title:
Jet effusion from a metal droplet irradiated by a polarized ultrashort
  laser pulse

abstract:
  Fragmentation of liquid metal droplets irradiated by linearly and circularly
polarized femtosecond laser pulses is observed in our experiment. The obtained
shadowgraph snapshots demonstrate that a circularly polarized pulse may produce
several randomly-oriented jets effused from the expanding droplets, while a
linearly polarized laser pulse generates strictly the cruciform jets. The
latter orientation is tied with polarization plane, rotation of which causes
rotation of the cruciform jets by the same angle. To shed light on the
experimental data we performed molecular dynamics simulation of droplet
expansion induced by angle-dependent heating.
  Our simulation shows that the jet directions are determined by an oriented
angle-dependent energy distribution within a frontal hemisphere layer of
droplet after absorption of linearly polarized light. As a result, the produced
flow velocity field guided by surface tension forms two high-speed opposite
jets oriented across the electric field vector as in our experiment.
  A shock-wave pulse generated in the frontal layer has angle-dependent
amplitude inherited from the oriented energy deposition. The release part of
shock pulse produces a cavitation zone nearby the droplet center, and thus an
expanding spherical shell is formed from the droplet. The flow velocities
within a rearside hemisphere of the shell, produced after reflection of the
shock wave from the rear side of droplet, generate two low-speed opposite jets
oriented along the electric field vector. Thus we found that the cruciform jets
are originated independently from the frontal and rear sides of droplet, and a
pair of frontal jets is faster than a pair of rearside jets.


title: Focal Length and Object Pose Estimation via Render and Compare

abstract:
  We introduce FocalPose, a neural render-and-compare method for jointly
estimating the camera-object 6D pose and camera focal length given a single RGB
input image depicting a known object. The contributions of this work are
twofold. First, we derive a focal length update rule that extends an existing
state-of-the-art render-and-compare 6D pose estimator to address the joint
estimation task. Second, we investigate several different loss functions for
jointly estimating the object pose and focal length. We find that a combination
of direct focal length regression with a reprojection loss disentangling the
contribution of translation, rotation, and focal length leads to improved
results. We show results on three challenging benchmark datasets that depict
known 3D models in uncontrolled settings. We demonstrate that our focal length
and 6D pose estimates have lower error than the existing state-of-the-art
methods.


title: Artificial Intelligence Enabled Spectral Reconfigurable Fiber Laser

abstract:
  The combinations of artificial intelligence and lasers provide powerful ways
to form smart light sources with ground-breaking functions. Here, a Raman fiber
laser (RFL) with reconfigurable and programmable spectra in an ultra-wide
bandwidth is developed based on spectral-spatial manipulation of light in
multimode fiber (MMF). The proposed fiber laser uses nonlinear gain from
cascaded stimulated Raman scattering, random distributed feedback from Rayleigh
scattering, and point feedback from an MMF-based smart spectral filter. Through
wavefront shaping controlled by a genetic algorithm, light of selective
wavelength(s) can be focused in the MMF, forming the filter that, together with
the active part of the laser, actively shape the output spectrum with a high
degree of freedom. We achieved arbitrary spectral shaping of the cascaded RFL
(e.g., continuously tunable single-wavelength and multi-wavelength laser with
customizable linewidth, mode separation, and power distribution) from the 1st-
to the 3rd-order Stokes emission by adjusting the pump power and
auto-optimization of the smart filter. Our research uses
artificial-intelligence controlled light manipulation in a fiber platform with
multi-eigenmodes and nonlinear gain, mapping the spatial control into the
spectral domain as well as extending the linear control of light in MMF to
active light emission, which is of great significance for applications in
optical communication, sensing, and spectroscopy.

categories: physics.optics cs.SY eess.SY physics.app-ph


title:
Regularized polysymplectic geometry and first steps towards Floer theory
  for covariant field theories

abstract:
  It is the goal of this paper to present the first steps for defining the
analogue of Hamiltonian Floer theory for covariant field theory, treating time
and space relativistically. While there already exist a number of competing
geometric frameworks for covariant field theory generalizing symplectic
geometry, none of them are readily suitable for variational techniques such as
Hamiltonian Floer theory, since the corresponding action functionals are too
degenerate. Instead, we show how a regularization procedure introduced by
Bridges leads to a new geometric framework for which we can show that the
finite energy $L^2$-gradient lines of the corresponding action functional,
called Floer curves, converge asymptotically to space-time periodic solutions.
As a concrete example we prove the existence of Floer curves, and hence also of
space-time periodic solutions, for a class of coupled particle-field systems
defined in this new framework.


title: Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning

abstract:
  We introduce a simple neural encoder architecture that can be trained using
an unsupervised contrastive learning objective which gets its positive samples
from data-augmented k-Nearest Neighbors search. We show that when built on top
of recent self-supervised audio representations, this method can be applied
iteratively and yield competitive SSE as evaluated on two tasks:
query-by-example of random sequences of speech, and spoken term discovery. On
both tasks our method pushes the state-of-the-art by a significant margin
across 5 different languages. Finally, we establish a benchmark on a
query-by-example task on the LibriSpeech dataset to monitor future improvements
in the field.


title:
The Carbon Footprint of Machine Learning Training Will Plateau, Then
  Shrink

abstract:
  Machine Learning (ML) workloads have rapidly grown in importance, but raised
concerns about their carbon footprint. Four best practices can reduce ML
training energy by up to 100x and CO2 emissions up to 1000x. By following best
practices, overall ML energy use (across research, development, and production)
held steady at <15% of Google's total energy use for the past three years. If
the whole ML field were to adopt best practices, total carbon emissions from
training would reduce. Hence, we recommend that ML papers include emissions
explicitly to foster competition on more than just model quality. Estimates of
emissions in papers that omitted them have been off 100x-100,000x, so
publishing emissions has the added benefit of ensuring accurate accounting.
Given the importance of climate change, we must get the numbers right to make
certain that we work on its biggest challenges.


title:
Euclidean operator radius inequalities of a pair of bounded linear
  operators and their applications

abstract:
  We obtain several sharp lower and upper bounds for the Euclidean operator
radius of a pair of bounded linear operators defined on a complex Hilbert
space. As applications of these bounds we deduce a chain of new bounds for the
classical numerical radius of a bounded linear operator which improve on the
existing ones. In particular, we prove that for a bounded linear operator $A,$
  \[\frac{1}{4} \|A^*A+AA^*\|+\frac{\mu}{2}\max \{\|\Re(A)\|,\|\Im(A)\|\}
  \leq w^2(A) \, \leq \, w^2( |\Re(A)| +i |\Im(A)|),\]
  where $\mu= \big| \|\Re(A)+\Im(A)\|-\|\Re(A)-\Im(A)\|\big|.$ This improve the
existing upper and lower bounds of the numerical radius, namely,
  \[ \frac14 \|A^*A+AA^*\|\leq w^2(A) \leq \frac12 \|A^*A+AA^*\|. \]


title: Metaethical Perspectives on 'Benchmarking' AI Ethics

abstract:
  Benchmarks are seen as the cornerstone for measuring technical progress in
Artificial Intelligence (AI) research and have been developed for a variety of
tasks ranging from question answering to facial recognition. An increasingly
prominent research area in AI is ethics, which currently has no set of
benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI
system. In this paper, drawing upon research in moral philosophy and
metaethics, we argue that it is impossible to develop such a benchmark. As
such, alternative mechanisms are necessary for evaluating whether an AI system
is 'ethical'. This is especially pressing in light of the prevalence of
applied, industrial AI research. We argue that it makes more sense to talk
about 'values' (and 'value alignment') rather than 'ethics' when considering
the possible actions of present and future AI systems. We further highlight
that, because values are unambiguously relative, focusing on values forces us
to consider explicitly what the values are and whose values they are. Shifting
the emphasis from ethics to values therefore gives rise to several new ways of
understanding how researchers might advance research programmes for robustly
safe or beneficial AI. We conclude by highlighting a number of possible ways
forward for the field as a whole, and we advocate for different approaches
towards more value-aligned AI research.


title:
Nonlinear Coarse-graining Models for 3D Printed Multi-material
  Biomimetic Composites

abstract:
  Bio-inspired composites are a great promise for mimicking the extraordinary
and highly efficient properties of natural materials. Recent developments in
voxel-by-voxel 3D printing have enabled extreme levels of control over the
material deposition, yielding complex micro-architected materials. However,
spatial complexity makes it a formidable challenge to find the optimal
distribution of both hard and soft phases. To address this, a nonlinear
coarse-graining approach is developed, where foam-based constitutive equations
are used to predict the mechanics of biomimetic composites. The proposed
approach is validated by comparing coarse-grained finite element predictions
against full-field strain distributions measured using digital image
correlation. To evaluate the degree of coarse-graining on model accuracy,
pre-notched specimens decorated with a binarized version of a renowned painting
were modeled. Subsequently, coarse-graining is used to predict the fracture
behavior of bio-inspired composites incorporating complex designs, such as
functional gradients and hierarchical organizations. Finally, as a showcase of
the proposed approach, the inverse coarse-graining is combined with a
theoretical model of bone tissue adaptation to optimize the microarchitecture
of a 3D-printed femur. The predicted properties were in exceptionally good
agreement with the corresponding experimental results. Therefore, the
coarse-graining method allows the design of advanced architected materials with
tunable and predictable properties.


title: Propagating-wave approximation in two-dimensional potential scattering

abstract:
  We introduce a nonperturbative approximation scheme for performing scattering
calculations in two dimensions that involves neglecting the contribution of the
evanescent waves to the scattering amplitude. This corresponds to replacing the
interaction potential $v$ with an associated energy-dependent nonlocal
potential ${\mathscr{V}}_k$ that does not couple to the evanescent waves. The
scattering solutions $\psi(\mathbf{r})$ of the Schr\"odinger equation,
$(-\nabla^2+{\mathscr{V}}_k)\psi(\mathbf{r})=k^2\psi(\mathbf{r})$, has the
remarkable property that their Fourier transform $\tilde\psi(\mathbf{p})$
vanishes unless $\mathbf{p}$ corresponds to the momentum of a classical
particle whose magnitude equals $k$. We construct a transfer matrix for this
class of nonlocal potentials and explore its representation in terms of the
evolution operator for an effective non-unitary quantum system. We show that
the above approximation reduces to the first Born approximation for weak
potentials, and similarly to the semiclassical approximation, becomes valid at
high energies. Furthermore, we identify an infinite class of complex potentials
for which this approximation scheme is exact. We also discuss the appealing
practical and mathematical aspects of this scheme.


title: Submodular Maximization Subject to Matroid Intersection on the Fly

abstract:
  Despite a surge of interest in submodular maximization in the data stream
model, there remain significant gaps in our knowledge about what can be
achieved in this setting, especially when dealing with multiple constraints. In
this work, we nearly close several basic gaps in submodular maximization
subject to $k$ matroid constraints in the data stream model. We present a new
hardness result showing that super polynomial memory in $k$ is needed to obtain
an $o(k / \log k)$-approximation. This implies near optimality of prior
algorithms. For the same setting, we show that one can nevertheless obtain a
constant-factor approximation by maintaining a set of elements whose size is
independent of the stream size. Finally, for bipartite matching constraints, a
well-known special case of matroid intersection, we present a new technique to
obtain hardness bounds that are significantly stronger than those obtained with
prior approaches. Prior results left it open whether a $2$-approximation may
exist in this setting, and only a complexity-theoretic hardness of $1.91$ was
known. We prove an unconditional hardness of $2.69$.


title:
Characterising eclipsing white dwarf M dwarf binaries from multi-band
  eclipse photometry

abstract:
  With the prevalence of wide-field, time-domain photometric sky surveys, the
number of eclipsing white dwarf systems being discovered is increasing
dramatically. An efficient method to follow these up will be key to determining
any population trends and finding any particularly interesting examples. We
demonstrate that multi-band eclipse photometry of binaries containing a white
dwarf and an M~dwarf can be used to determine the masses and temperatures of
the white dwarfs to better than 5 per cent. For the M~dwarfs we measure their
parameters to a precision of better than 6 per cent with the uncertainty
dominated by the intrinsic scatter of the M~dwarf mass-radius relationship.
This precision is better than what can typically be achieved with
low-resolution spectroscopy. The nature of this method means that it will be
applicable to LSST data in the future, enabling direct characterisation without
follow-up spectroscopy. Additionally, we characterise three new
post-common-envelope binaries from their eclipse photometry, finding two
systems containing hot helium-core white dwarfs with low-mass companions (one
near the brown dwarf transition regime) and a possible detached cataclysmic
variable at the lower edge of the period gap.


title: How to Listen? Rethinking Visual Sound Localization

abstract:
  Localizing visual sounds consists on locating the position of objects that
emit sound within an image. It is a growing research area with potential
applications in monitoring natural and urban environments, such as wildlife
migration and urban traffic. Previous works are usually evaluated with datasets
having mostly a single dominant visible object, and proposed models usually
require the introduction of localization modules during training or dedicated
sampling strategies, but it remains unclear how these design choices play a
role in the adaptability of these methods in more challenging scenarios. In
this work, we analyze various model choices for visual sound localization and
discuss how their different components affect the model's performance, namely
the encoders' architecture, the loss function and the localization strategy.
Furthermore, we study the interaction between these decisions, the model
performance, and the data, by digging into different evaluation datasets
spanning different difficulties and characteristics, and discuss the
implications of such decisions in the context of real-world applications. Our
code and model weights are open-sourced and made available for further
applications.


title: SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles

abstract:
  A critical concern in data-driven processes is to build models whose outcomes
do not discriminate against some demographic groups, including gender,
ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of
the group attributes is essential. However, in practice, these attributes may
not be available due to legal and ethical requirements. To address this
challenge, this paper studies a model that protects the privacy of the
individuals' sensitive information while also allowing it to learn
non-discriminatory predictors. A key characteristic of the proposed model is to
enable the adoption of off-the-selves and non-private fair models to create a
privacy-preserving and fair model. The paper analyzes the relation between
accuracy, privacy, and fairness, and the experimental evaluation illustrates
the benefits of the proposed models on several prediction tasks. In particular,
this proposal is the first to allow both scalable and accurate training of
private and fair models for very large neural networks.


title:
Gaining Insights into Unrecognized User Utterances in Task-Oriented
  Dialog Systems

abstract:
  The rapidly growing market demand for dialogue agents capable of
goal-oriented behavior has caused many tech-industry leaders to invest
considerable efforts into task-oriented dialog systems. The performance and
success of these systems is highly dependent on the accuracy of their intent
identification -- the process of deducing the goal or meaning of the user's
request and mapping it to one of the known intents for further processing.
Gaining insights into unrecognized utterances -- user requests the systems
fails to attribute to a known intent -- is therefore a key process in
continuous improvement of goal-oriented dialog systems.
  We present an end-to-end pipeline for processing unrecognized user
utterances, including a specifically-tailored clustering algorithm, a novel
approach to cluster representative extraction, and cluster naming. We evaluated
the proposed clustering algorithm and compared its performance to
out-of-the-box SOTA solutions, demonstrating its benefits in the analysis of
unrecognized user requests.


title:
Nonlinear point-coupling interaction for the relativistic
  Hartree-Bogoliubov approach

abstract:
  We propose a new nonlinear point-coupling parameterized interaction, PC-L3,
for the relativistic Hartree-Bogoliubov framework with a separable pairing
force by fitting to observables of 65 selected spherical nuclei, including the
binding energies and charge radii. Overall, from comparing with the
experimental data, the implementation of PC-L3 in relativistic
Hartree-Bogoliubov successfully yields the lowest root-mean-square deviation,
1.251 MeV, among currently and commonly used point-coupling interactions.
Meanwhile, PC-L3 is capable of estimating the saturation properties of the
symmetric nuclear matter and bulk properties of the finite nuclei and of
appropriately predicting the isospin and mass dependence of binding energy,
e.g., the isotopes of $Z$=20, 50, and 82, and isotones of $N$=20, 50, and 82.
The comparison of the estimated binding energies for $\sim$5500 neutron-rich
nuclei based on PC-L3 and other point-coupling interactions is also presented.


title:
Systematically Improvable Generalization of Self-Interaction Corrected
  Density Functional Theory

abstract:
  Perdew-Zunger self-interaction correction (PZSIC) reintroduces an exact
constraint to approximate density functional theory (DFT), but can
paradoxically degrade performance and is not systematically improvable. We use
the Adiabatic Projection formalism to derive PZSIC in terms of a reference
system experiencing only electron self-interaction. Generalization introduces
correlation into the reference system, systematically bridging from PZSIC to
exact wavefunction theory. Minimal active spaces resolve the PZSIC paradox,
accurately treating near-equilibrium and strongly-correlated systems.


title: Greatest common divisors of shifted primes and Fibonacci numbers

abstract:
  Let $(F_n)$ be the sequence of Fibonacci numbers and, for each positive
integer $k$, let $\mathcal{P}_k$ be the set of primes $p$ such that $\gcd(p -
1, F_{p - 1}) = k$. We prove that the relative density
$\text{r}(\mathcal{P}_k)$ of $\mathcal{P}_k$ exists, and we give a formula for
$\text{r}(\mathcal{P}_k)$ in terms of an absolutely convergent series.
Furthermore, we give an effective criterion to establish if a given $k$
satisfies $\text{r}(\mathcal{P}_k) > 0$, and we provide upper and lower bounds
for the counting function of the set of such $k$'s. As an application of our
results, we give a new proof of a lower bound for the counting function of the
set of integers of the form $\gcd(n, F_n)$, for some positive integer $n$. Our
proof is more elementary than the previous one given by Leonetti and Sanna,
which relies on a result of Cubre and Rouse.


title: From locality to factorizability: a novel escape from Bell's theorem

abstract:
  While initial versions of Bell's theorem captured the notion of locality with
the assumption of factorizability, in later presentations, Bell argued that
factorizability could be derived from the more fundamental principle of local
causality. Here we show that, contrary to what is commonly assumed, in order to
derive factorizability from the principle of local causality, a non-trivial
assumption, similar but strictly independent of settings independence, is
required. Loosely speaking, such an extra assumption demands independence
between the states of the measurement apparatuses. We conclude that it is
possible to construct a model, satisfying both the principle of local causality
and settings independence, but that, in virtue of violating this additional
assumption--and thus factorizability--is able to break Bell's inequality.


title:
Higher regulators of Siegel sixfolds and non-critical values of Spin
  $L$-functions

abstract:
  We construct classes in the middle degree plus one motivic cohomology of
Siegel sixfolds and we compute their image by Beilinson higher regulator in
terms of Rankin-Selberg type automorphic integrals. Using results of Pollack
and Shah, we relate the integrals to non-critical special values of the degree
$8$ Spin $L$-functions. Along the way, by defining and studying complexes of
tempered currents on smooth projective complex varieties endowed with a normal
crossings divisor, we provide a new description of Deligne--Beilinson
cohomology for any Shimura variety. This is particularly useful for
computations of higher regulators and fills a gap in the literature on the
subject.


title:
Generative Biomedical Entity Linking via Knowledge Base-Guided
  Pre-training and Synonyms-Aware Fine-tuning

abstract:
  Entities lie in the heart of biomedical natural language understanding, and
the biomedical entity linking (EL) task remains challenging due to the
fine-grained and diversiform concept names. Generative methods achieve
remarkable performances in general domain EL with less memory usage while
requiring expensive pre-training. Previous biomedical EL methods leverage
synonyms from knowledge bases (KB) which is not trivial to inject into a
generative method. In this work, we use a generative approach to model
biomedical EL and propose to inject synonyms knowledge in it. We propose
KB-guided pre-training by constructing synthetic samples with synonyms and
definitions from KB and require the model to recover concept names. We also
propose synonyms-aware fine-tuning to select concept names for training, and
propose decoder prompt and multi-synonyms constrained prefix tree for
inference. Our method achieves state-of-the-art results on several biomedical
EL tasks without candidate selection which displays the effectiveness of
proposed pre-training and fine-tuning strategies.


title:
Round Trees and Conformal Dimension in Random Groups: low density to
  high density

abstract:
  We investigate conformal dimension for the class of infinite hyperbolic
groups in the Gromov density model $\mathcal{G}^d_{m,l}$ of random groups with
$m \geq 2$ fixed generators, density $0 < d < 1/2$ and relator length $l \to
\infty$. Our main result is a lower bound linear in $l$ at all densities $0 < d
< 1/2$ achieved by building undistorted round trees coming directly from lower
density Gromov random groups.


title:
White-light generating molecular materials: correlation between the
  amorphous/crystalline structure and nonlinear optical properties

abstract:
  Amorphous materials are integral part of todays technology, they commonly are
performant and versatile in integration. Consequently, future applications
increasingly aim to harvest the potential of the amorphous state. Establishing
its structure-property relationship, however, is inherently challenging using
diffraction-based techniques yet is extremely desirable for developing advanced
functionalities. In this article, we introduce a set of transmission electron
microscopy-based techniques to locally quantify the structure of a material.
This unique approach allows to clearly identify the spatial distribution of
amorphous and crystalline regions and to quantify atomic arrangements of
amorphous regions of a representative model system. We study an ensemble of
well-defined, functionalized adamantane-type cluster molecules exhibiting
exceptionally promising nonlinear optical properties of unclear origin. The
nanoscopic structure for three model compounds ([(PhSn)4S6], [(NpSn)4S6],
[(CpSn)4S6]) correlates with their characteristic optical responses. These
results highlight the advantageous properties of amorphous molecular materials
when understanding the microscopic origin.


title:
On the "old" and "new" relativistic wave equations for the particle
  having spin s=3/2

abstract:
  Relativistic wave equation of motion without redundant components for the
particle having spin 3/2 has been considered. In order to show the newness a
comparison with the known equations for the spin s=3/2 field is given.
Therefore, the brief review of the relativistic wave equations for the particle
with spin s=3/2 is given. In our equation the wave function for the
particle-antiparticle doublet has only 8 components. The consideration is
carried out both at the level of relativistic quantum mechanics and at the
level of local field theory. The extended Foldy--Wouthuysen transformation,
which gives the operator link between these two levels is suggested.


title:
The Principle of Least Sensing: A Privacy-Friendly Sensing Paradigm for
  Urban Big Data Analytics

abstract:
  With the worldwide emergence of data protection regulations, how to conduct
law-regulated big data analytics becomes a challenging and fundamental problem.
This article introduces the principle of least sensing, a promising sensing
paradigm toward law-regulated big data analytics.


title:
Towards End-to-End Integration of Dialog History for Improved Spoken
  Language Understanding

abstract:
  Dialog history plays an important role in spoken language understanding (SLU)
performance in a dialog system. For end-to-end (E2E) SLU, previous work has
used dialog history in text form, which makes the model dependent on a cascaded
automatic speech recognizer (ASR). This rescinds the benefits of an E2E system
which is intended to be compact and robust to ASR errors. In this paper, we
propose a hierarchical conversation model that is capable of directly using
dialog history in speech form, making it fully E2E. We also distill semantic
knowledge from the available gold conversation transcripts by jointly training
a similar text-based conversation model with an explicit tying of acoustic and
semantic embeddings. We also propose a novel technique that we call DropFrame
to deal with the long training time incurred by adding dialog history in an E2E
manner. On the HarperValleyBank dialog dataset, our E2E history integration
outperforms a history independent baseline by 7.7% absolute F1 score on the
task of dialog action recognition. Our model performs competitively with the
state-of-the-art history based cascaded baseline, but uses 48% fewer
parameters. In the absence of gold transcripts to fine-tune an ASR model, our
model outperforms this baseline by a significant margin of 10% absolute F1
score.


title: Characterizing nonbilocal correlation: A geometric perspective

abstract:
  Exploiting the notion of measurement-induced nonlocality [Phys.Rev. Lett.
106, 120401 (2011)], we introduce a new measure to quantify the nonbilocal
correlation. We establish a simple relation between the nonlocal and nonbilocal
measures for the arbitrary pure input states. Considering the mixed states as
inputs, we derive two upper bounds of affinity-based nonbilocal measure.
Finally, we have studied the nonbilocality of a different combinations of input
states.


title:
Perfect photon indistinguishability from a set of dissipative quantum
  emitters

abstract:
  Single photon sources (SPS) based on semiconductor quantum dot (QD) platforms
are restricted to low temperature (T) operation due to the presence of strong
dephasing processes. Despite the integration of QD in optical cavities provides
an enhancement of its emission properties, the technical requirements for
maintaining high indistinguishability (I) at high T are beyond the state of the
art. Recently, new theoretical approaches have shown promising results by
implementing two-dipole-coupled-emitter systems. Here, we have developed a
theory to estimate I in a two-emitter system with strong dephasing coupled to a
photonic cavity. We have obtained an analytical expression for I that predicts
the cavity restrictions depending on the distance between the emitters.
Furthermore, we develop an alternative interpretation of I which provide
insigths for systems with a larger number of emitters. We find the optimal
configuration for maximum I in the case of a five-emitter system using a
machine-learning optimization procedure which models the Lindblad equation and
provides the optimal position of each emitter to maximize I. The optimized
configuration provides perfect I while relaxes the cavity requirements to more
experimentally accessible values.


title: Event Transformer

abstract:
  The event camera is a bio-vision inspired camera with high dynamic range,
high response speed, and low power consumption, recently attracting extensive
attention for its use in vast vision tasks. Unlike the conventional cameras
that output intensity frame at a fixed time interval, event camera records the
pixel brightness change (a.k.a., event) asynchronously (in time) and sparsely
(in space). Existing methods often aggregate events occurred in a predefined
temporal duration for downstream tasks, which apparently overlook varying
behaviors of fine-grained temporal events. This work proposes the Event
Transformer to directly process the event sequence in its native vectorized
tensor format. It cascades a Local Transformer (LXformer) for exploiting the
local temporal correlation, a Sparse Conformer (SCformer) for embedding the
local spatial similarity, and a Global Transformer (GXformer) for further
aggregating the global information in a serial means to effectively
characterize the time and space correlations from input raw events for the
generation of effective spatiotemporal features used for tasks. %In both
LXformer and SCformer, Experimental studies have been extensively conducted in
comparison to another fourteen existing algorithms upon five different datasets
widely used for classification. Quantitative results report the
state-of-the-arts classification accuracy and the least computational resource
requirements, of the Event Transformer, making it practically attractive for
event-based vision tasks.


title: Machine Learning State-of-the-Art with Uncertainties

abstract:
  With the availability of data, hardware, software ecosystem and relevant
skill sets, the machine learning community is undergoing a rapid development
with new architectures and approaches appearing at high frequency every year.
In this article, we conduct an exemplary image classification study in order to
demonstrate how confidence intervals around accuracy measurements can greatly
enhance the communication of research results as well as impact the reviewing
process. In addition, we explore the hallmarks and limitations of this
approximation. We discuss the relevance of this approach reflecting on a
spotlight publication of ICLR22. A reproducible workflow is made available as
an open-source adjoint to this publication. Based on our discussion, we make
suggestions for improving the authoring and reviewing process of machine
learning articles.


title: Perturbation in an interacting dark universe

abstract:
  In this paper we have considered an interacting model of dark energy and have
looked into the evolution of the dark sectors. By solving the perturbation
equations numerically, we have studied the imprints on the growth of matter as
well as dark energy fluctuations. It has been found that for higher rate of
interaction strength for the coupling term, visible imprints on the dark energy
density fluctuations are observed at the early epochs of evolution.


title: Partially Linear Models under Data Combination

abstract:
  We consider the identification of and inference on a partially linear model,
when the outcome of interest and some of the covariates are observed in two
different datasets that cannot be linked. This type of data combination problem
arises very frequently in empirical microeconomics. Using recent tools from
optimal transport theory, we derive a constructive characterization of the
sharp identified set. We then build on this result and develop a novel
inference method that exploits the specific geometric properties of the
identified set. Our method exhibits good performances in finite samples, while
remaining very tractable. Finally, we apply our methodology to study
intergenerational income mobility over the period 1850-1930 in the United
States. Our method allows to relax the exclusion restrictions used in earlier
work while delivering confidence regions that are informative.


title: Towards Painless Policy Optimization for Constrained MDPs

abstract:
  We study policy optimization in an infinite horizon, $\gamma$-discounted
constrained Markov decision process (CMDP). Our objective is to return a policy
that achieves large expected reward with a small constraint violation. We
consider the online setting with linear function approximation and assume
global access to the corresponding features. We propose a generic primal-dual
framework that allows us to bound the reward sub-optimality and constraint
violation for arbitrary algorithms in terms of their primal and dual regret on
online linear optimization problems. We instantiate this framework to use
coin-betting algorithms and propose the Coin Betting Politex (CBP) algorithm.
Assuming that the action-value functions are $\varepsilon_b$-close to the span
of the $d$-dimensional state-action features and no sampling errors, we prove
that $T$ iterations of CBP result in an $O\left(\frac{1}{(1 - \gamma)^3
\sqrt{T}} + \frac{\varepsilon_b\sqrt{d}}{(1 - \gamma)^2} \right)$ reward
sub-optimality and an $O\left(\frac{1}{(1 - \gamma)^2 \sqrt{T}} +
\frac{\varepsilon_b \sqrt{d}}{1 - \gamma} \right)$ constraint violation.
Importantly, unlike gradient descent-ascent and other recent methods, CBP does
not require extensive hyperparameter tuning. Via experiments on synthetic and
Cartpole environments, we demonstrate the effectiveness and robustness of CBP.


title:
The PartialSpoof Database and Countermeasures for the Detection of Short
  Generated Audio Segments Embedded in a Speech Utterance

abstract:
  Automatic speaker verification is susceptible to various manipulations and
spoofing, such as text-to-speech (TTS) synthesis, voice conversion (VC),
replay, tampering, and so on. In this paper, we consider a new spoofing
scenario called "Partial Spoof" (PS) in which synthesized or transformed audio
segments are embedded into a bona fide speech utterance. While existing
countermeasures (CMs) can detect fully spoofed utterances, there is a need for
their adaptation or extension to the PS scenario to detect utterances in which
only a part of the audio signal is generated and hence only a fraction of an
utterance is spoofed. For improved explainability, such new CMs should ideally
also be able to detect such short spoofed segments. Our previous study
introduced the first version of a speech database suitable for training CMs for
the PS scenario and showed that, although it is possible to train CMs to
execute the two types of detection described above, there is much room for
improvement. In this paper we propose various improvements to construct a
significantly more accurate CM that can detect short generated spoofed audio
segments at finer temporal resolutions. First, we introduce newly proposed
self-supervised pre-trained models as enhanced feature extractors. Second, we
extend the PartialSpoof database by adding segment labels for various temporal
resolutions, ranging from 20 ms to 640 ms. Third, we propose a new CM and
training strategies that enable the simultaneous use of the utterance-level and
segment-level labels at different temporal resolutions. We also show that the
proposed CM is capable of detecting spoofing at the utterance level with low
error rates, not only in the PS scenario but also in a related logical access
(LA) scenario. The equal error rates of utterance-level detection on the
PartialSpoof and the ASVspoof 2019 LA database were 0.47% and 0.59%,
respectively.


title:
A Unifying Framework for Flexible Excess Hazard Modeling with
  Applications in Cancer Epidemiology

abstract:
  Excess hazard modeling is one of the main tools in population-based cancer
survival research. Indeed, this setting allows for direct modeling of the
survival due to cancer even in the absence of reliable information on the cause
of death, which is common in population-based cancer epidemiology studies. We
propose a unifying link-based additive modeling framework for the excess hazard
that allows for the inclusion of many types of covariate effects, including
spatial and time-dependent effects, using any type of smoother, such as thin
plate, cubic splines, tensor products and Markov random fields. In addition,
this framework accounts for all types of censoring as well as left-truncation.
Estimation is conducted by using an efficient and stable penalized
likelihood-based algorithm whose empirical performance is evaluated through
extensive simulation studies. Some theoretical and asymptotic results are
discussed. Two case studies are presented using population-based cancer data
from patients diagnosed with breast (female), colon and lung cancers in
England. The results support the presence of non-linear and time-dependent
effects as well as spatial variation. The proposed approach is available in the
R package GJRM.


title:
Emergent superconductivity in van der Waals Kagome material Pd3P2S8
  under high pressure

abstract:
  Kagome lattice systems have been proposed to host rich physics, which provide
an excellent platform to explore unusual quantum states. Here, we report on the
discovery of superconductivity in van der Waals material Pd3P2S8 under
pressure. The superconductivity is observed in Pd3P2S8 for those pressures
where the temperature dependence of the resistivity changes from a
semiconducting-like behavior to that of a normal metal. The superconducting
transition temperature Tc increases with applied pressure and reaches ~ 6.83 K
at 79.5 GPa. Combining high-pressure XRD, Raman spectroscopy and theoretical
calculations, our results demonstrate that the observed superconductivity
induced by high pressure in Pd3P2S8 is closely related to the formation of
amorphous phase, which results from the structural instability due to the
enhanced coupling between interlayer Pd and S atoms upon compression.


title: Decoding the star forming properties of gas-rich galaxy pairs

abstract:
  We extend the analysis of Bok et al. (2020) in which the HI content of
isolated galaxies from the AMIGA sample and selected paired galaxies from
ALFALFA were examined as a potential driver of galaxy location on the WISE
mid-infrared SFR-Mstar sequence. By further characterizing the isolated and
pair galaxy samples, i.e. in terms of optical galaxy morphology, a more
detailed and quantitative description of local galaxy environment by way of the
local number density (eta) and tidal strength (Q) parameters, star formation
efficiency (SFE(HI)), and HI integrated profile asymmetries, we present
plausible pathways for the broadening of the pair sample HI deficiency
distribution towards both high and low deficiencies compared to the narrower
isolated galaxy sample distribution (i.e. sigma_pairs = 0.34 versus sigma_AMIGA
= 0.28). We associate the gas-rich tail of the pair deficiency distribution
with the highest Q values, large profile asymmetries, and low SFEs. From this
we infer that merger activity is enhancing gas supplies, as well as disrupting
the efficiency of star formation, via strong gravitational torques. The
gas-poor wing of the deficiency distribution appears to be populated with
galaxies in denser environments (with larger eta values on average), more akin
to groups. Despite our gas-rich selection criterion, there is a small
population of early-type galaxies in the pair sample, which primarily fall in
the positive deficiency wing of the distribution. These results suggest that a
combination of a denser galaxy environment, early-type morphology, and higher
stellar mass, is contributing to the broadening of the deficiency distribution
towards larger deficiencies.


title:
A spectral curve for the generation of bipartite maps in topological
  recursion

abstract:
  We derive an efficient way to obtain generating functions of bipartite maps
of arbitrary genus and boundary length using a spectral curve as initial data
for the framework of topological recursion. Based on an earlier result of
Chapuy and Fang counting these maps and having a structural proximity to
topological recursion, we deduce the corresponding spectral curve which has a
strong relation to the spectral curve giving rise to generating functions of
ordinary maps.


title: Towards Understanding Analytics in Software Startups

abstract:
  Analytics plays a crucial role in the data-informed decision-making processes
of modern businesses. Unlike established software companies, software startups
are not seen utilizing the potential of analytics even though a startup process
should be primarily data-driven. There has been little understanding in the
literature about analytics for software startups. This study set out to address
the knowledge gap by exploring how analytics is understood in the context of
software startups. To this end, we collected the qualitative data of three
analytics platforms that are mostly used by startups from multiple sources. We
covered platform documentation as well as experience reports of the software
startups using these platforms. The data was analyzed using content analysis
techniques. Four high-level concepts were identified that encapsulate the real
understanding of software startups on analytics, including instrumentation of
analytics, experimentation, diagnostic analysis, and getting insights. The
first concept describes how startups set up analytics and the latter three
illustrate the usage scenarios of analytics. This study is the first step
toward understanding analytics in the software startup context. The identified
concepts can guide further investigation of analytics in this context. It also
provides some insights for software startups to set up analytics for
data-informed decisions. Given the limitation of the data used in the study,
the immediate next step is to ground as well as validate the acquired
understanding using the primary data, by directly interacting with software
startups.


title:
Building an ASR Error Robust Spoken Virtual Patient System in a Highly
  Class-Imbalanced Scenario Without Speech Data

abstract:
  A Virtual Patient (VP) is a powerful tool for training medical students to
take patient histories, where responding to a diverse set of spoken questions
is essential to simulate natural conversations with a student. The performance
of such a Spoken Language Understanding system (SLU) can be adversely affected
by both the presence of Automatic Speech Recognition (ASR) errors in the test
data and a high degree of class imbalance in the SLU training data. While these
two issues have been addressed separately in prior work, we develop a novel
two-step training methodology that tackles both these issues effectively in a
single dialog agent. As it is difficult to collect spoken data from users
without a functioning SLU system, our method does not rely on spoken data for
training, rather we use an ASR error predictor to "speechify" the text data.
Our method shows significant improvements over strong baselines on the VP
intent classification task at various word error rate settings.


title:
Domain Adversarial Graph Convolutional Network Based on RSSI and
  Crowdsensing for Indoor Localization

abstract:
  In recent years, due to the wider WiFi coverage and the popularization of
mobile communication devices, the technology of indoor positioning using WiFi
fingerprints has been rapidly developed. Currently, most supervised methods
need to collect a large amount of data to construct fingerprint datasets, which
is labor-intensive and time-consuming. To solve the problem, we proposed a
novel WiDAGCN model that can be trained with a few labeled site survey data and
unlabeled crowdsensing WiFi fingerprints. To comprehensively represent the
topology structure of the data, we constructed heterogeneous graphs according
to the received signal strength indicators (RSSIs) between the waypoints and
WiFi access points (APs). We focus on the graph convolutional network (GCN)
method and the representation of graph-level features, which were rarely
involved in previous WiFi indoor localization studies. Then, we try to minimize
the difference between the source and target domains and make full use of the
unlabeled data in the target domain using the domain adversarial training
scheme. A public indoor localization dataset containing different buildings was
used to evaluate the performance of the model. The experimental results show
that our system can achieve a competitive localization accuracy in large
buildings such as shopping malls.


title: Uniform Complexity for Text Generation

abstract:
  Powerful language models such as GPT-2 have shown promising results in tasks
such as narrative generation which can be useful in an educational setup. These
models, however, should be consistent with the linguistic properties of
triggers used. For example, if the reading level of an input text prompt is
appropriate for low-leveled learners (ex. A2 in the CEFR), then the generated
continuation should also assume this particular level. Thus, we propose the
task of uniform complexity for text generation which serves as a call to make
existing language generators uniformly complex with respect to prompts used.
Our study surveyed over 160 linguistic properties for evaluating text
complexity and found out that both humans and GPT-2 models struggle in
preserving the complexity of prompts in a narrative generation setting.


title: Correcting Robot Plans with Natural Language Feedback

abstract:
  When humans design cost or goal specifications for robots, they often produce
specifications that are ambiguous, underspecified, or beyond planners' ability
to solve. In these cases, corrections provide a valuable tool for
human-in-the-loop robot control. Corrections might take the form of new goal
specifications, new constraints (e.g. to avoid specific objects), or hints for
planning algorithms (e.g. to visit specific waypoints). Existing correction
methods (e.g. using a joystick or direct manipulation of an end effector)
require full teleoperation or real-time interaction. In this paper, we explore
natural language as an expressive and flexible tool for robot correction. We
describe how to map from natural language sentences to transformations of cost
functions. We show that these transformations enable users to correct goals,
update robot motions to accommodate additional user preferences, and recover
from planning errors. These corrections can be leveraged to get 81% and 93%
success rates on tasks where the original planner failed, with either one or
two language corrections. Our method makes it possible to compose multiple
constraints and generalizes to unseen scenes, objects, and sentences in
simulated environments and real-world environments.


title: Meson and Glueball spectroscopy within the Graviton Soft-Wall model

abstract:
  In this contribution we present results of the calculations of several
hadronic spectra within the holographic graviton soft-wall (GSW) model. In
particular, we studied and compared with data for the ground state and
excitations of: glueballs, scalar, vector, axial and pseudo-scalar mesons. The
GSW model is found to be capable to describe these observable with only few
parameters.


title:
Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in
  End-to-End Speech-to-Intent Systems

abstract:
  Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) have
been primarily due to effective pretraining of speech representations. One such
pretraining paradigm is the distillation of semantic knowledge from
state-of-the-art text-based models like BERT to speech encoder neural networks.
This work is a step towards doing the same in a much more efficient and
fine-grained manner where we align speech embeddings and BERT embeddings on a
token-by-token basis. We introduce a simple yet novel technique that uses a
cross-modal attention mechanism to extract token-level contextual embeddings
from a speech encoder such that these can be directly compared and aligned with
BERT based contextual embeddings. This alignment is performed using a novel
tokenwise contrastive loss. Fine-tuning such a pretrained model to perform
intent recognition using speech directly yields state-of-the-art performance on
two widely used SLU datasets. Our model improves further when fine-tuned with
additional regularization using SpecAugment especially when speech is noisy,
giving an absolute improvement as high as 8% over previous results.


title:
MmWave 6D Radio Localization with a Snapshot Observation from a Single
  BS

abstract:
  Accurate and ubiquitous localization is crucial for a variety of applications
such as logistics, navigation, intelligent transport, monitoring, and control.
Exploiting mmWave signals in 5G and Beyond 5G systems can provide accurate
localization with limited infrastructure. We consider the single base station
localization problem and extend it to 3D position and 3D orientation estimation
of an unsynchronized multi-antenna user, using downlink MIMO-OFDM signals.
Through a Fisher information analysis, we show that the problem is often
identifiable, provided that there is at least one additional multipath
component, even if the position of corresponding incidence point is a priori
unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to
jointly estimate the 3D position and 3D orientation of the user as well as
several nuisance parameters (the user clock offset and the positions of
incidence points corresponding to the multipath). The ML problem is a
high-dimensional non-convex optimization problem over a product of Euclidean
and Riemannian manifolds. To avoid complex exhaustive search procedures, we
propose a geometric initial estimate of all parameters, which reduces the
problem to a 1-dimensional search over a finite interval. Numerical results
show the efficiency of the proposed ad-hoc estimation, whose gap to the CRB is
tightened using ML estimation.


title: Topological Jahn-Teller Mott insulators in twisted bilayer graphene

abstract:
  Small-angle twisted bilayer graphene is endowed with an emergent $U_v(1)$
valley-symmetry characterising both electronic structure and superlattice
normal modes. The $U_v(1)$ symmetric electron-phonon coupling realises an
$e\times E$ Jahn-Teller model featuring the same topological properties of the
band structure but now for combined electrons plus phonons. Indeed, when
Jahn-Teller effect is assumed static, and thus $U_v(1)$ broken, the
phonon-mediated attraction and Coulomb repulsion yield in mean-field an
insulator at charge neutrality that, like in the absence of interaction, is
invariant under the $P622$ space group and has the two occupied flat-bands
bearing opposite Chern numbers $\text{C}=\pm 2$. The rigid band-shift of that
parent state leads in mean-field to Jahn-Teller distorted topological
insulators at all other integer fillings $\nu=\pm n$, Chern number
$\text{C}=\pm 2$ for $n=2$ and $\text{C}=\pm 1$ for $n=1,3$, which break time
reversal symmetry, lower $P622\to P6$, and are spin-polarised for odd $n$. When
Jahn-Teller effect is instead dynamical, it may stabilise insulators with the
same properties as their mean-field counterparts, but now $U_v(1)$ invariant.
That envisages the noteworthy existence of topological Jahn-Teller Mott
insulators prone to become chiral $d$-wave superconductors upon doping.


title:
Meshfree Collocation for Elliptic Problems with Discontinuous
  Coefficients

abstract:
  We present a meshfree generalized finite difference method (GFDM) for solving
Poisson's equation with coefficients containing jump discontinuities up to
several orders of magnitude. To discretize the diffusion operator, we formulate
a strong form method that uses a smearing of the discontinuity, and a
conservative formulation based on locally computed Voronoi cells. Additionally,
we propose a novel conservative formulation of enforcing Neumann boundary
conditions that is compatible with the conservative formulation of the
diffusion operator. Finally, we introduce a way to switch between the strong
form and the conservative formulation to obtain a locally conservative and
positivity preserving scheme. The presented numerical methods are benchmarked
against four test cases with varying complexity and different jump magnitudes
on point clouds that are not aligned to the discontinuity. Our results show
that the new hybrid method that switches between the two formulations produces
better results than the standard GFDM approach for high jumps in the
diffusivity parameter.


title: Time-Adaptive Recurrent Neural Networks

abstract:
  Data are often sampled irregularly in time. Dealing with this using Recurrent
Neural Networks (RNNs) traditionally involved ignoring the fact, feeding the
time differences as additional inputs, or resampling the data. All these
methods have their shortcomings. We propose an elegant alternative approach
where instead the RNN is in effect resampled in time to match the time of the
data. We use Echo State Network (ESN) and Gated Recurrent Unit (GRU) as the
basis for our solution. Such RNNs can be seen as discretizations of
continuous-time dynamical systems, which gives a solid theoretical ground for
our approach. Similar recent observations have been made in feed-forward neural
networks as neural ordinary differential equations. Our Time-Adaptive ESN
(TAESN) and GRU (TAGRU) models allow for a direct model time setting and
require no additional training, parameter tuning, or computation compared to
the regular counterparts, thus retaining their original efficiency. We confirm
empirically that our models can effectively compensate for the
time-non-uniformity of the data and demonstrate that they compare favorably to
data resampling, classical RNN methods, and alternative RNN models proposed to
deal with time irregularities on several real-world nonuniform-time datasets.


title:
Worldwide city transport typology prediction with sentence-BERT based
  supervised learning via Wikipedia

abstract:
  An overwhelming majority of the world's human population lives in urban areas
and cities. Understanding a city's transportation typology is immensely
valuable for planners and policy makers whose decisions can potentially impact
millions of city residents. Despite the value of understanding a city's
typology, labeled data (city and it's typology) is scarce, and spans at most a
few hundred cities in the current transportation literature. To break this
barrier, we propose a supervised machine learning approach to predict a city's
typology given the information in its Wikipedia page. Our method leverages
recent breakthroughs in natural language processing, namely sentence-BERT, and
shows how the text-based information from Wikipedia can be effectively used as
a data source for city typology prediction tasks that can be applied to over
2000 cities worldwide. We propose a novel method for low-dimensional city
representation using a city's Wikipedia page, which makes supervised learning
of city typology labels tractable even with a few hundred labeled samples.
These features are used with labeled city samples to train binary classifiers
(logistic regression) for four different city typologies: (i) congestion, (ii)
auto-heavy, (iii) transit-heavy, and (iv) bike-friendly cities resulting in
reasonably high AUC scores of 0.87, 0.86, 0.61 and 0.94 respectively. Our
approach provides sufficient flexibility for incorporating additional variables
in the city typology models and can be applied to study other city typologies
as well. Our findings can assist a diverse group of stakeholders in
transportation and urban planning fields, and opens up new opportunities for
using text-based information from Wikipedia (or similar platforms) as data
sources in such fields.


title: Snowmass White Paper: Gravitational Waves and Scattering Amplitudes

abstract:
  We review recent progress and future prospects for harnessing powerful tools
from theoretical high-energy physics, such as scattering amplitudes and
effective field theory, to develop a precise and systematically improvable
framework for calculating gravitational-wave signals from binary systems
composed of black holes and/or neutron stars. This effort aims to provide
state-of-the-art predictions that will enable high-precision measurements at
future gravitational-wave detectors. In turn, applying the tools of quantum
field theory in this new arena will uncover theoretical structures that can
transform our understanding of basic phenomena and lead to new tools that will
further the cycle of innovation. While still in a nascent stage, this research
direction has already derived new analytic results in general relativity, and
promises to advance the development of highly accurate waveform models for ever
more sensitive detectors.


title: The KKL inequality and Rademacher type 2

abstract:
  We show that vector valued Kahn--Kalai--Linial inequality is equivalent to
Rademacher type 2.


title:
Automatically Learning Fallback Strategies with Model-Free Reinforcement
  Learning in Safety-Critical Driving Scenarios

abstract:
  When learning to behave in a stochastic environment where safety is critical,
such as driving a vehicle in traffic, it is natural for human drivers to plan
fallback strategies as a backup to use if ever there is an unexpected change in
the environment. Knowing to expect the unexpected, and planning for such
outcomes, increases our capability for being robust to unseen scenarios and may
help prevent catastrophic failures. Control of Autonomous Vehicles (AVs) has a
particular interest in knowing when and how to use fallback strategies in the
interest of safety. Due to imperfect information available to an AV about its
environment, it is important to have alternate strategies at the ready which
might not have been deduced from the original training data distribution.
  In this paper we present a principled approach for a model-free Reinforcement
Learning (RL) agent to capture multiple modes of behaviour in an environment.
We introduce an extra pseudo-reward term to the reward model, to encourage
exploration to areas of state-space different from areas privileged by the
optimal policy. We base this reward term on a distance metric between the
trajectories of agents, in order to force policies to focus on different areas
of state-space than the initial exploring agent. Throughout the paper, we refer
to this particular training paradigm as learning fallback strategies.
  We apply this method to an autonomous driving scenario, and show that we are
able to learn useful policies that would have otherwise been missed out on
during training, and unavailable to use when executing the control algorithm.


title: Compared analysis of time-like hyperon form factors

abstract:
  Data on hyperon form factors recently obtained from annihilation reactions
are compared each other, and to proton and neutron form factors, in terms of
two kinematical variables: the transferred momentum square, $q^2$, and the
modulus $P$ of the relative momentum between the outgoing baryons. They are
critically discussed in terms of possible correlated structures. The present
status of the time-like form factor data for baryons is described and
suggestions are given on the reactions and the kinematical range where data are
desirable in order to clarify the arisen questions.


title:
Coexistence of localization and transport in many-body two-dimensional
  Aubry-Andr\'e models

abstract:
  Whether disordered and quasiperiodic many-body quantum systems host a
long-lived localized phase in the thermodynamic limit has been the subject of
intense recent debate. While in one dimension substantial evidence for the
existence of such a many-body localized (MBL) phase exists, the behavior in
higher dimensions remains an open puzzle. In two-dimensional disordered
systems, for instance, it has been argued that rare regions may lead to
thermalization of the whole system through a mechanism dubbed the avalanche
instability. In quasiperiodic systems, rare regions are altogether absent and
the fate of a putative many-body localized phase has hitherto remained largely
unexplored. In this work, we investigate the localization properties of two
many-body quasiperiodic models, which are two-dimensional generalizations of
the Aubry-Andr\'e model. By studying the out-of-equilibrium dynamics of large
systems, we find a long-lived MBL phase, in contrast to random systems.
Furthermore, we show that deterministic lines of weak potential, which appear
in investigated quasiperiodic models, support large-scale transport, while the
system as a whole does not thermalize. Our results demonstrate that
quasiperiodic many-body systems have the remarkable and counter-intuitive
capability of exhibiting coexisting localization and transport properties - a
phenomenon reminiscent of the behavior of supersolids. Our findings are of
direct experimental relevance and can be tested, for instance, using
state-of-the-art cold atomic systems.


title:
The short-term effect of COVID-19 pandemic on China's crude oil futures
  market: A study based on multifractal analysis

abstract:
  The ongoing COVID-19 shocked financial markets globally, including China's
crude oil future market, which is the third most traded crude oil futures after
WTI and Brent. As China's first crude oil futures accessible to foreign
investors, the Shanghai crude oil futures (SC) have attracted significant
interest since launch at the Shanghai International Energy Exchange. The impact
of COVID-19 on the new crude oil futures is an important issue for investors
and policy makers. Therefore this paper studies the short-term influence of
COVID-19 pandemic on SC via multifractal analysis. We compare market efficiency
of SC before and during the pandemic with the multifractal detrended
fluctuation analysis and other commonly-used random walk tests. Then we
generate shuffled and surrogate data to investigate the components of
multifractal nature in SC. And we examine cross-correlations between SC returns
and other financial assets returns as well as SC trading volume changes by the
multifractal detrended cross-correlation analysis. The results show that market
efficiency of SC and its cross-correlations with other assets increase
significantly after the outbreak of COVID-19. Besides that, the sources of its
multifractal nature have changed since the pandemic. The findings provide
evidence for the short-term impacts of COVID-19 on SC. The results may have
important implications for assets allocation, investment strategies and risk
monitoring.


title: Human vs Objective Evaluation of Colourisation Performance

abstract:
  Automatic colourisation of grey-scale images is the process of creating a
full-colour image from the grey-scale prior. It is an ill-posed problem, as
there are many plausible colourisations for a given grey-scale prior. The
current SOTA in auto-colourisation involves image-to-image type Deep
Convolutional Neural Networks with Generative Adversarial Networks showing the
greatest promise. The end goal of colourisation is to produce full colour
images that appear plausible to the human viewer, but human assessment is
costly and time consuming. This work assesses how well commonly used objective
measures correlate with human opinion. We also attempt to determine what facets
of colourisation have the most significant effect on human opinion. For each of
20 images from the BSD dataset, we create 65 recolourisations made up of local
and global changes. Opinion scores are then crowd sourced using the Amazon
Mechanical Turk and together with the images this forms an extensible dataset
called the Human Evaluated Colourisation Dataset (HECD). While we find
statistically significant correlations between human-opinion scores and a small
number of objective measures, the strength of the correlations is low. There is
also evidence that human observers are most intolerant to an incorrect hue of
naturally occurring objects.


title:
A Post-Processing Tool and Feasibility Study for Three-Dimensional
  Imaging with Electrical Impedance Tomography During Deep Brain Stimulation
  Surgery

abstract:
  Electrical impedance tomography (EIT) is a promising technique for biomedical
imaging. The strength of EIT is its ability to reconstruct images of the body's
internal structures through radiation-safe techniques. EIT is regarded as safe
for patients' health, and it is currently being actively researched. This paper
investigates the application of EIT during deep brain stimulation (DBS) surgery
as a means to identify targets during operations. DBS involves a surgical
procedure in which a lead or electrode array is implanted in a specific target
area in the brain. Electrical stimulations are then used to modulate neural
circuits within the target area to reduce disabling neurological symptoms. The
main difficulty in performing DBS surgery is to accurately position the lead in
the target area before commencing the treatment. Brain tissue shifts during DBS
surgery can be as large as the target size when compared with the pre-operative
magnetic resonance imaging (MRI) or computed tomography (CT) images. To address
this problem, a solution based on open-domain EIT to reconstruct images
surrounding the probe during DBS surgery is proposed. Data acquisition and
image reconstruction were performed, and artificial intelligence was applied to
enhance the resulting images. The results showed that the proposed method is
rapid, produces valuable high-quality images, and constitutes a first step
towards in-vivo study.


title:
Perturbative region on non-Gaussian parameter space in single-field
  inflation

abstract:
  We calculate one-loop correction to the two-point functions of curvature
perturbation in single-field inflation generated by cubic self-interaction.
Incorporating the observed red-tilted spectrum of curvature perturbation, the
relevant one-loop correction takes a finite value and inversely proportional to
the spectral tilt. Requiring one-loop correction to be much smaller than the
tree-level contribution leads to an upper bound on primordial non-Gaussianity.
While observationally allowed region of non-Gaussian parameter space is found
to be entirely included by the region where one-loop correction is smaller than
the tree-level contribution, if we tighten the theoretical requirement to
require that the former should be smaller than, say, 10% or 1% of the latter,
significant parts of the observationally allowed region are ruled out. If
future observations conclude non-Gaussianity falls in such a region, then it
would be important to incorporate higher-order corrections to the spectrum in
order to achieve precise cosmology. In some extreme cases, it might indicate
breakdown of the cosmological perturbation theory in the context of
single-field inflation.


title:
CXR-FL: Deep Learning-based Chest X-ray Image Analysis Using Federated
  Learning

abstract:
  Federated learning enables building a shared model from multicentre data
while storing the training data locally for privacy. In this paper, we present
an evaluation (called CXR-FL) of deep learning-based models for chest X-ray
image analysis using the federated learning method. We examine the impact of
federated learning parameters on the performance of central models.
Additionally, we show that classification models perform worse if trained on a
region of interest reduced to segmentation of the lung compared to the full
image. However, focusing training of the classification model on the lung area
may result in improved pathology interpretability during inference. We also
find that federated learning helps maintain model generalizability. The
pre-trained weights and code are publicly available at
(https://github.com/SanoScience/CXR-FL).


title:
Automatic Adjoint Differentiation for special functions involving
  expectations

abstract:
  We explain how to compute gradients of functions of the form $G = \frac{1}{2}
\sum_{i=1}^{m} (E y_i - C_i)^2$, which often appear in the calibration of
stochastic models, using Automatic Adjoint Differentiation and parallelization.
We expand on the work of arXiv:1901.04200 and give faster and easier to
implement approaches. We also provide an implementation of our methods and
apply the technique to calibrate European options.


title: Rethinking Machine Learning Model Evaluation in Pathology

abstract:
  Machine Learning has been applied to pathology images in research and
clinical practice with promising outcomes. However, standard ML models often
lack the rigorous evaluation required for clinical decisions. Machine learning
techniques for natural images are ill-equipped to deal with pathology images
that are significantly large and noisy, require expensive labeling, are hard to
interpret, and are susceptible to spurious correlations. We propose a set of
practical guidelines for ML evaluation in pathology that address the above
concerns. The paper includes measures for setting up the evaluation framework,
effectively dealing with variability in labels, and a recommended suite of
tests to address issues related to domain shift, robustness, and confounding
variables. We hope that the proposed framework will bridge the gap between ML
researchers and domain experts, leading to wider adoption of ML techniques in
pathology and improving patient outcomes.


title:
Access to care: analysis of the geographical distribution of healthcare
  using Linked Open Data

abstract:
  Background: Access to medical care is strongly dependent on resource
allocation, such as the geographical distribution of medical facilities.
Nevertheless, this data is usually restricted to country official
documentation, not available to the public. While some medical facilities' data
is accessible as semantic resources on the Web, it is not consistent in its
modeling and has yet to be integrated into a complete, open, and specialized
repository. This work focuses on generating a comprehensive semantic dataset of
medical facilities worldwide containing extensive information about such
facilities' geo-location.
  Results: For this purpose, we collect, align, and link various open-source
databases where medical facilities' information may be present. This work
allows us to evaluate each data source along various dimensions, such as
completeness, correctness, and interlinking with other sources, all critical
aspects of current knowledge representation technologies.
  Conclusions: Our contributions directly benefit stakeholders in the
biomedical and health domain (patients, healthcare professionals, companies,
regulatory authorities, and researchers), who will now have a better overview
of the access to and distribution of medical facilities.


title: Constraining Global Solar Models through Helioseismic Analysis

abstract:
  Global hydrodynamic simulations of internal solar dynamics have focused on
replicating the conditions for solar-like differential rotation and meridional
circulation using the results of helioseismic inversions as a constraint.
Inferences of meridional circulation, however, have provided controversial
results showing the possibility of one, two, or multiple cells along the
radius. To resolve this controversy and develop a more robust understanding of
global flow regimes in the solar interior, we apply a "forward-modeling"
approach to the analysis of helioseismic signatures of meridional circulation
profiles obtained from numerical simulations. We employ the global acoustic
modeling code GALE to simulate the propagation of acoustic waves through
regimes of mean mass flows generated by global hydrodynamic and
magnetohydrodynamic models: EULAG, the Pencil Code, and the Rayleigh code.
These models are used to create synthetic dopplergram data products, used as
inputs for local time-distance helioseismology techniques. Helioseismic
travel-time signals from solutions obtained through global numerical
simulations are compared directly with inferences from solar observations, in
order to set additional constraints on global model parameters in a direct way.
We show that even though these models are able to replicate solar-like
differential rotation, the resulting rotationally-constrained convection
develops a multi-cell global meridional circulation profile that is measurably
inconsistent with local time-distance inferences of solar observations.
However, we find that the development of rotationally-unconstrained convection
close to the model surface is able to maintain solar-like differential
rotation, while having a significant impact on the helioseismic travel-time
signal, replicating solar observations within one standard deviation of the
error due to noise.


title: "FIJO": a French Insurance Soft Skill Detection Dataset

abstract:
  Understanding the evolution of job requirements is becoming more important
for workers, companies and public organizations to follow the fast
transformation of the employment market. Fortunately, recent natural language
processing (NLP) approaches allow for the development of methods to
automatically extract information from job ads and recognize skills more
precisely. However, these efficient approaches need a large amount of annotated
data from the studied domain which is difficult to access, mainly due to
intellectual property. This article proposes a new public dataset, FIJO,
containing insurance job offers, including many soft skill annotations. To
understand the potential of this dataset, we detail some characteristics and
some limitations. Then, we present the results of skill detection algorithms
using a named entity recognition approach and show that transformers-based
models have good token-wise performances on this dataset. Lastly, we analyze
some errors made by our best model to emphasize the difficulties that may arise
when applying NLP approaches.


title:
Can instability variations warn developers when open-source projects
  boost?

abstract:
  Although architecture instability has been studied and measured using a
variety of metrics, a deeper analysis of which project parts are less stable
and how such instability varies over time is still needed. While having more
information on architecture instability is, in general, useful for any software
development project, it is especially important in Open Source Software (OSS)
projects where the supervision of the development process is more difficult to
achieve. In particular, we are interested when OSS projects grow from a small
controlled environment (i.e., the cathedral phase) to a community-driven
project (i.e., the bazaar phase). In such a transition, the project often
explodes in terms of software size and number of contributing developers.
Hence, the complexity of the newly added features, and the frequency of the
commits and files modified may cause significant variations of the instability
of the structure of the classes and packages. Consequently, in this registered
report we suggest ways to analyze the instability in OSS projects, especially
during that sensitive phase where they become community-driven. We intend to
suggest ways to predict the evolution of the instability in several OSS
projects. Our preliminary results show that it seems possible to provide
meaningful estimations that can be useful for OSS teams before a project grows
in excess.


title:
Bridging the Gap between Language Models and Cross-Lingual Sequence
  Labeling

abstract:
  Large-scale cross-lingual pre-trained language models (xPLMs) have shown
effectiveness in cross-lingual sequence labeling tasks (xSL), such as
cross-lingual machine reading comprehension (xMRC) by transferring knowledge
from a high-resource language to low-resource languages. Despite the great
success, we draw an empirical observation that there is a training objective
gap between pre-training and fine-tuning stages: e.g., mask language modeling
objective requires local understanding of the masked token and the
span-extraction objective requires global understanding and reasoning of the
input passage/paragraph and question, leading to the discrepancy between
pre-training and xMRC. In this paper, we first design a pre-training task
tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM)
to eliminate the objective gap in a self-supervised manner. Second, we present
ContrAstive-Consistency Regularization (CACR), which utilizes contrastive
learning to encourage the consistency between representations of input parallel
sequences via unsupervised cross-lingual instance-wise training signals during
pre-training. By these means, our methods not only bridge the gap between
pretrain-finetune, but also enhance PLMs to better capture the alignment
between different languages. Extensive experiments prove that our method
achieves clearly superior results on multiple xSL benchmarks with limited
pre-training data. Our methods also surpass the previous state-of-the-art
methods by a large margin in few-shot data settings, where only a few hundred
training examples are available.


title: Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0

abstract:
  In this work, we explore whether the recently demonstrated zero-shot
abilities of the T0 model extend to Named Entity Recognition for
out-of-distribution languages and time periods. Using a historical newspaper
corpus in 3 languages as test-bed, we use prompts to extract possible named
entities. Our results show that a naive approach for prompt-based zero-shot
multilingual Named Entity Recognition is error-prone, but highlights the
potential of such an approach for historical languages lacking labeled
datasets. Moreover, we also find that T0-like models can be probed to predict
the publication date and language of a document, which could be very relevant
for the study of historical texts.


title:
Single-Turn Debate Does Not Help Humans Answer Hard
  Reading-Comprehension Questions

abstract:
  Current QA systems can generate reasonable-sounding yet false answers without
explanation or evidence for the generated answer, which is especially
problematic when humans cannot readily check the model's answers. This presents
a challenge for building trust in machine learning systems. We take inspiration
from real-world situations where difficult questions are answered by
considering opposing sides (see Irving et al., 2018). For multiple-choice QA
examples, we build a dataset of single arguments for both a correct and
incorrect answer option in a debate-style set-up as an initial step in training
models to produce explanations for two candidate answers. We use long contexts
-- humans familiar with the context write convincing explanations for
pre-selected correct and incorrect answers, and we test if those explanations
allow humans who have not read the full context to more accurately determine
the correct answer. We do not find that explanations in our set-up improve
human accuracy, but a baseline condition shows that providing human-selected
text snippets does improve accuracy. We use these findings to suggest ways of
improving the debate set up for future data collection efforts.


title:
Anisotropic magnetotransport in the layered antiferromagnet
  TaFe$_{1.25}$Te$_3$

abstract:
  The discovery of fascinating ways to control and manipulate antiferromagnetic
materials have garnered considerable attention as an attractive platform to
explore novel spintronic phenomena and functionalities. Layered
antiferromagnets (AFMs) exhibiting interesting magnetic structures, can serve
as an attractive starting point to establish novel functionalities down to the
two-dimensional limit. In this work, we explore the magnetoresistive properties
of the spin-ladder AFM TaFe$_{1.25}$Te$_3$. Magnetization studies reveal an
anisotropic magnetic behavior resulting in the stabilization of a spin-flop
configuration for H $\perp$ (10-1) plane (i.e., out-of-plane direction).
Angle-dependent longitudinal and transverse magnetoresistances show an unusual
anharmonic behavior. A significant anisotropic enhancement of magnetoresistance
when H $\perp$ (10-1) plane compared to H $\parallel$ (10-1) directions has
been observed. The present results deepen our understanding of the
magnetoresistive properties of low-dimensional layered AFMs, and point towards
the possibility of utilizing these novel material systems for antiferromagnetic
spintronics.


title:
An extended Rayleigh model: Properties, regression and COVID-19
  application

abstract:
  We define a four-parameter extended Rayleigh distribution, and obtain several
mathematical properties including a stochastic representation. We construct a
regression from the new distribution. The estimation is done by maximum
likelihood. The utility of the new models is proved in two real applications.


title: Simple Proof of Security of the multiparty Prepare and Measure QKD

abstract:
  The majority of research to date has concentrated on the quantum key
distribution (QKD) between two parties. In general, the QKD protocols proposed
for the multiparty scenario often involve the usage of a maximally entangled
state, such as GHZ, which is challenging to implement in practice. This paper
examines the prepare and measure version of multiparty communication through
quantum key distribution. It is sufficient to have the capability of preparing
and measuring a single qubit state. The security of the multiparty prepare and
measure QKD is demonstrated by utilizing the well-known techniques of quantum
error correction and entanglement purification. We begin by establishing the
security of the entangled-based version and progress through a series of
reductions to arrive at our equivalent multiparty prepare and measure version
of QKD. We establish the security proof of multiparty entangled-based version
by combining FTQC and classical statistics on the GHZ basis. Then proved the
conditional security of the multiparty prepare-and-measure QKD by the
equivalency.


title:
Lattice dynamics in the charge-density-wave metal at a
  van-Hove-singularity filling

abstract:
  The charge-density-wave (CDW) order with macroscopically occupied electrons
distorts the underlying lattice and usually causes the softening of the
associated phonon mode. However, previous studies demonstrated that the
spin-Peierls transition does not always induce an associated phonon softening,
but the central-peak scenario applied in the quasi-one-dimensional compound
CuGeO$_3$. We generalize the lattice-dynamics studies on the two-dimensional
CDW state at van-Hove-singularity (VHS) filling and find that the CDW ordering
could develop a central peak at zero frequency while the associated phonon
undergoes hardening. The particle-hole scatterings between VHS points give rise
to a low-energy increased charge-density susceptibility, and their coupling to
the lattice dynamics induces two poles in the Green function for the
CDW-associated phonon mode. The zero-frequency pole corresponds to the
collective charge-density and phonon coupling mode. The high-frequency one is
related to the high-temperature phonon mode that hardens as reaching the CDW
transition. Our result may have the potential implication for the recently
discovered Kagome metal $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs) in which no soft
phonon is observed during the CDW transition.


title: Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials

abstract:
  In this paper, we present a method for automated persona-driven video game
tutorial level generation. Tutorial levels are scenarios in which the player
can explore and discover different rules and game mechanics. Procedural
personas can guide generators to create content which encourages or discourages
certain playstyle behaviors. In this system, we use procedural personas to
calculate the behavioral characteristics of levels which are evolved using the
quality-diversity algorithm known as Constrained MAP-Elites. An evolved map's
quality is determined by its simplicity: the simpler it is, the better it is.
Within this work, we show that the generated maps can strongly encourage or
discourage different persona-like behaviors and range from simple solutions to
complex puzzle-levels, making them perfect candidates for a tutorial generative
system.


title:
An Optimal Experimental Design Approach for Light Configurations in
  Photometric Stereo

abstract:
  This paper presents a technique for finding the surface normal of an object
from a set of images obtained under different lighting positions. The method
presented is based on the principles of Photometric Stereo (PS) combined with
Optimum Experimental Design (OED) and Parameter Estimation (PE). Unclear by the
approach of photometric stereo, and many models based thereon, is how to
position the light sources. So far, this is done by using heuristic approaches
this leads to suboptimal and non-data driven positioning of the light sources.
But what if the optimal positions of the light sources are calculated for
photometric stereo? To this end, in this contribution, the effect of
positioning the light sources on the quality of the normal vector for PS is
evaluated. Furthermore, a new approach in this direction is derived and
formulated. For the calculation of the surface normal of a Lambertian surface,
the approach based on calibrated photometric stereo; for the estimation the
optimal position of the light sources the approach is premised on parameter
estimation and optimum experimental design. The approach is tested using
synthetic and real-data. Based on results it can be seen that the surface
normal estimated with the new method is more detailed than with conventional
methods.


title: Properties of IR selected Active Galactic Nuclei

abstract:
  Context. Active galactic nuclei (AGN) of galaxies play an important role in
the life and evolution of galaxies due to the impact they exert on certain
properties and the evolutionary path of galaxies. It is well known that
infrared (IR) emission is useful for selecting galaxies with AGNs, although it
has been observed that there is contamination by star-forming galaxies. Aims.
In this work we investigate galaxy properties hosting AGNs identified at mid
and near-IR wavelengths. The sample of AGNs selected at IR wavelengths was
confirmed using optical spectroscopy and X-ray photometry. We study the
near-UV, optical, near and mid-IR (MIR) properties, as well as [O III]
{\lambda}5007 luminosity, black hole mass and morphology properties of optical
and IR colour selected AGNs. Methods. We selected AGN candidates using two
mid-IR colour selection techniques, a power-law emission method and a
combination of mid and near-IR selection techniques. We confirm the AGN
selection with two line diagnostic diagrams that use the ratio [O III]/H\b{eta}
and the emission line width {\sigma} [O III] (kinematics-excitation diagram,
KEx) and the host galaxy stellar mass (mass-excitation diagram, MEx), as well
as X-ray photometry. Results. According to the diagnostic diagrams, the methods
with the greatest success in selecting AGNs are those that use a combination of
a mid and near-IR selection technique and a power-law emission. The method that
use a combination of mid and near-IR observation selects a large number of
AGNs, and is reasonably efficient in both the success rate (61%) and total
number of AGN recovered. We also find that the KEx method presents
contamination of SF galaxies within the AGN selection box. According to
morphological studies based on the S\'ersic index, AGN samples have higher
percentages of galaxy morphologies with bulge+disk components compared to
galaxies without AGNs.


title:
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot
  Object Detection

abstract:
  Few-shot object detection (FSOD) seeks to detect novel categories with
limited data by leveraging prior knowledge from abundant base data. Generalized
few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting
previously seen base classes and, thus, accounts for a more realistic scenario,
where both classes are encountered during test time. While current FSOD methods
suffer from catastrophic forgetting, G-FSOD addresses this limitation yet
exhibits a performance drop on novel tasks compared to the state-of-the-art
FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to
alleviate catastrophic forgetting, while achieving competitive results on the
novel task without increasing the model capacity. CFA adapts a continual
learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD.
Specifically, more constraints on the gradient search strategy are imposed from
which a new gradient update rule is derived, allowing for better knowledge
exchange between base and novel classes. To evaluate our method, we conduct
extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method
outperforms current FSOD and G-FSOD approaches on the novel task with minor
degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches
and operates as a plug-and-play module without increasing the model capacity or
inference time.


title: Hyperons, deconfinement and the speed of sound in neutron stars

abstract:
  The effects of the presence of hyperons and a phase transition to deconfined
quark matter on the speed of sound in neutron stars is investigated. For this
purpose a composite description consisting of a model of the covariant field
theory of hadrons and one for unbound quarks are used. A phase transition with
continuous and monotonous variation of the equation of state is assumed. The
predictions are contrasted with recent observational data on isolated neutron
stars as well as on binary systems. Only one candidate is finally obtained from
six different descriptions. According to the present calculations the onset of
the hyperons causes the equilibrium speed of sound to exceed the conformal
limit. Qualitative agreement with recent work about the influence of the speed
of sound on the g-modes of oscillation in neutron stars is obtained.


title: INTERSPEECH 2022 Audio Deep Packet Loss Concealment Challenge

abstract:
  Audio Packet Loss Concealment (PLC) is the hiding of gaps in audio streams
caused by data transmission failures in packet switched networks. This is a
common problem, and of increasing importance as end-to-end VoIP telephony and
teleconference systems become the default and ever more widely used form of
communication in business as well as in personal usage. This paper presents the
INTERSPEECH 2022 Audio Deep Packet Loss Concealment challenge. We first give an
overview of the PLC problem, and introduce some classical approaches to PLC as
well as recent work. We then present the open source dataset released as part
of this challenge as well as the evaluation methods and metrics used to
determine the winner. We also briefly introduce PLCMOS, a novel data-driven
metric that can be used to quickly evaluate the performance PLC systems.
Finally, we present the results of the INTERSPEECH 2022 Audio Deep PLC
Challenge, and provide a summary of important takeaways.


title:
Resource Allocation for Multiuser Edge Inference with Batching and Early
  Exiting (Extended Version)

abstract:
  The deployment of inference services at the network edge, called edge
inference, offloads computation-intensive inference tasks from mobile devices
to edge servers, thereby enhancing the former's capabilities and battery lives.
In a multiuser system, the joint allocation of communication-and-computation
($\text{C}^\text{2}$) resources (i.e., scheduling and bandwidth allocation) is
made challenging by adopting efficient inference techniques, batching and early
exiting, and further complicated by the heterogeneity in users' requirements on
accuracy and latency. Batching groups multiple tasks into one batch for
parallel processing to reduce time-consuming memory access and thereby boosts
the throughput (i.e., completed task per second). On the other hand, early
exiting allows a task to exit from a deep-neural network without traversing the
whole network to support a tradeoff between accuracy and latency. In this work,
we study optimal $\text{C}^\text{2}$ resource allocation with batching and
early exiting, which is an NP-complete integer program. A set of efficient
algorithms are designed under the criterion of maximum throughput by tackling
the challenge. Experimental results demonstrate that both optimal and
sub-optimal $\text{C}^\text{2}$ resource allocation algorithms can leverage
integrated batching and early exiting to achieve 200% throughput gain over
conventional schemes.


title:
Performance analysis of WDM in LoS communications with arbitrary
  orientation and position

abstract:
  This letter focuses on the wavenumber-division-multiplexing (WDM) scheme that
was recently proposed in [1] for line-of-sight communications between parallel
spatially-continuous electromagnetic segments. Our aim is to analyze the
performance of WDM, combined with different digital processing architectures,
when the electromagnetic segments have an arbitrary orientation and position.
To this end, we first show how the general electromagnetic MIMO (multiple-input
multiple-output) model from [1] can be particularized to the case of interest
and then use numerical results to evaluate the impact of system parameters
(e.g., horizontal and vertical distances, azimuth and elevation orientations).
It turns out that WDM performs satisfactorily also when the transmit and
receive segments are not in boresight direction of each other.


title: ZH production in gluon fusion at NLO in QCD

abstract:
  We present fully differential next-to-leading order results for Higgs
production in association with a $Z$ boson in gluon fusion. Our two-loop
virtual contributions are evaluated numerically using sector decomposition,
including full top-quark mass effects, and supplemented at high $p_T$ by an
analytic high-energy expansion to order ($m_Z^4, m_H^4, m_t^{32}$). Using the
expanded results we also present a study of the top-quark mass scheme
uncertainty at large $p_T$.


title:
A Gamma-ray Pulsar Timing Array Constrains the Nanohertz Gravitational
  Wave Background

abstract:
  After large galaxies merge, their central supermassive black holes are
expected to form binary systems whose orbital motion generates a gravitational
wave background (GWB) at nanohertz frequencies. Searches for this background
utilize pulsar timing arrays, which perform long-term monitoring of millisecond
pulsars (MSPs) at radio wavelengths. We use 12.5 years of Fermi Large Area
Telescope data to form a gamma-ray pulsar timing array. Results from 35 bright
gamma-ray pulsars place a 95\% credible limit on the GWB characteristic strain
of $1.0\times10^{-14}$ at 1 yr$^{-1}$, which scales as the observing time span
$t_{\mathrm{obs}}^{-13/6}$. This direct measurement provides an independent
probe of the GWB while offering a check on radio noise models.


title:
The self-learning AI controller for adaptive power beaming with
  fiber-array laser transmitter system

abstract:
  In this study we consider adaptive power beaming with fiber-array laser
transmitter system in presence of atmospheric turbulence. For optimization of
power transition through the atmosphere fiber-array is traditionally controlled
by stochastic parallel gradient descent (SPGD) algorithm where control feedback
is provided via radio frequency link by an optical-to-electrical power
conversion sensor, attached to a cooperative target. The SPGD algorithm
continuously and randomly perturbs voltages applied to fiber-array phase
shifters and fiber tip positioners in order to maximize sensor signal, i.e.
uses, so-called, "blind" optimization principle.
  In opposite to this approach a perspective artificially intelligent (AI)
control systems for synthesis of optimal control can utilize various pupil- or
target-plane data available for the analysis including wavefront sensor data,
photo-voltaic array (PVA) data, other optical or atmospheric parameters, and
potentially can eliminate well-known drawbacks of SPGD-based controllers. In
this study an optimal control is synthesized by a deep neural network (DNN)
using target-plane PVA sensor data as its input. A DNN training is occurred
online in sync with control system operation and is performed by applying of
small perturbations to DNN's outputs. This approach does not require initial
DNN's pre-training as well as guarantees optimization of system performance in
time. All theoretical results are verified by numerical experiments.