title:
Recycled detection of genuine multiparty entanglement of unlimitedly
  stretched array of parties and arbitrarily long series of sequential
  observers

abstract:
  We consider a scenario where spatially separated observers share a genuinely
multiparty entangled quantum state with each local observer possessing a single
qubit. A particular qubit is acted upon by sequential and independent
observers. We study the recycled detection of genuine multipartite entanglement
of multiqubit states by any one of the sequential observers and the rest of the
spatially separated parties. We show that for it is possible to sequentially
detect genuine multiparty entanglement, arbitrarily many times for an
arbitrarily large number of parties. Modified genuine multiparty entanglement
witness operators for unsharp measurements by sequential observers are deduced,
which are then employed to show that an arbitrary number of observers can
sequentially detect genuine multisite entanglement of
Greenberger-Horne-Zeilinger and cluster states of an arbitrary number of
parties. Extensions to multiparty generalized Greenberger-Horne-Zeilinger
states and a class of mixed states are also shown to be achievable.


title: QED corrections to Abraham and Aharonov-Casher force on Rydberg atoms

abstract:
  We calculate the Abraham force and the Aharonov-Casher force exerted by the
electromagnetic quantum vacuum on alkaline atoms in highly excited, long-living
Rydberg states. Because of their high polarizability and long-life time such
atoms are good candidates to observe these forces.


title: Mode Reduction for Markov Jump Systems

abstract:
  Switched systems are capable of modeling processes with underlying dynamics
that may change abruptly over time. To achieve accurate modeling in practice,
one may need a large number of modes, but this may in turn increase the model
complexity drastically. Existing work on reducing system complexity mainly
considers state space reduction, yet reducing the number of modes is less
studied. In this work, we consider Markov jump linear systems (MJSs), a special
class of switched systems where the active mode switches according to a Markov
chain, and several issues associated with its mode complexity. Specifically,
inspired by clustering techniques from unsupervised learning, we are able to
construct a reduced MJS with fewer modes that approximates well the original
MJS under various metrics. Furthermore, both theoretically and empirically, we
show how one can use the reduced MJS to analyze stability and design
controllers with significant reduction in computational cost while achieving
guaranteed accuracy.


title:
Do Different Deep Metric Learning Losses Lead to Similar Learned
  Features?

abstract:
  Recent studies have shown that many deep metric learning loss functions
perform very similarly under the same experimental conditions. One potential
reason for this unexpected result is that all losses let the network focus on
similar image regions or properties. In this paper, we investigate this by
conducting a two-step analysis to extract and compare the learned visual
features of the same model architecture trained with different loss functions:
First, we compare the learned features on the pixel level by correlating
saliency maps of the same input images. Second, we compare the clustering of
embeddings for several image properties, e.g. object color or illumination. To
provide independent control over these properties, photo-realistic 3D car
renders similar to images in the Cars196 dataset are generated. In our
analysis, we compare 14 pretrained models from a recent study and find that,
even though all models perform similarly, different loss functions can guide
the model to learn different features. We especially find differences between
classification and ranking based losses. Our analysis also shows that some
seemingly irrelevant properties can have significant influence on the resulting
embedding. We encourage researchers from the deep metric learning community to
use our methods to get insights into the features learned by their proposed
methods.


title:
Inference of signaling mechanism from cellular responses to multiple
  cues

abstract:
  Cell signaling networks are complex and often incompletely characterized,
making it difficult to obtain a comprehensive picture of the mechanisms they
encode. Mathematical modeling of these networks provides important clues, but
the models themselves are often complex, and it is not always clear how to
extract falsifiable predictions. Here we take an inverse approach, using
experimental data at the cell level to infer the minimal signaling network. We
focus on cells' response to multiple cues, specifically on the surprising case
in which the response is antagonistic: the response to multiple cues is weaker
than the response to the individual cues. We systematically build candidate
signaling networks one node at a time, using the ubiquitous ingredients of (i)
up- or down-regulation, (ii) molecular conversion, or (iii) reversible binding.
In each case, our method reveals a minimal, interpretable signaling mechanism
that explains the antagonistic response. Our work provides a systematic way to
infer molecular mechanisms from cell-level data.


title:
Measuring the Adiabatic Non-Hermitian Berry Phase in Feedback-Coupled
  Oscillators

abstract:
  The geometrical Berry phase is key to understanding the behaviour of quantum
states under cyclic adiabatic evolution. When generalised to non-Hermitian
systems with gain and loss, the Berry phase can become complex, and should
modify not only the phase but also the amplitude of the state. Here, we perform
the first experimental measurements of the adiabatic non-Hermitian Berry phase,
exploring a minimal two-site $\mathcal{PT}$-symmetric Hamiltonian that is
inspired by the Hatano-Nelson model. We realise this non-Hermitian model
experimentally by mapping its dynamics to that of a pair of classical
oscillators coupled by real-time measurement-based feedback. As we verify
experimentally, the adiabatic non-Hermitian Berry phase is a purely geometrical
effect that leads to significant amplification and damping of the amplitude
also for non-cyclical paths within the parameter space even when all
eigenenergies are real. We further observe a non-Hermitian analog of the
Aharonov--Bohm solenoid effect, observing amplification and attenuation when
encircling a region of broken $\mathcal{PT}$ symmetry that serves as a source
of imaginary flux. This experiment demonstrates the importance of geometrical
effects that are unique to non-Hermitian systems and paves the way towards the
further studies of non-Hermitian and topological physics in synthetic
metamaterials.

categories: cond-mat.mes-hall cond-mat.other physics.class-ph quant-ph


title: Robust population transfer of spin states by geometric formalism

abstract:
  Accurate population transfer of uncoupled or weakly coupled spin states is
crucial for many quantum information processing tasks. In this paper, we
propose a fast and robust scheme for population transfer which combines
invariant-based inverse engineering and geometric formalism for robust quantum
control. Our scheme is not constrained by the adiabatic condition and therefore
can be implemented fast. It can also effectively suppress the dominant noise in
spin systems, which together with the fast feature guarantees the accuracy of
the population transfer. Moreover, the control parameters of the driving
Hamiltonian in our scheme are easy to design because they correspond to the
curvature and torsion of a three-dimensional visual space curve derived by
using geometric formalism for robust quantum control. We test the efficiency of
our scheme by numerically simulating the ground-state population transfer in
$^{15}$N nitrogen vacancy centers and comparing our scheme with stimulated
Raman transition, stimulated Raman adiabatic passage and conventional shortcuts
to adiabaticity based schemes, three types of popularly used schemes for
population transfer. The numerical results clearly show that our scheme is
advantageous over these previous ones.


title:
Spin-wave localization on phasonic defects in one-dimensional magnonic
  quasicrystal

abstract:
  We report on the evolution of the spin-wave spectrum under induced structural
disorder in one-dimensional magnonic quasicrystal. We study theoretically a
system composed of ferromagnetic strips arranged in a Fibonacci sequence for
several stages of disorder in the form of phasonic defects, where different
rearrangements are introduced. By transition from the quasiperiodic order
towards disorder, we show a gradual degradation of the fine fractal spectra of
spin-waves, and the evolution of the band gaps. The analysis shows the mode
localization evolution, which is an inevitable property of the quasicrystals.
In particular, significant enhancement of the spin-wave mode localization and
disappearing of the van Hove singularities at the band gap edges with
increasing number of defects can be indication of the phasonic defects existing
in the structure. This work fills gap in the knowledge of defected magnonic
Fibonacci quasicrystals and opens the way to the study of the phasonic defect
in two-dimensional magnonic quasicrystals.


title: Thermalization of isolated harmonic networks under conservative noise

abstract:
  We study a scalar harmonic network with pair interactions and a binary
collision rule, exchanging the momenta of a randomly-chosen couple of sites. We
consider the case of the isolated network where the total energy is conserved.
In the first part, we recast the dynamics as a stochastic map in normal modes
(or action-angle) coordinates and provide a geometric interpretation of it. We
formulate the problem for generic networks but, for completeness, also
reconsider the translation-invariant lattices. In the second part, we examine
the kinetic limit and its range of validity. A general form of the linear
collision operator in terms of eigenstates of the network is given. This
defines an \textit{action network}, whose connectivity gives information on the
out-of-equilibrium dynamics. We present a few examples (ordered and disordered
chains and elastic networks) where the topology of connections in action spaces
can be determined in a neat way. As an application, we consider the classic
problem of relaxation to equipartition from the point of view of the dynamics
of linear actions. We compare the results based on the spectrum of the
collision operator with numerical simulation, performed with a novel scheme
based on direct solution of the equation of motion in normal modes coordinates.


title:
Utility-Based Context-Aware Multi-Agent Recommendation System for Energy
  Efficiency in Residential Buildings

abstract:
  A significant part of CO2 emissions is due to high electricity consumption in
residential buildings. Using load shifting can help to improve the households'
energy efficiency. To nudge changes in energy consumption behavior, simple but
powerful architectures are vital. This paper presents a novel algorithm of a
recommendation system generating device usage recommendations and suggests a
framework for evaluating its performance by analyzing potential energy cost
savings. As a utility-based recommender system, it models user preferences
depending on habitual device usage patterns, user availability, and device
usage costs. As a context-aware system, it requires an external hourly
electricity price signal and appliance-level energy consumption data. Due to a
multi-agent architecture, it provides flexibility and allows for adjustments
and further enhancements. Empirical results show that the system can provide
energy cost savings of 18% and more for most studied households.


title:
Blow-up solutions of damped Klein-Gordon equation on the Heisenberg
  group

abstract:
  Inthisnote,weprovetheblow-upofsolutionsofthesemilineardamped Klein-Gordon
equation in a finite time for arbitrary positive initial energy on the
Heisenberg group. This work complements the paper [21] by the first author and
Tokmagambetov, where the global in time well-posedness was proved for the small
energy solutions.


title:
Sound Event Classification in an Industrial Environment: Pipe Leakage
  Detection Use Case

abstract:
  In this work, a multi-stage Machine Learning (ML) pipeline is proposed for
pipe leakage detection in an industrial environment. As opposed to other
industrial and urban environments, the environment under study includes many
interfering background noises, complicating the identification of leaks.
Furthermore, the harsh environmental conditions limit the amount of data
collected and impose the use of low-complexity algorithms. To address the
environment's constraints, the developed ML pipeline applies multiple steps,
each addressing the environment's challenges. The proposed ML pipeline first
reduces the data dimensionality by feature selection techniques and then
incorporates time correlations by extracting time-based features. The resultant
features are fed to a Support Vector Machine (SVM) of low-complexity that
generalizes well to a small amount of data. An extensive experimental procedure
was carried out on two datasets, one with background industrial noise and one
without, to evaluate the validity of the proposed pipeline. The SVM
hyper-parameters and parameters specific to the pipeline steps were tuned as
part of the experimental procedure. The best models obtained from the dataset
with industrial noise and leaks were applied to datasets without noise and with
and without leaks to test their generalizability. The results show that the
model produces excellent results with 99\% accuracy and an F1-score of 0.93 and
0.9 for the respective datasets.


title:
A DG method for a stress formulation of the elasticity eigenproblem with
  strongly imposed symmetry

abstract:
  We introduce a pure--stress formulation of the elasticity eigenvalue problem
with mixed boundary conditions. We propose an H(div)-based discontinuous
Galerkin method that imposes strongly the symmetry of the stress for the
discretization of the eigenproblem. Under appropriate assumptions on the mesh
and the degree of polynomial approximation, we demonstrate the spectral
correctness of the discrete scheme and derive optimal rates of convergence for
eigenvalues and eigenfunctions. Finally, we provide numerical examples in two
and three dimensions.


title:
Meta-learning Feature Representations for Adaptive Gaussian Processes
  via Implicit Differentiation

abstract:
  We propose Adaptive Deep Kernel Fitting (ADKF), a general framework for
learning deep kernels by interpolating between meta-learning and conventional
learning. Our approach employs a bilevel optimization objective where we
meta-learn feature representations that are generally useful across tasks, in
the sense that task-specific Gaussian process models estimated on top of such
features achieve the lowest possible predictive loss on average across tasks.
We solve the resulting nested optimization problem using the implicit function
theorem. We show that ADKF contains Deep Kernel Learning and Deep Kernel
Transfer as special cases. Although ADKF is a completely general method, we
argue that it is especially well-suited for drug discovery problems and
demonstrate that it significantly outperforms previous state-of-the-art methods
on a variety of real-world few-shot molecular property prediction tasks and
out-of-domain molecular optimization tasks.


title: $\mathbb{Z}_2$ metallic spin liquid on a frustrated Kondo lattice

abstract:
  Metallic spin liquid has been reported in several correlated metals, but a
satisfactory theoretical description is not yet available. Here we propose a
potential route to realize the metallic spin liquid and construct an effective
$\mathbb{Z}_2$ gauge theory with charged fractionalized excitations on the
triangular Kondo lattice. This leads to a $\mathbb{Z}_2$ metallic spin liquid
featured with long-lived, heavy holon excitations of spin 0 and charge $+e$ and
a partially enlarged electron Fermi surface. It differs from the weak-coupling
FL$^*$ state proposed earlier and may be viewed as a fractionalized heavy
fermion liquid. Our theory provides a general framework to describe the
metallic spin liquid in frustrated Kondo lattice systems.


title:
Evaporating Rayleigh-B\'enard convection: prediction of interface
  temperature and global heat transfer modulation

abstract:
  We propose an analytical model to estimate the interface temperature
$\Theta_{\Gamma}$ and the Nusselt number $Nu$ for an evaporating two-layer
Rayleigh-B\'enard configuration in statistically stationary conditions. The
model is based on three assumptions: (i) the Grossmann-Lohse theory for thermal
convection can be applied on the liquid and gas layers separately, (ii) the
vapour content in the gas can be taken as the mean value at the gas-liquid
interface and (iii) the bulk gas temperature can be determined neglecting the
contributions of the thermal boundary layers. The resulting model can
accommodate non-Oberbeck-Boussinesq effects in the liquid and the gas phases,
as well as the variation of the liquid height due to evaporation. To obtain a
simplified scaling between $Nu$ and the Rayleigh number $Ra$, we specify the
model for the case of an Oberbeck-Boussinesq liquid and a gas phase with
uniform properties except for the gas density and the vapour diffusion
coefficient, which are functions of thermodynamic pressure, local temperature
and vapour composition. We validate this simplified setting using direct
numerical simulations for $Ra=10^6, 10^7$ and $10^8$ and for four values of the
temperature differential $\varepsilon=0.05,0.10,0.15$ and $0.20$, which
modulates the change of state variables in the gas layer. The proposed model
agrees very well with the numerical simulations in the entire range of
$Ra-\varepsilon$ investigated.


title:
Hybrid CNN Based Attention with Category Prior for User Image Behavior
  Modeling

abstract:
  User historical behaviors are proved useful for Click Through Rate (CTR)
prediction in online advertising system. In Meituan, one of the largest
e-commerce platform in China, an item is typically displayed with its image and
whether a user clicks the item or not is usually influenced by its image, which
implies that user's image behaviors are helpful for understanding user's visual
preference and improving the accuracy of CTR prediction. Existing user image
behavior models typically use a two-stage architecture, which extracts visual
embeddings of images through off-the-shelf Convolutional Neural Networks (CNNs)
in the first stage, and then jointly trains a CTR model with those visual
embeddings and non-visual features. We find that the two-stage architecture is
sub-optimal for CTR prediction. Meanwhile, precisely labeled categories in
online ad systems contain abundant visual prior information, which can enhance
the modeling of user image behaviors. However, off-the-shelf CNNs without
category prior may extract category unrelated features, limiting CNN's
expression ability. To address the two issues, we propose a hybrid CNN based
attention module, unifying user's image behaviors and category prior, for CTR
prediction. Our approach achieves significant improvements in both online and
offline experiments on a billion scale real serving dataset.


title:
Thermodynamics of the RN-AdS black hole with cloud of strings and
  quintessence in stationary and free-fall frame in rainbow gravity

abstract:
  In this paper, we investigate the thermodynamic properties of the RN-AdS
black hole with cloud of strings and quintessence in rainbow gravity with the
stationary (ST) orthonormal frame and the free-fall (FF) orthonormal frame.
After the SF and the FF rainbow metric is obtained, we get the Hawking
temperature and the entropy, and their physical meanings are discussed. We find
that, for the ST rainbow RN-AdS black hole with cloud of strings and
quintessence, the effect of rainbow gravity is to increase the Hawking
temperature but decrease the entropy of the black hole. However, for the FF
rainbow case, rainbow gravity turns out to decrease the Hawking temperature but
increase the entropy of the black hole, which seems that the effects rainbow
gravity has are quite model-dependent.


title: Coding IxD: Enabling Interdisciplinary Education by Sparking Reflection

abstract:
  Educating students from diverse disciplinary backgrounds is challenging. In
this article, we report on our interdisciplinary course coding interaction and
design (Coding IxD), which is designed for computer science and design students
alike. This course has been developed over several years by consciously
deliberating on existing hurdles within the educational concept. First, we
motivate the need for Coding IxD and introduce the teaching principles that
helped shape the course's general structure. Our teaching principles
materialize in four method-based phases derived from research through design.
Each phase consists of several methods that emerged to be suitable in an
interdisciplinary context. Then, based on two selected student projects, we
exemplify how interdisciplinary teams can arrive at novel interactive
prototypes. We conclude by reflecting on our teaching practice as essential for
a meaningful learning experience.


title:
Neural Rendering in a Room: Amodal 3D Understanding and Free-Viewpoint
  Rendering for the Closed Scene Composed of Pre-Captured Objects

abstract:
  We, as human beings, can understand and picture a familiar scene from
arbitrary viewpoints given a single image, whereas this is still a grand
challenge for computers. We hereby present a novel solution to mimic such human
perception capability based on a new paradigm of amodal 3D scene understanding
with neural rendering for a closed scene. Specifically, we first learn the
prior knowledge of the objects in a closed scene via an offline stage, which
facilitates an online stage to understand the room with unseen furniture
arrangement. During the online stage, given a panoramic image of the scene in
different layouts, we utilize a holistic neural-rendering-based optimization
framework to efficiently estimate the correct 3D scene layout and deliver
realistic free-viewpoint rendering. In order to handle the domain gap between
the offline and online stage, our method exploits compositional neural
rendering techniques for data augmentation in the offline training. The
experiments on both synthetic and real datasets demonstrate that our two-stage
design achieves robust 3D scene understanding and outperforms competing methods
by a large margin, and we also show that our realistic free-viewpoint rendering
enables various applications, including scene touring and editing. Code and
data are available on the project webpage:
https://zju3dv.github.io/nr_in_a_room/.


title:
High-refractive index and mechanically cleavable non-van der Waals
  InGaS3

abstract:
  The growing families of two-dimensional crystals derived from naturally
occurring van der Waals materials offer an unprecedented platform to
investigate elusive physical phenomena and could be of use in a diverse range
of devices. Of particular interest are recently reported atomic sheets of
non-van der Waals materials, which could allow a better comprehension of the
nature of structural bonds and increase the functionality of prospective
heterostructures. Here, we study the optostructural properties of ultrathin
non-van der Waals InGaS3 sheets produced by standard mechanical cleavage. Our
ab initio calculation results suggest an emergence of authentically delicate
out-of-plane covalent bonds within its unit cell, and, as a consequence, an
artificial generation of layered structure within the material. Those yield to
singular layer isolation energies of around 50 meVA-2, which is comparable with
the conventional van der Waals material's monolayer isolation energies of 20 -
60 meVA-2. In addition, we provide a comprehensive analysis of the structural,
vibrational, and optical properties of the materials presenting that it is a
wide bandgap (2.73 eV) semiconductor with a high-refractive index (higher than
2.5) and negligible losses in the visible and infrared spectral ranges. It
makes it a perfect candidate for further establishment of visible-range
all-dielectric nanophotonics.


title:
TV and Video Game Streaming with a Quantum Receiver: A Study on a
  Rydberg atom-based receivers bandwidth and reception clarity

abstract:
  We demonstrate the ability to receive live color analog television and video
game signals with the use of the Rydberg atom receiver. The typical signal
expected for traditional 480i NSTC format video signals requires a bandwidth of
over 3 MHz. We determine the beam sizes, powers, and detection method required
for the Rydberg atoms to receive this type of signal. The beam size affects the
average time the atoms remain in the interaction volume, which is inversely
proportional to the bandwidth of the receiver. We find that small beam
diameters (less than 100 {\mu}m) lead to much faster responses and allow for
color reception. We demonstrate the effect of beam size on bandwidth by
receiving a live 480i video stream with the Rydberg atom receiver. The best
fidelity was achieved with a beam width of 85 {\mu}m full-width at half-max.


title: BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection

abstract:
  Temporal action detection (TAD) is extensively studied in the video
understanding community by following the object detection pipelines in images.
However, complex designs are not uncommon in TAD, such as two-stream feature
extraction, multi-stage training, complex temporal modeling, and global context
fusion. In this paper, we do not aim to introduce any novel technique for TAD.
Instead, we study a simple, straightforward, yet must-known baseline given the
current status of complex design and low efficiency in TAD. In our simple
baseline (BasicTAD), we decompose the TAD pipeline into several essential
components: data sampling, backbone design, neck construction, and detection
head. We empirically investigate the existing techniques in each component for
this baseline and, more importantly, perform end-to-end training over the
entire pipeline thanks to the simplicity in design. Our BasicTAD yields an
astounding RGB-Only baseline very close to the state-of-the-art methods with
two-stream inputs. In addition, we further improve the BasicTAD by preserving
more temporal and spatial information in network representation (termed as
BasicTAD Plus). Empirical results demonstrate that our BasicTAD Plus is very
efficient and significantly outperforms the previous methods on the datasets of
THUMOS14 and FineAction. Our approach can serve as a strong baseline for TAD.
The code will be released at https://github.com/MCG-NJU/BasicTAD.


title: Optimal subsampling for functional quantile regression

abstract:
  Subsampling is an efficient method to deal with massive data. In this paper,
we investigate the optimal subsampling for linear quantile regression when the
covariates are functions. The asymptotic distribution of the subsampling
estimator is first derived. Then, we obtain the optimal subsampling
probabilities based on the A-optimality criterion. Furthermore, the modified
subsampling probabilities without estimating the densities of the response
variables given the covariates are also proposed, which are easier to implement
in practise. Numerical experiments on synthetic and real data show that the
proposed methods always outperform the one with uniform sampling and can
approximate the results based on full data well with less computational
efforts.


title: Communication-Efficient Adaptive Federated Learning

abstract:
  Federated learning is a machine learning training paradigm that enables
clients to jointly train models without sharing their own localized data.
However, the implementation of federated learning in practice still faces
numerous challenges, such as the large communication overhead due to the
repetitive server-client synchronization and the lack of adaptivity by
SGD-based model updates. Despite that various methods have been proposed for
reducing the communication cost by gradient compression or quantization, and
the federated versions of adaptive optimizers such as FedAdam are proposed to
add more adaptivity, the current federated learning framework still cannot
solve the aforementioned challenges all at once. In this paper, we propose a
novel communication-efficient adaptive federated learning method (FedCAMS) with
theoretical convergence guarantees. We show that in the nonconvex stochastic
optimization setting, our proposed FedCAMS achieves the same convergence rate
of $O(\frac{1}{\sqrt{TKm}})$ as its non-compressed counterparts. Extensive
experiments on various benchmarks verify our theoretical analysis.


title:
Integrable systems on hexagonal lattices and consistency on polytopes
  with quadrilateral and hexagonal faces

abstract:
  The new concept of a system of hex equations is introduced as an
overdetermined system of six five-point face-centered quad equations defined on
six vertices of a hexagon. For a consistent system of hex equations, two
variables on neighbouring vertices of the hexagon can be solved for uniquely in
terms of the other four. A consistent system of hex equations has a
well-defined unique evolution in the hexagonal lattice under suitable initial
value problems defined on a single connected staircase of points.
Multidimensional consistency for systems of hex equations is proposed in terms
of their consistency on certain polytopes which have both hexagonal and
quadrilateral faces, and specific examples are presented for the hexagonal
prism, the elongated dodecahedron, the truncated octahedron, and the
6-6-duoprism. Consistent systems of hex equations on such polytopes may be
constructed from face-centered quad equations which satisfy
consistency-around-a-face-centered-cube, in combination with regular quad
equations that satisfy consistency-around-a-cube.


title:
Waserstein model reduction approach for parametrized flow problems in
  porous media

abstract:
  The aim of this work is to build a reduced-order model for parametrized
porous media equations. The main challenge of this type of problems is that the
Kolmogorov width of the solution manifold typically decays quite slowly and
thus makes usual linear model-order reduction methods inappropriate. In this
work, we investigate an adaptation of the methodology proposed in a previous
work, based on the use of Wasserstein barycenters, to the case of
non-conservative problems. Numerical examples in one-dimensional test cases
illustrate the advantages and limitations of this approach and suggest further
research directions that we intend to explore in the future.


title: Connecting Scalar Amplitudes using The Positive Tropical Grassmannian

abstract:
  The biadjoint scalar partial amplitude, $m_n(\mathbb{I},\mathbb{I})$, can be
expressed as a single integral over the positive tropical Grassmannian thus
producing a Global Schwinger Parameterization. The first result in this work is
an extension to all partial amplitudes $m_n(\alpha,\beta)$ using a limiting
procedure on kinematic invariants that produces indicator functions in the
integrand. The same limiting procedure leads to an integral representation of
$\phi^4$ amplitudes where indicator functions turn into Dirac delta functions.
Their support decomposes into $\textrm{C}_{n/2-1}$ regions, with $\textrm{C}_q$
the $q^{\rm th}$-Catalan number. The contribution from each region is
identified with a $m_{n/2+1}(\alpha,\mathbb{I})$ amplitude. We provide a
combinatorial description of the regions in terms of non-crossing chord
diagrams and propose a general formula for $\phi^4$ amplitudes using the
Lagrange inversion construction. We start the exploration of $\phi^p$ theories,
finding that their regions are encoded in non-crossing $(p-2)$-chord diagrams.
The structure of the expansion of $\phi^p$ amplitudes in terms of $\phi^3$
amplitudes is the same as that of Green functions in terms of connected Green
functions in the planar limit of $\Phi^{p-1}$ matrix models. We also discuss
possible connections to recent constructions based on Stokes polytopes and
accordiohedra.


title:
A rank-two relaxed parallel splitting version of the augmented
  Lagrangian method with step size in (0,2) for separable convex programming

abstract:
  The augmented Lagrangian method (ALM) is classic for canonical convex
programming problems with linear constraints, and it finds many applications in
various scientific computing areas. A major advantage of the ALM is that the
step for updating the dual variable can be further relaxed with a step size in
$(0,2)$, and this advantage can easily lead to numerical acceleration for the
ALM. When a separable convex programming problem is discussed and a
corresponding splitting version of the classic ALM is considered, convergence
may not be guaranteed and thus it is seemingly impossible that a step size in
$(0,2)$ can be carried on to the relaxation step for updating the dual
variable. We show that for a parallel splitting version of the ALM, a step size
in $(0,2)$ can be maintained for further relaxing both the primal and dual
variables if the relaxation step is simply corrected by a rank-two matrix.
Hence, a rank-two relaxed parallel splitting version of the ALM with a step
size in $(0,2)$ is proposed for separable convex programming problems. We
validate that the new algorithm can numerically outperform existing algorithms
of the same kind significantly by testing some applications.


title: Implicit N-grams Induced by Recurrence

abstract:
  Although self-attention based models such as Transformers have achieved
remarkable successes on natural language processing (NLP) tasks, recent studies
reveal that they have limitations on modeling sequential transformations (Hahn,
2020), which may prompt re-examinations of recurrent neural networks (RNNs)
that demonstrated impressive results on handling sequential data. Despite many
prior attempts to interpret RNNs, their internal mechanisms have not been fully
understood, and the question on how exactly they capture sequential features
remains largely unclear. In this work, we present a study that shows there
actually exist some explainable components that reside within the hidden
states, which are reminiscent of the classical n-grams features. We evaluated
such extracted explainable features from trained RNNs on downstream sentiment
analysis tasks and found they could be used to model interesting linguistic
phenomena such as negation and intensification. Furthermore, we examined the
efficacy of using such n-gram components alone as encoders on tasks such as
sentiment analysis and language modeling, revealing they could be playing
important roles in contributing to the overall performance of RNNs. We hope our
findings could add interpretability to RNN architectures, and also provide
inspirations for proposing new architectures for sequential data.


title:
Signal of Cosmic Strings in Cross-Correlation of 21-cm Redshift and CMB
  Polarization Maps

abstract:
  We study the signal of cosmic string wakes present before the time of
reionization in the cross-correlation signal of 21-cm redshift and B-mode CMB
polarization maps. The specific non-Gaussian signal of strings in the position
space cross-correlation maps can be extracted by means of a matched filtering
analysis. Signals of strings with tension somewhat lower than those
corresponding to the current upper bound can be identified when embedded in a
background of Gaussian fluctuations from a Planck best-fit LCDM model.


title: Asymptotic Efficiency Bounds for a Class of Experimental Designs

abstract:
  We consider an experimental design setting in which units are assigned to
treatment after being sampled sequentially from an infinite population. We
derive asymptotic efficiency bounds that apply to data from any experiment that
assigns treatment as a (possibly randomized) function of covariates and past
outcome data, including stratification on covariates and adaptive designs. For
estimating the average treatment effect of a binary treatment, our results show
that no further first order asymptotic efficiency improvement is possible
relative to an estimator that achieves the Hahn (1998) bound in an experimental
design where the propensity score is chosen to minimize this bound. Our results
also apply to settings with multiple treatments with possible constraints on
treatment, as well as covariate based sampling of a single outcome.


title: Replicating Data Pipelines with GrimoireLab

abstract:
  In this paper, we present our MSR Hackathon 2022 project that replicates an
existing Gitter study using GrimoireLab. We compare the previous study's
pipeline with our GrimoireLab implementation in terms of speed, data
consistency, organization, and the learning curve to get started. We believe
our experience with GrimoireLab can help future researchers in making the right
choice while implementing their data pipelines over Gitter and Github data.


title:
CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic
  Terms

abstract:
  Euphemisms have not received much attention in natural language processing,
despite being an important element of polite and figurative language.
Euphemisms prove to be a difficult topic, not only because they are subject to
language change, but also because humans may not agree on what is a euphemism
and what is not. Nevertheless, the first step to tackling the issue is to
collect and analyze examples of euphemisms. We present a corpus of potentially
euphemistic terms (PETs) along with example texts from the GloWbE corpus.
Additionally, we present a subcorpus of texts where these PETs are not being
used euphemistically, which may be useful for future applications. We also
discuss the results of multiple analyses run on the corpus. Firstly, we find
that sentiment analysis on the euphemistic texts supports that PETs generally
decrease negative and offensive sentiment. Secondly, we observe cases of
disagreement in an annotation task, where humans are asked to label PETs as
euphemistic or not in a subset of our corpus text examples. We attribute the
disagreement to a variety of potential reasons, including if the PET was a
commonly accepted term (CAT).


title:
Direct Imaging Discovery and Dynamical Mass of a Substellar Companion
  Orbiting an Accelerating Hyades Sun-like Star with SCExAO/CHARIS

abstract:
  We present the direct-imaging discovery of a substellar companion in orbit
around a Sun-like star member of the Hyades open cluster. So far, no other
substellar companions have been unambiguously confirmed via direct imaging
around main-sequence stars in Hyades. The star HIP 21152 is an accelerating
star as identified by the astrometry from the Gaia and Hipparcos satellites. We
have detected the companion, HIP 21152 B, in multi-epoch using the
high-contrast imaging from SCExAO/CHARIS and Keck/NIRC2. We have also obtained
the stellar radial-velocity data from the Okayama 188cm telescope. The CHARIS
spectroscopy reveals that HIP 21152 B's spectrum is consistent with the L/T
transition, best fit by an early T dwarf. Our orbit modeling determines the
semi-major axis and the dynamical mass of HIP 21152 B to be
17.0$^{+7.1}_{-3.5}$ au and 27.5$^{+8.7}_{-5.3}$ $M_{\rm{Jup}}$, respectively.
The mass ratio of HIP 21152 B relative to its host is $\approx$2\%, near the
planet/brown dwarf boundary suggested from recent surveys. Mass estimates
inferred from luminosity evolution models are slightly higher (33--42
$M_{\rm{Jup}}$). With a dynamical mass and a well-constrained age due to the
system's Hyades membership, HIP 21152 B will become a critical benchmark in
understanding the formation, evolution, and atmosphere of a substellar object
as a function of mass and age. Our discovery is yet another key
proof-of-concept for using precision astrometry to select direct imaging
targets.


title:
State Estimation Methods for Continuous-Discrete Nonlinear Systems
  involving Stochastic Differential Equations

abstract:
  In this work, we present methods for state estimation in continuous-discrete
nonlinear systems involving stochastic differential equations. We present the
extended Kalman filter, the unscented Kalman filter, the ensemble Kalman
filter, and a particle filter. We implement the state estimation methods in
Matlab. We evaluate the performance of the methods on a simulation of the
modified four-tank system. We implement the state estimation methods for
non-stiff systems, i.e., using an explicit numerical integration scheme. The
implementation of the extended Kalman filter utilises the Joseph stabilising
form for numerical stability. We evaluate the accuracy of the state estimation
methods in terms of the mean absolute percentage error over the simulation
horizon. We show that each method successfully estimates the states and
unmeasured disturbances of the simulated modified four-tank system. Finally, we
present conclusions.


title:
Characterizing player's playing styles based on Player Vectors for each
  playing position in the Chinese Football Super League

abstract:
  Characterizing playing style is important for football clubs on scouting,
monitoring and match preparation. Previous studies considered a player's style
as a combination of technical performances, failing to consider the spatial
information. Therefore, this study aimed to characterize the playing styles of
each playing position in the Chinese Football Super League (CSL) matches,
integrating a recently adopted Player Vectors framework. Data of 960 matches
from 2016-2019 CSL were used. Match ratings, and ten types of match events with
the corresponding coordinates for all the lineup players whose on-pitch time
exceeded 45 minutes were extracted. Players were first clustered into 8
positions. A player vector was constructed for each player in each match based
on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF
process was run on the player vectors to extract different types of playing
styles. The resulting player vectors discovered 18 different playing styles in
the CSL. Six performance indicators of each style were investigated to observe
their contributions. In general, the playing styles of forwards and midfielders
are in line with football performance evolution trends, while the styles of
defenders should be reconsidered. Multifunctional playing styles were also
found in high rated CSL players.


title: Optimal Information Provision for Strategic Hybrid Workers

abstract:
  We study the problem of information provision by a strategic central planner
who can publicly signal about an uncertain infectious risk parameter.
Signalling leads to an updated public belief over the parameter, and agents
then make equilibrium choices on whether to work remotely or in-person. The
planner maintains a set of desirable outcomes for each realization of the
uncertain parameter and seeks to maximize the probability that agents choose an
acceptable outcome for the true parameter. We distinguish between stateless and
stateful objectives. In the former, the set of desirable outcomes does not
change as a function of the risk parameter, whereas in the latter it does. For
stateless objectives, we reduce the problem to maximizing the probability of
inducing mean beliefs that lie in intervals computable from the set of
desirable outcomes. We derive the optimal signalling mechanism and show that it
partitions the parameter domain into at most two intervals with the signals
generated according to an interval-specific distribution. For the stateful
case, we consider a practically relevant situation in which the planner can
enforce in-person work capacity limits that progressively get more stringent as
the risk parameter increases. We show that the optimal signalling mechanism for
this case can be obtained by solving a linear program. We numerically verify
the improvement in achieving desirable outcomes using our information design
relative to no information and full information benchmarks.


title: Sparse Large-Scale Fading Decoding in Cell-Free Massive MIMO Systems

abstract:
  Cell-free massive multiple-input multiple-output (CF mMIMO) systems are
characterized by having many more access points (APs) than user equipments
(UEs). A key challenge is to determine which APs should serve which UEs.
Previous work has tackled this combinatorial problem heuristically. This paper
proposes a sparse large-scale fading decoding (LSFD) design for CF mMIMO to
jointly optimize the association and LSFD. We formulate a group sparsity
problem and then solve it using a proximal algorithm with block-coordinate
descent. Numerical results show that sparse LSFD achieves almost the same
spectral efficiency as optimal LSFD, thus achieving a higher energy efficiency
since the processing and signaling are reduced.


title: Percolation critical probabilities of matching lattice-pairs

abstract:
  A necessary and sufficient condition is established for the strict inequality
$p_c(G_*)<p_c(G)$ between the critical probabilities of site percolation on a
quasi-transitive, plane graph $G$ and on its matching graph $G_*$. It is
assumed that $G$ is properly embedded in either the Euclidean or the hyperbolic
plane. When $G$ is transitive, strict inequality holds if and only if $G$ is
not a triangulation. The basic approach is the standard method of enhancements,
but its implemention has complexity arising from the non-Euclidean (hyperbolic)
space, the study of site (rather than bond) percolation, and the generality of
the assumption of quasi-transitivity. This result is complementary to the work
of the authors ("Hyperbolic site percolation", arXiv:2203.00981) on the
equality $p_u(G) + p_c(G_*) = 1$, where $p_u$ is the critical probability for
the existence of a unique infinite open cluster. More specifically, it implies
for transitive $G$ that $p_u(G) + p_c(G) \ge 1$, with equality if and only if
$G$ is a triangulation.


title:
The VVV Open Cluster Project II. Near-infrared sequences of 37 open
  clusters on eight-dimensional parameter space

abstract:
  Open clusters are key coeval structures that help us understand star
formation, stellar evolution and trace the physical properties of our Galaxy.
In the past years, the isolation of open clusters from the field has been
heavily alleviated by the access to accurate large-scale stellar parallaxes and
proper motions along a determined line of sight. Still, there are limitations
regarding their completeness since large-scale studies rely on optical
wavelengths. Here we extend the open clusters sequences towards fainter
magnitudes complementing the Gaia photometric and astrometric information with
near-infrared data from the VVV survey. We performed a homogeneous analysis on
37 open clusters implementing two coarse-to-fine characterization methods:
extreme deconvolution Gaussian mixture models coupled with an unsupervised
machine learning method on 8-dimensional parameter space. The process allowed
us to separate the clusters from the field at near-infrared wavelengths. We
report an increase of $\sim$47\% new member candidates on average in our sample
(considering only sources with high membership probability p$\geqq$0.9). This
study is the second in a series intended to reveal open cluster near-infrared
sequences homogeneously.


title:
A Structured Analysis of Journalistic Evaluations for News Source
  Reliability

abstract:
  In today's era of information disorder, many organizations are moving to
verify the veracity of news published on the web and social media. In
particular, some agencies are exploring the world of online media and, through
a largely manual process, ranking the credibility and transparency of news
sources around the world. In this paper, we evaluate two procedures for
assessing the risk of online media exposing their readers to m/disinformation.
The procedures have been dictated by NewsGuard and The Global Disinformation
Index, two well-known organizations combating d/misinformation via practices of
good journalism. Specifically, considering a fixed set of media outlets, we
examine how many of them were rated equally by the two procedures, and which
aspects led to disagreement in the assessment. The result of our analysis shows
a good degree of agreement, which in our opinion has a double value: it
fortifies the correctness of the procedures and lays the groundwork for their
automation.


title:
Koopman pose predictions for temporally consistent human walking
  estimations

abstract:
  We tackle the problem of tracking the human lower body as an initial step
toward an automatic motion assessment system for clinical mobility evaluation,
using a multimodal system that combines Inertial Measurement Unit (IMU) data,
RGB images, and point cloud depth measurements. This system applies the factor
graph representation to an optimization problem that provides 3-D skeleton
joint estimations. In this paper, we focus on improving the temporal
consistency of the estimated human trajectories to greatly extend the range of
operability of the depth sensor. More specifically, we introduce a new factor
graph factor based on Koopman theory that embeds the nonlinear dynamics of
several lower-limb movement activities. This factor performs a two-step
process: first, a custom activity recognition module based on spatial temporal
graph convolutional networks recognizes the walking activity; then, a Koopman
pose prediction of the subsequent skeleton is used as an a priori estimation to
drive the optimization problem toward more consistent results. We tested the
performance of this module on datasets composed of multiple clinical lowerlimb
mobility tests, and we show that our approach reduces outliers on the skeleton
form by almost 1 m, while preserving natural walking trajectories at depths up
to more than 10 m.


title:
Dynamical Gibbs Variational Principles for Irreversible Interacting
  Particle Systems with Applications to Attractor Properties

abstract:
  We consider irreversible translation-invariant interacting particle systems
on the $d$-dimensional cubic lattice with finite local state space, which admit
at least one Gibbs measure as a time-stationary measure. Under some mild
degeneracy conditions on the rates and the specification we prove, that zero
relative entropy loss of a translation-invariant measure implies, that the
measure is Gibbs w.r.t. the same specification as the time-stationary Gibbs
measure. As an application, we obtain the attractor property for irreversible
interacting particle systems, which says that any weak limit point of any
trajectory of translation-invariant measures is a Gibbs measure w.r.t. the same
specification as the time-stationary measure. This extends previously known
results to fairly general irreversible interacting particle systems.


title:
Negative refractive index in dielectric crystals containing
  stoichiometric rare-earth ions

abstract:
  We investigate the prospect of achieving negative permittivity and
permeability at optical frequencies in a dielectric crystal containing
stoichiometric rare-earth ions. We derive the necessary transition linewidth,
ion density and electric and magnetic oscillator strengths using a simplified
model of non-interacting dipoles. We identify Erbium crystals in a magnetically
ordered phase as the most promising material to meet these conditions, and
describe initial optical measurements of two potential candidates, \ercl{} and
${}^7$\lierf{}, which display linewidths of 3~GHz and 250~MHz, respectively.
The properties of ${}^7$\lierf{} satisfied our criterion for negative
permeability.


title: P-Operators on Hilbert Spaces

abstract:
  A real square matrix $A$ is called a P-matrix if all its principal minors are
positive. Using the sign non-reversal property of matrices, the notion of
P-matrix has been recently extended by Kannan and Sivakumar to
infinite-dimensional Banach spaces relative to a given Schauder basis.
Motivated by their work, we discuss P-operators on separable real Hilbert
spaces. We also investigate P-operators relative to various orthonormal bases.


title: Based-CE white-box adversarial attack will not work using super-fitting

abstract:
  Deep Neural Networks (DNN) are widely used in various fields due to their
powerful performance, but recent studies have shown that deep learning models
are vulnerable to adversarial attacks-by adding a slight perturbation to the
input, the model will get wrong results. It is especially dangerous for some
systems with high security requirements, so this paper proposes a new defense
method by using the model super-fitting status. Model's adversarial robustness
(i.e., the accuracry under adversarial attack) has been greatly improved in
this status. This paper mathematically proves the effectiveness of
super-fitting, and proposes a method to make the model reach this status
quickly-minimaze unrelated categories scores (MUCS). Theoretically,
super-fitting can resist any existing (even future) Based on CE white-box
adversarial attack. In addition, this paper uses a variety of powerful attack
algorithms to evaluate the adversarial robustness of super-fitting and other
nearly 50 defense models from recent conferences. The experimental results show
that super-fitting method in this paper can make the trained model obtain the
highest adversarial performance robustness.


title:
High-accuracy three-dimensional surface detection in smoothed particle
  hydrodynamics for free-surface flows

abstract:
  In this study, we investigate high-accuracy three-dimensional surface
detection in smoothed particle hydrodynamics for free-surface flows. A new
geometrical method is first developed to enhance the accuracy of free-surface
particle detection in complex flows. This method detects free-surface particles
via continuous global scanning inside the sphere of a particle through a cone
region whose vertex corresponds to the particle position. The particle is
identified as a free-surface particle if there exists a cone region with no
neighboring particles. Next, an efficient semi-geometrical method is proposed
based on the geometrical method to reduce the computational cost. It consists
of finding particles near the free surface via position divergence and then
detecting these particles using the geometrical method to identify free-surface
particles. The accuracy and robustness of the proposed method are demonstrated
by performing tests on several model problems.


title: Rethinking Classifier And Adversarial Attack

abstract:
  Various defense models have been proposed to resist adversarial attack
algorithms, but existing adversarial robustness evaluation methods always
overestimate the adversarial robustness of these models (i.e. not approaching
the lower bound of robustness). To solve this problem, this paper first uses
the Decouple Space method to divide the classifier into two parts: non-linear
and linear. On this basis, this paper defines the representation vector of
original example (and its space, i.e., the representation space) and uses
Absolute Classification Boundaries Initialization (ACBI) iterative optimization
to obtain a better attack starting point (i.e. attacking from this point can
approach the lower bound of robustness faster). Particularly, this paper apply
ACBI to nearly 50 widely-used defense models (including 8 architectures).
Experimental results show that ACBI achieves lower robust accuracy in all
cases.


title: Streaming and Sketching Complexity of CSPs: A survey

abstract:
  In this survey we describe progress over the last decade or so in
understanding the complexity of solving constraint satisfaction problems (CSPs)
approximately in the streaming and sketching models of computation. After
surveying some of the results we give some sketches of the proofs and in
particular try to explain why there is a tight dichotomy result for sketching
algorithms working in subpolynomial space regime.


title: New Nonrelativistic Quantum Theory of Cold Dark Matter

abstract:
  Cold dark matter is conceived as a gas of massive particles that undergo
collisions, interact gravitationaly, and exchange quanta of energy. A new
nonrelativistic quantum theory is presented for this model of dark matter,
based on recently discovered equation for a spinless, no charge, and free
particle. This theory describes the quantum processes undergoing by the
particles, specifies the required characteristic wavelength of the quanta of
energy, gives constraints on mass of dark matter particles, and predicts a
detectable gravitational wave background associated with dark matter halos.


title: Two Novel Observational Tests of General Relativity

abstract:
  We propose two novel observational tests of general relativistic predictions:
(i) Detecting the memory effect from a massive black hole merger at the
Galactic Center through Lunar Ranging; and (ii) Violation of a limiting flux
versus redshift as a flag of new physics. First, I show that a gravitational
wave pulse from a major merger of massive black holes at the Galactic center
induces a permanent increase in the Earth-Moon separation. For black holes of
millions of solar masses, the shift in the local gravitational potential is
comparable to the Earth-Moon potential, leading to the Moon being perturbed
relative to the Earth during the passage of the pulse. The permanent increase
in the Earth-Moon separation is a fraction of a millimeter, measurable by lunar
ranging for future merger events. Second, I show that General Relativity sets
an absolute upper limit on the energy flux observed from a cosmological source
as a function of its redshift. Detecting a brighter source in gravitational
waves, neutrinos or light, would flag new physics. The derived flux limit can
also be used to determine the maximum redshift possible for any source with an
unknown origin.


title:
Optimizing Polarizability Distributions for Metasurface Apertures with
  Lorentzian-Constrained Radiators

abstract:
  We present a design strategy for selecting the effective polarizability
distribution for a metasurface aperture needed to form a desired radiation
pattern. A metasurface aperture consists of an array of subwavelength
metamaterial elements, each of which can be conceptualized as a radiating,
polarizable dipole. An ideal polarizability distribution can be determined by
using a holographic approach to first obtain the necessary aperture fields,
which can then be converted to a polarizability distribution using equivalence
principles. To achieve this ideal distribution, the polarizability of each
element would need to have unconstrained magnitude and phase; however, for a
single, passive, metamaterial resonator the magnitude and phase of the
effective polarizability are inextricably linked through the properties of the
Lorentzian resonance, with the range of phase values restricted to a span of at
most 180 degrees. Here, we introduce a family of mappings from the ideal to the
available polarizability distributions, easily visualized by plotting both
polarizabilities in the complex plane. Using one of these mappings it is
possible to achieve highly optimized beam patterns from a metasurface antenna,
despite the inherent resonator limitations. We introduce the mapping technique
and provide several specific examples, with numerical simulations used to
confirm the design approach.


title:
Compressed AFM-IR hyperspectral nanoimaging of single Leishmania
  parasites

abstract:
  Infrared hyperspectral imaging is a powerful approach in the field of
materials and life sciences. However, the extension to modern sub-diffraction
nanoimaging still remains a highly inefficient technique, as it acquires data
via inherent sequential schemes. Here, we introduce the mathematical technique
of low-rank matrix reconstruction to the sub-diffraction scheme of atomic force
microscopy-based infrared spectroscopy (AFM-IR), for efficient hyperspectral
infrared nanoimaging. To demonstrate its application potential, we chose the
trypanosomatid unicellular parasites Leishmania species as a realistic target
of biological importance. The mid-infrared spectral fingerprint window covering
the spectral range from 1300 to 1900 cm$^{-1}$ was chosen and a step width of
220 nm was applied for nanoimaging of single parasites. Multivariate statistics
approaches such as hierarchical cluster analysis (HCA) were used for extracting
the chemically distinct spatial locations. Subsequently, we randomly selected
only 5% from an originally gathered data cube of 134 (x) $\times$ 50 (y)
$\times$ 148 (spectral) AFM-IR measurements and reconstructed the full data set
by low-rank matrix recovery. The technique is evaluated by showing agreement in
the cluster regions between full and reconstructed data cubes. We conclude that
the corresponding measurement time of more than 14 hours can be reduced to less
than 1 hour. These findings can significantly boost the practical applicability
of hyperspectral nanoimaging in both academic and industrial settings involving
nano- and bio-materials.

categories: eess.IV physics.bio-ph physics.data-an physics.optics


title: Automating Reasoning with Standpoint Logic via Nested Sequents

abstract:
  Standpoint logic is a recently proposed formalism in the context of knowledge
integration, which advocates a multi-perspective approach permitting reasoning
with a selection of diverse and possibly conflicting standpoints rather than
forcing their unification. In this paper, we introduce nested sequent calculi
for propositional standpoint logics--proof systems that manipulate trees whose
nodes are multisets of formulae--and show how to automate standpoint reasoning
by means of non-deterministic proof-search algorithms. To obtain worst-case
complexity-optimal proof-search, we introduce a novel technique in the context
of nested sequents, referred to as "coloring," which consists of taking a
formula as input, guessing a certain coloring of its subformulae, and then
running proof-search in a nested sequent calculus on the colored input. Our
technique lets us decide the validity of standpoint formulae in CoNP since
proof-search only produces a partial proof relative to each permitted coloring
of the input. We show how all partial proofs can be fused together to construct
a complete proof when the input is valid, and how certain partial proofs can be
transformed into a counter-model when the input is invalid. These
"certificates" (i.e. proofs and counter-models) serve as explanations of the
(in)validity of the input.


title:
Region-to-region kernel interpolation of acoustic transfer function with
  directional weighting

abstract:
  A method of interpolating the acoustic transfer function (ATF) between
regions that takes into account both the physical properties of the ATF and the
directionality of region configurations is proposed. Most spatial ATF
interpolation methods are limited to estimation in the region of receivers. A
kernel method for region-to-region ATF interpolation makes it possible to
estimate the ATFs for both source and receiver regions from a discrete set of
ATF measurements. We newly formulate the reproducing kernel Hilbert space and
associated kernel function incorporating directional weight to enhance the
interpolation accuracy. We also investigate hyperparameter optimization methods
for this kernel function. Numerical experiments indicate that the proposed
method outperforms the method without the use of directional weighting.


title: Tilted Hardy paradoxes for device-independent randomness extraction

abstract:
  The device-independent paradigm has had spectacular successes in randomness
generation, key distribution and self-testing, however most of these results
have been obtained under the assumption that parties hold trusted and private
random seeds. In efforts to relax the assumption of measurement independence,
Hardy's non-locality tests have been proposed as ideal candidates. In this
paper, we introduce a family of tilted Hardy paradoxes that allow to self-test
general pure two-qubit entangled states, as well as certify up to $1$ bit of
local randomness with arbitrarily limited measurement independence. We then use
these tilted Hardy tests to obtain an improvement in the generation rate in the
state-of-art randomness amplification protocols against adversaries holding
quantum as well as non-signaling side information. Finally, we propose a family
of Hardy tests for maximally entangled states of local dimension $4, 8$ that
allow to certify up to the maximum possible $2 \log d$ bits of global
randomness.


abstract:
  A collection of invited non-archival papers for the Conference on Health,
Inference, and Learning (CHIL) 2022. This index is incomplete as some authors
of invited non-archival presentations opted not to include their papers in this
index.


title:
Encrypted, Anonymized System for Protected Health Information
  Verification Built via Proof of Stake

abstract:
  Digital Health Passes (DHP), systems of digitally validating quarantine and
vaccination status such as the New York IBM Excelsior Pass, demonstrate a
lawful means to approach some benefits offered by "true elimination" treatment
strategies-which focus on the complete elimination of cases instead of
investing more in controlling the progression of the disease-of COVID-19.
Current implementations of DHPs require region-based control and central
storage of Protected Health Information (PHI)-creating a challenge to
widespread use across different jurisdictions with incompatible data management
systems and a lack of standardized patient privacy controls. In this work, a
mechanism for decentralized PHI storage and validation is proposed through a
novel two-stage handshaking mechanism update to blockchain proof-of-stake
consensus. The proposed mechanism, when used to support a DHP, allows
individuals to validate their quarantine and testing universally with any
jurisdiction while allowing their right of independent movement and the
protection of their PHI. Implementational details on the protocol are given,
and the protocol is shown to withstand a 1% disturbance attack at only 923
participants via a Monte-Carlo simulation: further validating its stability.


title:
Accelerating Path Planning for Autonomous Driving with Hardware-assisted
  Memorization

abstract:
  Path planning for autonomous driving with dynamic obstacles poses a challenge
because it needs to perform a higher-dimensional search (including time) while
still meeting real-time constraints. This paper proposes an algorithm-hardware
co-optimization approach to accelerate path planning with high-dimensional
search space. First, we reduce the time for a nearest neighbor search and
collision detection by mapping nodes and obstacles to a lower-dimensional space
and memorizing recent search results. Then, we propose a hardware extension for
efficient memorization. The experimental results on a modern processor and a
cycle-level simulator show that the execution time is reduced significantly.


title:
A lower bound for the logarithmic energy on $\mathbb{S}^2$ and for the
  Green energy on $\mathbb{S}^n$

abstract:
  We show an alternative proof of the sharpest known lower bound for the
logarithmic energy on the unit sphere $\mathbb{S}^2$. We then generalize this
proof to get new lower bounds for the Green energy on the unit $n$-sphere
$\mathbb{S}^n$.


title: Eigenvalues and Diagonal Elements

abstract:
  In this paper we discuss some relations between the eigenvalues and the
diagonal entries of Hermitian matrices.


title:
Transverse momentum distributions of valence quark in light and heavy
  vector mesons

abstract:
  We study the leading-twist time-reversal even transverse momentum dependent
parton distribution functions (TMDs) of light and heavy vector mesons, i.e.,
the $\rho$, $J/\psi$ and $\Upsilon$. We employ the leading Fock-state light
front wave functions (LF-LFWFs) of $\rho$ and $J/\psi$ from our recent study,
and supplement with $\Upsilon$'s LF-LFWFs. These LF-LFWFs are extracted from
dynamically solved Bethe-Salpeter wave functions. The vector meson TMDs are
then studied with the light front overlap representation at leading Fock-state.
All the obtained TMDs are non-vanishing and evolve with current quark mass, in
particular the tensor polarized TMDs $f_{1LT}$ and $f_{1TT}$ which undergo a
sign flip. The $\rho$ TMDs are compared with other model studies and agreement
is found, aside from $f_{1LT}$ and $f_{1TT}$. Finally, the collinear PDFs of
vector mesons are studied. The $\rho$'s valence PDFs $f_{1,v}(x)$ and
$g_{1L,v}(x)$ are evolved to the scale of 2.4 GeV, with their first three
moments compared to lattice QCD prediction. The qualitative behavior of tensor
polarized PDF $f_{1LL}(x)$ in $\rho$ at large $x$ is also discussed.


title:
Quantitative Measures for Integrating Resilience Assessment into
  Transportation Planning Practice: Study of the State of Texas

abstract:
  The objective of this study is to propose a system-level framework with
quantitative measures to assess the resilience of road networks. The framework
proposed in this paper can help transportation agencies incorporate resilience
considerations into planning and project development proactively and to
understand the resilience performance of the current road networks effectively.
This study identified and implemented four quantitative metrics to classify the
criticality of road segments based on critical dimensions of the resilience of
road networks, and two integrated metrics were proposed to combine all metrics
to show the overall performance of road segments in terms of resilience. A case
study was conducted on the Texas road networks to demonstrate the effectiveness
of implementing this framework in a practical scenario. Since the data used in
this study is available to other states and countries, the framework presented
in this study can be adopted by other transportation agencies across the globe
for regional transportation resilience assessments.


title:
Stochastic Relative Degree and Path-wise Control of Nonlinear Stochastic
  Systems

abstract:
  We address the path-wise control of systems described by a set of nonlinear
stochastic differential equations. For this class of systems, we introduce a
notion of stochastic relative degree and a change of coordinates which
transforms the dynamics to a stochastic normal form. The normal form is
instrumental for the design of a state-feedback control which linearises and
makes the dynamics deterministic. We observe that this control is idealistic,
i.e. it is not practically implementable because it employs a feedback of the
Brownian motion (which is never available) to cancel the noise. Using the
idealistic control as a starting point, we introduce a hybrid control
architecture which achieves \emph{practical} path-wise control. This hybrid
controller uses measurements of the state to perform periodic compensations for
the noise contribution to the dynamics. We prove that the hybrid controller
retrieves the idealistic performances in the limit as the compensating period
approaches zero. We address the problem of asymptotic output tracking, solving
it in the idealistic and in the practical framework. We finally validate the
theory by means of a numerical example.


title: General sum stochastic games with networked information flows

abstract:
  Inspired by applications such as supply chain management, epidemics, and
social networks, we formulate a stochastic game model that addresses three key
features common across these domains: 1) network-structured player
interactions, 2) pair-wise mixed cooperation and competition among players, and
3) limited global information toward individual decision-making. In
combination, these features pose significant challenges for black box
approaches taken by deep learning-based multi-agent reinforcement learning
(MARL) algorithms and deserve more detailed analysis. We formulate a networked
stochastic game with pair-wise general sum objectives and asymmetrical
information structure, and empirically explore the effects of information
availability on the outcomes of different MARL paradigms such as individual
learning and centralized learning decentralized execution.


title: Quantum computers as an amplifier for existential risk

abstract:
  Quantum computing is expected to have a profound impact on society. In this
work we discuss the potential consequences on existential risk for humanity.
Even with the timeline for large-scale fault-tolerant quantum computing still
unclear, it is highly likely that quantum computers will eventually realize an
exponential speedup for certain practical applications. We identify quantum
simulation as the most relevant application in this regard and we qualitatively
outline different risk trajectories. Both amplifying and mitigating effects of
quantum computing for existential risk are anticipated. In order to prevent
quantum computing from being an amplifier of existential risk, we call for
increased efforts by the scientific community towards reducing potential future
quantum risk. This viewpoint seeks to add a new perspective to the discussion
on technological risk of quantum computing.


title:
The crucial role of adhesion in the transmigration of active droplets
  through interstitial orifices

abstract:
  Active fluid droplets are a class of soft materials exhibiting autonomous
motion sustained by an energy supply. Such systems have been shown to capture
motility regimes typical of biological cells and are ideal candidates as
building-block for the fabrication of soft biomimetic materials of interest in
pharmacology, tissue engineering and lab on chip devices. While their behavior
is well established in unconstrained environments, much less is known about
their dynamics under strong confinement. Here, we numerically study the physics
of a droplet of active polar fluid migrating within a microchannel hosting a
constriction with adhesive properties, and report evidence of a striking
variety of dynamic regimes and morphological features, whose properties
crucially depend upon droplet speed and elasticity, degree of confinement
within the constriction and adhesiveness to the pore. Our results suggest that
adhesion forces are instrumental in enabling the crossing through narrow
orifices, in contrast to larger gaps where a careful balance between speed and
elasticity is sufficient to guarantee the transition. These observations may be
useful for improving the design of artificial micro-swimmers, of interest in
material science and pharmaceutics, and potentially for cell sorting in
microfluidic devices.


title:
Confined Continuous-Flow Plasma Source For High-Average-Power Laser
  Plasma Acceleration

abstract:
  Over the last decades, significant advances in high-power laser systems have
enabled rapid progress in the development of laser-driven plasma accelerators.
Today, the results obtained in beam stability and reproducibility present laser
plasma acceleration as a viable and promising alternative to conventional
accelerators. As several electron beam and secondary sources applications
require high average currents, a major focus is now on increasing the beam's
repetition rate. In the following, we introduce a novel plasma source for kHz
electron acceleration, providing a continuous and spatially confined gas flow,
while minimising the gas load in the acceleration chamber.


title:
Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge
  Computing

abstract:
  The Metaverse is a virtual environment where users are represented by avatars
to navigate a virtual world, which has strong links with the physical one.
State-of-the-art Metaverse architectures rely on a cloud-based approach for
avatar physics emulation and graphics rendering computation. Such centralized
design is unfavorable as it suffers from several drawbacks caused by the long
latency required for cloud access, such as low quality visualization. To solve
this issue, in this paper, we propose a Fog-Edge hybrid computing architecture
for Metaverse applications that leverage an edge-enabled distributed computing
paradigm, which makes use of edge devices computing power to fulfil the
required computational cost for heavy tasks such as collision detection in
virtual universe and computation of 3D physics in virtual simulation. The
computational cost related to an entity in the Metaverse such as collision
detection or physics emulation are performed at the end-device of the
associated physical entity. To prove the effectiveness of the proposed
architecture, we simulate a distributed social metaverse application.
Simulation results shows that the proposed architecture can reduce the latency
by 50% when compared with the legacy cloud-based Metaverse applications.


title: Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE

abstract:
  In this paper, we propose, implement, and test two novel downlink LTE
scheduling algorithms. The implementation and testing of these algorithms were
in Matlab, and they are based on the use of Reinforcement Learning, more
specifically, the Qlearning technique for scheduling two types of users. The
first algorithm is called a Collaborative scheduling algorithm, and the second
algorithm is called a Competitive scheduling algorithm. The first type of the
scheduled users is the Primary Users, and they are the licensed subscribers
that pay for their service. The second type of the scheduled users is the
Secondary Users, and they could be unlicensed subscribers that dont pay for
their service, device to device communications, or sensors. Each user whether
it is a primary or secondary is considered as an agent. In the Collaborative
scheduling algorithm, the primary user agents will collaborate in order to make
a joint scheduling decision about allocating the resource blocks to each one of
them, then the secondary user agents will compete among themselves to use the
remaining resource blocks. In the Competitive scheduling algorithm, the primary
user agents will compete among themselves over the available resources, then
the secondary user agents will compete among themselves over the remaining
resources. Experimental results show that both scheduling algorithms converged
to almost ninety percent utilization of the spectrum, and provided fair shares
of the spectrum among users.


title:
An end-to-end KNN-based PIV approach for high-resolution measurements
  and uncertainty quantification

abstract:
  We introduce a novel approach to improving the resolution of PIV
measurements. The method blends information from different snapshots without
the need for time-resolved measurements on grounds of similarity of flow
regions in different snapshots. The main hypothesis is that, with a
sufficiently large ensemble of statistically-independent snapshots, the
identification of flow structures that are morphologically similar but
occurring at different time instants is feasible. Measured individual vectors
from different snapshots with similar flow organisation can thus be merged,
resulting in an artificially increased particle concentration. This allows to
refine the interrogation region and, consequently, increase the spatial
resolution. The measurement domain is split in subdomains. The similarity is
enforced only on a local scale, i.e. morphologically-similar regions are sought
only among subdomains corresponding to the same flow region. The identification
of locally-similar snapshots is based on unsupervised K-nearest neighbours
search in a space of significant flow features. Such features are defined in
terms of a Proper Orthogonal Decomposition, performed in subdomains on the
original low-resolution data, obtained either with standard cross-correlation
or with binning of Particle Tracking Velocimetry data with a relatively large
bin size. A refined bin size is then selected according to the number of
"sufficiently close" snapshots identified. The statistical dispersion of the
velocity vectors within the bin is then used to estimate the uncertainty and to
select the optimal K which minimises it. The method is tested and validated
against datasets with a progressively increasing level of complexity: two
virtual experiments based on direct simulations of the wake of a fluidic
pinball and a channel flow and the experimental data collected in a turbulent
boundary layer.


title: Spiking Graph Convolutional Networks

abstract:
  Graph Convolutional Networks (GCNs) achieve an impressive performance due to
the remarkable representation ability in learning the graph information.
However, GCNs, when implemented on a deep network, require expensive
computation power, making them difficult to be deployed on battery-powered
devices. In contrast, Spiking Neural Networks (SNNs), which perform a
bio-fidelity inference process, offer an energy-efficient neural architecture.
In this work, we propose SpikingGCN, an end-to-end framework that aims to
integrate the embedding of GCNs with the biofidelity characteristics of SNNs.
The original graph data are encoded into spike trains based on the
incorporation of graph convolution. We further model biological information
processing by utilizing a fully connected layer combined with neuron nodes. In
a wide range of scenarios (e.g. citation networks, image graph classification,
and recommender systems), our experimental results show that the proposed
method could gain competitive performance against state-of-the-art approaches.
Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear
advantage of energy efficiency into graph data analysis, which demonstrates its
great potential to construct environment-friendly machine learning models.


title: Ab-initio theory of free-carrier absorption in semiconductors

abstract:
  The absorption of light by free carriers in semiconductors results in optical
loss for all photon wavelengths. Since free-carrier absorption competes with
optical transitions across the band gap, it also reduces the efficiency of
optoelectronic devices such as solar cells because it does not generate
electron-hole pairs. In this work, we develop a first-principles theory of
free-carrier absorption taking both single particle excitations and the
collective Drude term into account, and we demonstrate its application to the
case of doped Si. We determine the free-carrier absorption coefficient as a
function of carrier concentration and we obtain excellent agreement with
experimental data. We identify the dominant mechanisms that contribute to
free-carrier absorption processes at various photon wavelengths, and analyze
the results to evaluate the impact of this loss mechanism on the efficiency of
Si-based optoelectronic devices.


title: On the NLO QCD Corrections to Gluon-Initiated ZH Production

abstract:
  We compute the QCD corrections at next-to-leading order for the process $gg
\rightarrow ZH$, including both the virtual two-loop terms and real-emission
contributions. The two-loop box diagrams in the virtual corrections are
approximated analytically over the complete phase space, combining the results
of an expansion in the limit of small transverse momentum and an expansion in
the regime of high energy. We obtain both inclusive and differential results
for the cross section. We find that the NLO QCD corrections are of the same
size as the LO contribution up to $ZH$ invariant masses close to 1 TeV, but
they increase significantly when higher energies are considered, due to a class
of real-emission diagrams in which the $Z$ boson is radiated from an open quark
line. Finally, we estimate the uncertainty due to the renormalization scheme
used for the top-quark mass both on the total and differential cross section.


title: Additive properties of fractal sets on the parabola

abstract:
  Let $0 \leq s \leq 1$, and let $\mathbb{P} := \{(t,t^{2}) \in \mathbb{R}^{2}
: t \in [-1,1]\}$. If $K \subset \mathbb{P}$ is a closed set with
$\dim_{\mathrm{H}} K = s$, it is not hard to see that $\dim_{\mathrm{H}} (K +
K) \geq 2s$. The main corollary of the paper states that if $0 < s < 1$, then
adding $K$ once more makes the sum slightly larger: $$\dim_{\mathrm{H}} (K + K
+ K) \geq 2s + \epsilon, $$ where $\epsilon = \epsilon(s) > 0$. This
information is deduced from an $L^{6}$ bound for the Fourier transforms of
Frostman measures on $\mathbb{P}$. If $0 < s < 1$, and $\mu$ is a Borel measure
on $\mathbb{P}$ satisfying $\mu(B(x,r)) \leq r^{s}$ for all $x \in \mathbb{P}$
and $r > 0$, then there exists $\epsilon = \epsilon(s) > 0$ such that $$
\|\hat{\mu}\|_{L^{6}(B(R))}^{6} \leq R^{2 - (2s + \epsilon)} $$ for all
sufficiently large $R \geq 1$. The proof is based on a reduction to a
$\delta$-discretised point-circle incidence problem, and eventually to the
$(s,2s)$-Furstenberg set problem.


title: Finding Bipartite Components in Hypergraphs

abstract:
  Hypergraphs are important objects to model ternary or higher-order relations
of objects, and have a number of applications in analysing many complex
datasets occurring in practice. In this work we study a new heat diffusion
process in hypergraphs, and employ this process to design a polynomial-time
algorithm that approximately finds bipartite components in a hypergraph. We
theoretically prove the performance of our proposed algorithm, and compare it
against the previous state-of-the-art through extensive experimental analysis
on both synthetic and real-world datasets. We find that our new algorithm
consistently and significantly outperforms the previous state-of-the-art across
a wide range of hypergraphs.


title:
Entropic propagation of chaos for mean field diffusion with
  $L_t^q-L_x^p$ interaction, linear growth interaction and fractional noise

abstract:
  Three quantitative propagation of chaos results for mean field diffusions are
proved via local and global entropy estimates. In the first result we consider
singular interactions that satisfy Krylov's condition, i.e. lying in
$L^q([0,T];L^p(\mathbb{R}^d))$ where $\frac{d}{p}+\frac{2}{q}<1$ and $d$ is the
dimension of space. We prove a $O(k^{2+\epsilon}/n^2)$ convergence rate, for
each $\epsilon>0$, in relative entropy between the $k$-marginal laws of the
particle system and its limiting law at each time $t$, as long as the
convergence holds with rate $O(k^2/n^2)$ at $t=0$. The proof is strongly
inspired by Lacker arxiv:2105.02983. We extend his framework, previously only
known to cover locally bounded interactions, to all singular interactions that
satisfy the aforementioned integrability condition. Then we prove convergence
results of the same type when the driving noise is replaced by a fractional
Brownian motion $B^H$, $H\in(0,\frac{1}{2})$ and when the interaction belongs
to a wide class of locally bounded functions. The proof of the previous two
results are based on a local entropy estimate. We complement them with a
quantitative convergence result for interactions that are only assumed to have
linear growth. SubGaussian concentration inequalities are used instead and
yield a $O((k/n)^{1/2})$ convergence rate in total variation because the local
estimate is not known under such generality. These results as a whole give a
rather complete list of quantitative propagation of chaos results for the
various McKean-Vlasov SDEs recently solved in arxiv:2204.05709.


title:
Tensor network calculation of the logarithmic correction exponent in the
  XY model

abstract:
  We study the logarithmic correction to the scaling of the first Lee-Yang (LY)
zero in the classical $XY$ model on square lattices by using tensor
renormalization group methods. In comparing the higher-order tensor
renormalization group (HOTRG) and the loop-optimized tensor network
renormalization (LoopTNR), we find that the entanglement filtering in LoopTNR
is crucial to gaining high accuracy for the characterization of the logarithmic
correction, while HOTRG still proposes empirical bounds associated with the
different bond-merging algorithms. Using the LoopTNR data computed up to the
system size of $L=1024$ in the $L \times L$ lattices, we estimate the
logarithmic correction exponent $r = -0.0643(9)$ from the extrapolation of the
finite-size-evaluated effective exponent, which is comparable to the
renormalization group prediction of $r = -1/16$.


title:
Synthetic turbulence generator for lattice Boltzmann method at the
  interface between RANS and LES

abstract:
  The paper presents a synthetic turbulence generator (STG) for the lattice
Boltzmann method (LBM) at the interface of the Reynolds averaged Naiver-Stokes
(RANS) equations and the LBM Large Eddy Simulation (LES). We first obtain the
RANS velocity field from a finite volume solver at the interface. Then, we
apply a numerical interpolation from the RANS velocity field to the LBM
velocity field due to the different grid types of RANS and LBM. The STG method
generates the velocity fluctuations, and the regularized LBM reconstructs the
particle distribution functions at the interface. We perform a turbulent
channel flow simulation at Re_{\tau} = 180 with the STG at the inlet and the
pressure-free boundary condition at the outlet. The velocity field is
quantitatively compared with the periodic lattice Boltzmann based LES (LES-LBM)
channel flow and the direct numerical simulation (DNS) channel flow. Both
adaptation length and time for the STG method are evaluated. Also, we compare
the STG-LBM channel flow results with the existing LBM synthetic eddies method
(SEM-LBM) results. Our numerical investigations show good agreement with the
DNS and periodic LES-LBM channel flow within a short adaptation length. The
adaptation time for the turbulent channel flow is quantitatively analyzed and
matches the DNS around 1.5 to 3 domain flow-through time. Finally, we check the
auto-correlation for the velocity components at different cross-sections of the
streamwise direction. The proposed STG-LBM is observed to be both fast and
robust. The findings show good potential for the hybrid RANS/LES-LBM based
solver on the aerodynamics simulations and a broad spectrum of engineering
applications.


title:
Embedded Multilevel Regression and Poststratification: Model-based
  Inference with Incomplete Auxiliary Information

abstract:
  Health disparity research often evaluates health outcomes across subgroups.
Multilevel regression and poststratification (MRP) is a popular approach for
small subgroup estimation due to its ability to stabilize estimates by fitting
multilevel models and to adjust for selection bias by poststratifying on
auxiliary variables, which are population characteristics predictive of the
analytic outcome. However, the granularity and quality of the estimates
produced by MRP are limited by the availability of the auxiliary variables'
joint distribution; data analysts often only have access to the marginal
distributions. To overcome this limitation, we develop an integrative inference
framework that embeds the estimation of population cell counts needed for
poststratification into the MRP workflow: embedded MRP (EMRP). Under EMRP, we
generate synthetic populations of the auxiliary variables before implementing
MRP. All sources of estimation uncertainty are propagated with a fully Bayesian
framework. Through simulation studies, we compare different methods and
demonstrate EMRP's improvements over classical MRP on the bias-variance
tradeoff to yield valid subpopulation inferences of interest. As an
illustration, we estimate food insecurity prevalence among vulnerable groups in
New York City by applying EMRP to the Longitudinal Survey of Wellbeing. We find
that the improvement is primarily on subgroup estimation with efficiency gains.


title: Optical gravity in a graviton spacetime

abstract:
  While the Hubble redshift is generally linked to expansion of spacetime, an
open question concerns where the energy lost from redshifted photons, gravitons
and gravitational waves goes. One possibility is that it gives rise to gravity.
In the optical-mechanical analogy in general relativity, relativistic light
deflection is treated as refraction in an optical medium of varying density
gradient. We now model the optical medium analogue of spacetime as a real
graviton conjugate interlinking all masses. Gravitons in local mass systems are
assumed to be organized in coherent quantum states overlapping with those of
particles within spacetime structures. With the Hubble redshift, however,
gravitons acquire longer wavelengths as they are shifted to less coherent
states. Assuming that the momentum and energy lost from gravitons is
transferrable to particles, mutual screening by masses then gives rise to an
attractive force with a magnitude consistent with gravity. Some possible
cosmological implications of the model are discussed.


title: Relative Habitability of Exoplanet Systems with Two Giant Planets

abstract:
  The architecture of a planetary system can influence the habitability of a
planet via orbital effects, particularly in the areas of stability and
eccentricity. Some of these effects are readily apparent, particularly when
they occur on short timescales that are easily numerically calculable. However,
the appearance and evolution of life can take place on gigayear timescales,
long enough that secular effects become important. These effects are difficult
to investigate, as a direct integration requires significant computational
time. In this paper, we apply a semi-analytic framework in conjunction with
N-body integrations and predictive techniques to determine the relative
habitability for an Earth-like planet in a system with two giant companions
over a multidimensional parameter space. Relative habitability quantifies the
integrated habitability probability compared to a system containing only a
single Earth-like planet. We find trends with mass, eccentricity, location,
spacing, inclination, and alignment of the giant planets, including
configurations where the system is more habitable due to the giant planets. As
long as the system remains stable, a moderate eccentricity excitation of the
terrestrial planet can be beneficial by increasing the outer boundary of the
habitable zone through higher mean irradiance. In our simulations, the median
($\pm 1 \sigma$) habitable planet has an eccentricity of 0.11 (+0.16, -0.08),
though it started circular. Low-mass, widely separated, and moderately
eccentric perturbing giants can accomplish this, an "ultra-habitable"
configuration of companions.


title: An Algorithm for Computing the Stratonovich's Value of Information

abstract:
  We consider an algorithm for computing Stratonovich's value of information
(VoI) that can be regarded as an analogue of the distortion-rate function. We
construct an alternating optimization algorithm for VoI under a general
information leakage constraint and derive a convergence condition. Furthermore,
we discuss algorithms for computing VoI under specific information leakage
constraints, such as Shannon's mutual information (MI), $f$-leakage, Arimoto's
MI, Sibson's MI, and Csiszar's MI.


title: Non-distributable key sharing for improving the security in IoT networks

abstract:
  Quantum key distribution (QKD) constitutes the most widespread family of
information preservation techniques in the context of Quantum Cryptography.
However, these techniques must deal with a series of technological challenges
that prevent their efficient implementation, in space, as well as, in
exclusively terrestrial configurations. Moreover, the current smallsat
con-stellations of Low Earth Orbit (LEO with an altitude of approx. 500 km)
added to other satellites in Medium Earth Orbit (MEO), and geostationary orbits
(GEO), plus a large amount of space de-bris present around the planet, have
become a serious obstacle for Astronomy. In this work, a classic alternative to
QKD is presented, also based on a symmetric key, but with low cost, and high
efficiency, which dispenses with all the implementation problems present in the
QKD pro-tocols and does not require the use of satellites, and which we will
call non-distributable key sharing (NDKS). Due to its low cost, simplicity of
implementation, and high efficiency, NDKS is presented as an ideal solution to
the problem of cybersecurity in IoT, in general, and fog clouds, in particular.


title: Alcubierre Warp Drive in Bohmian Quantum Gravity

abstract:
  Alcubierre warp drive metric is coupled to quantum mechanical scalar matter
field. The requirement of the exotic matter for the warp drive is mapped into a
conformal wave equation. This result into a fourth order partial differential
equation in terms of the quantum mechanical density. Finding a proper quantum
mechanical density obeying the proposed partial differential equation will be a
resolution to the exotic matter problem of Alcubierre warp drive in Bohmian
Quantum Gravity context.


title:
Mass distributions of dijet resonances from excited quarks at
  proton-proton colliders

abstract:
  We study the expected experimental mass distributions of dijet resonances
from excited quarks in proton-proton collisions at energies $\sqrt{s}=$ 13, 14,
27, 100, 300, and 500 TeV. We explore in detail the expected shapes at both the
generator and experimental levels, and identify within the distributions the
effects of the excited quark natural width, parton momentum distributions of
the proton, radiation, and experimental resolution. We present both
differential and cumulative probability distributions as a function of dijet
mass, and the signal acceptance of a window in dijet mass centered on each
resonance. We find that for a range of resonance masses, between 10\% and 50\%
of $\sqrt{s}$, the dijet mass distributions and window acceptance are
practically universal, approximately invariant under changes in resonance mass
and $\sqrt{s}$. This work supports our Snowmass 2021 study on the sensitivity
to dijet resonances at proton-proton colliders.


title:
Long-Range Bell States from Local Measurements and Many-Body
  Teleportation without Time-Reversal

abstract:
  In this work, we study quantum many-body teleportation, where a single qubit
is teleported through a strongly-interacting quantum system, as a result of a
scrambling unitary and local measurements on a few qubits. While usual
many-body teleportation protocols require rewinding time and a double copy, we
demonstrate that teleportation is possible in more general systems, including
the 2D spin-$1/2$ XY model, without these constraints. Teleportation success
requires an entanglement resource, such as long-ranged entangled Bell states.
We outline a specific protocol for preparing this highly entangled many-body
state by evolving a product state and performing iterative measurements on only
two qubits with feedback control.