact calculation and approximations

abstract:
  Accurate covariance matrices are required for a reliable estimation of
cosmological parameters from pseudo-power spectrum estimators. In this work, we
focus on the analytical calculation of covariance matrices. We consider the
case of observations of the Cosmic Microwave Background in temperature and
polarization on a small footprint such as in the SPT-3G experiment, which
observes 4% of the sky. Power spectra evaluated on small footprints are
expected to have large correlations between modes, and these need to be
accurately modelled. We present, for the first time, an algorithm that allows
an efficient (but computationally expensive) exact calculation of analytic
covariance matrices. Using it as our reference, we test the accuracy of
existing fast approximations of the covariance matrix. We find that, when the
power spectrum is binned in wide bandpowers, current approaches are correct up
to the 5% level on the SPT-3G small sky footprint. Furthermore, we propose a
new approximation which improves over the previous ones reaching a precision of
1% in the wide bandpowers case and generally more than 4 times more accurate
than current approaches. Finally, we derive the covariance matrices for
mask-corrected power spectra estimated by the PolSpice code. In particular, we
include, in the case of a small sky fraction, the effect of the apodization of
the large scale modes. While we considered the specific case of the CMB, our
results are applicable to any other cosmological probe which requires the
calculation of pseudo-power spectrum covariance matrices.


title:
Weyl-Luttinger phase transition in pyrochlore iridates revealed by Raman
  scattering

abstract:
  Pyrochlore iridates are thought to be a fertile ground for the realization of
topologically non-trivial and strongly correlated states of matter. Here we
provide a compelling evidence of interacting Weyl electrons in
Nd$_2$Ir$_2$O$_7$ and quadratic band touching in Pr$_2$Ir$_2$O$_7$ by Raman
scattering. We observe a magnetically-driven phase transition between the
topological Weyl semimetal and a Luttinger semimetal with quadratic band
touching in Nd$_2$Ir$_2$O$_7$. Our theoretical analysis of the Raman scattering
from Weyl and Luttinger quasiparticles agrees with experimental observations,
and enables a characterization of the material parameters while revealing
interaction and disorder effects through a relatively short quasiparticle
lifetime.


title:
Stellar Populations in the Transition Region of Nuclear Star Cluster and
  Nuclear Stellar Disc

abstract:
  The Milky Way nuclear star cluster (NSC) is located within the nuclear
stellar disc (NSD) in the Galactic centre. It is not fully understood if the
formation and evolution of these two components are connected, and how they
influence each other. We study the stellar populations in the transition region
of NSC and NSD. We observed two $\sim$4.3 pc$^2$ fields with the integral-field
spectrograph KMOS (VLT), located at r$\sim$20 pc (> 4 R$_e$) to the Galactic
East and West of the NSC. We extract and analyse medium-resolution stellar
spectra of >200 stars per field. The data contain in total nine young star
candidates. We use stellar photometry to estimate the stellar masses, effective
temperatures, and spectral types of the young stars. The stars are consistent
with an age of 4-6 Myr, they may have formed inside the Quintuplet cluster, but
were dispersed in dynamical interactions. Most stars in the two fields are red
giant stars, and we measure their stellar metallicities [M/H] using full
spectral fitting. We compare our [M/H] distributions to the NSC and NSD, using
data from the literature, and find that the overall metallicity decreases from
the central NSC, over the transition region, to the NSD. The steep decrease of
[M/H] from the NSC to the region dominated by the NSD indicates that the two
components have distinct stellar populations and formation histories.


title: Predicting magnetic edge behaviour in graphene using neural networks

abstract:
  Magnetic moments near zigzag edges in graphene allow complex nanostructures
with customised spin properties to be realised. However, computational costs
restrict theoretical investigations to small or perfectly periodic structures.
Here we demonstrate that a machine-learning approach, using only geometric
input, can accurately estimate magnetic moment profiles, allowing arbitrarily
large and disordered systems to be quickly simulated. Excellent agreement is
found with mean-field Hubbard calculations, and important electronic, magnetic
and transport properties are reproduced using the estimated profiles. This
approach allows the magnetic moments of experimental-scale systems to be
quickly and accurately predicted, and will speed-up the identification of
promising geometries for spintronic applications. While machine-learning
approaches to many-body interactions have largely been limited to exact
solutions of complex models at very small scales, this work establishes that
they can be successfully applied at very large scales at mean-field levels of
accuracy.


title: Spectral Evidence of Solar Neighborhood Analogs in CALIFA Galaxies

abstract:
  We introduce a novel non-parametric method to find solar neighborhood analogs
(SNAs) in extragalactic IFS surveys. The main ansatz is that the physical
properties of the solar neighborhood (SN) should be encoded in its optical
stellar spectrum. We assume that our best estimate of such spectrum is the one
extracted from the analysis performed by the Code for Stellar properties
Heuristic Assignment (\textsc{CoSHA}) from the MaStar stellar library. It
follows that finding SNAs in other galaxies consist in matching, in a $\chi^2$
sense, the SN reference spectrum across the optical extent of the observed
galaxies. We apply this procedure to a selection of CALIFA galaxies, by
requiring a close to face-on projection, relative isolation and non-AGN. We
explore how the local and global properties of the SNAs (stellar age,
metallicity, dust extinction, mass-to-light ratio, stellar surface mass and
star-formation densities and galactocentric distance) and their corresponding
host galaxies (morphological type, total stellar mass, star-formation rate,
effective radius) compare with those of the SN and the Milky Way (MW). We find
that SNAs are located preferentially in S(B)a~--~S(B)c galaxies, in a ring-like
structure, which radii seems to scale with the galaxy size. Despite the known
sources of systematics and errors, {most} properties present a considerable
agreement with the literature on the SN. We conclude that the solar
neighborhood is relatively common in our sample of SNAs. Our results warrants a
systematic exploration of correlations among the physical properties of the
SNAs and their host galaxies. We reckon that our method should inform current
models of the galactic habitable zone in our MW and other galaxies.


title: Auctioning Multiple Goods without Priors

abstract:
  I consider a mechanism design problem of selling multiple goods to multiple
bidders when the designer has minimal amount of information. I assume that the
designer only knows the upper bounds of bidders' values for each good and has
no additional distributional information. The designer takes a minimax regret
approach. The expected regret from a mechanism given a joint distribution over
value profiles and an equilibrium is defined as the difference between the full
surplus and the expected revenue. The designer seeks a mechanism, referred to
as a minimax regret mechanism, that minimizes her worst-case expected regret
across all possible joint distributions over value profiles and all equilibria.
I find that a separate second-price auction with random reserves is a minimax
regret mechanism for general upper bounds. Under this mechanism, the designer
holds a separate auction for each good; the formats of these auctions are
second-price auctions with random reserves.


title:
The impact of ultraviolet heating and cooling on the dynamics and
  observability of lava planet atmospheres

abstract:
  Lava planets have non-global, condensible atmospheres similar to icy bodies
within the solar system. Because they depend on interior dynamics, studying the
atmospheres of lava planets can lead to understanding unique geological
processes driven by their extreme environment. Models of lava planet
atmospheres have thus far focused on either radiative transfer or
hydrodynamics. In this study, we couple the two processes by introducing
ultraviolet and infrared radiation to a turbulent boundary layer model. We also
test the effect of different vertical temperature profiles on atmospheric
dynamics. Results from the model show that UV radiation affects the atmosphere
much more than IR. UV heating and cooling work together to produce a
horizontally isothermal atmosphere away from the sub-stellar point regardless
of the vertical temperature profile. We also find that stronger temperature
inversions induce stronger winds and hence cool the atmosphere. Our simulated
transmission spectra of the bound atmosphere show a strong SiO feature in the
UV that would be challenging to observe in the planet's transit spectrum due to
the precision required. Our simulated emission spectra are more promising, with
significant SiO spectral features at 4.5 and 9 $\mu$m that can be observed with
the James Webb Space Telescope. Different vertical temperature profiles produce
discernible dayside emission spectra, but not in the way one would expect.


title: A quasispecies continuous contact model in a subcritical regime

abstract:
  We study a non-equilibrium dynamical model: a marked continuous contact model
in $d$-dimensional space, $d \ge 1$. In contrast with the continuous contact
model in a critical regime, see \cite{KKP}, \cite{KPZ}, the model under
consideration is in the subcritical regime and it contains an additional
spontaneous spatially homogeneous birth from an external source. We prove that
this system has an invariant measure. We prove also that the process starting
from any initial distribution converges to this invariant measure.


title: Quasi-polynomial representations of double affine Hecke algebras

abstract:
  We introduce an explicit family of representations of the double affine Hecke
algebra $\mathbb{H}$ acting on spaces of quasi-polynomials, defined in terms of
truncated Demazure-Lusztig type operators. We show that these quasi-polynomial
representations provide concrete realizations of a natural family of cyclic
$Y$-parabolically induced $\mathbb{H}$-representations. We recover Cherednik's
well-known polynomial representation as a special case.
  The quasi-polynomial representation gives rise to a family of commuting
operators acting on spaces of quasi-polynomials. These generalize the Cherednik
operators, which are fundamental in the study of Macdonald polynomials. We
provide a detailed study of their joint eigenfunctions, which may be regarded
as quasi-polynomial, multi-parametric generalizations of nonsymmetric Macdonald
polynomials. We also introduce generalizations of symmetric Macdonald
polynomials, which are invariant under a multi-parametric generalization of the
standard Weyl group action.
  We connect our results to the representation theory of metaplectic covers of
reductive groups over non-archimedean local fields. We introduce root system
generalizations of the metaplectic polynomials from our previous work by taking
a suitable restriction and reparametrization of the quasi-polynomial
generalizations of Macdonald polynomials. We show that metaplectic
Iwahori-Whittaker functions can be recovered by taking the Whittaker limit of
these metaplectic polynomials.


title:
Direct Air-to-Underwater Optical Wireless Communication: Statistical
  Characterization and Outage Performance

abstract:
  In general, a buoy relay is used to connect the underwater communication to
the terrestrial network over a radio or optical wireless communication (OWC)
link. The use of relay deployment may pose security and deployment issues. This
paper investigates the feasibility of direct air-to-underwater (A2UW)
communication from an over-the-sea OWC system to an underwater submarine
without deploying a relaying node. We analyze the statistical performance of
the direct transmission over the combined channel fading effect of atmospheric
turbulence, random fog, air-to-water interface, oceanic turbulence, and
pointing errors. We develop novel analytical expressions for the probability
density function (PDF) and cumulative distribution function (CDF) of the
resultant signal-to-noise ratio (SNR) in terms of bivariate Meijer-G and Fox-H
functions. We use the derived statistical results to analyze the system
performance by providing exact and asymptotic results of the outage probability
in terms of system parameters. We use computer simulations to demonstrate the
performance of direct A2UW transmissions compared to the relay-assisted system.


title: An Intriguing Property of Geophysics Inversion

abstract:
  Inversion techniques are widely used to reconstruct subsurface physical
properties (e.g., velocity, conductivity, and others) from surface-based
geophysical measurements (e.g., seismic, electric/magnetic (EM) data). The
problems are governed by partial differential equations~(PDEs) like the wave or
Maxwell's equations. Solving geophysical inversion problems is challenging due
to the ill-posedness and high computational cost. To alleviate those issues,
recent studies leverage deep neural networks to learn the inversion mappings
from geophysical measurements to the geophysical property directly.
  In this paper, we show that such a mapping can be well modeled by a
\textit{very shallow}~(but not wide) network with only five layers. This is
achieved based on our new finding of an intriguing property: \textit{a
near-linear relationship between the input and output, after applying integral
transform in high dimensional space.} In particular, when dealing with the
inversion from seismic data to subsurface velocity governed by a wave equation,
the integral results of velocity with Gaussian kernels are linearly correlated
to the integral of seismic data with sine kernels. Furthermore, this property
can be easily turned into a light-weight encoder-decoder network for inversion.
The encoder contains the integration of seismic data and the linear
transformation without need for fine-tuning. The decoder only consists of a
single transformer block to reverse the integral of velocity.
  Experiments show that this interesting property holds for two geophysics
inversion problems over four different datasets. Compared to much deeper
InversionNet~\cite{wu2019inversionnet}, our method achieves comparable
accuracy, but consumes significantly fewer parameters.


title: Multilevel Optimization for Inverse Problems

abstract:
  Inverse problems occur in a variety of parameter identification tasks in
engineering. Such problems are challenging in practice, as they require
repeated evaluation of computationally expensive forward models. We introduce a
unifying framework of multilevel optimization that can be applied to a wide
range of optimization-based solvers. Our framework provably reduces the
computational cost associated with evaluating the expensive forward maps
stemming from various physical models. To demonstrate the versatility of our
analysis, we discuss its implications for various methodologies including
multilevel (accelerated, stochastic) gradient descent, a multilevel ensemble
Kalman inversion and a multilevel Langevin sampler. We also provide numerical
experiments to verify our theoretical findings.


title:
A look into the "hedgehog" events in pp collisions using a new event
  shape -- flatenicity

abstract:
  The UA1 and CDF collaborations have reported the presence of events with a
very extended structure of low momentum tracks filling in a uniform way the
$\eta-\varphi$ phase space. However, these events have not caused interest with
respect to the details of the interaction that originate them. We have
undertaken to study whether such events are predicted by present event
generators like PYTHIA 8. We report the existence of events without any
discernible jet structure in high-multiplicity pp collisions. In the
simulations those events originate from a moderate number of parton-parton
scatterings within the same pp collision. A new event shape, $\rho$
(flatenicity), is proposed to allow for easily triggering on such events.


title:
Flexible and scalable privacy assessment for very large datasets, with
  an application to official governmental microdata

abstract:
  We present a systematic refactoring of the conventional treatment of privacy
analyses, basing it on mathematical concepts from the framework of Quantitative
Information Flow (QIF). The approach we suggest brings three principal
advantages: it is flexible, allowing for precise quantification and comparison
of privacy risks for attacks both known and novel; it can be computationally
tractable for very large, longitudinal datasets; and its results are
explainable both to politicians and to the general public. We apply our
approach to a very large case study: the Educational Censuses of Brazil,
curated by the governmental agency INEP, which comprise over 90 attributes of
approximately 50 million individuals released longitudinally every year since
2007. These datasets have only very recently (2018-2021) attracted legislation
to regulate their privacy -- while at the same time continuing to maintain the
openness that had been sought in Brazilian society. INEP's reaction to that
legislation was the genesis of our project with them. In our conclusions here
we share the scientific, technical, and communication lessons we learned in the
process.


title: What's the (Dark) Matter with Cosmological Bubbles?

abstract:
  Despite their tremendous successes, modern-day cosmology and particle physics
harbor a variety of unresolved mysteries. Two of the biggest are the origin of
the baryon asymmetry of the Universe and the existence and nature of dark
matter. In the present thesis, the author addresses these topics in various
ways. The first part of the thesis is concerned with cosmological first-order
phase transitions that may have occurred shortly after the Big Bang. Such
transitions proceed via the nucleation and expansion of true vacuum bubbles and
give rise to a rich phenomenology. The author suggests a mechanism to
simultaneously explain the baryon asymmetry and dark matter, based on the
out-of-equilibrium dynamics at the boundary of a dark phase transition with
large order parameter. The same class of phase transitions can, in the
parameter regime of small dark matter Yukawa couplings, lead to the production
of primordial black holes via the compression of the plasma in shrinking false
vacuum regions, as the author demonstrates with a sophisticated numerical
simulation. In a third project regarding cosmological phase transitions, the
author investigates the possibility of sub-MeV hidden sectors that are
decoupled from the remaining plasma and cold enough to be reconciled with
cosmological constraints, but at the same time give rise to a detectable
gravitational-wave spectrum produced during bubble collisions. In the second
part of the thesis, the author assesses the prospects for new physics searches
at the DUNE near detector, focusing on the DUNE-PRISM concept, which suggests
consecutive measurements at different on- and off-axis positions. This setup
achieves improved signal-to-background ratios and reduces systematic
uncertainties.


title:
The narrowing of literature use and the restricted mobility of papers in
  the sciences

abstract:
  It is a matter of debate whether a shrinking proportion of scholarly
literature is getting most of the references over time. It is also less well
understood how a narrowing literature usage would affect the circulation of
ideas in the sciences. Here we show, that the utilization of scientific
literature follows dual tendencies over time: while a larger proportion of
literature is cited at least a few times, citations are also concentrating more
on the top of the citation distribution. Parallel to the latter trend, a
paper's future importance increasingly depends on its past citation
performance. A random network model shows that the citation concentration is
directly related to the greater stability of citation performance. The
presented evidence suggests that the growing heterogeneity of citation impact
restricts the mobility of research articles that do not gain attention early
on. While concentration grows from the beginning of the studied period in 1970,
citation dispersion manifest itself significantly only from the mid-1990s when
the popularity of freshly published papers has also risen. Most likely,
advanced information technologies to disseminate papers are behind both of
these latter trends.


title: Extricating IoT Devices from Vendor Infrastructure with Karl

abstract:
  Most consumer IoT devices are vertically integrated with cloud-side
infrastructure. Such architectures present enormous risk to user data,
exacerbated by vendor heterogeneity and the inability for users to audit
cloud-side activity. A more promising approach would be to leverage local
hardware, providing users control over how their data is processed and why it
can be shared with other devices or the Internet.
  Karl is a new smart-home framework designed to host IoT computation and
storage on user-chosen devices. A key insight in Karl's modular programming
model is that a familiar interface (inspired by serverless) can capture most
modern cloud-side IoT components under a single framework, which executes
modules agnostic of hardware location. While local hosting eliminates many
flows, modularity enables all remaining flows to be justified using
fine-grained primitives. We introduce two IoT security mechanisms: pipeline
permissions that permit device data to be shared given some justification and
exit policies that block flows unless specific conditions are met. We evaluate
Karl through two end-to-end applications.


title:
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer
  for Missing Data Imputation

abstract:
  Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical
practice as each contrast provides complementary information. However, the
availability of each contrast may vary amongst patients in reality. This poses
challenges to both radiologists and automated image analysis algorithms. A
general approach for tackling this problem is missing data imputation, which
aims to synthesize the missing contrasts from existing ones. While several
convolutional neural network (CNN) based algorithms have been proposed, they
suffer from the fundamental limitations of CNN models, such as requirement for
fixed numbers of input and output channels, inability to capture long-range
dependencies, and lack of interpretability. In this paper, we formulate missing
data imputation as a sequence-to-sequence learning problem and propose a
multi-contrast multi-scale Transformer (MMT), which can take any subset of
input contrasts and synthesize those that are missing. MMT consists of a
multi-scale Transformer encoder that builds hierarchical representations of
inputs combined with a multi-scale Transformer decoder that generates the
outputs in a coarse-to-fine fashion. Thanks to the proposed multi-contrast Swin
Transformer blocks, it can efficiently capture intra- and inter-contrast
dependencies for accurate image synthesis. Moreover, MMT is inherently
interpretable. It allows us to understand the importance of each input contrast
in different regions by analyzing the in-built attention maps of Transformer
blocks in the decoder. Extensive experiments on two large-scale multi-contrast
MRI datasets demonstrate that MMT outperforms the state-of-the-art methods
quantitatively and qualitatively.


title:
Global analysis of regulatory network dynamics: equilibria and
  saddle-node bifurcations

abstract:
  In this paper we describe a combined combinatorial/numerical approach to
studying equilibria and bifurcations in network models arising in Systems
Biology. Often interactions are only coarsely known in terms of a regulatory or
signalling network topology. Consequently, ODE models of the dynamics suffer
from high dimensional parameters which presents a significant obstruction to
studying the global dynamics via numerical methods.
  Given a network topology describing state variables which regulate one
another via monotone and bounded functions, we first use the Dynamic Signatures
Generated by Regulatory Networks (DSGRN) software to obtain a combinatorial
description which summarizes the dynamics. This summary is coarse but global
and we use this information as a first pass to identify "interesting" subsets
of parameters in which to focus. We construct an associated ODE model with high
parameter dimension using our Network Dynamics Modeling and Analysis (NDMA)
Python library. We introduce algorithms for efficiently investigating the
dynamics in these ODE models restricted to these parameter subsets. Finally, we
perform a statistical validation of the method and several interesting
dynamical applications including finding saddle-node bifurcations in a 54
parameter model.


title:
Black-Scholes Option Pricing on Intel CPUs and GPUs: Implementation on
  SYCL and Optimization Techniques

abstract:
  The Black-Scholes option pricing problem is one of the widely used financial
benchmarks. We explore the possibility of developing a high-performance
portable code using the SYCL (Data Parallel C++) programming language. We start
from a C++ code parallelized with OpenMP and show optimization techniques that
are beneficial on modern Intel Xeon CPUs. Then, we port the code to SYCL and
consider important optimization aspects on CPUs and GPUs (device-friendly
memory access patterns, relevant data management, employing vector data types).
We show that the developed SYCL code is only 10% inferior to the optimized C++
code when running on CPUs while achieving reasonable performance on Intel GPUs.
We hope that our experience of developing and optimizing the code on SYCL can
be useful to other researchers who plan to port their high-performance C++
codes to SYCL to get all the benefits of single-source programming.


title:
On the Arithmetic and Geometric Fusion of Beliefs for Distributed
  Inference

abstract:
  We study the asymptotic learning rates under linear and log-linear
combination rules of belief vectors in a distributed hypothesis testing
problem. We show that under both combination strategies, agents are able to
learn the truth exponentially fast, with a faster rate under log-linear fusion.
We examine the gap between the rates in terms of network connectivity and
information diversity. We also provide closed-form expressions for special
cases involving federated architectures and exchangeable networks.


title:
Twin-width and Limits of Tractability of FO Model Checking on Geometric
  Graphs

abstract:
  The complexity of the problem of deciding properties expressible in FO logic
on graphs -- the FO model checking problem (parameterized by the respective FO
formula), is well-understood on so-called sparse graph classes, but much less
understood on hereditary dense graph classes. Regarding the latter, a recent
concept of twin-width [Bonnet et al., FOCS 2020] appears to be very useful. For
instance, the question of these authors [CGTA 2019] about where is the exact
limit of fixed-parameter tractability of FO model checking on permutation
graphs has been answered by Bonnet et al. in 2020 quite easily, using the newly
introduced twin-width. We prove that such exact characterization of hereditary
subclasses with tractable FO model checking naturally extends from permutation
to circle graphs (the intersection graphs of chords in a circle). Namely, we
prove that under usual complexity assumptions, FO model checking of a
hereditary class of circle graphs is in FPT if and only if the class excludes
some permutation graph. We also prove a similar excluded-subgraphs
characterization for hereditary classes of interval graphs with FO model
checking in FPT, which concludes the line a research of interval classes with
tractable FO model checking started in [Ganian et al., ICALP 2013]. The
mathematical side of the presented characterizations -- about when subclasses
of the classes of circle and permutation graphs have bounded twin-width,
moreover extends to so-called bounded perturbations of these classes.


title: HiNER: A Large Hindi Named Entity Recognition Dataset

abstract:
  Named Entity Recognition (NER) is a foundational NLP task that aims to
provide class labels like Person, Location, Organisation, Time, and Number to
words in free text. Named Entities can also be multi-word expressions where the
additional I-O-B annotation information helps label them during the NER
annotation process. While English and European languages have considerable
annotated data for the NER task, Indian languages lack on that front -- both in
terms of quantity and following annotation standards. This paper releases a
significantly sized standard-abiding Hindi NER dataset containing 109,146
sentences and 2,220,856 tokens, annotated with 11 tags. We discuss the dataset
statistics in all their essential detail and provide an in-depth analysis of
the NER tag-set used with our data. The statistics of tag-set in our dataset
show a healthy per-tag distribution, especially for prominent classes like
Person, Location and Organisation. Since the proof of resource-effectiveness is
in building models with the resource and testing the model on benchmark data
and against the leader-board entries in shared tasks, we do the same with the
aforesaid data. We use different language models to perform the sequence
labelling task for NER and show the efficacy of our data by performing a
comparative evaluation with models trained on another dataset available for the
Hindi NER task. Our dataset helps achieve a weighted F1 score of 88.78 with all
the tags and 92.22 when we collapse the tag-set, as discussed in the paper. To
the best of our knowledge, no available dataset meets the standards of volume
(amount) and variability (diversity), as far as Hindi NER is concerned. We fill
this gap through this work, which we hope will significantly help NLP for
Hindi. We release this dataset with our code and models at
https://github.com/cfiltnlp/HiNER


title:
GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial
  Differential Equations

abstract:
  This paper introduces a novel two-stream deep model based on graph
convolutional network (GCN) architecture and feed-forward neural networks
(FFNN) for learning the solution of nonlinear partial differential equations
(PDEs). The model aims at incorporating both graph and grid input
representations using two streams corresponding to GCN and FFNN models,
respectively. Each stream layer receives and processes its own input
representation. As opposed to FFNN which receives a grid-like structure, the
GCN stream layer operates on graph input data where the neighborhood
information is incorporated through the adjacency matrix of the graph. In this
way, the proposed GCN-FFNN model learns from two types of input
representations, i.e. grid and graph data, obtained via the discretization of
the PDE domain. The GCN-FFNN model is trained in two phases. In the first
phase, the model parameters of each stream are trained separately. Both streams
employ the same error function to adjust their parameters by enforcing the
models to satisfy the given PDE as well as its initial and boundary conditions
on grid or graph collocation (training) data. In the second phase, the learned
parameters of two-stream layers are frozen and their learned representation
solutions are fed to fully connected layers whose parameters are learned using
the previously used error function. The learned GCN-FFNN model is tested on
test data located both inside and outside the PDE domain. The obtained
numerical results demonstrate the applicability and efficiency of the proposed
GCN-FFNN model over individual GCN and FFNN models on 1D-Burgers,
1D-Schr\"odinger, 2D-Burgers and 2D-Schr\"odinger equations.


title: The WISE-2MASS Survey: Red Quasars Into the Radio Quiet Regime

abstract:
  We present a highly complete sample of broad-line (Type 1) QSOs out to z ~ 3
selected by their mid-infrared colors, a method that is minimally affected by
dust reddening. We remove host galaxy emission from the spectra and fit for
excess reddening in the residual QSOs, resulting in a Gaussian distribution of
colors for unreddened (blue) QSOs, with a tail extending toward heavily
reddened (red) QSOs, defined as having E(B - V) > 0.25. This radio-independent
selection method enables us to compare red and blue QSO radio properties in
both the FIRST (1.4 GHz) and VLASS (2 - 4 GHz) surveys. Consistent with recent
results from optically-selected QSOs from SDSS, we find that red QSOs have a
significantly higher detection fraction and a higher fraction of compact radio
morphologies at both frequencies. We employ radio stacking to investigate the
median radio properties of the QSOs including those that are undetected in
FIRST and VLASS, finding that red QSOs have significantly brighter radio
emission and steeper radio spectral slopes compared with blue QSOs. Finally, we
find that the incidence of red QSOs is strongly luminosity dependent, where red
QSOs make up > 40% of all QSOs at the highest luminosities. Overall, red QSOs
comprise ~ 40% of higher luminosity QSOs, dropping to only a few percent at
lower luminosities. Furthermore, red QSOs make up a larger percentage of the
radio-detected QSO population. We argue that dusty AGN-driven winds are
responsible for both the obscuration as well as excess radio emission seen in
red QSOs.


title:
CAVES: A Dataset to facilitate Explainable Classification and
  Summarization of Concerns towards COVID Vaccines

abstract:
  Convincing people to get vaccinated against COVID-19 is a key societal
challenge in the present times. As a first step towards this goal, many prior
works have relied on social media analysis to understand the specific concerns
that people have towards these vaccines, such as potential side-effects,
ineffectiveness, political factors, and so on. Though there are datasets that
broadly classify social media posts into Anti-vax and Pro-Vax labels, there is
no dataset (to our knowledge) that labels social media posts according to the
specific anti-vaccine concerns mentioned in the posts. In this paper, we have
curated CAVES, the first large-scale dataset containing about 10k COVID-19
anti-vaccine tweets labelled into various specific anti-vaccine concerns in a
multi-label setting. This is also the first multi-label classification dataset
that provides explanations for each of the labels. Additionally, the dataset
also provides class-wise summaries of all the tweets. We also perform
preliminary experiments on the dataset and show that this is a very challenging
dataset for multi-label explainable classification and tweet summarization, as
is evident by the moderate scores achieved by some state-of-the-art models. Our
dataset and codes are available at: https://github.com/sohampoddar26/caves-data


title:
Optical properties of low background PEN structural components for the
  LEGEND-200 experiment

abstract:
  Polyethylene Naphthalate (PEN) plastic scintillator has been identified as
potential self-vetoing structural material in low-background physics
experiments. Scintillating components have been produced radio-pure from PEN
using injection compression molding technology. These low-background PEN
components will be used as active holders to mount the Germanium detectors in
the \legend-$200$ neutrinoless double beta decay experiment. In this paper we
present the measurement of the optical properties of these PEN components.
Thus, the emission spectrum, time constant, attenuation and bulk absorption
length as well as light output and light yield are reported. In addition, the
surface of these PEN components has been characterized and an estimation of the
surface roughness is presented. Moreover, the light output of the final
\legend-$200$ detector holders has been measured and is reported. These
measurements were used to estimate the self-vetoing efficiency of these
holders.


title:
Intermediate Valence State in $\mathrm{YbB_4}$ Revealed by Resonant
  X-ray Emission Spectroscopy

abstract:
  We report the temperature dependence of the Yb valence in the geometrically
frustrated compound $\mathrm{YbB_4}$ from 12 to 300 K using resonant X-ray
emission spectroscopy at the Yb $L_{\alpha_1}$ transition. We find that the Yb
valence, $v$, is hybridized between the $v=2$ and $v=3$ valence states,
increasing from $v=2.61\pm0.01$ at 12 K to $v=2.67\pm0.01$ at 300 K, confirming
that $\mathrm{YbB_4}$ is a Kondo system in the intermediate valence regime.
This result indicates that the Kondo interaction in $\mathrm{YbB_4}$ is
substantial, and is likely to be the reason why $\mathrm{YbB_4}$ does not order
magnetically at low temperature, rather than this being an effect of geometric
frustration. Furthermore, the zero-point valence of the system is extracted
from our data and compared with other Kondo lattice systems. The zero-point
valence seems to be weakly dependent on the Kondo temperature scale, but not on
the valence change temperature scale $T_v$.


title: Learning to Split for Automatic Bias Detection

abstract:
  Classifiers are biased when trained on biased datasets. As a remedy, we
propose Learning to Split (ls), an algorithm for automatic bias detection.
Given a dataset with input-label pairs, ls learns to split this dataset so that
predictors trained on the training split generalize poorly to the testing
split. This performance gap provides a proxy for measuring the degree of bias
in the learned features and can therefore be used to reduce biases. Identifying
non-generalizable splits is challenging as we don't have any explicit
annotations about how to split. In this work, we show that the prediction
correctness of the testing example can be used as a source of weak supervision:
generalization performance will drop if we move examples that are predicted
correctly away from the testing split, leaving only those that are
mispredicted. We evaluate our approach on Beer Review, Waterbirds, CelebA and
MNLI. Empirical results show that ls is able to generate astonishingly
challenging splits that correlate with human-identified biases. Moreover, we
demonstrate that combining robust learning algorithms (such as group DRO) with
splits identified by ls enables automatic de-biasing. Compared with previous
state-of-the-arts, we substantially improves the worst-group performance (23.4%
on average) when the source of biases is unknown during training and
validation.


title: A First Runtime Analysis of the NSGA-II on a Multimodal Problem

abstract:
  Very recently, the first mathematical runtime analyses of the multi-objective
evolutionary optimizer NSGA-II have been conducted (AAAI 2022, GECCO 2022 (to
appear), arxiv 2022). We continue this line of research with a first runtime
analysis of this algorithm on a benchmark problem consisting of two multimodal
objectives. We prove that if the population size $N$ is at least four times the
size of the Pareto front, then the NSGA-II with four different ways to select
parents and bit-wise mutation optimizes the OneJumpZeroJump benchmark with jump
size~$2 \le k \le n/4$ in time $O(N n^k)$. When using fast mutation, a recently
proposed heavy-tailed mutation operator, this guarantee improves by a factor of
$k^{\Omega(k)}$. Overall, this work shows that the NSGA-II copes with the local
optima of the OneJumpZeroJump problem at least as well as the global SEMO
algorithm.


title: BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms

abstract:
  Barren plateaus are a notorious problem in the optimization of variational
quantum algorithms and pose a critical obstacle in the quest for more efficient
quantum machine learning algorithms. Many potential reasons for barren plateaus
have been identified but few solutions have been proposed to avoid them in
practice. Existing solutions are mainly focused on the initialization of
unitary gate parameters without taking into account the changes induced by
input data. In this paper, we propose an alternative strategy which initializes
the parameters of a unitary gate by drawing from a beta distribution. The
hyperparameters of the beta distribution are estimated from the data. To
further prevent barren plateau during training we add a novel perturbation at
every gradient descent step. Taking these ideas together, we empirically show
that our proposed framework significantly reduces the possibility of a complex
quantum neural network getting stuck in a barren plateau.


title: The Geometry of the Permutohedral Variety as a Hessenberg Variety

abstract:
  We construct a concrete isomorphism from the permutohedral variety to the
regular semisimple Hessenberg variety associated to the Hessenberg function
$h_+(i)=i+1$, $1\le i\le n-1$. As applications, we first identify torus orbit
closures of permutohedral variety as components of disconnected Hessenberg
varieties. Using the isomorphisms we constructed, we are also able to describe
the geometric structure of regular semisimple Hessenberg varieties associated
to the Hessenberg function represented by $h_k= (2,3, \cdots, k+1, n,\cdots,n)$
for some $k\le n-3$.


title:
High Dimensional Bayesian Optimization with Kernel Principal Component
  Analysis

abstract:
  Bayesian Optimization (BO) is a surrogate-based global optimization strategy
that relies on a Gaussian Process regression (GPR) model to approximate the
objective function and an acquisition function to suggest candidate points. It
is well-known that BO does not scale well for high-dimensional problems because
the GPR model requires substantially more data points to achieve sufficient
accuracy and acquisition optimization becomes computationally expensive in high
dimensions. Several recent works aim at addressing these issues, e.g., methods
that implement online variable selection or conduct the search on a
lower-dimensional sub-manifold of the original search space. Advancing our
previous work of PCA-BO that learns a linear sub-manifold, this paper proposes
a novel kernel PCA-assisted BO (KPCA-BO) algorithm, which embeds a non-linear
sub-manifold in the search space and performs BO on this sub-manifold.
Intuitively, constructing the GPR model on a lower-dimensional sub-manifold
helps improve the modeling accuracy without requiring much more data from the
objective function. Also, our approach defines the acquisition function on the
lower-dimensional sub-manifold, making the acquisition optimization more
manageable.
  We compare the performance of KPCA-BO to the vanilla BO and PCA-BO on the
multi-modal problems of the COCO/BBOB benchmark suite. Empirical results show
that KPCA-BO outperforms BO in terms of convergence speed on most test
problems, and this benefit becomes more significant when the dimensionality
increases. For the 60D functions, KPCA-BO surpasses PCA-BO in many test cases.
Moreover, it efficiently reduces the CPU time required to train the GPR model
and optimize the acquisition function compared to the vanilla BO.


title: Philosophical Uses of Categoricity Arguments

abstract:
  Mathematicians and philosophers have appealed to categoricity arguments in a
surprisingly varied range of contexts. One familiar example calls on
second-order categoricity in an attempt to show that the Continuum Hypothesis,
despite its formal independence, has a determinate truth value, but this does
not exhaust the uses of categoricity even in set theory, not to mention its
appearance in various roles in discussions of arithmetic. Here we compare and
contrast a sampling of these deployments to get a sense of when these arguments
tend to succeed and when they tend to fail. Our story begins with two
historical landmarks, Dedekind and Zermelo, on arithmetic and set theory,
respectively, and ends with two leading contemporary writers, Charles Parsons
and the co-authors Tim Button and Sean Walsh, again on arithmetic and set
theory, respectively. In between, we pause over the well-known contribution of
Georg Kreisel. In each case we ask: what does the author set out to accomplish,
philosophically?; what do they actually do (or what can be done),
mathematically?; and does what is done (or can be done) accomplish what they
set out to do? We find this focus on context illuminating: these authors have
qualitatively different philosophical goals, and what works for one might not
work for another.


title:
Hybrid Eyes: Design and Evaluation of the Prediction-level Cooperative
  Driving with a Real-world Automated Driving System

abstract:
  Currently, there are still various situations in which automated driving
systems (ADS) cannot perform as well as a human driver, particularly in
predicting the behaviour of surrounding traffic. As humans are still surpassing
state-of-the-art ADS in this task, a new concept enabling human driver to help
ADS to better anticipate the behaviour of other road users was developed.
Preliminary results suggested that the collaboration at the prediction level
can effectively enhance the experience and comfort of ADS. For an in-depth
investigation of the concept, we implemented an interactive prototype, called
Prediction-level Cooperative Automated Driving system (PreCoAD), adapting an
existing ADS that has been previously validated on the public road. The results
of a driving simulator study among 15 participants in different highway
scenarios showed that PreCoAD could enhance automated driving performance and
provide a positive user experience. Follow-up interviews with participants also
provided insights into the improvement of the system.


title: What model companionship can say about the Continuum problem

abstract:
  We present recent results on the model companions of set theory, placing them
in the context of the current debate in the philosophy of mathematics. We start
by describing the dependence of the notion of model companionship on the
signature, and then we analyze this dependence in the specific case of set
theory. We argue that the most natural model companions of set theory describe
(as the signature in which we axiomatize set theory varies) theories of
$H_{\kappa^+}$, as $\kappa$ ranges among the infinite cardinals. We also single
out $2^{\aleph_0}=\aleph_2$ as the unique solution of the Continuum problem
which can (and does) belong to some model companion of set theory (enriched
with large cardinal axioms). Finally this model-theoretic approach to
set-theoretic validities is explained and justified in terms of a form of
maximality inspired by Hilbert's axiom of completeness.


title: Network Creation with Homophilic Agents

abstract:
  Network Creation Games are an important framework for understanding the
formation of real-world networks. These games usually assume a set of
indistinguishable agents strategically buying edges at a uniform price leading
to a network among them. However, in real life, agents are heterogeneous and
their relationships often display a bias towards similar agents, say of the
same ethnic group. This homophilic behavior on the agent level can then lead to
the emergent global phenomenon of social segregation. We initiate the study of
Network Creation Games with multiple types of homophilic agents and non-uniform
edge cost. Specifically, we introduce and compare two models, focusing on the
perception of same-type and different-type neighboring agents, respectively.
Despite their different initial conditions, both our theoretical and
experimental analysis show that the resulting stable networks are almost
identical in the two models, indicating a robust structure of social networks
under homophily. Moreover, we investigate the segregation strength of the
formed networks and thereby offer new insights on understanding segregation.


title:
Using EDT0L systems to solve equations in the solvable Baumslag-Solitar
  groups

abstract:
  We investigate the solution sets to equations in the solvable
Baumslag-Solitar groups $BS(1,k)$, $k\geq2$, and show that these sets are
represented by EDT0L languages in some cases. In particular, we prove that the
multiplication table of such a group forms an \E language in an appropriate
sense.


title:
Cavity induced chiral edge currents and spontaneous magnetization in
  two-dimensional electron systems

abstract:
  We consider a laterally confined two-dimensional electron gas (2DEG), placed
inside a gyrotropic cavity. Splitting of the circularly polarized
electromagnetic modes leads to the emergence of the ground state spontaneous
magnetization, anomalous Hall effect and chiral edge currents in 2DEG. We
examine the dependence of the magnetization and edge current density on the
system size for two particular cases of the confining potential: infinite wall
and parabolic potentials. We show that paramagnetic and diamagnetic
contributions to the edge currents have qualitatively different dependence on
the system size. These findings pave the route to the design quantum
electrodynamic engineering of the material properties of the mesoscopic
electron systems.


title: Turbulent universal galactic Kolmogorov velocity cascade over 6 decades

abstract:
  We report the evidence for the existence of the universal, continuous
turbulent cascade of velocity fluctuations with Kolmogorov -5/3 slope spanning
6 orders of length scales, from $10^4$ pc down to $10^{-2}$ pc. This was
achieved by applying our innovative technique of separating density and
velocity fluctuations to a set of spectroscopic surveys featuring various
galactic spectral lines. This unified velocity cascade involves different
interstellar phases from diffuse galactic media to dense self-gravitating
clouds and persists despite interstellar phase transitions. However, the
turbulent density fluctuations do not show this universality as the value of
the spectral slope changes in different interstellar phases. This agrees with
the expectation of compressible turbulence theory and demonstrates that the
density is only an indirect tracer of interstellar turbulence. We report that
the density fluctuations for clouds and filaments that are preferentially
parallel to magnetic fields exhibit the spectral slope of -2. The universal
grand turbulent velocity cascade that is established in our paper has
significant implications for key galactic physical processes, including star
formation, cosmic ray transport etc. We anticipate our result to be a starting
point for in vitro models of multiphase interstellar turbulence studies with a
significant impact for modeling of spiral galaxies.


title:
Faithful to the Document or to the World? Mitigating Hallucinations via
  Entity-linked Knowledge in Abstractive Summarization

abstract:
  Despite recent advances in abstractive summarization, current summarization
systems still suffer from content hallucinations where models generate text
that is either irrelevant or contradictory to the source document. However,
prior work has been predicated on the assumption that any generated facts not
appearing explicitly in the source are undesired hallucinations. Methods have
been proposed to address this scenario by ultimately improving `faithfulness'
to the source document, but in reality, there is a large portion of entities in
the gold reference targets that are not directly in the source. In this work,
we show that these entities are not aberrations, but they instead require
utilizing external world knowledge to infer reasoning paths from entities in
the source. We show that by utilizing an external knowledge base, we can
improve the faithfulness of summaries without simply making them more
extractive, and additionally, we show that external knowledge bases linked from
the source can benefit the factuality of generated summaries.


title:
Three-dimensional photonic topological insulator induced by lattice
  dislocations

abstract:
  The hallmark of topological insulators is the scatter-free propagation of
waves in topologically protected edge channels. This transport is strictly
chiral on the outer edge of the medium, and therefore capable of bypassing
sharp corners and imperfections, even in the presence of substantial disorder.
In photonics, two-dimensional topological edge states have been demonstrated on
several different platforms, and are emerging as a promising tool for robust
lasers, quantum devices, and other applications. However, three-dimensional
photonic topological insulators, specifically those supporting topologically
protected edge states in all 3D, have thus far remained out of experimental
reach. Here, we demonstrate a three-dimensional photonic topological insulator
with protected topological edge states. The topological protection is enabled
by a screw dislocation. For this purpose, we utilize the concept of synthetic
dimensions in a 2D photonic waveguide array by introducing an additional modal
dimension to transform the system into a 3D photonic topological insulator. The
lattice dislocation endows the system with edge states propagating along
three-dimensional trajectories, with topological protection akin to strong
photonic topological insulators. Our work paves the way for utilizing
three-dimensional topology in photonic science and technology.


title: Enabling PSD-capability for a High-density Channel Imager

abstract:
  Pulse shape discrimination (PSD) is crucial for non-proliferation and
security applications, where fast neutrons need to be identified and measured
in the presence of a strong gamma-ray background. The traditional
charge-integration-based PSD method requires the storage and processing of
hundreds of samples for every single pulse, which is time- and memory-consuming
for high-density channel applications. In this work, we explored the
possibility of implementing PSD using a commercial ASIC that allows the user to
adjust the pulse shaping time. We demonstrated that PSD can be achieved by
maximizing the difference between the pulse shaping circuit's responses to
neutron and gamma-ray pulses.


title: Homological algebra of Nakayama algebras and 321-avoiding permutations

abstract:
  Linear Nakayama algebras over a field $K$ are in natural bijection to Dyck
paths and Dyck paths are in natural bijection to 321-avoiding bijections via
the Billey-Jockusch-Stanley bijection. Thus to every 321-avoiding permutation
$\pi$ we can associate in a natural way a linear Nakayama algebra $A_{\pi}$. We
give a homological interpretation of the fixed points statistic of 321-avoiding
permutations using Nakayama algebras with a linear quiver. We furthermore show
that the space of self-extension for the Jacobson radical of a linear Nakayama
algebra $A_{\pi}$ is isomorphic to $K^{\mathfrak{s}(\pi)}$, where
$\mathfrak{s}(\pi)$ is defined as the cardinality $k$ such that $\pi$ is the
minimal product of transpositions of the form $s_i=(i,i+1)$ and $k$ is the
number of distinct $s_i$ that appear.


title:
Probabilistic Permutation Graph Search: Black-Box Optimization for
  Fairness in Ranking

abstract:
  There are several measures for fairness in ranking, based on different
underlying assumptions and perspectives. PL optimization with the REINFORCE
algorithm can be used for optimizing black-box objective functions over
permutations. In particular, it can be used for optimizing fairness measures.
However, though effective for queries with a moderate number of repeating
sessions, PL optimization has room for improvement for queries with a small
number of repeating sessions.
  In this paper, we present a novel way of representing permutation
distributions, based on the notion of permutation graphs. Similar to PL, our
distribution representation, called PPG, can be used for black-box optimization
of fairness. Different from PL, where pointwise logits are used as the
distribution parameters, in PPG pairwise inversion probabilities together with
a reference permutation construct the distribution. As such, the reference
permutation can be set to the best sampled permutation regarding the objective
function, making PPG suitable for both deterministic and stochastic rankings.
Our experiments show that PPG, while comparable to PL for larger session
repetitions (i.e., stochastic ranking), improves over PL for optimizing
fairness metrics for queries with one session (i.e., deterministic ranking).
Additionally, when accurate utility estimations are available, e.g., in tabular
models, the performance of PPG in fairness optimization is significantly
boosted compared to lower quality utility estimations from a learning to rank
model, leading to a large performance gap with PL. Finally, the pairwise
probabilities make it possible to impose pairwise constraints such as "item
$d_1$ should always be ranked higher than item $d_2$." Such constraints can be
used to simultaneously optimize the fairness metric and control another
objective such as ranking performance.


title:
Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA
  Communications

abstract:
  A multi-cell cluster-free NOMA framework is proposed, where both intra-cell
and inter-cell interference are jointly mitigated via flexible cluster-free
successive interference cancellation (SIC) and coordinated beamforming design,
respectively. The joint design problem is formulated to maximize the system sum
rate while satisfying the SIC decoding requirements and users' data rate
constraints. To address this highly complex and coupling non-convex mixed
integer nonlinear programming (MINLP), a novel distributed auto-learning graph
neural network (AutoGNN) architecture is proposed to alleviate the overwhelming
information exchange burdens among base stations (BSs). The proposed AutoGNN
can train the GNN model weights whilst automatically learning the optimal GNN
architecture, namely the GNN network depth and message embedding sizes, to
achieve communication-efficient distributed scheduling. Based on the proposed
architecture, a bi-level AutoGNN learning algorithm is further developed to
efficiently approximate the hypergradient in model training. It is
theoretically proved that the proposed bi-level AutoGNN learning algorithm can
converge to a stationary point. Numerical results reveal that: 1) the proposed
cluster-free NOMA framework outperforms the conventional cluster-based NOMA
framework in the multi-cell scenario; and 2) the proposed AutoGNN architecture
significantly reduces the computation and communication overheads compared to
the conventional convex optimization-based methods and the conventional GNN
with a fixed architecture.


title:
Triformer: Triangular, Variable-Specific Attentions for Long Sequence
  Multivariate Time Series Forecasting--Full Version

abstract:
  A variety of real-world applications rely on far future information to make
decisions, thus calling for efficient and accurate long sequence multivariate
time series forecasting. While recent attention-based forecasting models show
strong abilities in capturing long-term dependencies, they still suffer from
two key limitations. First, canonical self attention has a quadratic complexity
w.r.t. the input time series length, thus falling short in efficiency. Second,
different variables' time series often have distinct temporal dynamics, which
existing studies fail to capture, as they use the same model parameter space,
e.g., projection matrices, for all variables' time series, thus falling short
in accuracy. To ensure high efficiency and accuracy, we propose Triformer, a
triangular, variable-specific attention. (i) Linear complexity: we introduce a
novel patch attention with linear complexity. When stacking multiple layers of
the patch attentions, a triangular structure is proposed such that the layer
sizes shrink exponentially, thus maintaining linear complexity. (ii)
Variable-specific parameters: we propose a light-weight method to enable
distinct sets of model parameters for different variables' time series to
enhance accuracy without compromising efficiency and memory usage. Strong
empirical evidence on four datasets from multiple domains justifies our design
choices, and it demonstrates that Triformer outperforms state-of-the-art
methods w.r.t. both accuracy and efficiency. This is an extended version of
"Triformer: Triangular, Variable-Specific Attentions for Long Sequence
Multivariate Time Series Forecasting", to appear in IJCAI 2022 [Cirstea et al.,
2022a], including additional experimental results.


title:
Ultrastrong Coupling of Band-Nested Excitons in Few-Layer Molybdenum
  Disulphide

abstract:
  The two-dimensional transition-metal dichalcogenides (2D TMDCs) are an
intriguing platform for studying light-matter interactions because they combine
the electronic properties of conventional semiconductors with the optical
resonances found in organic systems. However, the coupling strengths
demonstrated in strong exciton-polariton coupling remain much lower than those
found in organic systems. In this paper, we take on a new approach by utilizing
the large oscillator strength of the above-band gap C exciton in few-layer
molybdenum disulphide ($\text{FL-MoS}_2$). We show a k-space Rabi splitting of
293 meV when coupling $\text{FL-MoS}_2$ C excitons to surface plasmon
polaritons at room temperature. This value is 11% of the uncoupled exciton
energy (2.67 eV or 464 nm), ~2x what is typically seen in the TMDCs, placing
the system in the ultrastrong coupling regime. Our results take a step towards
finally achieving the efficient quantum coherent processes of ultrastrong
coupling in a CMOS-compatible system -- the 2D TMDCs -- in the visible
spectrum.


title:
Nonvanishing quadrature derivatives in the analytical gradients of
  density functional energies in crystals and helices

abstract:
  It is shown that the quadrature derivatives in some analytical gradients of
energies evaluated with a multi-center radial-angular grid do not vanish even
in the limit of an infinitely dense grid, causing severe errors when neglected.
The gradients in question are those with respect to a lattice constant of a
crystal or to the helical angle of a chain with screw axis symmetry. This is in
contrast with the quadrature derivatives in atomic gradients, which can be made
arbitrarily small by grid extension. The disparate behavior is traced to
whether the grid points depend on the coordinate with respect to which the
derivative of energy is taken.


title: Complex surfaces and null conformal Killing vector fields

abstract:
  We study the relation between the existence of null conformal Killing vector
fields and existence of compatible complex and para-hypercomplex structures on
a pseudo-Riemannian manifold with metric of signature (2,2). We establish first
the topological types of pseudo-Hermitian surfaces admitting a nowhere
vanishing null vector field. Then we show that a pair of orthogonal, pointwise
linearly independent, null, conformal Killing vector fields defines a
para-hyperhermitian structure and use this fact for a classification of the
smooth compact four-manifolds admitting such a pair of vector fields. We also
provide examples of neutral metrics with two orthogonal, pointwise linearly
independent, null Killing vector fields on most of these manifolds.


title:
On operator estimates in homogenization of non-local operators of
  convolution type

abstract:
  The paper studies a bounded symmetric operator ${\mathbf{A}}_\varepsilon$ in
$L_2(\mathbf{R}^d)$ with $$ ({\mathbf{A}}_\varepsilon u) (x) =
\varepsilon^{-d-2} \int_{\mathbf{R}^d} a((x-y)/\varepsilon) \mu(x/\varepsilon,
y/\varepsilon) \left( u(x) - u(y) \right)\,dy; $$ here $\varepsilon$ is a small
positive parameter. It is assumed that $a(x)$ is a non-negative
$L_1(\mathbf{R}^d)$ function such that $a(-x)=a(x)$ and the moments $M_k
=\int_{\mathbf{R}^d} |x|^k a(x)\,dx$, $k=1,2,3$, are finite. It is also assumed
that $\mu(x,y)$ is $\mathbf{Z}^d$-periodic both in $x$ and $y$ function such
that $\mu(x,y) = \mu(y,x)$ and $0< \mu_- \leq \mu(x,y) \leq \mu_+< \infty$. Our
goal is to study the limit behaviour of the resolvent
$({\mathbf{A}}_\varepsilon + I)^{-1}$, as $\varepsilon\to0$. We show that, as
$\varepsilon \to 0$, the operator $({\mathbf{A}}_\varepsilon + I)^{-1}$
converges in the operator norm in $L_2(\mathbf{R}^d)$ to the resolvent
$({\mathbf{A}}^0 + I)^{-1}$ of the effective operator ${\mathbf{A}}^0$ being a
second order elliptic differential operator with constant coefficients of the
form ${\mathbf{A}}^0= - \operatorname{div} g^0 \nabla$. We then obtain sharp in
order estimates of the rate of convergence.