title: Flexibility of Fluorinated Graphene-Based Materials

abstract:
  The resistivity of different films and structures containing fluorinated
graphene (FG) flakes and chemical vapor deposition (CVD) grown graphene of
various fluorination degrees under tensile and compressive strains due to
bending deformations was studied. Graphene and multilayer graphene films grown
by means of the CVD method were transferred onto the flexible substrate by
laminating and were subjected to fluorination. They demonstrated a weak
fluorination degree (F/C lower 20%). Compressive strains led to a strong
(one-two orders of magnitude) decrease in the resistivity in both cases, which
was most likely connected with the formation of additional conductive paths
through fluorinated graphene. Tensile strain up to 3% caused by the bending of
both types of CVD-grown FG led to a constant value of the resistivity or to an
irreversible increase in the resistivity under repeated strain cycles. In the
case of the structures with the FG thin film printed on polyvinyl alcohol, a
stable bipolar resistive switching was observed up to 6.5% of the tensile
strain (bending radius was 2 mm). The excellent performance of the crossbar
memristor structures under tensile strain shows that the FG films and
structures created from suspension are especially promising for flexible
electronics.


title:
Observation of sizeable $\omega$ contribution to
  $\chi_{c1}(3872)\to\pi^+\pi^-J/\psi$ decays

abstract:
  Resonant structures in the dipion mass spectrum from
$\chi_{c1}(3872)\to\pi^+\pi^- J/\psi$ decays, produced via $B^+\to
K^+\chi_{c1}(3872)$ decays, are analyzed using proton-proton collision data
collected by the LHCb experiment, corresponding to an integrated luminosity of
9 $fb^{-1}$. A sizeable contribution from the isospin conserving
$\chi_{c1}(3872)\to\omega J/\psi$ decay is established for the first time,
$(21.4\pm2.3\pm2.0)\%$, with a significance of more than $7.1\sigma$. The
amplitude of isospin violating decay, $\chi_{c1}(3872)\to\rho^0 J/\psi$,
relative to isospin conserving decay, $\chi_{c1}(3872)\to\omega J/\psi$, is
properly determined, and it is a factor of six larger than expected for a pure
charmonium state.


title: Molecular and Serologic Diagnostic Technologies for SARS-CoV-2

abstract:
  The COVID-19 pandemic has presented many challenges that have spurred
biotechnological research to address specific problems. Diagnostics is one area
where biotechnology has been critical. Diagnostic tests play a vital role in
managing a viral threat by facilitating the detection of infected and/or
recovered individuals. From the perspective of what information is provided,
these tests fall into two major categories, molecular and serological.
Molecular diagnostic techniques assay whether a virus is present in a
biological sample, thus making it possible to identify individuals who are
currently infected. Additionally, when the immune system is exposed to a virus,
it responds by producing antibodies specific to the virus. Serological tests
make it possible to identify individuals who have mounted an immune response to
a virus of interest and therefore facilitate the identification of individuals
who have previously encountered the virus. These two categories of tests
provide different perspectives valuable to understanding the spread of
SARS-CoV-2. Within these categories, different biotechnological approaches
offer specific advantages and disadvantages. Here we review the categories of
tests developed for the detection of the SARS-CoV-2 virus or antibodies against
SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic.


title: Tolerance is Necessary for Stability: Single-Peaked Swap Schelling Games

abstract:
  Residential segregation in metropolitan areas is a phenomenon that can be
observed all over the world. It is characterized by the emergence of large
regions populated by residents that are homogeneous in terms of ethnicity or
other traits. In a recent research trend in the AI community this phenomenon
was investigated via game-theoretic models. There, selfish agents of two types
are equipped with a monotone utility function that ensures higher utility if an
agent has more same-type neighbors. The agents strategically choose their
location on a given graph that serves as residential area to maximize their
utility. However, sociological polls suggest that real-world agents are
actually favoring mixed-type neighborhoods, and hence should be modeled via
non-monotone utility functions.
  We study Swap Schelling Games with non-monotone utility functions that are
single-peaked. In these games pairs of agents may improve their utility by
swapping their locations. Our main finding is that tolerance, i.e., that the
agents favor fifty-fifty neighborhoods or even being in the minority, is
necessary for equilibrium existence on almost regular or bipartite graphs. We
show equilibrium existence on almost regular graphs via a potential function
argument and we prove that this approach is impossible on arbitrary graphs even
with tolerant agents. Regarding the quality of equilibria, we consider the
recently introduced degree of integration, that counts the number of agents
that live in a heterogeneous neighborhood, as social welfare function. We
derive (almost) tight bounds on the Price of Anarchy and the Price of
Stability. In particular, we show that the latter is constant on bipartite and
almost regular graphs. Moreover, we prove that computing approximations of the
social optimum placement and the equilibrium with maximum social welfare is
NP-hard even on cubic graphs.


title:
X-ray Phase Contrast Tomography to assess the sequential evolution of
  multi-organ damage in an animal model of multiple sclerosis

abstract:
  We use X-ray phase-contrast tomography (XPCT) in a multi-organ approach to
identify early imaging markers predictive of multiple sclerosis (MS) in EAE
animal model. As the majority of neurodegenerative diseases, MS is
characterized by a progressive accumulation of biological deficits across
different organs and systems. A simultaneous imaging of different
disease-relevant networks and a multiscale imaging, ranging from the single
cell through to the organ as a whole, are required to provide complete reliable
information. XPCT offers the unprecedented possibility to investigate
structural and cellular alterations at brain, gut, and eye levels in a
multiscale approach. The comparison between naive mice and EAE-affected mice
sacrificed at different time points and the correlation of the data from
different organs and different time points, unveils the identification of early
changes in organs possible predictive of the disease.


title:
Observations From an Online Security Competition and Its Implications on
  Crowdsourced Security

abstract:
  The crowd sourced security industry, particularly bug bounty programs, has
grown dramatically over the past years and has become the main source of
software security reviews for many companies. However, the academic literature
has largely omitted security teams, particularly in crowd work contexts. As
such, we know very little about how distributed security teams organize,
collaborate, and what technology needs they have. We fill this gap by
conducting focus groups with the top five teams (out of 18,201 participating
teams) of a computer security Capture-the-Flag (CTF) competition. We find that
these teams adopted a set of strategies centered on specialties, which allowed
them to reduce issues relating to dispersion, double work, and lack of previous
collaboration. Observing the current issues of a model centered on individual
workers in security crowd work platforms, our study cases that scaling security
work to teams is feasible and beneficial. Finally, we identify various areas
which warrant future work, such as issues of social identity in high-skilled
crowd work environments.


title: NNLO+PS Monte Carlo simulation of photon pair production with MiNNLOPS

abstract:
  We present a NNLO QCD accurate event generator for direct photon pair
production at hadron colliders, based on the MiNNLOPS formalism, within the
POWHEG BOX RES framework. Despite the presence of the photons requires the use
of isolation criteria, our generator is built such that no technical cuts are
needed at any stage of the event generation. Therefore, our predictions can be
used to simulate kinematic distributions with arbitrary fiducial cuts.
Furthermore, we describe a few modifications of the MiNNLOPS formalism in order
to allow for a setting of the renormalization and factorization scales more
similar to that of a fixed-order computation, thus reducing the numerical
impact of higher-order terms beyond the nominal accuracy. Finally, we show
several phenomenological distributions of physical interest obtained by
showering the generated events with Pythia8, and we compare them with the 13
TeV data from the ATLAS Collaboration.


title: A cloud-cloud collision in Sgr B2? 3D simulations meet SiO observations

abstract:
  We compare the properties of shocked gas in Sgr B2 with maps obtained from 3D
simulations of a collision between two fractal clouds. In agreement with
$^{13}$CO(1-0) observations, our simulations show that a cloud-cloud collision
produces a region with a highly turbulent density substructure with an average
$N_{\rm H2}\gtrsim 5\times10^{22}\,\rm cm^{-2}$. Similarly, our numerical
multi-channel shock study shows that colliding clouds are efficient at
producing internal shocks with velocities of $5-50\,\rm km\,s^{-1}$ and Mach
numbers of $\sim4-40$, which are needed to explain the $\sim 10^{-9}$ SiO
abundances inferred from our SiO(2-1) IRAM observations of Sgr B2. Overall, we
find that both the density structure and the shocked gas morphology in Sgr B2
are consistent with a $\lesssim 0.5\,\rm Myr$-old cloud-cloud collision.
High-velocity shocks are produced during the early stages of the collision and
can ignite star formation, while moderate- and low-velocity shocks are
important over longer time-scales and can explain the extended SiO emission in
Sgr B2.


title:
Information-theoretic multi-time-scale partially observable systems with
  relevance to leukemia treatment

abstract:
  Inspired by a leukemia treatment challenge, we study a partially observable
non-linear stochastic system with unknown parameters, where the given time
scales of the states and measurements may be distinct. Key words: Stochastic
control; Non-linear systems; Partially observable systems; System
identification; Biomedical systems.


title:
Zero-Touch Network on Industrial IoT: An End-to-End Machine Learning
  Approach

abstract:
  Industry 4.0-enabled smart factory is expected to realize the next revolution
for manufacturers. Although artificial intelligence (AI) technologies have
improved productivity, current use cases belong to small-scale and single-task
operations. To unbound the potential of smart factory, this paper develops
zero-touch network systems for intelligent manufacturing and facilitates
distributed AI applications in both training and inferring stages in a
large-scale manner. The open radio access network (O-RAN) architecture is first
introduced for the zero-touch platform to enable globally controlling
communications and computation infrastructure capability in the field. The
designed serverless framework allows intelligent and efficient learning
assignments and resource allocations. Hence, requested learning tasks can be
assigned to appropriate robots, and the underlying infrastructure can be used
to support the learning tasks without expert knowledge. Moreover, due to the
proposed network system's flexibility, powerful AI-enabled networking
algorithms can be utilized to ensure service-level agreements and superior
performances for factory workloads. Finally, three open research directions of
backward compatibility, end-to-end enhancements, and cybersecurity are
discussed for zero-touch smart factory.


title:
Pauli-limit violation in lanthanide infinite-layer nickelate
  superconductors

abstract:
  Superconductivity can be destroyed by a magnetic field with an upper bound
known as the Pauli-limit in spin-singlet superconductors. Almost all the
discovered superconductors are spin-singlet, with the highest transition
temperature $T_c$ at ambient pressure achieved in the cuprate family. The
closest cuprate analogue is the recently discovered infinite-layer nickelate,
which hosts substantial structural and electronic similarity to the cuprate. A
previous magnetotransport study on Nd$_{0.775}$Sr$_{0.225}$NiO$_2$ has observed
an isotropic Pauli-limited upper critical field. Here, we report a large
violation (>2 times) of Pauli-limit in every crystallographic directions in
La$_{1-x}$(Ca/Sr)$_x$NiO$_2$ regardless of the doping $x$. Such a large
violation of the Pauli-limit in all directions in La$_{1-x}$(Ca/Sr)$_x$NiO$_2$
is unexpected and unlikely accounted by a Fulde Ferrell-Larkin-Ovchinnikov
(FFLO)-state, strong spin-orbit-coupling, strong-coupling or a large pseudogap.
On the other hand, in agreement with the previous report, we observe a
Pauli-limiting critical field in Nd$_{1-x}$Sr$_x$NiO$_2$ and the
superconducting anisotropy decreases as doping increases, suggesting a
spin-singlet pairing. Therefore, superconductivity in
La$_{1-x}$(Ca/Sr)$_x$NiO$_2$ could be driven by a non-spin-singlet Cooper
pairing mechanism with an attractive high-$T_c$ at 10 K, an order of magnitude
higher than the known spin triplet superconductors, favourably extending the
application of spin-triplet superconductivity in topological matter,
non-dissipative spintronics, and quantum computing.


title: Heading perception and the structure of the optic acceleration field

abstract:
  Visual estimation of heading in the human brain is widely believed to be
based on instantaneous optic flow, the velocity of retinal image motion.
However, we previously found that humans are unable to use instantaneous optic
flow to accurately estimate heading and require time-varying optic flow
(Burlingham and Heeger, 2020). We proposed the hypothesis that heading
perception is computed from optic acceleration, the temporal derivative of
optic flow, based on the observation that heading is aligned perfectly with a
point on the retina with zero optic acceleration. However, this result was
derived for a specific scenario used in our experiments, when retinal heading
and rotational velocity are constant over time. We previously speculated that
as the change over time in heading or rotation increases, the bias of the
estimator would increase proportionally, based on the idea that our derived
case would approximate what happens in a small interval of time (when heading
and rotation are nearly constant). In this technical report, we characterize
the properties of the optic acceleration field and derive the bias of this
estimator for the more common case of a fixating observer, i.e., one that moves
while counter-rotating their eyes to stabilize an object on the fovea. For a
fixating observer tracking a point on a fronto-parallel plane, there are in
fact two singularities of optic acceleration: one that is always at fixation
(due to image stabilization) and a second whose bias scales inversely with
heading, inconsistent with human behavior. For movement parallel to a ground
plane, there is only one singularity of optic acceleration (at the fixation
point), which is uninformative about heading. We conclude that the singularity
of optic acceleration is not an accurate estimator of heading under natural
conditions.


title: Efficient Machine Translation Domain Adaptation

abstract:
  Machine translation models struggle when translating out-of-domain text,
which makes domain adaptation a topic of critical importance. However, most
domain adaptation methods focus on fine-tuning or training the entire or part
of the model on every new domain, which can be costly. On the other hand,
semi-parametric models have been shown to successfully perform domain
adaptation by retrieving examples from an in-domain datastore (Khandelwal et
al., 2021). A drawback of these retrieval-augmented models, however, is that
they tend to be substantially slower. In this paper, we explore several
approaches to speed up nearest neighbor machine translation. We adapt the
methods recently proposed by He et al. (2021) for language modeling, and
introduce a simple but effective caching strategy that avoids performing
retrieval when similar contexts have been seen before. Translation quality and
runtimes for several domains show the effectiveness of the proposed solutions.


title:
A 3-Approximation Algorithm for a Particular Case of the Hamiltonian
  p-Median Problem

abstract:
  Given a weighted graph $G$ with $n$ vertices and $m$ edges, and a positive
integer $p$, the Hamiltonian $p$-median problem consists in finding $p$ cycles
of minimum total weight such that each vertex of $G$ is in exactly one cycle.
We introduce an $O(n^6)$ 3-approximation algorithm for the particular case in
which $p \leq \lceil \frac{n-2\lceil \frac{n}{5} \rceil}{3} \rceil$. An
approximation ratio of 2 might be obtained depending on the number of
components in the optimal 2-factor of $G$. We present computational experiments
comparing the approximation algorithm to an exact algorithm from the
literature. In practice much better ratios are obtained. For large values of
$p$, the exact algorithm is outperformed by our approximation algorithm.


title:
Yu-Shiba-Rusinov states, the BCS-BEC crossover, and the exact solution
  in the flat-band limit

abstract:
  We study the subgap Yu-Shiba-Rusinov (YSR) states in the Richardson's model
of a superconductor with a magnetic impurity for different electron pairing
strengths from the weak-coupling Bardeen-Cooper-Schrieffer (BCS) regime to the
strong-coupling Bose-Einstein-condensate (BEC) regime. We observe that the
effect of the increasing pairing strength on the YSR excitation spectrum as a
function of hybridisation strength and impurity on-site potential is only
quantitative and, in fact, rather weak when the results are appropriately
rescaled. We furthermore show that the problem is analytically solvable in the
deep BEC limit which is equivalent to flat-band superconductivity. This exact
solution can be related to a zero-bandwidth (ZBW) effective BCS mean field
Hamiltonian where the superconductor is described by a single electron level
with onsite pairing. The small difference between the BCS and BEC regimes of
the Richardson's model explains the success of the simple ZBW calculations for
BCS mean-field Hamiltonians. A ZBW model requires only a suitable parameter
rescaling to become useful as a quantitative predictive tool for the full
problem.


title:
SBMLDiagrams: A python package to process and visualize SBML layout and
  render

abstract:
  Summary: The Systems Biology Markup Language (SBML) is an extensible standard
format for exchanging biochemical models. One of the extensions for SBML is the
SBML Layout and Render package. This allows modelers to describe a biochemical
model as a pathway diagram. However, up to now there has been little support to
help users easily add and retrieve such information from SBML. In this
application note we describe a new Python package called SBMLDiagrams. This
package allows a user to add layout and render information or retrieve it using
a straight forward Python API. The package uses skia-python to support
rendering of the diagrams, allowing export to commons formats such as PNG or
PDF. Availability: SBMLDiagrams is publicly available and licensed under the
liberal MIT open-source license. The package is available for all major
platforms. The source code has been deposited at GitHub
(github.com/sys-bio/SBMLDiagrams). Users can install the package using the
standard pip installation mechanism: pip install SBMLDiagrams. Availability:
SBMLDiagrams is publicly available and licensed under the liberal MIT
open-source license. The package is available for all major platforms. The
source code has been deposited at GitHub (github.com/sys-bio/SBMLDiagrams).
Users can install the package using the standard pip installation mechanism:
pip install SBMLDiagrams. Contact: hsauro@uw.edu.


title: Approximations of Hecke-Maass L-functions by short Dirichlet polynomials

abstract:
  By assuming the Ramanujan conjecture, we approximate L-functions associated
to $SL_{3}(\mathbb{Z})$ Hecke-Maass cusp forms (on average) by short Dirichlet
polynomials. For the proof, we apply a variant of the Kuznetsov trace formula.


title: Formal exponentials and linearisations of QP-manifolds

abstract:
  We define formal exponential maps for any graded manifold as maps from the
formal tangent bundle (that we also define) into the graded manifold. We show
that each such map uniquely determines and is determined by its associated
Grothendieck connection, which is shown to be flat, and to furnish a resolution
of the ring of functions. We then show how a recent construction involving the
data of a connection on the tangent bundle recovers a large class of formal
exponentials in our definition.
  As an application, we use a formal exponential map to linearise a QP-manifold
at a point. This gives the formal tangent space at each point the structure of
an $L_\infty$-algebra with invariant inner product.


title: MaxSAT with Absolute Value Functions: A Parameterized Perspective

abstract:
  The natural generalization of the Boolean satisfiability problem to
optimization problems is the task of determining the maximum number of clauses
that can simultaneously be satisfied in a propositional formula in conjunctive
normal form. In the weighted maximum satisfiability problem each clause has a
positive weight and one seeks an assignment of maximum weight. The literature
almost solely considers the case of positive weights. While the general case of
the problem is only restricted slightly by this constraint, many special cases
become trivial in the absence of negative weights. In this work we study the
problem with negative weights and observe that the problem becomes
computationally harder - which we formalize from a parameterized perspective in
the sense that various variations of the problem become W[1]-hard if negative
weights are present.
  Allowing negative weights also introduces new variants of the problem:
Instead of maximizing the sum of weights of satisfied clauses, we can maximize
the absolute value of that sum. This turns out to be surprisingly expressive
even restricted to monotone formulas in disjunctive normal form with at most
two literals per clause. In contrast to the versions without the absolute
value, however, we prove that these variants are fixed-parameter tractable. As
technical contribution we present a kernelization for an auxiliary problem on
hypergraphs in which we seek, given an edge-weighted hypergraph, an induced
subgraph that maximizes the absolute value of the sum of edge-weights.


title: From Sand to Flour: The Next Leap in Granular Computing with NanoSort

abstract:
  The granularity of distributed computing is limited by communication time:
there is no point in farming out smaller and smaller tasks if the communication
overhead dominates the decrease in processing time due to the added
parallelism. In this work, we leverage the low communication latency of a new
NIC/CPU hardware design, the nanoPU, to explore a new extreme of granularity in
distributed computation, where a problem is partitioned into tens of thousands
of nanosecond-scale tasks.
  To evaluate the feasibility and practicality of extremely fine-grained
computing, we built NanoSort, a distributed sorting algorithm running on the
nanoPU. Using cycle-accurate FireSim simulations of 65,536 nanoPU cores, we
show that NanoSort can sort 1M keys in 68$\mu$s, an order of magnitude faster
than MilliSort, the current state-of-the-art.


title: Thomson scattering on the Large Plasma Device

abstract:
  We have developed a non-collective Thomson scattering diagnostic for
measurements of electron density and temperature on the Large Plasma Device. A
triple grating spectrometer with a tunable notch filter is used to discriminate
the faint scattering signal from the stray light. In this paper we describe the
diagnostic and its calibration via Raman scattering, and present the first
measurements performed with the fully commissioned system. Depending on the
discharge conditions, the measured densities and temperatures range from
4.0$\times$10$^{12}$ cm$^{-3}$ to 2.8$\times$10$^{13}$ cm$^{-3}$, and from 1.2
eV to 6.8 eV, respectively. The variation of the measurement error with plasma
parameters and discharges averaged is also discussed.


title: Spread of COVID-19: Adult Detention Facilities in LA County

abstract:
  We analyze the spread of COVID-19 cases within adult detention facilities in
Los Angeles (LA) county. Throughout the analysis we review the data to explore
the range of positive cases in each center and see the percentage of people who
were positive for COVID-19 against the amount of people who were tested.
Additionally, we see if there is any correlation between the surrounding
community of each detention center and the number of positive cases in each
center and explore the protocols in place at each detention center. We use the
cloud visualization tool SAP Analytics Cloud (SAC) with the data from the
California government website through adult detention facilities in LA County.
We found that (1) the number of confirmed cases at the facilities and the
surrounding communities are not related, (2) the data does not represent all
positive cases at the facility, and (3) there are not enough tests at the
facilities.


title:
Towards compact phase-matched and waveguided nonlinear optics in
  atomically layered semiconductors

abstract:
  Nonlinear frequency conversion provides essential tools for light generation,
photon entanglement, and manipulation. Transition metal dichalcogenides (TMDs)
possess huge nonlinear susceptibilities and 3R-stacked TMD crystals further
combine broken inversion symmetry and aligned layering, representing ideal
candidates to boost the nonlinear optical gain with minimal footprint. Here, we
report on the efficient frequency conversion of 3R-MoS2, revealing the
evolution of its exceptional second-order nonlinear processes along the
ordinary (in-plane) and extraordinary (out-of-plane) directions. By measuring
second harmonic generation (SHG) of 3R-MoS2 with various thickness - from
monolayer (~0.65 nm) to bulk (~1 {\mu}m) - we present the first measurement of
the in-plane SHG coherence length (~530 nm) at 1520 nm and achieve record
nonlinear optical enhancement from a van der Waals material, >10^4 stronger
than a monolayer. It is found that 3R-MoS2 slabs exhibit similar conversion
efficiencies of lithium niobate, but within propagation lengths >100-fold
shorter at telecom wavelengths. Furthermore, along the extraordinary axis, we
achieve broadly tunable SHG from 3R-MoS2 in a waveguide geometry, revealing the
coherence length in such structure for the first time. We characterize the full
refractive index spectrum and quantify both birefringence components in
anisotropic 3R-MoS2 crystals with near-field nano-imaging. Empowered with these
data we assess the intrinsic limits of the conversion efficiency and nonlinear
optical processes in 3R-MoS2 attainable in waveguide geometries. Our analysis
highlights the potential of 3R-stacked TMDs for integrated photonics, providing
critical parameters for designing highly efficient on-chip nonlinear optical
devices including periodically poled structures, resonators, compact optical
parametric oscillators and amplifiers, and optical quantum circuits.


title: Decoding noisy messages: a method that just shouldn't work

abstract:
  This paper is about receiving text messages through a noisy and costly line.
Because the line is noisy we need redundancy, but because it is costly we can
afford very little of it. I start by using well-known machinery for decoding
noisy messages (compressed sensing), then I attempt to reduce the redundancy
(using random projections), until I get to a point where I use more orthogonal
vectors than the space dimension allows. Instead of grinding to a halt or
spurting out noise, this method is still able to decode messages correctly or
almost correctly. I have no idea why the method works: this is my first reason
for writing this paper using a narrative instead of formal scientific style
(the second one is that I am tired of writing semi-formal prose, and long for a
change).


title: Rate-Constrained Remote Contextual Bandits

abstract:
  We consider a rate-constrained contextual multi-armed bandit (RC-CMAB)
problem, in which a group of agents are solving the same contextual multi-armed
bandit (CMAB) problem. However, the contexts are observed by a remotely
connected entity, i.e., the decision-maker, that updates the policy to maximize
the returned rewards, and communicates the arms to be sampled by the agents to
a controller over a rate-limited communications channel. This framework can be
applied to personalized ad placement, whenever the content owner observes the
website visitors, and hence has the context, but needs to transmit the ads to
be shown to a controller that is in charge of placing the marketing content.
Consequently, the rate-constrained CMAB (RC-CMAB) problem requires the study of
lossy compression schemes for the policy to be employed whenever the constraint
on the channel rate does not allow the uncompressed transmission of the
decision-maker's intentions. We characterize the fundamental information
theoretic limits of this problem by letting the number of agents go to
infinity, and study the regret that can be achieved, identifying the two
distinct rate regions leading to linear and sub-linear regrets respectively. We
then analyze the optimal compression scheme achievable in the limit with
infinite agents, when using the forward and reverse KL divergence as distortion
metric. Based on this, we also propose a practical coding scheme, and provide
numerical results.


title: A sharp upper bound for sampling numbers in $L_{2}$

abstract:
  For a class $F$ of complex-valued functions on a set $D$, we denote by
$g_n(F)$ its sampling numbers, i.e., the minimal worst-case error on $F$,
measured in $L_2$, that can be achieved with a recovery algorithm based on $n$
function evaluations. We prove that there is a universal constant $c>0$ such
that, if $F$ is the unit ball of a separable reproducing kernel Hilbert space,
then \[ g_{cn}(F)^2 \,\le\, \frac{1}{n}\sum_{k\geq n} d_k(F)^2, \] where
$d_k(F)$ are the Kolmogorov widths (or approximation numbers) of $F$ in $L_2$.
We also obtain similar upper bounds for more general classes $F$, including all
compact subsets of the space of continuous functions on a bounded domain
$D\subset \mathbb{R}^d$, and show that these bounds are sharp by providing
examples where the converse inequality holds up to a constant.


title: Named Entity Recognition for Audio De-Identification

abstract:
  Data anonymization is often a task carried out by humans. Automating it would
reduce the cost and time required to complete this task. This paper presents a
pipeline to automate the anonymization of audio data in French. We propose a
pipeline, which takes audio files with their transcriptions and removes the
named entities (NEs) present in the audio. Our pipeline is made up of a forced
aligner, which aligns words in an audio transcript with speech and a model that
performs named entity recognition (NER). Then, the audio segments that
correspond to NEs are substituted with silence to anonymize audio. We compared
forced aligners and NER models to find the best ones for our scenario. We
evaluated our pipeline on a small hand-annotated dataset, achieving an F1 score
of 0.769. This result shows that automating this task is feasible.


title:
SANA: Cross-Species Prediction of Gene Ontology GO Annotations via
  Topological Network Alignment

abstract:
  Topological network alignment aims to align two networks node-wise in order
to maximize the observed common connection (edge) topology between them. The
topological alignment of two Protein-Protein Interaction (PPI) networks should
thus expose protein pairs with similar interaction partners allowing, for
example, the prediction of common Gene Ontology (GO) terms. Unfortunately, no
network alignment algorithm based on topology alone has been able to achieve
this aim, though those that include sequence similarity have seen some success.
We argue that this failure of topology alone is due to the sparsity and
incompleteness of the PPI network data of almost all species, which provides
the network topology with a small signal-to-noise ratio that is effectively
swamped when sequence information is added to the mix. Here we show that the
weak signal can be detected using multiple stochastic samples of "good"
topological network alignments, which allows us to observe regions of the two
networks that are robustly aligned across multiple samples. The resulting
Network Alignment Frequency (NAF) strongly correlates with GO-based Resnik
semantic similarity and enables the first successful cross-species predictions
of GO terms based on topology-only network alignments. Our best predictions
have an AUPR of about 0.4, which is competitive with state-of-the-art
algorithms, even when there is no observable sequence similarity and no known
homology relationship. While our results provide only a "proof of concept" on
existing network data, we hypothesize that predicting GO terms from
topology-only network alignments will become increasingly practical as the
volume and quality of PPI network data increase.


title:
Evaluation of Self-taught Learning-based Representations for Facial
  Emotion Recognition

abstract:
  This work describes different strategies to generate unsupervised
representations obtained through the concept of self-taught learning for facial
emotion recognition (FER). The idea is to create complementary representations
promoting diversity by varying the autoencoders' initialization, architecture,
and training data. SVM, Bagging, Random Forest, and a dynamic ensemble
selection method are evaluated as final classification methods. Experimental
results on Jaffe and Cohn-Kanade datasets using a leave-one-subject-out
protocol show that FER methods based on the proposed diverse representations
compare favorably against state-of-the-art approaches that also explore
unsupervised feature learning.


title: H-mode inhibition in negative triangularity tokamak reactor plasmas

abstract:
  Instability to high toroidal mode number ($n$) ballooning modes has been
proposed as the primary gradient-limiting mechanism for tokamak equilibria with
negative triangularity ($\delta$) shaping, preventing access to strong H-mode
regimes when $\delta\ll0$. To understand how this mechanism extrapolates to
reactor conditions, we model the infinite-$n$ ballooning stability as a
function of internal profiles and equilibrium shape using a combination of the
CHEASE and BALOO codes. While the critical $\delta$ required for avoiding
$2^\mathrm{nd}$ stability to high-$n$ modes is observed to depend in a
complicated way on various shaping parameters, including the equilibrium aspect
ratio, elongation and squareness, equilibria with negative triangularity are
robustly prohibited from accessing the $2^\mathrm{nd}$ stability region,
offering the prediction that that negative triangularity reactors should
maintain L-mode-like operation. In order to access high-$n$ $2^\mathrm{nd}$
stability, the local shear over the entire bad curvature region must be
sufficiently negative to overcome curvature destabilization on the low field
side. Scalings of the ballooning-limited pedestal height are provided as a
function of plasma parameters to aid future scenario design.


title: GCD of sums of $k$ consecutive squares of generalized Fibonacci numbers

abstract:
  In 2021, Guyer and Mbirika gave two equivalent formulas that computed the
greatest common divisor (GCD) of all sums of $k$ consecutive terms in the
generalized Fibonacci sequence $\left(G_n\right)_{n \geq 0}$ given by the
recurrence $G_n = G_{n-1} + G_{n-2}$ for all $n \geq 2$ with integral initial
conditions $G_0$ and $G_1$. In this current paper, we extend their results to
the GCD of all sums of $k$ consecutive squares of these numbers. Denoting these
GCD values by the symbol $\mathcal{G}_{G_0, G_1}^2\!(k)$, we prove
$\mathcal{G}_{G_0, G_1}^2\!(k) = \gcd\left(G_k G_{k+1} - G_0 G_1,\; G_{k+1}^2 -
G_1^2,\; G_{k+2}^2 - G_2^2\right)$. Moreover, we provide very tantalizing
closed forms in the specific settings of the Fibonacci, Lucas, and generalized
Fibonacci numbers. We close with a number of open questions for further
research.


title: Ironing out the crease

abstract:
  The crease is a surface operator folded by a finite angle along an infinite
line. Several realisations of it in the 6d ${\mathcal N}=(2,0)$ theory are
studied here. It plays a role similar to the generalised quark-antiquark
potential, or the cusp anomalous dimension, in gauge theories. We identify a
finite quantity that can be studied despite the conformal anomalies ubiquitous
with surface operators and evaluate it in free field theory and in the
holographic dual. We also find a subtle difference between the infinite crease
and its conformal transform to a compact observable comprised of two glued
hemispheres, reminiscent of the circular Wilson loop. We prove by a novel
application of defect CFT techniques for the $SO(2,1)$ symmetry along the fold
that the near-BPS behaviour of the crease is determined as the derivative of
the compact observable with respect to its angle, as in the bremsstrahlung
function. We also comment about the lightlike limit of the crease in Minkowski
space.


title:
$\mathbb{S}^6$ (or any of $\mathbb{S}^2 \times \mathbb{S}^4$,
  $\mathbb{S}^2\times\mathbb{S}^6$, or $\mathbb{S}^6\times \mathbb{S}^6$,
  respectively) is not diffeomorphic to a complex manifold

abstract:
  We identify all metrics on a closed $n$-manifold with their Nash isometric
embeddings into a standard sphere of large, but fixed, dimension, and use
Palais' isotopic extension theorem conveniently to analyze their deformations
corresponding to variations of the metrics within a conformal class. If $n\geq
3$, we characterize metrics of constant scalar curvature in terms of properties
of extrinsic quantities of their associated embeddings, and prove that any
metric of constant positive scalar curvature that can be minimally embedded
into this background sphere is a Yamabe metric in its conformal class. We then
use Simons' gap theorem to study the extrinsic quantities of almost complex
Hermitian deformations by Yamabe metrics of the standard minimal almost complex
isometric embeddings of $\mathbb{S}^6$, $\mathbb{S}^2 \times \mathbb{S}^4$,
$\mathbb{S}^2\times\mathbb{S}^6$, and $\mathbb{S}^6\times \mathbb{S}^6$,
respectively, and deduce that none of these manifolds carry integrable almost
complex structures.


title:
Gaussian Kernel Variance For an Adaptive Learning Method on Signals Over
  Graphs

abstract:
  This paper discusses a special kind of a simple yet possibly powerful
algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning
method predicting unknown nodal values in a network using known nodal values
and the network structure. We aim to find out how to configure the special kind
of the model in applying the algorithm. To be more specific, we focus on SKG
with a Gaussian kernel and specify how to find a suitable variance for the
kernel. To do so, we introduce two variables with which we are able to set up
requirements on the variance of the Gaussian kernel to achieve (near-) optimal
performance and can better understand how SKG works. Our contribution is that
we introduce two variables as analysis tools, illustrate how predictions will
be affected under different Gaussian kernels, and provide an algorithm finding
a suitable Gaussian kernel for SKG with knowledge about the training network.
Simulation results on real datasets are provided.


title:
The SAMI Galaxy Survey: The Link Between [$\alpha$/Fe] and Kinematic
  Morphology

abstract:
  We explore a sample of 1492 galaxies with measurements of the mean stellar
population properties and the spin parameter proxy, $\lambda_{R_{\rm{e}}}$,
drawn from the SAMI Galaxy Survey. We fit a global
$\left[\alpha/\rm{Fe}\right]$-$\sigma$ relation, finding that
$\left[\alpha/\rm{Fe}\right]=(0.395\pm0.010)\rm{log}_{10}\left(\sigma\right)-(0.627\pm0.002)$.
We observe an anti-correlation between the residuals
$\Delta\left[\alpha/\rm{Fe}\right]$ and the inclination-corrected
$\lambda_{\,R_{\rm{e}}}^{\rm{\,eo}}$, which can be expressed as
$\Delta\left[\alpha/\rm{Fe}\right]=(-0.057\pm0.008)\lambda_{\,R_{\rm{e}}}^{\rm{\,eo}}+(0.020\pm0.003)$.
The anti-correlation appears to be driven by star-forming galaxies, with a
gradient of
$\Delta\left[\alpha/\rm{Fe}\right]\sim(-0.121\pm0.015)\lambda_{\,R_{\rm{e}}}^{\rm{\,eo}}$,
although a weak relationship persists for the subsample of galaxies for which
star formation has been quenched. We take this to be confirmation that
disk-dominated galaxies have an extended duration of star formation. At a
reference velocity dispersion of 200 km s$^{-1}$, we estimate an increase in
half-mass formation time from $\sim$0.5 Gyr to $\sim$1.2 Gyr from low- to
high-$\lambda_{\,R_{\rm{e}}}^{\rm{\,eo}}$ galaxies. Slow rotators do not appear
to fit these trends. Their residual $\alpha$-enhancement is indistinguishable
from other galaxies with $\lambda_{\,R_{\rm{e}}}^{\rm{\,eo}}\lessapprox0.4$,
despite being both larger and more massive. This result shows that galaxies
with $\lambda_{\,R_{\rm{e}}}^{\rm{\,eo}}\lessapprox0.4$ experience a similar
range of star formation histories, despite their different physical structure
and angular momentum.


title:
Digital Radio Arrays for the Detection of Air Showers Initiated by
  Ultra-High-Energy Particles (Epiphany 2022)

abstract:
  Digital radio arrays have become an effective tool to measure air showers at
energies around and above 100 PeV. Compared to optical techniques, the radio
technique is not restricted to clear nights. Thanks to recent progress on
computational analysis techniques, radio arrays can provide an equally accurate
measurement of the energy and the depth of the shower maximum. Stand-alone
radio arrays offer an economic way towards huge apertures, e.g., for the search
for ultra-high-energy neutrinos, but still require technical demonstration on
large scales. Hybrid arrays combining radio antennas and particle detectors
have already started to contribute to cosmic-ray physics in the energy range of
the presumed Galactic-to-extragalactic transition. In particular, the
combination of radio and muon detectors can pave a path to unprecedented
accuracy for the mass composition of cosmic rays. This proceeding reviews
recent developments regarding the radio technique and highlights selected
running and planned antennas arrays, such as GCOS, GRAND, the SKA, the
AugerPrime Upgrade of the Pierre Auger Observatory, and IceCube-Gen2.


title: Testing the Ability of Language Models to Interpret Figurative Language

abstract:
  Figurative and metaphorical language are commonplace in discourse, and
figurative expressions play an important role in communication and cognition.
However, figurative language has been a relatively under-studied area in NLP,
and it remains an open question to what extent modern language models can
interpret nonliteral phrases. To address this question, we introduce Fig-QA, a
Winograd-style nonliteral language understanding task consisting of correctly
interpreting paired figurative phrases with divergent meanings. We evaluate the
performance of several state-of-the-art language models on this task, and find
that although language models achieve performance significantly over chance,
they still fall short of human performance, particularly in zero- or few-shot
settings. This suggests that further work is needed to improve the nonliteral
reasoning capabilities of language models.


title: Developing Universal Dependency Treebanks for Magahi and Braj

abstract:
  In this paper, we discuss the development of treebanks for two low-resourced
Indian languages - Magahi and Braj based on the Universal Dependencies
framework. The Magahi treebank contains 945 sentences and Braj treebank around
500 sentences marked with their lemmas, part-of-speech, morphological features
and universal dependencies. This paper gives a description of the different
dependency relationship found in the two languages and give some statistics of
the two treebanks. The dataset will be made publicly available on Universal
Dependency (UD) repository
(https://github.com/UniversalDependencies/UD_Magahi-MGTB/tree/master) in the
next(v2.10) release.


title:
Discrete-Time Adaptive Control of a Class of Nonlinear Systems Using
  High-Order Tuners

abstract:
  This paper concerns the adaptive control of a class of discrete-time
nonlinear systems with all states accessible. Recently, a high-order tuner
algorithm was developed for the minimization of convex loss functions with
time-varying regressors in the context of an identification problem. Based on
Nesterov's algorithm, the high-order tuner was shown to guarantee bounded
parameter estimation when regressors vary with time, and to lead to accelerated
convergence of the tracking error when regressors are constant. In this paper,
we apply the high-order tuner to the adaptive control of a particular class of
discrete-time nonlinear dynamical systems. First, we show that for plants of
this class, the underlying dynamical error model can be causally converted to
an algebraic error model. Second, we show that using this algebraic error
model, the high-order tuner can be applied to provably stabilize the class of
dynamical systems around a reference trajectory.


title:
Multivariate and regression models for directional data based on
  projected P\'olya trees

abstract:
  Projected distributions have proved to be useful in the study of circular and
directional data. Although any multivariate distribution can be used to produce
a projected model, these distributions are typically parametric. In this
article we consider a multivariate P\'olya tree on $R^k$ and project it to the
unit hypersphere $S^k$ to define a new Bayesian nonparametric model for
directional data. We study the properties of the proposed model and in
particular, concentrate on the implied conditional distributions of some
directions given the others to define a directional-directional regression
model. We also define a multivariate linear regression model with P\'olya tree
error and project it to define a linear-directional regression model. We obtain
the posterior characterisation of all models and show their performance with
simulated and real datasets.


title:
Reentrant Rigidity Percolation in Structurally Correlated Filamentous
  Networks

abstract:
  Many biological tissues feature a heterogeneous network of fibers whose
tensile and bending rigidity contribute substantially to these tissues' elastic
properties. Rigidity percolation has emerged as a important paradigm for
relating these filamentous tissues' mechanics to the concentrations of their
constituents. Past studies have generally considered tuning of networks by
spatially homogeneous variation in concentration, while ignoring structural
correlation. We here introduce a model in which dilute fiber networks are built
in a correlated manner that produces alternating sparse and dense regions. Our
simulations indicate that structural correlation consistently allows tissues to
attain rigidity with less material. We further find that the percolation
threshold varies non-monotonically with the degree of correlation, such that it
decreases with moderate correlation and once more increases for high
correlation. We explain the eventual reentrance in the dependence of the
rigidity percolation threshold on correlation as the consequence of large,
stiff clusters that are too poorly coupled to transmit forces across the
network. Our study offers deeper understanding of how spatial heterogeneity may
enable tissues to robustly adapt to different mechanical contexts.


title:
Meta-Learning Based Early Fault Detection for Rolling Bearings via
  Few-Shot Anomaly Detection

abstract:
  Early fault detection (EFD) of rolling bearings can recognize slight
deviation of the health states and contribute to the stability of mechanical
systems. In practice, very limited target bearing data are available to conduct
EFD, which makes it hard to adapt to the EFD task of new bearings. To address
this problem, many transfer learning based EFD methods utilize historical data
to learn transferable domain knowledge and conduct early fault detection on new
target bearings. However, most existing methods only consider the distribution
drift across different working conditions but ignore the difference between
bearings under the same working condition, which is called Unit-to-Unit
Variability (UtUV). The setting of EFD with limited target data considering
UtUV can be formulated as a Few-shot Anomaly Detection task. Therefore, this
paper proposes a novel EFD method based on meta-learning considering UtUV. The
proposed method can learn a generic metric based on Relation Network (RN) to
measure the similarity between normal data and the new arrival target bearing
data. Besides, the proposed method utilizes a health state embedding strategy
to decrease false alarms. The performance of proposed method is tested on two
bearing datasets. The results show that the proposed method can detect
incipient faults earlier than the baselines with lower false alarms.


title: Driven one dimensional noisy Kitaev chain

abstract:
  We study one dimensional Kitaev chain driven by the anisotropy parameter,
$J_{-}=t/\tau_Q$ (with $t$ running from $-\infty$ to $\infty$), in the presence
of weak Gaussian time dependent noise in the parameter $J_{+}$. The system is
prepared in the ground state of the Hamiltonian at the initial time
$t\rightarrow -\infty$. We map the problem to a set of noisy two-level
Landau-Zener (LZ) problem in the momentum space and obtain the excitation
probability. The defect density and the residual energy at the end of the drive
protocol reveals anti-Kibble Zurek (AKZ) behavior, i.e., slower drive results
in the increase of both the quantity as compared to the noiseless scenario. In
the context of the Kitaev chain, the two-point spin correlators are short
ranged, therefore the hidden topological order across the quantum phase
transition point is probed via the non-local string order parameters. We have
calculated the two-point Majorana correlator and a hidden string order
parameters in the final decohered state at the end of the noisy drive protocol
and find that it is consistent with the AKZ picture. We also evaluate the
entropy density at the end of the protocol which also shows the signature of
AKZ behavior.


title:
Executive Function: A Contrastive Value Policy for Resampling and
  Relabeling Perceptions via Hindsight Summarization?

abstract:
  We develop the few-shot continual learning task from first principles and
hypothesize an evolutionary motivation and mechanism of action for executive
function as a contrastive value policy which resamples and relabels perception
data via hindsight summarization to minimize attended prediction error, similar
to an online prompt engineering problem. This is made feasible by the use of a
memory policy and a pretrained network with inductive biases for a grammar of
learning and is trained to maximize evolutionary survival. We show how this
model of executive function can be used to implement hypothesis testing as a
stream of consciousness and may explain observations of human few-shot learning
and neuroanatomy.


title:
Optimal Closeness Testing of Discrete Distributions Made (Complex)
  Simple

abstract:
  In this note, we revisit the recent work of Diakonikolas, Gouleakis, Kane,
Peebles, and Price (2021), and provide an alternative proof of their main
result. Our argument does not rely on any specific property of Poisson random
variables (such as stability and divisibility) nor on any "clever trick," but
instead on an identity relating the expectation of the absolute value of any
random variable to the integral of its characteristic function:
  \[
  \mathbb{E}[|X|] = \frac{2}{\pi}\int_0^\infty \frac{1-\Re(\mathbb{E}[e^{i
tX}])}{t^2}\, dt
  \]
  Our argument, while not devoid of technical aspects, is arguably conceptually
simpler and more general; and we hope this technique can find additional
applications in distribution testing.


title: Framework for disruptive AI/ML Innovation

abstract:
  This framework enables C suite executive leaders to define a business plan
and manage technological dependencies for building AI/ML Solutions. The
business plan of this framework provides components and background information
to define strategy and analyze cost. Furthermore, the business plan represents
the fundamentals of AI/ML Innovation and AI/ML Solutions. Therefore, the
framework provides a menu for managing and investing in AI/ML. Finally, this
framework is constructed with an interdisciplinary and holistic view of AI/ML
Innovation and builds on advances in business strategy in harmony with
technological progress for AI/ML. This framework incorporates value chain,
supply chain, and ecosystem strategies.


title:
Impacts of Variable-Impedance-Based Power Flow Control on Renewable
  Energy Integration

abstract:
  The electric power grid has evolved significantly over the past two decades
in response to climate change. Increased levels of renewable energy generation,
as a prominent feature of this evolution, have led to new congestion patterns
in the transmission network. The transmission system is originally designed for
conventional energy sources, with predictable flow patterns. Insufficient
transfer capability in congested transmission systems results in commitment of
more expensive power plants and higher levels of renewable energy curtailment.
One way to mitigate congestion is adoption of power flow control through
variable-impedance flexible ac transmission system (FACTS) devices. In this
paper the impacts of power flow control on generation cost, carbon emissions
and renewable energy curtailment are studied under a wide range of scenarios,
including generation mix from major US regional transmission organizations, and
different load curves, representing seasonal variations. A two-stage stochastic
unit commitment, including FACTS adjustment, is used to evaluate the impacts of
FACTS devices on various types and penetration levels of renewable energy. The
results show that FACTS installation effectively reduces generation cost,
carbon emissions, and renewable energy curtailment. Location of renewable
energy resources, peak-hour demand and the system's generation mix are among
the influential factors.


title:
Layer formation in a stably-stratified fluid cooled from above. Towards
  an analog for Jupiter and other gas giants

abstract:
  It is theorized that in gas giants, an outer convection zone advances into
the interior as the surface cools, and multiple convective layers form beneath
that convective front. To study layer formation below an outer convection zone
in a similar scenario, we investigate the evolution of a stably-stratified
fluid with a linear composition gradient that is constantly being cooled from
above. We use the Boussinesq approximation in a series of 2D simulations at low
and high Prandtl numbers ($\mathrm{Pr} = 0.5$ and 7), initialized with
different temperature stratifications, and cooled at different rates. We find
that simulations initialized with an isothermal temperature profile form
multiple convective layers at $\mathrm{Pr} = 7$. These layers result from an
instability of a diffusive thermal boundary layer below the outer convection
zone. At low Pr, layers do not form. Double-diffusive instabilities drive the
fluid below the outer convection zone into a state of turbulent diffusion
rather than layered convection. Changing the initial distribution of
temperature to decrease linearly with depth results in lower values of the
inverse density ratio $R^{-1}\equiv S_{z}/T_{z}$ (given the normalization in
this work), and consequently, the spontaneous formation of multiple convective
layers at low Pr. For the stratifications used in this study, on the long-term
the composition gradient is an ineffective barrier against the propagation of
the outer convection zone and the entire fluid becomes fully-mixed, whether
layers form or not. Our results challenge 1D evolutionary models of gas giant
planets, which predict that layers are long-lived and that the outer convective
envelope stops advancing inwards. We discuss what is needed for future work to
build more realistic models.

categories: astro-ph.EP astro-ph.SR physics.ao-ph physics.flu-dyn


title: Quantum deformation of cubic string field theory

abstract:
  In this paper, we will analyze a quantum deformation of cubic string field
theory. This will be done by first constructing a quantum deformation of string
theory, in a covariant gauge, and then using the quantum deformed stringy
theory to construct a quantum deformation of string field theory. This quantum
deformed string field will then be used to contract a quantum deformed version
of cubic string field theory. We will explicitly demonstrate that the axioms of
cubic string field theory hold even after quantum deformation. Finally, we will
analyze the effect of the quantum deformation of string field theory on the
string vertices.


title:
Circular cartograms via the elastic beam algorithm originated from
  cartographic generalization

abstract:
  The circular cartogram, also known as the Dorling map, is a widely used tool
to visualize statistical data. The circular cartogram represents regions as
circles with their sizes in proportion to the statistical values, and these
circles are displaced with quality requirements such as no overlaps and
contiguity maintenance satisfied. The displacement is a basic operation in
cartographic generalization to fulfill map requirements, and many algorithms
for map objects' displacements have been introduced. The circles in a circular
cartogram can also be considered as map objects. Thus, we develop a new
approach by using the elastic beam displacement algorithm in cartographic
generalization to displace circles in circular cartogram production. First, the
initial circles are generated with their sizes in proportion to the statistical
values to avoid huge gaps or overlaps. Second, an elastic beam structure is
built as a proximity graph by analyzing the spatial relationships between the
circles. Third, the circles violating the quality requirements are considered
to have a force on the node of a beam. Fourth, the elastic beam algorithm to
achieve global optimization is applied to assign forces for each node to decide
the new positions of the circles. Steps 2 to 4 are repeated until a circular
cartogram that satisfies the defined quality requirements is obtained. The
evaluation results indicate that the circular cartograms generated by the
proposed approach have a higher quality for the maintenance of topology
relations, contiguities, and relative relations with smaller displacement
distances compared to the existing approaches.


title:
Electron spill-out effect on third-order optical nonlinearity of
  metallic nanostructure

abstract:
  Over the last three decades, plasmonics using metallic nanostructures has
become central to nanophotonics research. Recently, its targets have been
extended to nonlinear optical phenomena. In a nonlinear regime, quantum
mechanical effects, as exemplified by electron spill-out on the surface of
nanostructures, significantly influence the optical response. Quantum
hydrodynamic theory (QHT) is a promising basis for the analysis of nonlinear
optical responses involving such quantum mechanical effects. Herein, QHT was
applied to calculate the third-order optical nonlinearity of a spherical
metallic nanostructure. The results demonstrate how electron spill-out strongly
affects plasmon resonance and third-order optical nonlinearity.


title: A new consistency test for $\Lambda$CDM cosmology using galaxy surveys

abstract:
  We propose a new consistency test for the $\Lambda$CDM cosmology using
baryonic acoustic oscillations (BAO) and redshift space distortion (RSD)
measurements from galaxy redshift surveys. Specifically, we determine the peak
position of $f\sigma_8(z)$ in redshift $z$ offered by a RSD measurement, and
compare it to the one predicted by the BAO observables assuming a flat
$\Lambda$CDM cosmology. We demonstrate this new test using the simulated data
for the DESI galaxy survey, and argue that this test complements those using
the background observables alone, and is less subject to systematics in the RSD
analysis, compared to traditional methods using values of $f\sigma_8(z)$
directly.


title: Generating Examples From CLI Usage: Can Transformers Help?

abstract:
  Continuous evolution in modern software often causes documentation,
tutorials, and examples to be out of sync with changing interfaces and
frameworks. Relying on outdated documentation and examples can lead programs to
fail or be less efficient or even less secure. In response, programmers need to
regularly turn to other resources on the web such as StackOverflow for examples
to guide them in writing software. We recognize that this inconvenient,
error-prone, and expensive process can be improved by using machine learning
applied to software usage data. In this paper, we present our practical system
which uses machine learning on large-scale telemetry data and documentation
corpora, generating appropriate and complex examples that can be used to
improve documentation. We discuss both feature-based and transformer-based
machine learning approaches and demonstrate that our system achieves 100%
coverage for the used functionalities in the product, providing up-to-date
examples upon every release and reduces the numbers of PRs submitted by
software owners writing and editing documentation by >68%. We also share
valuable lessons learnt during the 3 years that our production quality system
has been deployed for Azure Cloud Command Line Interface (Azure CLI).


title:
Study on the Fairness of Speaker Verification Systems on
  Underrepresented Accents in English

abstract:
  Speaker verification (SV) systems are currently being used to make sensitive
decisions like giving access to bank accounts or deciding whether the voice of
a suspect coincides with that of the perpetrator of a crime. Ensuring that
these systems are fair and do not disfavor any particular group is crucial. In
this work, we analyze the performance of several state-of-the-art SV systems
across groups defined by the accent of the speakers when speaking English. To
this end, we curated a new dataset based on the VoxCeleb corpus where we
carefully selected samples from speakers with accents from different countries.
We use this dataset to evaluate system performance for several SV systems
trained with VoxCeleb data. We show that, while discrimination performance is
reasonably robust across accent groups, calibration performance degrades
dramatically on some accents that are not well represented in the training
data. Finally, we show that a simple data balancing approach mitigates this
undesirable bias, being particularly effective when applied to our
recently-proposed discriminative condition-aware backend.


title:
Investigating the Emergence of Online Learning in Different Countries
  using the 5 W's and 1 H Approach

abstract:
  The rise of the Internet of Everything lifestyle in the last decade has had a
significant impact on the increased emergence and adoption of online learning
in almost all countries across the world. E-learning 3.0 is expected to become
the norm of learning globally in almost all sectors in the next few years. The
pervasiveness of the Semantic Web powered by the Internet of Everything
lifestyle is expected to play a huge role towards seamless and faster adoption
of the emerging paradigms of E-learning 3.0. Therefore, this paper presents an
exploratory study to analyze multimodal components of Semantic Web behavior
data to investigate the emergence of online learning in different countries
across the world. The work specifically involved investigating relevant web
behavior data to interpret the 5 W's and 1 H - Who, What, When Where, Why, and
How related to online learning. Based on studying the E-learning Index of 2021,
the study was performed for all the countries that are member states of the
Organization for Economic Cooperation and Development. The results presented
and discussed help to interpret the emergence of online learning in each of
these countries in terms of the associated public perceptions, queries,
opinions, behaviors, and perspectives. Furthermore, to support research and
development in this field, we have published the web behavior-based Big Data
related to online learning that was mined for all these 38 countries, in the
form of a dataset, which is avail-able at
https://dx.doi.org/10.21227/xbvs-0198.


title:
Generating Self-Serendipity Preference in Recommender Systems for
  Addressing Cold Start Problems

abstract:
  Classical accuracy-oriented Recommender Systems (RSs) typically face the
cold-start problem and the filter-bubble problem when users suffer the
familiar, repeated, and even predictable recommendations, making them boring
and unsatisfied. To address the above issues, serendipity-oriented RSs are
proposed to recommend appealing and valuable items significantly deviating from
users' historical interactions and thus satisfying them by introducing
unexplored but relevant candidate items to them. In this paper, we devise a
novel serendipity-oriented recommender system (\textbf{G}enerative
\textbf{S}elf-\textbf{S}erendipity \textbf{R}ecommender \textbf{S}ystem,
\textbf{GS$^2$-RS}) that generates users' self-serendipity preferences to
enhance the recommendation performance. Specifically, this model extracts
users' interest and satisfaction preferences, generates virtual but convincible
neighbors' preferences from themselves, and achieves their self-serendipity
preference. Then these preferences are injected into the rating matrix as
additional information for RS models. Note that GS$^2$-RS can not only tackle
the cold-start problem but also provides diverse but relevant recommendations
to relieve the filter-bubble problem. Extensive experiments on benchmark
datasets illustrate that the proposed GS$^2$-RS model can significantly
outperform the state-of-the-art baseline approaches in serendipity measures
with a stable accuracy performance.


title:
Correlated metallic two-particle bound states in Wannier--Stark
  flatbands

abstract:
  Tight-binding single-particle models on simple Bravais lattices in space
dimension $d \geq 2$, when exposed to commensurate DC fields, result in the
complete absence of transport due to the formation of Wannier--Stark flatbands
[Phys. Rev. Res. $\textbf{3}$, 013174 (2021)]. The single-particle states
localize in a factorial manner, i.e., faster than exponential. Here, we
introduce interaction among two such particles that partially lifts the
localization and results in metallic two-particle bound states that propagate
in the directions perpendicular to the DC field. We demonstrate this effect
using a square lattice with Hubbard interaction. We apply perturbation theory
in the regime of interaction strength $(U)$ $\ll$ hopping strength $(t)$ $\ll$
field strength $(\mathcal{F})$, and obtain estimates for the group velocity of
the bound states in the direction perpendicular to the field. The two-particle
group velocity scales as $U {(t/\mathcal{F})}^\nu$. We calculate the dependence
of the exponent $\nu$ on the DC field direction and on the dominant
two-particle configurations related to the choices of unperturbed flatbands.
Numerical simulations confirm our predictions from the perturbative analysis.

categories: cond-mat.quant-gas cond-mat.mtrl-sci cond-mat.str-el quant-ph


title:
Inferring Late Stage Enrichment of Exoplanet Atmospheres from Observed
  Interstellar Comets

abstract:
  The discovery of the first two interstellar comets implies that, on average,
every star contributes a substantial amount of material to the galactic
population by ejecting such bodies from the host system. Since scattering is a
chaotic process, a comparable amount of material should be injected into the
inner regions of each system that ejects comets. For comets that are
transported inwards and interact with planets, this Letter estimates the
fraction of material that is accreted or outward-scattered as a function of
planetary masses and orbital parameters. These calculations indicate that
planets with escape velocities smaller than their current day orbital
velocities will efficiently accrete comets. We estimate the accretion
efficiency for members of the current census of extrasolar planets, and find
that planetary populations including but not limited to hot and warm Jupiters,
sub-Neptunes and super-Earths can efficiently capture incoming comets. This
cometary enrichment may have important ramifications for post-formation
atmospheric composition and chemistry. As a result, future detections and
compositional measurements of interstellar comets will provide direct
measurements of material that potentially enriched a sub-population of the
extrasolar planets. Finally, we estimate the efficiency of this enrichment
mechanism for extrasolar planets that will be observed with the $\textit{James
Webb Space Telescope}$ (JWST). With JWST currently operational and these
observations imminently forthcoming, it is of critical importance to
investigate how enrichment from interstellar comet analogues may affect the
interpretations of exoplanet atmospheric compositions.


title:
Trends in Remote Learning-based Google Shopping in the United States due
  to COVID-19

abstract:
  The United States of America has been the worst affected country in terms of
the number of cases and deaths on account of the severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) or COVID-19, a highly transmissible and
pathogenic coronavirus that started spreading globally in late 2019. On account
of the surge of infections, accompanied by hospitalizations and deaths due to
COVID-19, and lack of a definitive cure at that point, a national emergency was
declared in the United States on March 13, 2020. To prevent the rapid spread of
the virus, several states declared stay at home and remote work guidelines
shortly after this declaration of an emergency. Such guidelines caused schools,
colleges, and universities, both private and public, in all the 50-United
States to switch to remote or online forms of teaching for a significant period
of time. As a result, Google, the most widely used search engine in the United
States, experienced a surge in online shopping of remote learning-based
software, systems, applications, and gadgets by both educators and students
from all the 50-United States, due to both these groups responding to the
associated needs and demands related to switching to remote teaching and
learning. This paper aims to investigate, analyze, and interpret these trends
of Google Shopping related to remote learning that emerged since March 13,
2020, on account of COVID-19 and the subsequent remote learning adoption in
almost all schools, colleges, and universities, from all the 50-United States.
The study was performed using Google Trends, which helps to track and study
Google Shopping-based online activity emerging from different geolocations. The
results and discussions show that the highest interest related to Remote
Learning-based Google Shopping was recorded from Oregon, which was followed by
Illinois, Florida, Texas, California, and the other states.


title:
Chiral Hinge Transports in Disordered Non-Hermitian Second-Order
  Topological Insulators

abstract:
  The generalized bulk-boundary correspondence predicts the existence of the
chiral hinge states in three-dimensional second-order topological insulators
(3DSOTIs), resulting in a quantized Hall effect in three dimensions. Chiral
hinge states in Hermitian 3DSOTIs are characterized by the quantized
transmission coefficients with zero fluctuations, even in the presence of
disorders. Here, we show that chiral hinge transport in disordered
non-Hermitian systems deviates from the paradigm of the Hermitian case. Our
numerical calculations prove the robustness of hinge states of disordered
non-Hermitian 3DSOTIs. The mean transmission coefficients may or may not equal
the number of chiral hinge channels, depending on the Hermiticity of chiral
hinge states, while the fluctuations of transmission coefficients are always
non-zero. Such fluctuations are not due to the broken chirality of hinge states
but the incoherent scatterings of non-Hermitian potentials. The physics
revealed here should also be true for one-dimensional chiral channels in
topological materials that support chiral boundary states, such as Chern
insulators, three-dimensional anomalous Hall insulators, and Weyl semimetals.


title: SCGC : Self-Supervised Contrastive Graph Clustering

abstract:
  Graph clustering discovers groups or communities within networks. Deep
learning methods such as autoencoders (AE) extract effective clustering and
downstream representations but cannot incorporate rich structural information.
While Graph Neural Networks (GNN) have shown great success in encoding graph
structure, typical GNNs based on convolution or attention variants suffer from
over-smoothing, noise, heterophily, are computationally expensive and typically
require the complete graph being present. Instead, we propose Self-Supervised
Contrastive Graph Clustering (SCGC), which imposes graph-structure via
contrastive loss signals to learn discriminative node representations and
iteratively refined soft cluster labels. We also propose SCGC*, with a more
effective, novel, Influence Augmented Contrastive (IAC) loss to fuse richer
structural information, and half the original model parameters. SCGC(*) is
faster with simple linear units, completely eliminate convolutions and
attention of traditional GNNs, yet efficiently incorporates structure. It is
impervious to layer depth and robust to over-smoothing, incorrect edges and
heterophily. It is scalable by batching, a limitation in many prior GNN models,
and trivially parallelizable. We obtain significant improvements over
state-of-the-art on a wide range of benchmark graph datasets, including images,
sensor data, text, and citation networks efficiently. Specifically, 20% on ARI
and 18% on NMI for DBLP; overall 55% reduction in training time and overall,
81% reduction on inference time. Our code is available at :
https://github.com/gayanku/SCGC


title:
Modeling dynamic volatility under uncertain environment with fuzziness
  and randomness

abstract:
  Predicting the dynamic volatility in financial market provides a promising
method for risk prediction, asset pricing and market supervision.
Barndorff-Nielsen and Shephard model (BN-S) model, used to capture the
stochastic behavior of high-frequency time series, is an accepted stochastic
volatility model with L\' evy process. Although this model is attractive and
successful in theory, it needs to be improved in application. We build a new
generalized BN-S model suitable for uncertain environment with fuzziness and
randomness. This new model considers the delay phenomenon between price
fluctuation and volatility changes, solves the problem of the lack of
long-range dependence of classic models. Calculation results show that new
model outperforms the classic model in volatility forecasting. Experiments on
Dow Jones Industrial Average futures price data are conducted to verify
feasibility and practicability of our proposed approach. Numerical examples are
provided to illustrate the theoretical result. Three machine learning
algorithms are applied to estimate new model parameter. Compared with the
classical model, our method effectively combines the uncertain environmental
characteristics, which makes the prediction of dynamic volatility more flexible
and has ideal performance.


title:
Measuring a Robot Hand's Graspable Region using Power and Precision
  Grasps

abstract:
  The variety of robotic hand designs and actuation schemes makes it difficult
to measure a hand's graspable volume. For end-users, this lack of standardized
measurements makes it challenging to determine a priori if a robot hand is the
right size for grasping an object. We propose a practical hand measurement
standard, based on precision and power grasps, that is applicable to a wide
variety of robot hand designs. The resulting measurements can be used to both
determine if an object will fit in the hand and characterize the size of an
object with respect to the hand. Our measurement procedure uses a functional
approach, based on grasping a hypothetical cylinder, that allows the measurer
choose the exact hand orientation and finger configurations that are used for
the measurements. This ensures that the measurements are functionally
comparable while relying on the human to determine the finger configurations
that best match the intended grasp. We demonstrate using our measurement
standard with three commercial robot hand designs and objects from the YCB data
set.


title: Timing and Evolution of PSR B0950+08

abstract:
  We present timing solutions of PSR B0950+08, using 14 years of observations
at Nanshan 26-m Radio Telescope of Xinjiang Astronomical Observatory. The
braking index of PSR B0950+08 varies from --367 392 to 168 883, which shows an
oscillation with large amplitude ($\sim 10^5$) and uncertainty. Considering the
variation of braking indices and the most probable kinematic age of PSR
B0950+08, a model withe long-term magnetic field decay modulated by short-term
oscillations is proposed to explain the timing data. With this magnetic field
decay model, we discuss the spin and thermal evolution of PSR B0950+08. The
uncertainties of its age are also considered. The results show that
three-component oscillations are the more reasonable for the spin-frequency
derivative distributions of PSR B0950+08, and the initial spin period of PSR
B0950+08 must be shorter than $97\rm\ ms$ when the age is equal to the lower
bound of its kinematic age. The standard cooling model could explain the
surface temperature of PSR B0950+08 with its most probable kinematic age.
Vortex creep heating with a long-term magnetic field decay could maintain a
relatively high temperature at the later stages of evolution and explain the
thermal emission data of old and warm pulsars. Coupling with the long-term
magnetic field decay, an explanation of the temperature of PSR B0950+08 with
roto-chemical heating needs an implausibly short initial rotation period ($P_0
\lesssim 17\rm{ ms}$). The spin and thermal evolution of pulsars should be
studied simultaneously. Future timing, ultraviolet or X-ray observations are
essential for studying the evolution and interior properties of pulsars.


title: Prediction of drag components on rough surfaces using effective models

abstract:
  Owing to the multiscale nature and the consequent high computational cost
associated with simulations of flows over rough surfaces, effective models are
being developed as a practical means of dealing with such flows. Existing
effective models focus primarily on accurately predicting interface velocities
using slip length. Moreover, they are concerned mainly with flat interfaces and
do not directly address the drag computation. In this work, we formulate the
Transpiration-Resistance model in polar coordinates and address the challenge
of computing drag components on rough surfaces. Like slip length, we introduce
two constitutive parameters called shear and pressure correction factors that
encompass information about how the total drag is partitioned into viscous and
pressure components. Computation of these non-empirical parameters does not
necessitate solving additional microscale problems; they can be obtained from
the same microscale problem used for the slip-length calculation. We
demonstrate the effectiveness of the proposed parameters for the Couette flow
over rough surfaces. Moreover, using the flow over a rough cylinder as an
example, we present the accuracy of predicting interface velocity and drag
components by comparing the effective model results with those obtained from
geometry-resolved simulations. Numerical simulations presented in this paper
prove that we can accurately capture both viscous and pressure drag over rough
surfaces for flat- and circular-interface problems by using the proposed
constitutive parameters.


title: One-shot trajectory learning of open quantum systems dynamics

abstract:
  Nonadiabatic quantum dynamics are important for understanding
light-harvesting processes, but their propagation with traditional methods can
be rather expensive. Here we present a one-short trajectory learning approach
that allows to directly make ultra-fast prediction of the entire trajectory for
a new set of such simulation parameters as temperature and reorganization
energy. The whole 2.5 ps long propagation takes 50 milliseconds as we
demonstrate on the comparatively large quantum system, the Fenna-Matthews-Olsen
(FMO) complex. Our approach also significantly reduces time and memory
requirements for training.


title: Better Query Graph Selection for Knowledge Base Question Answering

abstract:
  This paper presents a novel approach based on semantic parsing to improve the
performance of Knowledge Base Question Answering (KBQA). Specifically, we focus
on how to select an optimal query graph from a candidate set so as to retrieve
the answer from knowledge base (KB). In our approach, we first propose to
linearize the query graph into a sequence, which is used to form a sequence
pair with the question. It allows us to use mature sequence modeling, such as
BERT, to encode the sequence pair. Then we use a ranking method to sort
candidate query graphs. In contrast to the previous studies, our approach can
efficiently model semantic interactions between the graph and the question as
well as rank the candidate graphs from a global view. The experimental results
show that our system achieves the top performance on ComplexQuestions and the
second best performance on WebQuestions.


title:
Understanding A Class of Decentralized and Federated Optimization
  Algorithms: A Multi-Rate Feedback Control Perspective

abstract:
  Distributed algorithms have been playing an increasingly important role in
many applications such as machine learning, signal processing, and control.
Significant research efforts have been devoted to developing and analyzing new
algorithms for various applications. In this work, we provide a fresh
perspective to understand, analyze, and design distributed optimization
algorithms. Through the lens of multi-rate feedback control, we show that a
wide class of distributed algorithms, including popular decentralized/federated
schemes, can be viewed as discretizing a certain continuous-time feedback
control system, possibly with multiple sampling rates, such as decentralized
gradient descent, gradient tracking, and federated averaging. This key
observation not only allows us to develop a generic framework to analyze the
convergence of the entire algorithm class. More importantly, it also leads to
an interesting way of designing new distributed algorithms. We develop the
theory behind our framework and provide examples to highlight how the framework
can be used in practice.


title: Investigation of spin rotators in CEPC at Z-pole

abstract:
  Longitudinal polarization is an important design aspect of the future 100
km-scale Circular Electron Position Collider (CEPC). Spin rotators are needed
in CEPC collider rings to make the beam polarization along the longitudinal
direction at the interaction points (IPs). This paper focuses on the design of
spin rotators for CEPC at Z-pole (45.6 GeV). The design of spin rotators in
CEPC at Z-pole is based on solenoid magnets and horizontal bending magnets
sections. The coupling of transverse motion introduced by solenoids is
compensated with quadrupole lenses. Adjustments have been made to the layout to
implement the spin rotators into the collider rings.Longitudinal polarized beam
can be achieved at the IPs with the spin rotators. High degree of polarization
is attainable, while the effect of spin rotators on orbital motion is
acceptable. The detailed simulation results will be presented.A solenoid-based
spin rotator configuration is designed and integrated into the CEPC collider
ring lattice. According to the simulation results, the polarization
requirements can be satisfied.


title:
Relational Abstractions for Generalized Reinforcement Learning on
  Symbolic Problems

abstract:
  Reinforcement learning in problems with symbolic state spaces is challenging
due to the need for reasoning over long horizons. This paper presents a new
approach that utilizes relational abstractions in conjunction with deep
learning to learn a generalizable Q-function for such problems. The learned
Q-function can be efficiently transferred to related problems that have
different object names and object quantities, and thus, entirely different
state spaces. We show that the learned generalized Q-function can be utilized
for zero-shot transfer to related problems without an explicit, hand-coded
curriculum. Empirical evaluations on a range of problems show that our method
facilitates efficient zero-shot transfer of learned knowledge to much larger
problem instances containing many objects.


title: Transit Frequency Setting Problem with Demand Uncertainty

abstract:
  Public transit systems are the backbone of efficient and sustainable urban
mobility systems in the era of urbanization. This paper proposes a transit
frequency setting model for a single transit line to generate transit schedules
that could better serve passengers. The proposed model optimizes both service
patterns and frequency for transit routes during a service period, and crowding
levels on transit vehicles are also considered. To handle demand uncertainties
when designing transit schedules, both stochastic programming and robust
optimization techniques are introduced to protect transit schedules against
inaccurate demand estimates. To address the computation complexity in both
extended models under large-scale demand matrices, a Bender decomposition
algorithm and two dimension reduction techniques are designed. The proposed
models will be tested with real-world data from Chicago Transit Authority
(CTA).


title: MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation

abstract:
  Test-time adaptation approaches have recently emerged as a practical solution
for handling domain shift without access to the source domain data. In this
paper, we propose and explore a new multi-modal extension of test-time
adaptation for 3D semantic segmentation. We find that directly applying
existing methods usually results in performance instability at test time
because multi-modal input is not considered jointly. To design a framework that
can take full advantage of multi-modality, where each modality provides
regularized self-supervisory signals to other modalities, we propose two
complementary modules within and across the modalities. First, Intra-modal
Pseudolabel Generation (Intra-PG) is introduced to obtain reliable pseudo
labels within each modality by aggregating information from two models that are
both pre-trained on source data but updated with target data at different
paces. Second, Inter-modal Pseudo-label Refinement (Inter-PR) adaptively
selects more reliable pseudo labels from different modalities based on a
proposed consistency scheme. Experiments demonstrate that our regularized
pseudo labels produce stable self-learning signals in numerous multi-modal
test-time adaptation scenarios for 3D semantic segmentation. Visit our project
website at https://www.nec-labs.com/~mas/MM-TTA.


title: Adaptable Text Matching via Meta-Weight Regulator

abstract:
  Neural text matching models have been used in a range of applications such as
question answering and natural language inference, and have yielded a good
performance. However, these neural models are of a limited adaptability,
resulting in a decline in performance when encountering test examples from a
different dataset or even a different task. The adaptability is particularly
important in the few-shot setting: in many cases, there is only a limited
amount of labeled data available for a target dataset or task, while we may
have access to a richly labeled source dataset or task. However, adapting a
model trained on the abundant source data to a few-shot target dataset or task
is challenging. To tackle this challenge, we propose a Meta-Weight Regulator
(MWR), which is a meta-learning approach that learns to assign weights to the
source examples based on their relevance to the target loss. Specifically, MWR
first trains the model on the uniformly weighted source examples, and measures
the efficacy of the model on the target examples via a loss function. By
iteratively performing a (meta) gradient descent, high-order gradients are
propagated to the source examples. These gradients are then used to update the
weights of source examples, in a way that is relevant to the target
performance. As MWR is model-agnostic, it can be applied to any backbone neural
model. Extensive experiments are conducted with various backbone text matching
models, on four widely used datasets and two tasks. The results demonstrate
that our proposed approach significantly outperforms a number of existing
adaptation methods and effectively improves the cross-dataset and cross-task
adaptability of the neural text matching models in the few-shot setting.


title: On Local Energy decay for solution of the Benjamin-Ono equation

abstract:
  We consider the long time dynamics of large solutions to the Benjamin-Ono
equation. Using virial techniques, we describe regions of space where every
solution in a suitable Sobolev space must decay to zero along sequences of
times. Moreover, in the case of exterior regions, we prove complete decay for
any sequence of times. The remaining regions not treated here are essentially
the strong dispersion and soliton regions.


title:
SVD Perspectives for Augmenting DeepONet Flexibility and
  Interpretability

abstract:
  Deep operator networks (DeepONets) are powerful architectures for fast and
accurate emulation of complex dynamics. As their remarkable generalization
capabilities are primarily enabled by their projection-based attribute, we
investigate connections with low-rank techniques derived from the singular
value decomposition (SVD). We demonstrate that some of the concepts behind
proper orthogonal decomposition (POD)-neural networks can improve DeepONet's
design and training phases. These ideas lead us to a methodology extension that
we name SVD-DeepONet. Moreover, through multiple SVD analyses, we find that
DeepONet inherits from its projection-based attribute strong inefficiencies in
representing dynamics characterized by symmetries. Inspired by the work on
shifted-POD, we develop flexDeepONet, an architecture enhancement that relies
on a pre-transformation network for generating a moving reference frame and
isolating the rigid components of the dynamics. In this way, the physics can be
represented on a latent space free from rotations, translations, and stretches,
and an accurate projection can be performed to a low-dimensional basis. In
addition to flexibility and interpretability, the proposed perspectives
increase DeepONet's generalization capabilities and computational efficiencies.
For instance, we show flexDeepONet can accurately surrogate the dynamics of 19
variables in a combustion chemistry application by relying on 95% less
trainable parameters than the ones of the vanilla architecture. We argue that
DeepONet and SVD-based methods can reciprocally benefit from each other. In
particular, the flexibility of the former in leveraging multiple data sources
and multifidelity knowledge in the form of both unstructured data and
physics-informed constraints has the potential to greatly extend the
applicability of methodologies such as POD and PCA.

categories: cs.LG math.AP physics.comp-ph physics.data-an


title: The symmetry for two class of steady stratified periodic water waves

abstract:
  In this paper, we mainly consider two class of travelling stratified periodic
water waves, one with negative (or without) surface tension and the other with
constant Bernoulli's function and stagnation points. We first establish the
symmetry result for stratified water waves with negative (or without) surface
tension, but without stagnation by using the modified maximum principle.
Furthermore, the symmetry property of stratified water waves with constant
Bernoulli's function and stagnation points is also obtained provided the
monotonic property is known.


title: Data-Driven Adaptive Simultaneous Machine Translation

abstract:
  In simultaneous translation (SimulMT), the most widely used strategy is the
wait-k policy thanks to its simplicity and effectiveness in balancing
translation quality and latency. However, wait-k suffers from two major
limitations: (a) it is a fixed policy that can not adaptively adjust latency
given context, and (b) its training is much slower than full-sentence
translation. To alleviate these issues, we propose a novel and efficient
training scheme for adaptive SimulMT by augmenting the training corpus with
adaptive prefix-to-prefix pairs, while the training complexity remains the same
as that of training full-sentence translation models. Experiments on two
language pairs show that our method outperforms all strong baselines in terms
of translation quality and latency.


title: Topological synchronization of chaotic systems

abstract:
  A chaotic dynamics is typically characterized by the emergence of strange
attractors with their fractal or multifractal structure. On the other hand,
chaotic synchronization is a unique emergent self-organization phenomenon in
nature. Classically, synchronization was characterized in terms of macroscopic
parameters, such as the spectrum of Lyapunov exponents. Recently, however, we
attempted a microscopic description of synchronization, called topological
synchronization, and showed that chaotic synchronization is, in fact, a
continuous process that starts in low-density areas of the attractor. Here we
analyze the relation between the two emergent phenomena by shifting the
descriptive level of topological synchronization to account for the
multifractal nature of the visited attractors. Namely, we measure the
generalized dimension of the system and monitor how it changes while increasing
the coupling strength. We show that during the gradual process of topological
adjustment in phase space, the multifractal structures of each strange
attractor of the two coupled oscillators continuously converge, taking a
similar form, until complete topological synchronization ensues. According to
our results, chaotic synchronization has a specific trait in various systems,
from continuous systems and discrete maps to high dimensional systems:
synchronization initiates from the sparse areas of the attractor, and it
creates what we termed as the zipper effect, a distinctive pattern in the
multifractal structure of the system that reveals the microscopic buildup of
the synchronization process. Topological synchronization offers, therefore, a
more detailed microscopic description of chaotic synchronization and reveals
new information about the process even in cases of high mismatch parameters.


title:
Span-level Bidirectional Cross-attention Framework for Aspect Sentiment
  Triplet Extraction

abstract:
  Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment
analysis task that aims to extract triplets of aspect terms, sentiments, and
opinion terms from review sentences. Recently, span-level models achieve
gratifying results on ASTE task by taking advantage of whole span predictions.
However, all the spans generated by these methods inevitably share at least one
token with some others, and these method suffer from the similarity of these
spans due to their similar distributions. Moreover, since either the aspect
term or opinion term can trigger a sentiment triplet, it is challenging to make
use of the information more comprehensively and adequately. To address these
concerns, we propose a span-level bidirectional cross-attention framework.
Specifically, we design a similar span separation loss to detach the spans with
shared tokens and a bidirectional cross-attention structure that consists of
aspect and opinion decoders to decode the span-level representations in both
aspect-to-opinion and opinion-to-aspect directions. With differentiated span
representations and bidirectional decoding structure, our model can extract
sentiment triplets more precisely and efficiently. Experimental results show
that our framework significantly outperforms state-of-the-art methods,
achieving better performance in predicting triplets with multi-token entities
and extracting triplets in sentences with multi-triplets.


title: Crowdsourcing Felt Reports using the MyShake smartphone app

abstract:
  MyShake is a free citizen science smartphone app that provides a range of
features related to earthquakes. Features available globally include rapid
post-earthquake notifications, live maps of earthquake damage as reported by
MyShake users, safety tips and various educational features. The app also uses
the accelerometer to detect earthquake shaking and to record and submit
waveforms to a central archive. In addition, MyShake delivers earthquake early
warning alerts in California, Oregon and Washington. In this study we compare
the felt shaking reports provided by MyShake users in California with the US
Geological Survey's "Did You Feel It?" intensity reports. The MyShake app
simply asks "What strength of shaking did you feel" and users report on a
five-level scale. When the reports are averaged in spatial bins, we find strong
correlations with the Modified Mercalli Intensity scale values reported by the
USGS based on the much more complex DYFI surveys. The MyShake felt reports can
therefore also be used to generate shaking intensity maps.