title: Nonlocal Effect on a Generalized Ohta-Kawasaki Model

abstract:
  We propose a nonlocal Ohta-Kawasaki model to study the nonlocal effect on the
pattern formation of some binary systems with general long-range interactions.
While the nonlocal Ohta-Kawasaki model displays similar bubble patterns as the
standard Ohta-Kawasaki model, by performing Fourier analysis, we find that the
optimal number of bubbles for the nonlocal model may have an upper bound no
matter how large the repulsive strength is. The existence of such an upper
bound is characterized by the eigenvalues of the nonlocal kernels. Additionally
we explore the conditions under which the nonlocal horizon parameter may
promote or demote the bubble splitting, and apply the analysis framework to
several case studies for various nonlocal operators.


title: The ASEP speed process

abstract:
  For ASEP with step initial data and a second class particle started at the
origin we prove that as time goes to infinity the second class particle almost
surely achieves a velocity that is uniformly distributed on $[-1,1]$. This
positively resolves Conjecture 1.9 and 1.10 of [Amir, Angel and Valko, "The
TASEP speed process", Annals of Probability 39, 1205--1242, 2011] and allows us
to construct the ASEP speed process.


title:
A conjectural formula for $\lambda_g\mathrm{DR}_g(a,-a)$ is true in
  Gorenstein quotient

abstract:
  In this note we prove that a conjectural formula for the class $\lambda_g
\mathrm{DR}_g(a,-a)\in R^{2g}(\overline{\mathcal{M}}_{g,2})$ proposed recently
by Buryak-Iglesias-Shadrin is true in the Gorenstein quotient of the ring
$R^*(\overline{\mathcal{M}}_{g,2})$. As a corollary we prove the strong
$\mathrm{DR} \backslash \mathrm{DZ}$ equivalence conjecture for one-point
correlators.


title:
Accelerated Design and Deployment of Low-Carbon Concrete for Data
  Centers

abstract:
  Concrete is the most widely used engineered material in the world with more
than 10 billion tons produced annually. Unfortunately, with that scale comes a
significant burden in terms of energy, water, and release of greenhouse gases
and other pollutants; indeed 8% of worldwide carbon emissions are attributed to
the production of cement, a key ingredient in concrete. As such, there is
interest in creating concrete formulas that minimize this environmental burden,
while satisfying engineering performance requirements including compressive
strength. Specifically for computing, concrete is a major ingredient in the
construction of data centers.
  In this work, we use conditional variational autoencoders (CVAEs), a type of
semi-supervised generative artificial intelligence (AI) model, to discover
concrete formulas with desired properties. Our model is trained just using a
small open dataset from the UCI Machine Learning Repository joined with
environmental impact data from standard lifecycle analysis. Computational
predictions demonstrate CVAEs can design concrete formulas with much lower
carbon requirements than existing formulations while meeting design
requirements. Next we report laboratory-based compressive strength experiments
for five AI-generated formulations, which demonstrate that the formulations
exceed design requirements. The resulting formulations were then used by Ozinga
Ready Mix -- a concrete supplier -- to generate field-ready concrete
formulations, based on local conditions and their expertise in concrete design.
Finally, we report on how these formulations were used in the construction of
buildings and structures in a Meta data center in DeKalb, IL, USA. Results from
field experiments as part of this real-world deployment corroborate the
efficacy of AI-generated low-carbon concrete mixes.


title: An answer to an open question in the incremental SVD

abstract:
  Incremental singular value decomposition (SVD) was proposed by Brand to
efficiently compute the SVD of a matrix. The algorithm needs to compute
thousands or millions of orthogonal matrices and to multiply them together.
However, the multiplications may corrode the orthogonality. Hence many
reorthogonalizations are needed in practice. In [Linear Algebra and its
Applications 415 (2006) 20-30], Brand asked "It is an open question how often
this is necessary to guarantee a certain overall level of numerical precision;
it does not change the overall complexity." In this paper, we answer this
question and the answer is we can avoid computing the large amount of those
orthogonal matrices and hence the reorthogonalizations are not necessary by
modifying his algorithm. We prove that the modification does not change the
outcomes of the algorithm. Numerical experiments are presented to illustrate
the performance of our modification.


title:
Near-field radiative heat transfer between irregularly shaped dielectric
  particles modeled with the discrete system Green's function method

abstract:
  Near-field radiative heat transfer (NFRHT) between irregularly shaped
dielectric particles made of SiO2 and morphology characterized by Gaussian
random spheres is studied. Particles are modeled with a new numerical method
based on fluctuational electrodynamics, called the discrete system Green's
function (DSGF) approach, that defines system interactions deterministically
via a self-consistent Green's function equation and is applicable to finite,
three-dimensional objects. The DSGF method is deemed suitable to model NFRHT
between irregularly shaped particles after verification against the analytical
solution for chains of two and three SiO2 spheres. The NFRHT results reveal
that geometric irregularity in particles leads to a reduction of the total
conductance from that of comparable perfect spheres at small separation
distances where NFRHT is a surface phenomenon. At larger separation distances
where NFRHT becomes a volumetric process, the total conductance between
irregularly shaped particles converges to that of comparable perfect spheres.
Spectral analysis reveals, however, that particle irregularity leads to damping
and broadening of resonances at all separation distances, thereby highlighting
the importance of the DSGF method for spectral engineering in the near field.
The reduced spectral coherence when particle size is larger than the vacuum
separation distance is attributed to coupling of surface phonon-polaritons
within the randomly generated, distorted particle features. For particle size
smaller than the vacuum separation distance, resonance broadening and damping
is linked with the multiple localized surface phonon modes supported by the
composite spherical harmonic morphologies of the Gaussian random spheres.


title: Transfer Learning for Autonomous Chatter Detection in Machining

abstract:
  Large-amplitude chatter vibrations are one of the most important phenomena in
machining processes. It is often detrimental in cutting operations causing a
poor surface finish and decreased tool life. Therefore, chatter detection using
machine learning has been an active research area over the last decade. Three
challenges can be identified in applying machine learning for chatter detection
at large in industry: an insufficient understanding of the universality of
chatter features across different processes, the need for automating feature
extraction, and the existence of limited data for each specific
workpiece-machine tool combination. These three challenges can be grouped under
the umbrella of transfer learning. This paper studies automating chatter
detection by evaluating transfer learning of prominent as well as novel chatter
detection methods. We investigate chatter classification accuracy using a
variety of features extracted from turning and milling experiments with
different cutting configurations. The studied methods include Fast Fourier
Transform (FFT), Power Spectral Density (PSD), the Auto-correlation Function
(ACF), Wavelet Packet Transform (WPT), and Ensemble Empirical Mode
Decomposition (EEMD). We also examine more recent approaches based on
Topological Data Analysis (TDA) and similarity measures of time series based on
Discrete Time Warping (DTW). We evaluate the transfer learning potential of
each approach by training and testing both within and across the turning and
milling data sets. Our results show that carefully chosen time-frequency
features can lead to high classification accuracies albeit at the cost of
requiring manual pre-processing and the tagging of an expert user. On the other
hand, we found that the TDA and DTW approaches can provide accuracies and F1
scores on par with the time-frequency methods without the need for manual
preprocessing.


title:
Chemical state analysis of reactively sputtered zinc vanadium nitride:
  The Auger parameter as a tool in materials design

abstract:
  Photoelectron spectroscopy is an important tool for the development of new
materials. However, especially for nitride semiconductors, the formation of
surface oxides, surface band bending as well as the lack of a suitable charge
reference often prevent a robust analysis. Here, we perform a comprehensive
chemical state analysis of the Zn-V-N phase space using the Auger parameter
concept, which is less sensitive to such uncertainties. Phase-pure Zn2VN3, VN,
and Zn3N2 samples are analyzed using XPS/HAXPES after transfer in inert-gas
atmosphere. In addition, high-throughput chemical state analysis is performed
on combinatorial Zn1-xVxN thin film libraries. The evolution of the Zn Auger
parameter in Zn1-xVxN is consistent with previous mapping of the structural and
functional properties. Strikingly, the study reveals a narrower stability range
of wurtzite Zn1-xVxN than our previous high-throughput XRD screening,
highlighting the sensitivity of the measurement approach. The procedures
applied here are transferable to many other material systems and could be
particularly useful for the high-throughput development of materials with low
crystallinity where insights from XRD screenings are limited.


title:
On $SL(2,\mathbb{R})$-cocycles over irrational rotations with secondary
  collisions

abstract:
  We consider a skew product $F_{A} = (\sigma_{\omega}, A)$ over irrational
rotation $\sigma_{\omega}(x) = x + \omega$ of a circle $\mathbb{T}^{1}$. It is
supposed that the transformation $A: \mathbb{T}^{1} \to SL(2, \mathbb{R})$
being a $C^{1}$-map has the form $A(x) = R(\varphi(x)) Z(\lambda(x))$, where
$R(\varphi)$ is a rotation in $\mathbb{R}^{2}$ over the angle $\varphi$ and
$Z(\lambda)= diag\{\lambda, \lambda^{-1}\}$ is a diagonal matrix. Assuming that
$\lambda(x) \ge \lambda_{0} > 1$ with a sufficiently large constant
$\lambda_{0}$ and the function $\varphi$ be such that $\cos \varphi(x)$
possesses only simple zeroes, we study hyperbolic properties of the cocycle
generated by $F_{A}$. We apply the critical set method to show that, under some
additional requirements on the derivative of the function $\varphi$, the
secondary collisions compensate weakening of the hyperbolicity due to primary
collisions and the cocycle generated by $F_{A}$ becomes hyperbolic in contrary
to the case when secondary collisions can be partially eliminated.


title:
Optimal brokerage contracts in Almgren-Chriss model with multiple
  clients

abstract:
  This paper constructs optimal brokerage contracts for multiple
(heterogeneous) clients trading a single asset whose price follows the
Almgren-Chriss model. The distinctive features of this work are as follows: (i)
the reservation values of the clients are determined endogenously, and (ii) the
broker is allowed to not offer a contract to some of the potential clients,
thus choosing her portfolio of clients strategically. We find a computationally
tractable characterization of the optimal portfolios of clients (up to a
digital optimization problem, which can be solved efficiently if the number of
potential clients is small) and conduct numerical experiments which illustrate
how these portfolios, as well as the equilibrium profits of all market
participants, depend on the price impact coefficients.


title:
Renormalization and a non-adiabatic vacuum choice in a
  radiation-dominated universe

abstract:
  Vacuum and particles can be naturally defined in the adiabatic regime of an
expanding universe. In general, however, there is no preferred choice of a
vacuum state, unless the spacetime background possesses special symmetries. In
the absence of symmetries the standard viewpoint is to construct distinguished
adiabatic states permitting renormalizability of the stress-energy tensor. We
study a special non-adiabatic vacuum for a massive scalar field in a
radiation-dominated universe defined by imposing early-times conformal
symmetry. We show that this state is consistent with renormalization, despite
its ultraviolet behaviour is not the conventional one due to the emergence of
oscillatory terms.


title: MPC-Based Emergency Vehicle-Centered Multi-Intersection Traffic Control

abstract:
  This paper proposes a traffic control scheme to alleviate traffic congestion
in a network of interconnected signaled lanes/roads. The proposed scheme is
emergency vehicle-centered, meaning that it provides an efficient and timely
routing for emergency vehicles. In the proposed scheme, model predictive
control is utilized to control inlet traffic flows by means of network gates,
as well as configuration of traffic lights across the network. Two schemes are
considered in this paper: i) centralized; and ii) decentralized. In the
centralized scheme, a central unit controls the entire network. This scheme
provides the optimal solution, even though it might not fulfil real-time
computation requirements for large networks. In the decentralized scheme, each
intersection has its own control unit, which sends local information to an
aggregator. The main responsibility of this aggregator is to receive local
information from all control units across the network as well as the emergency
vehicle, to augment the received information, and to share it with the control
units. Since the decision-making in decentralized scheme is local and the
aggregator should fulfil the above-mentioned tasks during a traffic cycle which
takes a long period of time, the decentralized scheme is suitable for large
networks, even though it may provide a sub-optimal solution. Extensive
simulation studies are carried out to validate the proposed schemes, and assess
their performance. Notably, the obtained results reveal that traveling times of
emergency vehicles can be reduced up to ~50% by using the centralized scheme
and up to ~30% by using the decentralized scheme, without causing congestion in
other lanes.


title: Chaos for rescaled measures on Kac's sphere

abstract:
  In this article we study a relatively novel way of constructing chaotic
sequences of probability measures supported on Kac's sphere, which are obtained
as the law of a vector of $N$ i.i.d. variables after it is rescaled to have
unit average energy. We show that, as $N$ increases, this sequence is chaotic
in the sense of Kac, with respect to the Wasserstein distance, in $L^1$, in the
entropic sense, and in the Fisher information sense. For many of these results,
we provide explicit rates of polynomial order in $N$. In the process, we
improve a quantitative entropic chaos result of Haurey and Mischler by relaxing
the finite moment requirement on the densities from order $6$ to $4+\epsilon$.


title: Tissue fluidization by cell-shape-controlled active stresses

abstract:
  Biological cells can actively tune their intracellular architecture according
to their overall shape. Here we explore the rheological implication of such
coupling in a minimal model of a dense cellular material where each cell exerts
an active mechanical stress along its axis of elongation. Increasing the active
stress amplitude leads to several transitions. An initially hexagonal crystal
motif is first destabilized into a solid with anisotropic cells. Increasing
activity further, we find a re-entrant transition to a regime with finite
hexatic order and finite shear modulus, in which cells arrange according to a
rhombile pattern with periodically arranged rosette structures. The shear
modulus vanishes again at a third threshold beyond which spontaneous tissue
flows arise. In this last regime, we observe the emergence of cell shape
patterns called topological defects, with flow and stress fields around defects
agreeing with those observed in epithelial tissue experiments. We further
provide a testable prediction of cell-cell rearrangement hotspots near
topological defects. Overall, our work connects seemingly distinct features -
e.g. rosettes and topological defects - observed across various types of
epithelial tissues.


title: Log canonical thresholds and coregularity

abstract:
  We prove the ascending chain condition for log canonical thresholds of
bounded coregularity.


title: Unified Speech-Text Pre-training for Speech Translation and Recognition

abstract:
  We describe a method to jointly pre-train speech and text in an
encoder-decoder modeling framework for speech translation and recognition. The
proposed method incorporates four self-supervised and supervised subtasks for
cross modality learning. A self-supervised speech subtask leverages unlabelled
speech data, and a (self-)supervised text to text subtask makes use of abundant
text training data. Two auxiliary supervised speech tasks are included to unify
speech and text modeling space. Our contribution lies in integrating linguistic
information from the text corpus into the speech pre-training. Detailed
analysis reveals learning interference among subtasks. Two pre-training
configurations for speech translation and recognition, respectively, are
presented to alleviate subtask interference. Our experiments show the proposed
method can effectively fuse speech and text information into one model. It
achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the
MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the
Librispeech speech recognition task.


title:
Multi-spacecraft Analysis of the Properties of Magnetohydrodynamic
  Fluctuations in Sub-Alfv\'enic Solar Wind Turbulence at 1 AU

abstract:
  We present observations of three-dimensional magnetic power spectra in
wavevector space to investigate the anisotropy and scalings of sub-Alfv\'enic
solar wind turbulence in low$-\beta_p$ plasma at magnetohydrodynamic (MHD)
scale using the Magnetospheric Multiscale spacecraft. The magnetic power
distributions are organized in a new coordinate determined by wavevectors (k)
and background magnetic field ($b_0$) in Fourier space. This study utilizes two
approaches to determine wavevectors: the singular value decomposition method
and multi-spacecraft timing analysis. The combination of both methods allows an
examination of magnetic field fluctuation properties in terms of mode
compositions without spatiotemporal hypothesis. Observations show that
fluctuations ($\delta B_{\perp1}$) in the direction perpendicular to k and
$b_0$ prominently cascade perpendicular to $b_0$, and such anisotropy increases
with wavenumber. The reduced power spectra of $\delta B_{\perp1}$ follow
Goldreich-Sridhar scalings: $P(k_\perp)\sim k_\perp^{-5/3}$ and $P(k_{||}) \sim
k_{||}^{-2}$. In contrast, fluctuations within $kb_0$ plane show isotropic
behaviors: perpendicular power distributions are approximately the same as
parallel distributions. The reduced power spectra of fluctuations within $kb_0$
plane follow the scalings: $P(k_\perp)\sim k_\perp^{-3/2}$ and $P(k_{||})\sim
k_{||}^{-3/2}$. Comparing frequency-wavevector spectra with theoretical
dispersion relations of MHD modes, we find that $\delta B_{\perp1}$ are
probably associated with Alfv\'en modes. On the other hand, magnetic field
fluctuations within $kb_0$ plane more likely originate from fast modes in
low-$\beta_p$ plasma based on their isotropic behaviors. The observations of
anisotropy and scalings of different magnetic field components are consistent
with the predictions of current compressible MHD theory.


title:
$\{\text{PF}\}^2\text{ES}$: Parallel Feasible Pareto Frontier Entropy
  Search for Multi-Objective Bayesian Optimization Under Unknown Constraints

abstract:
  We present Parallel Feasible Pareto Frontier Entropy Search
($\{\text{PF}\}^2$ES) -- a novel information-theoretic acquisition function for
multi-objective Bayesian optimization. Although information-theoretic
approaches regularly provide state-of-the-art optimization, they are not yet
widely used in the context of constrained multi-objective optimization. Due to
the complexity of characterizing mutual information between candidate
evaluations and (feasible) Pareto frontiers, existing approaches must employ
severe approximations that significantly hamper their performance. By instead
using a variational lower bound, $\{\text{PF}\}^2$ES provides a low cost and
accurate estimate of the mutual information for the parallel setting (where
multiple evaluations must be chosen for each optimization step). Moreover, we
are able to interpret our proposed acquisition function by exploring direct
links with other popular multi-objective acquisition functions. We benchmark
$\{\text{PF}\}^2$ES across synthetic and real-life problems, demonstrating its
competitive performance for batch optimization across synthetic and real-world
problems including vehicle and electronic filter design.


title:
NEOWISE Observations Of The Potentially Hazardous Asteroid (99942)
  Apophis

abstract:
  Large potentially hazardous asteroids (PHAs) are capable of causing a global
catastrophe in the event of a planetary collision. Thus, rapid assessment of
such an object's physical characteristics is crucial for determining its
potential risk scale. We treated the near-Earth asteroid (99942) Apophis as a
newly discovered object during its 2020-2021 close-approach as part of a mock
planetary defense exercise. The object was detected by the Near-Earth Object
Wide-field Infrared Survey Explorer (NEOWISE), and data collected by the two
active bands (3.4 ${\mu}$m and 4.6 ${\mu}$m) were analyzed using thermal and
thermophysical modeling. Our results indicate that Apophis is an elongated
object with an effective spherical diameter D$_{eff}$ = 340 $\pm$ 70 m, a
geometric visual albedo p$_{V}$ = 0.31 $\pm$ 0.09, and a thermal inertia
$\Gamma$ $\sim$ 150 - 2850 Jm$^{-2}$s$^{-0.5}$K$^{-1}$ with a best-fit value of
550 Jm$^{-2}$s$^{-0.5}$K$^{-1}$. NEOWISE "discovery" observations reveal that
(99942) Apophis is a potentially hazardous asteroid that would likely cause
damage at a regional level and not a global one.


title:
A Switching Thrust Tracking Controller for Load Constrained Wind
  Turbines

abstract:
  Wind turbines are prone to structural degradation, particularly in offshore
locations. Based on the structural health condition of the tower, power
de-rating strategies can be used to reduce structural loads at the cost of
power losses.This paper introduces a novel closed-loop switching control
architecture to constrain the thrust in individual turbines. By taking
inspiration from developments in the field of reference governors, an existing
demanded power tracking controller is extended by a thrust tracking controller.
The latter is activated only when a user-defined constraint on fore-aft thrust
force is exceeded, which can be set based on the actual damage status of the
turbine. Having a down-regulation with monotonic aerodynamic load response, a
simple linear thrust tracking controller is proposed. Such a scheme can reduce
aerodynamic loads while incurring acceptable losses on power production which,
in a wind farm setting, can be compensated for by other turbines. Large eddy
simulations demonstrate the performance of the proposed scheme on satisfying
thrust constraints.


title: Ultraviolet regularity, CPT and the Big Bang quantum vacuum

abstract:
  In this paper, we further analyze the CPT-invariant vacua in a
radiation-dominated spacetime, recently described in [Phys. Rev. Lett. 121,
251301 (2018), Annals Phys. 438, 168767], by imposing the quantum states to be
ultraviolet regular. To this end we provide an alternative description of the
CPT-invariant vacuum states by naturally setting invariant "initial" conditions
at the big bang. We identify a family of CPT-invariant and ultraviolet regular
vacuum states for both scalars and spin-$1/2$ fields. We give the asymptotic
conditions that these states must satisfy and discuss some interesting vacuum
choices.


title: Bayesian Parameter Estimation for Dynamical Models in Systems Biology

abstract:
  Dynamical systems modeling, particularly via systems of ordinary differential
equations, has been used to effectively capture the temporal behavior of
different biochemical components in signal transduction networks. Despite the
recent advances in experimental measurements, including sensor development and
'-omics' studies that have helped populate protein-protein interaction networks
in great detail, systems biology modeling lacks systematic methods to estimate
kinetic parameters and quantify associated uncertainties. This is because of
multiple reasons, including sparse and noisy experimental measurements, lack of
detailed molecular mechanisms underlying the reactions, and missing biochemical
interactions. Additionally, the inherent nonlinearities with respect to the
states and parameters associated with the system of differential equations
further compound the challenges of parameter estimation. In this study, we
propose a comprehensive framework for Bayesian parameter estimation and
complete quantification of the effects of uncertainties in the data and models.
We apply these methods to a series of signaling models of increasing
mathematical complexity. Systematic analysis of these dynamical systems showed
that parameter estimation depends on data sparsity, noise level, and model
structure, including the existence of multiple steady states. These results
highlight how focused uncertainty quantification can enrich systems biology
modeling and enable additional quantitative analyses for parameter estimation.


title: Mass optimization problem with convex cost

abstract:
  In this paper we consider a mass optimization problem in the case of scalar
state function, where instead of imposing a constraint on the total mass of the
competitors, we penalize the classical compliance by a convex functional
defined on the space of measures. We obtain a characterization of optimal
solutions to the problem through a suitable PDE. This generalizes the case
considered in the literature of a linear cost and applies to the optimization
of a conductor where very low and very high conductivities have both a high
cost, and then the study of nonlinear models becomes relevant.


title:
Active power control of wind farms: an instantaneous approach on waked
  conditions

abstract:
  This paper presents a closed-loop controller for wind farms to provide active
power control services using a high-fidelity computational fluid dynamics based
wind plant simulator. The proposed design enhances power tracking stability and
allows for simple understanding, where each turbine is considered as a pure
time-delay system. The paper investigates the control performance with
different nominal power distributions in a fully waked condition and limited
power availability. Results demonstrate the improvement in power production
obtained by closing the control loop, compared to greedy operation.
Additionally, power tracking capabilities are enhanced with a nominal power
distribution favored by axial-induction, as well as the occurrence of turbine
saturation and the distribution of loads.


title: Hadron and light nucleus radii from electron scattering

abstract:
  Conceptually, radii are amongst the simplest Poincar\'e-invariant properties
that can be associated with hadrons and light nuclei. Accurate values of these
quantities are necessary so that one may judge the character of putative
solutions to the strong interaction problem within the Standard Model. However,
limiting their ability to serve in this role, recent measurements and new
analyses of older data have revealed uncertainties and imprecisions in the
radii of the proton, pion, kaon, and deuteron. In the context of radius
measurement using electron + hadron elastic scattering, the past decade has
shown that reliable extraction requires complete elimination of bias associated
with practitioner-dependent choices of data fitting functions. Different
answers to that challenge have been offered; and this perspective describes the
statistical Schlessinger point method (SPM), in unifying applications to
proton, pion, kaon, and deuteron radii. Grounded in analytic function theory,
independent of assumptions about underlying dynamics, free from
practitioner-induced bias, and applicable in the same form to diverse systems
and observables, the SPM returns an objective expression of the information
contained in any data under consideration. Its robust nature and versatility
make it suitable for use in many branches of experiment and theory.


title:
Can Self-Supervised Learning solve the problem of child speech
  recognition?

abstract:
  Despite recent advancements in deep learning technologies, Child Speech
Recognition remains a challenging task. Current Automatic Speech Recognition
(ASR) models required substantial amounts of annotated data for training, which
is scarce. In this work, we explore using the ASR model, wav2vec2, with
different pretraining and finetuning configurations for self supervised
learning (SSL) towards improving automatic child speech recognition. The
pretrained wav2vec2 models were finetuned using different amounts of child
speech training data to discover the optimum amount of data required to
finetune the model for the task of child ASR. Our trained model receives the
best word error rate (WER) of 8.37 on the in domain MyST dataset and WER of
10.38 on the out of domain PFSTAR dataset. We do not use any Language Models
(LM) in our experiments.


title:
On the Dirichlet problem for Lagrangian phase equation with critical and
  supercritical phase

abstract:
  In this paper, we solve the Dirichlet problem for Lagrangian phase equation
with critical and supercritical phase. A crucial ingredient is the interior
$C^2$ estimate. Our result is sharp in the sense that there exist singular
solutions in the subcritical phase case.


title: A Survey on Crowdsourcing Applications in Smart Cities

abstract:
  With the emergence of the Internet of things (IoT), human life is now
progressing towards smartification faster than ever before. Thus, smart cities
become automated in different aspects such as business, education, economy,
medicine, and urban areas. Since smartification requires a variety of dynamic
information in different urban dimensions, mobile crowdsourcing has gained
importance in smart cities. This chapter systematically reviews the related
applications of smart cities that use mobile crowdsourcing for data
acquisition. For this purpose, the applications are classified as
environmental, urban life, and transportation categories and then investigated
in detail. This survey helps in understanding the current situation of smart
cities from the viewpoint of crowdsourcing and discusses the future research
directions in this field.


title: SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks

abstract:
  Spiking Neural Networks (SNNs) have gained huge attention as a potential
energy-efficient alternative to conventional Artificial Neural Networks (ANNs)
due to their inherent high-sparsity activation. Recently, SNNs with
backpropagation through time (BPTT) have achieved a higher accuracy result on
image recognition tasks compared to other SNN training algorithms. Despite the
success on the algorithm perspective, prior works neglect the evaluation of the
hardware energy overheads of BPTT, due to the lack of a hardware evaluation
platform for SNN training algorithm design. Moreover, although SNNs have been
long seen as an energy-efficient counterpart of ANNs, a quantitative comparison
between the training cost of SNNs and ANNs is missing. To address the
above-mentioned issues, in this work, we introduce SATA (Sparsity-Aware
Training Accelerator), a BPTT-based training accelerator for SNNs. The proposed
SATA provides a simple and re-configurable accelerator architecture for the
general-purpose hardware evaluation platform, which makes it easier to analyze
the training energy for SNN training algorithms. Based on SATA, we show
quantitative analyses on the energy efficiency of SNN training and make a
comparison between the training cost of SNNs and ANNs. The results show that
SNNs consume $1.27\times$ more total energy with considering sparsity (spikes,
gradient of firing function, and gradient of membrane potential) when compared
to ANNs. We find that such high training energy cost is from time-repetitive
convolution operations and data movements during backpropagation. Moreover, to
guide the future SNN training algorithm design, we provide several observations
on energy efficiency with respect to different SNN-specific training
parameters.


title:
Automated Task Updates of Temporal Logic Specifications for
  Heterogeneous Robots

abstract:
  Given a heterogeneous group of robots executing a complex task represented in
Linear Temporal Logic, and a new set of tasks for the group, we define the task
update problem and propose a framework for automatically updating individual
robot tasks given their respective existing tasks and capabilities. Our
heuristic, token-based, conflict resolution task allocation algorithm generates
a near-optimal assignment for the new task. We demonstrate the scalability of
our approach through simulations of multi-robot tasks.


title: Beam Decoding with Controlled Patience

abstract:
  Text generation with beam search has proven successful in a wide range of
applications. The commonly-used implementation of beam decoding follows a first
come, first served heuristic: it keeps a set of already completed sequences
over time steps and stops when the size of this set reaches the beam size. We
introduce a patience factor, a simple modification to this decoding algorithm,
that generalizes the stopping criterion and provides flexibility to the depth
of search. Extensive empirical results demonstrate that the patience factor
improves decoding performance of strong pretrained models on news text
summarization and machine translation over diverse language pairs, with a
negligible inference slowdown. Our approach only modifies one line of code and
can be thus readily incorporated in any implementation.


title:
Transition-Potential Coupled Cluster II: Optimization of the Core
  Orbital Occupation Number

abstract:
  The issue of orbital relaxation in computational core-hole spectroscopy,
specifically x-ray absorption, has been a major problem for methods such as
equation-of-motion coupled cluster with singles and doubles (EOM-CCSD). The
transition-potential coupled cluster (TP-CC) method is utilized to address this
problem by including an explicit treatment of orbital relaxation via the use of
reference orbitals with a fractional core occupation number. The value of the
fractional occupation parameter $\lambda$ was optimized for both TP-CCSD and
XTP-CCSD methods in an element-specific manner due to the differences in atomic
charge and energy scale. Additionally, TP-CCSD calculations using the optimized
parameters were performed for the K-edge absorption spectra of gas-phase
adenine and thymine. TP-CCSD reproduces the valence region well and requires
smaller overall energy shifts in comparison to EOM-CCSD, while also improving
on the relative position and intensities of several absorption peaks.


title: ProtoTEx: Explaining Model Decisions with Prototype Tensors

abstract:
  We present ProtoTEx, a novel white-box NLP classification architecture based
on prototype networks. ProtoTEx faithfully explains model decisions based on
prototype tensors that encode latent clusters of training examples. At
inference time, classification decisions are based on the distances between the
input text and the prototype tensors, explained via the training examples most
similar to the most influential prototypes. We also describe a novel
interleaved training algorithm that effectively handles classes characterized
by the absence of indicative features. On a propaganda detection task, ProtoTEx
accuracy matches BART-large and exceeds BERT-large with the added benefit of
providing faithful explanations. A user study also shows that prototype-based
explanations help non-experts to better recognize propaganda in online news.


title: Generalizing Adversarial Explanations with Grad-CAM

abstract:
  Gradient-weighted Class Activation Mapping (Grad- CAM), is an example-based
explanation method that provides a gradient activation heat map as an
explanation for Convolution Neural Network (CNN) models. The drawback of this
method is that it cannot be used to generalize CNN behaviour. In this paper, we
present a novel method that extends Grad-CAM from example-based explanations to
a method for explaining global model behaviour. This is achieved by introducing
two new metrics, (i) Mean Observed Dissimilarity (MOD) and (ii) Variation in
Dissimilarity (VID), for model generalization. These metrics are computed by
comparing a Normalized Inverted Structural Similarity Index (NISSIM) metric of
the Grad-CAM generated heatmap for samples from the original test set and
samples from the adversarial test set. For our experiment, we study adversarial
attacks on deep models such as VGG16, ResNet50, and ResNet101, and wide models
such as InceptionNetv3 and XceptionNet using Fast Gradient Sign Method (FGSM).
We then compute the metrics MOD and VID for the automatic face recognition
(AFR) use case with the VGGFace2 dataset. We observe a consistent shift in the
region highlighted in the Grad-CAM heatmap, reflecting its participation to the
decision making, across all models under adversarial attacks. The proposed
method can be used to understand adversarial attacks and explain the behaviour
of black box CNN models for image analysis.


title:
A Multilingual Perspective Towards the Evaluation of Attribution Methods
  in Natural Language Inference

abstract:
  Most evaluations of attribution methods focus on the English language. In
this work, we present a multilingual approach for evaluating attribution
methods for the Natural Language Inference (NLI) task in terms of plausibility
and faithfulness properties. First, we introduce a novel cross-lingual strategy
to measure faithfulness based on word alignments, which eliminates the
potential downsides of erasure-based evaluations. We then perform a
comprehensive evaluation of attribution methods, considering different output
mechanisms and aggregation methods. Finally, we augment the XNLI dataset with
highlight-based explanations, providing a multilingual NLI dataset with
highlights, which may support future exNLP studies. Our results show that
attribution methods performing best for plausibility and faithfulness are
different.


title: Computing a Sparse Projection into a Box

abstract:
  We describe a procedure to compute a projection of $w \in \mathbb{R}^n$ into
the intersection of the so-called \emph{zero-norm} ball $k \mathbb{B}_0$ of
radius $k$, i.e., the set of $k$-sparse vectors, with a box centered at a point
of $k \mathbb{B}_0$. The need for such projection arises in the context of
certain trust-region methods for nonsmooth regularized optimization. Although
the set into which we wish to project is nonconvex, we show that a solution may
be found in $O(n \log(n))$ operations. We describe our Julia implementation and
illustrate our procedure in the context of two trust-region methods for
nonsmooth regularized optimization.


title: On the polyharmonic polynomials

abstract:
  In the paper we study the orthogonal projection of the homogeneous
polynomials onto the space of the homogeneous polyharmonic polynomials. To do
this we derive some decompositions of homogeneous polynomials in the terms of
the Kelvin transform of some differential operators. We consider also the basis
of the space of the homogeneous polyharmonic polynomials and study the problem
of convergence of orthogonal series.


title:
The classification problem for extensions of torsion-free abelian
  groups, I

abstract:
  Let $C,A$ be countable abelian groups. In this paper we determine the
complexity of classifying extensions $C$ by $A$, in the cases when $C$ is
torsion-free and $A$ is a $p$-group, a torsion group with bounded primary
components, or a free $R$-module for some subring $R\subseteq \mathbb{Q}$.
Precisely, for such $C$ and $A$ we describe in terms of $C$ and $A$ the
potential complexity class in the sense of Borel complexity theory of the
equivalence relation $\mathcal{R}_{\mathbf{Ext}\left( C,A\right) }$ of
isomorphism of extensions of $C$ by $A$. This complements a previous result by
the same author, settling the case when $C$ is torsion and $A$ is arbitrary. We
establish the main result within the framework of Borel-definable homological
algebra, recently introduced in collaboration with Bergfalk and
Panagiotopoulos. As a consequence of our main results, we will obtain that if
$C$ is torsion-free and $A$ is either a free $R$-module or a torsion group with
bounded components, then an extension of $C$ by $A$ splits if and only if it
splits on all finite-rank subgroups of $C$. This is a purely algebraic
statements obtained with methods from Borel-definable homological algebra.


title:
A Simple Approach to Adversarial Robustness in Few-shot Image
  Classification

abstract:
  Few-shot image classification, where the goal is to generalize to tasks with
limited labeled data, has seen great progress over the years. However, the
classifiers are vulnerable to adversarial examples, posing a question regarding
their generalization capabilities. Recent works have tried to combine
meta-learning approaches with adversarial training to improve the robustness of
few-shot classifiers. We show that a simple transfer-learning based approach
can be used to train adversarially robust few-shot classifiers. We also present
a method for novel classification task based on calibrating the centroid of the
few-shot category towards the base classes. We show that standard adversarial
training on base categories along with calibrated centroid-based classifier in
the novel categories, outperforms or is on-par with state-of-the-art advanced
methods on standard benchmarks for few-shot learning. Our method is simple,
easy to scale, and with little effort can lead to robust few-shot classifiers.
Code is available here: \url{https://github.com/UCDvision/Simple_few_shot.git}


title:
Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic
  Surgery

abstract:
  In recent decades, the tremendous benefits surgical robots have brought to
surgeons and patients have been witnessed. With the dexterous operation and the
great precision, surgical robots can offer patients less recovery time and less
hospital stay. However, the controls for current surgical robots in practical
usage are fully carried out by surgeons via teleoperation. During the surgery
process, there exists a lot of repetitive but simple manipulation, which can
cause unnecessary fatigue to the surgeons. In this paper, we proposed a deep
reinforcement learning-based semi-autonomous control framework for robotic
surgery. The user study showed that the framework can reduce the completion
time by 19.1% and the travel length by 58.7%.


title: Cosmology with the Laser Interferometer Space Antenna

abstract:
  The Laser Interferometer Space Antenna (LISA) has two scientific objectives
of cosmological focus: to probe the expansion rate of the universe, and to
understand stochastic gravitational-wave backgrounds and their implications for
early universe and particle physics, from the MeV to the Planck scale. However,
the range of potential cosmological applications of gravitational wave
observations extends well beyond these two objectives. This publication
presents a summary of the state of the art in LISA cosmology, theory and
methods, and identifies new opportunities to use gravitational wave
observations by LISA to probe the universe.


title:
Quantification of high dimensional non-Gaussianities and its implication
  to Fisher analysis in cosmology

abstract:
  It is well known that the power spectrum is not able to fully characterize
the statistical properties of non-Gaussian density fields. Recently, many
different statistics have been proposed to extract information from
non-Gaussian cosmological fields that perform better than the power spectrum.
The Fisher matrix formalism is commonly used to quantify the accuracy with
which a given statistic can constrain the value of the cosmological parameters.
However, these calculations typically rely on the assumption that the
likelihood of the considered statistic follows a multivariate Gaussian
distribution. In this work we follow Sellentin & Heavens (2017) and use two
different statistical tests to identify non-Gaussianities in different
statistics such as the power spectrum, bispectrum, marked power spectrum, and
wavelet scatering transform (WST). We remove the non-Gaussian components of the
different statistics and perform Fisher matrix calculations with the
\textit{Gaussianized} statistics using Quijote simulations. We show that
constraints on the parameters can change by a factor of $\sim 2$ in some cases.
We show with simple examples how statistics that do not follow a multivariate
Gaussian distribution can achieve artificially tight bounds on the cosmological
parameters when using the Fisher matrix formalism. We think that the
non-Gaussian tests used in this work represent a powerful tool to quantify the
robustness of Fisher matrix calculations and their underlying assumptions. We
release the code used to compute the power spectra, bispectra, and WST that can
be run on both CPUs and GPUs.


title: Heterogeneous Acceleration Pipeline for Recommendation System Training

abstract:
  Recommendation systems are unique as they show a conflation of compute and
memory intensity due to their deep learning and massive embedding tables.
Training these models typically involve a hybrid CPU-GPU mode, where GPUs
accelerate the deep learning portion and the CPUs store and process the
memory-intensive embedding tables. The hybrid mode incurs a substantial
CPU-to-GPU transfer time and relies on main memory bandwidth to feed embeddings
to GPU for deep learning acceleration. Alternatively, we can store the entire
embeddings across GPUs to avoid the transfer time and utilize the GPU's High
Bandwidth Memory (HBM). This approach requires GPU-to-GPU backend communication
and scales the number of GPUs with the size of the embedding tables. To
overcome these concerns, this paper offers a heterogeneous acceleration
pipeline, called Hotline.
  Hotline leverages the insight that only a small number of embedding entries
are accessed frequently, and can easily fit in a single GPU's HBM. Hotline
implements a data-aware and model-aware scheduling pipeline that utilizes the
(1) CPU main memory for not-frequently-accessed embeddings and (2) GPUs' local
memory for frequently-accessed embeddings. Hotline improves the training
throughput by dynamically stitching the execution of popular and not-popular
inputs through a novel hardware accelerator and feeding to the GPUs. Results on
real-world datasets and recommender models show that Hotline reduces the
average training time by 3x and 1.8x in comparison to Intel-optimized CPU-GPU
DLRM and HugeCTR-optimized GPU-only baseline, respectively. Hotline increases
the overall training throughput to 35.7 epochs/hour in comparison to 5.3
epochs/hour for the Intel-optimized DLRM baseline


title: Implementing Online Reinforcement Learning with Temporal Neural Networks

abstract:
  A Temporal Neural Network (TNN) architecture for implementing efficient
online reinforcement learning is proposed and studied via simulation. The
proposed T-learning system is composed of a frontend TNN that implements online
unsupervised clustering and a backend TNN that implements online reinforcement
learning. The reinforcement learning paradigm employs biologically plausible
neo-Hebbian three-factor learning rules. As a working example, a prototype
implementation of the cart-pole problem (balancing an inverted pendulum) is
studied via simulation.


title:
GPU Parallel algorithm for the generation of polygonal meshes based on
  terminal-edge regions

abstract:
  This paper presents a GPU parallel algorithm to generate a new kind of
polygonal meshes obtained from Delaunay triangulations. To generate the
polygonal mesh, the algorithm first uses a classification system to label each
edge of an input triangulation; second it builds polygons (simple or not) from
terminal-edge regions using the label system, and third it transforms each
non-simple polygon from the previous phase into simple ones, convex or not
convex polygons. We show some preliminary experiments to test the scalability
of the algorithm and compare it with the sequential version. We also run a very
simple test to show that these meshes can be useful for the virtual element
method.


title: A Web-Scale Analysis of the Community Origins of Image Memes

abstract:
  Where do the most popular online cultural artifacts such as image memes
originate? Media narratives suggest that cultural innovations often originate
in peripheral communities and then diffuse to the mainstream core; behavioral
science suggests that intermediate network positions that bridge between the
periphery and the core are especially likely to originate many influential
cultural innovations. Research has yet to fully adjudicate between these
predictions because prior work focuses on individual platforms such as Twitter;
however, any single platform is only a small, incomplete part of the larger
online cultural ecosystem. In this paper, we perform the first analysis of the
origins and diffusion of image memes at web scale, via a one-month crawl of all
indexible online communities that principally share meme images with English
text overlays. Our results suggest that communities at the core of the network
originate the most highly diffused image memes: the top 10% of communities by
network centrality originate the memes that generate 62% of the image meme
diffusion events on the web. A zero-inflated negative binomial regression
confirms that memes from core communities are more likely to diffuse than those
from peripheral communities even when controlling for community size and
activity level. However, a replication analysis that follows the traditional
approach of testing the same question only within a single large community,
Reddit, finds the regression coefficients reversed -- underscoring the
importance of engaging in web-scale, cross-community analyses. The
ecosystem-level viewpoint of this work positions the web as a highly
centralized generator of cultural artifacts such as image memes.


title:
Lost Vibration Test Data Recovery Using Convolutional Neural Network: A
  Case Study

abstract:
  Data loss in Structural Health Monitoring (SHM) networks has recently become
one of the main challenges for engineers. Therefore, a data recovery method for
SHM, generally an expensive procedure, is essential. Lately, some techniques
offered to recover this valuable raw data using Neural Network (NN) algorithms.
Among them, the convolutional neural network (CNN) based on convolution, a
mathematical operation, can be applied to non-image datasets such as signals to
extract important features without human supervision. However, the effect of
different parameters has not been studied and optimized for SHM applications.
Therefore, this paper aims to propose different architectures and investigate
the effects of different hyperparameters for one of the newest proposed
methods, which is based on a CNN algorithm for the Alamosa Canyon Bridge as a
real structure. For this purpose, three different CNN models were considered to
predict one and two malfunctioned sensors by finding the correlation between
other sensors, respectively. Then the CNN algorithm was trained by experimental
data, and the results showed that the method had a reliable performance in
predicting Alamosa Canyon Bridge's missed data. The accuracy of the model was
increased by adding a convolutional layer. Also, a standard neural network with
two hidden layers was trained with the same inputs and outputs of the CNN
models. Based on the results, the CNN model had higher accuracy, lower
computational cost, and was faster than the standard neural network.


title:
Origin of nonsymmorphic bosonization formulas in generalized
  antiferromagnetic Kitaev spin-1/2 chains from a renormalization group point
  of view

abstract:
  Recently, in the Luttinger liquid phase of the one-dimensional generalized
antiferromagnetic Kitaev spin-1/2 model, it has been found that the abelian
bosonization formulas of the local spin operators only respect the exact
discrete nonsymmorphic symmetry group of the model, not the emergent U(1)
symmetry. In this work, we perform a renormalization group (RG) study to
provide explanations for the origin of the U(1) breaking terms in the
bosonization formulas. We find that the lack of U(1) symmetry originates from
the wavefunction renormalization effects in the spin operators along the RG
flow induced by the U(1) breaking interactions in the microscopic Hamiltonian.
In addition, the RG analysis can give predictions to the signs and order of
magnitudes of the coefficients in the bosonization formulas. Our work is
helpful to understand the rich nonsymmorphic physics in one-dimensional Kitaev
spin models.


title: Unramified case of the Grothendieck-Serre conjecture about torsors

abstract:
  We prove the unramified case of the Grothendieck-Serre conjecture: let $R$ be
an unramified regular local ring (that is a local ring such that all its
$\mathbb Z$-fibers are regular) and $\mathbf G$ be a reductive $R$-group
scheme. Then a $\mathbf G$-torsor over $R$ is trivial, provided that it is
trivial over the fraction field of $R$. In fact, we prove a slight
generalization of that: $R$ is allowed to be a semilocal ring geometrically
regular over a Dedekind domain.


title: A Protocol for Validating Social Navigation Policies

abstract:
  Enabling socially acceptable behavior for situated agents is a major goal of
recent robotics research. Robots should not only operate safely around humans,
but also abide by complex social norms. A key challenge for developing
socially-compliant policies is measuring the quality of their behavior. Social
behavior is enormously complex, making it difficult to create reliable metrics
to gauge the performance of algorithms. In this paper, we propose a protocol
for social navigation benchmarking that defines a set of canonical social
navigation scenarios and an in-situ metric for evaluating performance on these
scenarios using questionnaires. Our experiments show this protocol is
realistic, scalable, and repeatable across runs and physical spaces. Our
protocol can be replicated verbatim or it can be used to define a social
navigation benchmark for novel scenarios. Our goal is to introduce a protocol
for benchmarking social scenarios that is homogeneous and comparable.


title:
Constraining the $^{30}$P($p,\gamma)^{31}$S reaction rate in ONe novae
  via the weak, low-energy, $\beta$-delayed proton decay of $^{31}$Cl

abstract:
  The $^{30}$P$(p,\gamma)^{31}$S reaction plays an important role in
understanding nucleosynthesis of $A\geq 30$ nuclides in oxygen-neon novae. The
Gaseous Detector with Germanium Tagging was used to measure $^{31}$Cl
$\beta$-delayed proton decay through the key $J^{\pi}=3/2^{+}$, 260-keV
resonance. The intensity $I^{260}_{\beta p} = 8.3^{+1.2}_{-0.9} \times 10^{-6}$
represents the weakest $\beta$-delayed, charged-particle emission ever measured
below 400 keV, resulting in a proton branching ratio of $\Gamma_p / \Gamma =
2.5^{+0.4}_{-0.3} \times 10^{-4}$. By combining this measurement with
shell-model calculations for $\Gamma_{\gamma}$ and past work on other
resonances, the total $^{30}$P$(p,\gamma)^{31}$S rate has been determined with
reduced uncertainty. The new rate has been used in hydrodynamic simulations to
model the composition of nova ejecta, leading to a concrete prediction of
$^{30}$Si/$^{28}$Si excesses in presolar nova grains and the calibration of
nuclear thermometers.


title:
Small Footprint Multi-channel ConvMixer for Keyword Spotting with
  Centroid Based Awareness

abstract:
  It is critical for a keyword spotting model to have a small footprint as it
typically runs on-device with low computational resources. However, maintaining
the previous SOTA performance with reduced model size is challenging. In
addition, a far-field and noisy environment with multiple signals interference
aggravates the problem causing the accuracy to degrade significantly. In this
paper, we present a multi-channel ConvMixer for speech command recognitions.
The novel architecture introduces an additional audio channel mixing for
channel audio interaction in a multi-channel audio setting to achieve better
noise-robust features with more efficient computation. Besides, we proposed a
centroid based awareness component to enhance the system by equipping it with
additional spatial geometry information in the latent feature projection space.
We evaluate our model using the new MISP challenge 2021 dataset. Our model
achieves significant improvement against the official baseline with a 55% gain
in the competition score (0.152) on raw microphone array input and a 63%
(0.126) boost upon front-end speech enhancement.


title:
Identifying the Dynamics of a System by Leveraging Data from Similar
  Systems

abstract:
  We study the problem of identifying the dynamics of a linear system when one
has access to samples generated by a similar (but not identical) system, in
addition to data from the true system. We use a weighted least squares approach
and provide finite sample performance guarantees on the quality of the
identified dynamics. Our results show that one can effectively use the
auxiliary data generated by the similar system to reduce the estimation error
due to the process noise, at the cost of adding a portion of error that is due
to intrinsic differences in the models of the true and auxiliary systems. We
also provide numerical experiments to validate our theoretical results. Our
analysis can be applied to a variety of important settings. For example, if the
system dynamics change at some point in time (e.g., due to a fault), how should
one leverage data from the prior system in order to learn the dynamics of the
new system? As another example, if there is abundant data available from a
simulated (but imperfect) model of the true system, how should one weight that
data compared to the real data from the system? Our analysis provides insights
into the answers to these questions.


title: The Stability of Transient Relationships

abstract:
  In contrast to stable relationships, far less is known about the temporal
evolution of transient relationships, although these constitute a substantial
fraction of people's communication networks. Previous literature suggests that
ratings of relationship emotional intensity decay gradually until the
relationship ends. Using mobile phone data from three countries (US, UK, and
Italy), we demonstrate that the volume of communication between ego and its
alters is stable for most of the duration of a transient relationship. Alters
with longer durations receive more calls, with the duration of the relationship
being predictable from call volume within the first few weeks of first contact.
Relationships typically start with an early elevated period of communication,
settling into the longer steady regime until eventually terminating abruptly.
This is observed across all three countries, which include samples of egos at
different life stages. These results are consistent with the suggestion that
individuals initially engage frequently with a new alter so as to evaluate
their potential as a tie, and then settle back into a contact frequency
commensurate with the alter's match to ego on the so-called Seven Pillars of
Friendship.


title:
Financial technologies (FinTech) for mental health: The potential of
  objective financial data to better understand the relationships between
  financial behavior and mental health

abstract:
  In this paper, we present novel research methods for collecting and analyzing
personal financial data alongside mental health factors, illustrated through a
N=1 case study using data from one individual with bipolar disorder. While we
have not found statistically significant trends nor our findings are
generalizable beyond this case, our approach provides an insight into the
challenges of accessing objective financial data. We outline what data is
currently available, what can be done with it, and what factors to consider
when working with financial data. More specifically, using these methods
researchers might be able to identify symptomatic traces of mental ill health
in personal financial data such as identifying early warning signs and thereby
enable preemptive care for individuals with serious mental illnesses. Based on
this work, we have also explored future directions for developing interventions
to support financial wellbeing. Furthermore, we have described the technical,
ethical, and equity challenges for financial data-driven assessments and
intervention methods, as well as provided a broad research agenda to address
these challenges. By leveraging objective, personalized financial data in a
privacy-preserving and ethical manner help lead to a shift in mental health
care.


title:
Neural Processes with Stochastic Attention: Paying more attention to the
  context dataset

abstract:
  Neural processes (NPs) aim to stochastically complete unseen data points
based on a given context dataset. NPs essentially leverage a given dataset as a
context representation to derive a suitable identifier for a novel task. To
improve the prediction accuracy, many variants of NPs have investigated context
embedding approaches that generally design novel network architectures and
aggregation functions satisfying permutation invariant. In this work, we
propose a stochastic attention mechanism for NPs to capture appropriate context
information. From the perspective of information theory, we demonstrate that
the proposed method encourages context embedding to be differentiated from a
target dataset, allowing NPs to consider features in a target dataset and
context embedding independently. We observe that the proposed method can
appropriately capture context embedding even under noisy data sets and
restricted task distributions, where typical NPs suffer from a lack of context
embeddings. We empirically show that our approach substantially outperforms
conventional NPs in various domains through 1D regression, predator-prey model,
and image completion. Moreover, the proposed method is also validated by
MovieLens-10k dataset, a real-world problem.


title:
A self-paced BCI system with low latency for motor imagery onset
  detection based on time series prediction paradigm

abstract:
  In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsets
of the MI commands presented in a continuous electroencephalogram (EEG) signal
are unknown. To detect these onsets, most self-paced approaches apply a window
function on the continuous EEG signal and split it into long segments for
further analysis. As a result, the system has a high latency. To reduce the
system latency, we propose an algorithm based on the time series prediction
concept and use the data of the previously received time samples to predict the
upcoming time samples. Our predictor is an encoder-decoder (ED) network built
with long short-term memory (LSTM) units. The onsets of the MI commands are
detected shortly by comparing the incoming signal with the predicted signal.
The proposed method is validated on dataset IVc from BCI competition III. The
simulation results show that the proposed algorithm improves the average
F1-score achieved by the winner of the competition by 26.7% for latencies
shorter than one second.


title: Surrogate-based optimization for variational quantum algorithms

abstract:
  Variational quantum algorithms are a class of techniques intended to be used
on near-term quantum computers. The goal of these algorithms is to perform
large quantum computations by breaking the problem down into a large number of
shallow quantum circuits, complemented by classical optimization and feedback
between each circuit execution. One path for improving the performance of these
algorithms is to enhance the classical optimization technique. Given the
relative ease and abundance of classical computing resources, there is ample
opportunity to do so. In this work, we introduce the idea of learning surrogate
models for variational circuits using few experimental measurements, and then
performing parameter optimization using these models as opposed to the original
data. We demonstrate this idea using a surrogate model based on kernel
approximations, through which we reconstruct local patches of variational cost
functions using batches of noisy quantum circuit results. Through application
to the quantum approximate optimization algorithm and preparation of ground
states for molecules, we demonstrate the superiority of surrogate-based
optimization over commonly-used optimization techniques for variational
algorithms.


title:
Bayesian optimization with experimental failure for high-throughput
  materials growth

abstract:
  A crucial problem in achieving innovative high-throughput materials growth
with machine learning and automation techniques, such as Bayesian optimization
(BO) and robotic experimentation, has been a lack of an appropriate way to
handle missing data due to experimental failures. Here, we propose a new BO
algorithm that complements the missing data in the optimization of materials
growth parameters. The proposed method provides a flexible optimization
algorithm capable of searching a wide multi-dimensional parameter space. We
demonstrate the effectiveness of the method with simulated data as well as in
its implementation for actual materials growth, namely
machine-learning-assisted molecular beam epitaxy (ML-MBE) of SrRuO3, which is
widely used as a metallic electrode in oxide electronics. Through the
exploitation and exploration in a wide three-dimensional parameter space, while
complementing the missing data, we attained tensile-strained SrRuO3 film with a
high residual resistivity ratio of 80.1, the highest among tensile-strained
SrRuO3 films ever reported, in only 35 MBE growth runs.


title: Glass Segmentation with RGB-Thermal Image Pairs

abstract:
  This paper proposes a new glass segmentation method utilizing paired RGB and
thermal images. Due to the large difference between the transmission property
of visible light and that of the thermal energy through the glass where most
glass is transparent to the visible light but opaque to thermal energy, glass
regions of a scene are made more distinguishable with a pair of RGB and thermal
images than solely with an RGB image. To exploit such a unique property, we
propose a neural network architecture that effectively combines an RGB-thermal
image pair with a new multi-modal fusion module based on attention, and
integrate CNN and transformer to extract local features and long-range
dependencies, respectively. As well, we have collected a new dataset containing
5551 RGB-thermal image pairs with ground-truth segmentation annotations. The
qualitative and quantitative evaluations demonstrate the effectiveness of the
proposed approach on fusing RGB and thermal data for glass segmentation. Our
code and data are available at
https://github.com/Dong-Huo/RGB-T-Glass-Segmentation.


title: Are Multimodal Transformers Robust to Missing Modality?

abstract:
  Multimodal data collected from the real world are often imperfect due to
missing modalities. Therefore multimodal models that are robust against
modal-incomplete data are highly preferred. Recently, Transformer models have
shown great success in processing multimodal data. However, existing work has
been limited to either architecture designs or pre-training strategies; whether
Transformer models are naturally robust against missing-modal data has rarely
been investigated. In this paper, we present the first-of-its-kind work to
comprehensively investigate the behavior of Transformers in the presence of
modal-incomplete data. Unsurprising, we find Transformer models are sensitive
to missing modalities while different modal fusion strategies will
significantly affect the robustness. What surprised us is that the optimal
fusion strategy is dataset dependent even for the same Transformer model; there
does not exist a universal strategy that works in general cases. Based on these
findings, we propose a principle method to improve the robustness of
Transformer models by automatically searching for an optimal fusion strategy
regarding input data. Experimental validations on three benchmarks support the
superior performance of the proposed method.


title: Quantum entangled states of a classically radiating macroscopic spin

abstract:
  Entanglement constitutes a main feature that distinguishes quantum from
classical physics and is a key resource of quantum technologies. Here we show,
however, that entanglement may also serve as the essential ingredient for the
emergence of classical behavior in a composite nonlinear radiating system. We
consider the radiation from a macroscopic spin emitter, such as the collective
radiation from an atomic ensemble. We introduce a new class of macroscopic spin
states, the coherently radiating spin states (CRSS), defined as the asymptotic
eigenstates of the SU(2) lowering operator. We find that a spin emitter in a
CRSS radiates classical-like coherent light, although the CRSS itself is a
quantum entangled state exhibiting spin squeezing. We further show that CRSS
are naturally produced in Dicke superradiance and underlie the dissipative
Dicke phase transition. Our CRSS theory thus provides new concepts for studying
the quantum physics of radiation, with applications in current platforms
involving collections of atoms or spins, their consideration in quantum
technologies such as metrology and lasing, and the many-body theory of spin
systems.


title: Resolving Muon g-2 Anomaly With Partial Compositeness

abstract:
  We consider the scenario of composite Higgs with partial compositeness to
address the muon g-2 anomaly. We show that this anomaly is resolved by one-loop
correction due to composite muon partners with mass scale of order TeVs and
large Yukawa coupling to composite Higgs, which is different from
interpretations of vectorlike lepton models. We present parameter space by
imposing indirect constraints from precise measurements on Higgs, Z and oblique
electroweak parameters. We analyze direct constraints in light of both
Drell-Yan and Higgs-associated productions of the composite muon partners at
high-luminosity LHC. It turns out that the surviving parameter regions with the
lighter composite muon mass below $\sim 328$ GeV can be excluded at 2$\sigma$
order by the Drell-Yan processes.


title: Coevolutionary Pareto Diversity Optimization

abstract:
  Computing diverse sets of high quality solutions for a given optimization
problem has become an important topic in recent years. In this paper, we
introduce a coevolutionary Pareto Diversity Optimization approach which builds
on the success of reformulating a constrained single-objective optimization
problem as a bi-objective problem by turning the constraint into an additional
objective. Our new Pareto Diversity optimization approach uses this
bi-objective formulation to optimize the problem while also maintaining an
additional population of high quality solutions for which diversity is
optimized with respect to a given diversity measure. We show that our standard
co-evolutionary Pareto Diversity Optimization approach outperforms the recently
introduced DIVEA algorithm which obtains its initial population by generalized
diversifying greedy sampling and improving the diversity of the set of
solutions afterwards. Furthermore, we study possible improvements of the Pareto
Diversity Optimization approach. In particular, we show that the use of
inter-population crossover further improves the diversity of the set of
solutions.


title: Frobenius-Perron theory of the bound quiver algebras containing loops

abstract:
  The spectral radius of matrix, also known as Frobenius-Perron dimension, is a
useful tool for studying linear algebras and plays an important role in the
classification of the representation categories of algebras. In this paper, we
study the Frobenius-Perron theory of the representation categories of bound
quiver algebras containing loops, find a way to calculate the Frobenius-Perron
dimension of these algebras when they satisfy the commutativity condition of
loops. As an application, we prove that the Frobenius-Perron dimension of the
representation category of a modified ADE bounded quiver algebra is equal to
the maximum number of loops at a vertex. Finally, we point out that there also
exists infinite dimensional algebras whose Frobenius-Perron dimension is equal
to the maximal number of loops by giving an example.


title:
Easy Adaptation to Mitigate Gender Bias in Multilingual Text
  Classification

abstract:
  Existing approaches to mitigate demographic biases evaluate on monolingual
data, however, multilingual data has not been examined. In this work, we treat
the gender as domains (e.g., male vs. female) and present a standard domain
adaptation model to reduce the gender bias and improve performance of text
classifiers under multilingual settings. We evaluate our approach on two text
classification tasks, hate speech detection and rating prediction, and
demonstrate the effectiveness of our approach with three fair-aware baselines.


title:
CorrectSpeech: A Fully Automated System for Speech Correction and Accent
  Reduction

abstract:
  This study extends our previous work on text-based speech editing to
developing a fully automated system for speech correction and accent reduction.
Consider the application scenario that a recorded speech audio contains certain
errors, e.g., inappropriate words, mispronunciations, that need to be
corrected. The proposed system, named CorrectSpeech, performs the correction in
three steps: recognizing the recorded speech and converting it into
time-stamped symbol sequence, aligning recognized symbol sequence with target
text to determine locations and types of required edit operations, and
generating the corrected speech. Experiments show that the quality and
naturalness of corrected speech depend on the performance of speech recognition
and alignment modules, as well as the granularity level of editing operations.
The proposed system is evaluated on two corpora: a manually perturbed version
of VCTK and L2-ARCTIC. The results demonstrate that our system is able to
correct mispronunciation and reduce accent in speech recordings. Audio samples
are available online for demonstration
https://daxintan-cuhk.github.io/CorrectSpeech/ .


title:
Discovery of ATLAS17jrp as an Optical, X-ray and Infrared Bright TDE in
  a Star-forming Galaxy

abstract:
  We hereby report the discovery of ATLAS17jrp as an extraordinary TDE in
star-forming galaxy SDSSJ162034.99+240726.5 in our recent sample of
mid-infrared outbursts in nearby galaxies. Its optical/UV light curves rise to
a peak luminosity $\sim1.06\times10^{44}\rm\,erg\,s^{-1}$ in about a month and
then decay as $\rm t^{-5/3}$ with a roughly constant temperature around
19000~K, and the optical spectra show a blue continuum and very broad Balmer
lines with FWHM$\sim$15000 km/s which gradually narrowed to 1400 km/s within 4
years, all agreeing well with other optical TDEs. A delayed and rapidly rising
X-ray flare with a peak luminosity $\rm \sim 1.27\times10^{43}\,erg\,s^{-1}$
was detected at $\rm \sim$ 170 days after the optical peak. The high MIR
luminosity of ATLAS17jrp ($\sim2\times10^{43} \rm\,erg\,s^{-1}$) has revealed a
distinctive dusty environment with covering factor as high as $\sim0.2$, that
is comparable with that of torus in active galactic nuclei but at least one
order of magnitude higher than normal optical TDEs. Therefore, ATLAS17jrp turns
out to be one of the rare unambiguous TDE found in star-forming galaxies and
its high dust covering factor implies that the dust extinction could play an
important role in the absence of optical TDEs in star-forming galaxies.


title: Out-Of-Distribution Detection In Unsupervised Continual Learning

abstract:
  Unsupervised continual learning aims to learn new tasks incrementally without
requiring human annotations. However, most existing methods, especially those
targeted on image classification, only work in a simplified scenario by
assuming all new data belong to new tasks, which is not realistic if the class
labels are not provided. Therefore, to perform unsupervised continual learning
in real life applications, an out-of-distribution detector is required at
beginning to identify whether each new data corresponds to a new task or
already learned tasks, which still remains under-explored yet. In this work, we
formulate the problem for Out-of-distribution Detection in Unsupervised
Continual Learning (OOD-UCL) with the corresponding evaluation protocol. In
addition, we propose a novel OOD detection method by correcting the output bias
at first and then enhancing the output confidence for in-distribution data
based on task discriminativeness, which can be applied directly without
modifying the learning procedures and objectives of continual learning. Our
method is evaluated on CIFAR-100 dataset by following the proposed evaluation
protocol and we show improved performance compared with existing OOD detection
methods under the unsupervised continual learning scenario.


title:
Construction of high-order robust theta-methods with applications in
  anomalous models

abstract:
  A general conversion strategy by involving a shifted parameter $\theta$ is
proposed to construct high-order accuracy difference formulas for fractional
calculus operators. By converting the second-order backward difference formula
with such strategy, a novel $\theta$-scheme with correction terms is developed
for the subdiffusion problem with nonsmooth data, which is robust even for very
small $\alpha$ and can resolve the initial singularity.The optimal error
estimates are carried out with essential arguments and are verified by
numerical tests.


title: Lipschitz Functions on Quasiconformal Trees

abstract:
  We first identify (up to linear isomorphism) the Lipschitz free spaces of
quasiarcs. By decomposing quasiconformal trees into quasiarcs as done in an
article of David, Eriksson-Bique, and Vellis, we then identify the Lipschitz
free spaces of quasiconformal trees and prove that quasiconformal trees have
Lipschitz dimension 1. Generalizing the aforementioned decomposition, we define
a geometric tree-like decomposition of a metric space. Our results pertaining
to quasiconformal trees are in fact special cases of results about metric
spaces admitting a geometric tree-like decomposition. Furthermore, the methods
employed in our study of Lipschitz free spaces yield a decomposition of any
(weak) quasiarc into rectifiable and purely unrectifiable subsets, which may be
of independent interest.


title:
Exact formulae and Tur\'{a}n inequalities for Vafa-Witten invariants of
  $K3$ surfaces

abstract:
  We consider three different families of Vafa-Witten invariants of $K3$
surfaces. In each case, the partition function that encodes the Vafa-Witten
invariants is given by combinations of twisted Dedekind $\eta$-functions. By
utilising known properties of these $\eta$-functions, we obtain exact formulae
for each of the invariants and prove that they asymptotically satisfy all
higher-order Tur\'{a}n inequalities.


title:
Independent Natural Policy Gradient Methods for Potential Games:
  Finite-time Global Convergence with Entropy Regularization

abstract:
  A major challenge in multi-agent systems is that the system complexity grows
dramatically with the number of agents as well as the size of their action
spaces, which is typical in real world scenarios such as autonomous vehicles,
robotic teams, network routing, etc. It is hence in imminent need to design
decentralized or independent algorithms where the update of each agent is only
based on their local observations without the need of introducing complex
communication/coordination mechanisms.
  In this work, we study the finite-time convergence of independent
entropy-regularized natural policy gradient (NPG) methods for potential games,
where the difference in an agent's utility function due to unilateral deviation
matches exactly that of a common potential function. The proposed
entropy-regularized NPG method enables each agent to deploy symmetric,
decentralized, and multiplicative updates according to its own payoff. We show
that the proposed method converges to the quantal response equilibrium (QRE) --
the equilibrium to the entropy-regularized game -- at a sublinear rate, which
is independent of the size of the action space and grows at most sublinearly
with the number of agents. Appealingly, the convergence rate further becomes
independent with the number of agents for the important special case of
identical-interest games, leading to the first method that converges at a
dimension-free rate. Our approach can be used as a smoothing technique to find
an approximate Nash equilibrium (NE) of the unregularized problem without
assuming that stationary policies are isolated.


title: RGB-D Semantic SLAM for Surgical Robot Navigation in the Operating Room

abstract:
  Gaining spatial awareness of the Operating Room (OR) for surgical robotic
systems is a key technology that can enable intelligent applications aiming at
improved OR workflow. In this work, we present a method for semantic dense
reconstruction of the OR scene using multiple RGB-D cameras attached and
registered to the da Vinci Xi surgical system. We developed a novel SLAM
approach for robot pose tracking in dynamic OR environments and dense
reconstruction of the static OR table object. We validated our techniques in a
mock OR by collecting data sequences with corresponding optical tracking
trajectories as ground truth and manually annotated 100 frame segmentation
masks. The mean absolute trajectory error is $11.4\pm1.9$ mm and the mean
relative pose error is $1.53\pm0.48$ degrees per second. The segmentation DICE
score is improved from 0.814 to 0.902 by using our SLAM system compared to
single frame. Our approach effectively produces a dense OR table reconstruction
in dynamic clinical environments as well as improved semantic segmentation on
individual image frames.


title: Centre Vortices in the Presence of Dynamical Fermions

abstract:
  The behaviour of centre vortices in the presence of dynamical fermions is
studied for the first time in the context of the static quark potential and the
Landau-gauge gluon propagator. These results indicate that in the presence of
dynamical fermions, centre vortices are able to better encapsulate the
long-range behaviour of QCD, compared to previous studies on pure Yang-Mills
lattices. This work provides strong evidence that centre vortices are
responsible for the long-range linear potential and the enhanced infrared gluon
propagator, which are indicative of confinement in QCD.


title:
Perturbations and stability conditions of k-essence and kinetic gravity
  braiding models in two-field measure theory

abstract:
  We study cosmological perturbations for k-essence and kinetic gravity
braiding models in the context of the two-field measure theory (TMT).
Considering scalar perturbations and the uniform field gauge, we obtain the
sound speed of the fields and present a stability analysis by means of the
kinetic matrix and the mass eigenvalues. For k-essence models, in the two-field
measure theory, the speed of propagation of the field is modified completely
due to the new measure field and it gives rise to crucial differences with
respect to the case without new measure. The stability analysis gives a
physical viable model for the Universe. For the kinetic gravity braiding models
in the two-field measure theory we get that, in general, the speed of
perturbations is equal to the speed of light which is a consequence of the
properties of the new measure field. In the later case, there is always a ghost
field. Furthermore, we calculate general expressions for the mass eigenvalues
and find, for an explicit example, the existence of tachyonic instabilities.


title: Thermodynamic geometry analysis of new Schwarzschild black holes

abstract:
  This article puts forward a hypothesis.In this article, the derivative of the
cosmological constant is positive, and there is a possibility that the constant
evolves from negative in the early universe to positive in the later period. We
see that when the black hole is an extreme black hole $r_{+}=r_{-}$, the
curvature scalar vanishes to zero. In general, the curvature scalar is not
equal to zero. When $r_{+}=3 r_{-}$, the curvature scalar is equal to negative
infinity. According to Ruppeiner's theory, this time represents the phase
transition of the system. It is very interesting that the divergence point of
the curvature scalar happens to be the Davis transition point. We have found
the phase transition point of the new Schwarzschild black hole.


title:
Key Management Based on Ownership of Multiple Authenticators in Public
  Key Authentication

abstract:
  Public key authentication (PKA) has been deployed in various services to
provide stronger authentication to users. In PKA, a user manages private keys
on her devices called authenticators, and services bind the corresponding
public keys to her account. To protect private keys, a user uses authenticators
which never export private keys outside. On the other hand, a user regularly
uses multiple authenticators like PCs and smartphones. She replaces some of her
authenticators according to their lifecycle, such as purchasing new devices and
losing devices. It is a burden for a user to register, update and revoke public
keys in many services every time she registers new accounts with services and
replaces some of her authenticators. To ease the burden, we propose a mechanism
where users and services manage public keys based on the owner of
authenticators and users can access services with PKA using any of their
authenticators. We introduce a key pair called an Ownership Verification Key
(OVK), which consists of the private key (OVSK) and the corresponding public
key (OVPK). All authenticators owned by a user derive the same OVSK from the
pre-shared secret called the seed. Services verify the ownership of the
authenticators using the corresponding OVPK to determine whether binding the
requested public key to her account. To protect user privacy while maintaining
convenience, authenticators generate a different OVK for each service from the
seed independently. We demonstrate the feasibility through the Proof of Concept
implementation, show that our proposed mechanism achieves some security goals,
and discuss how the mechanism mitigates threats not completely handled.


title: Breaking Fair Binary Classification with Optimal Flipping Attacks

abstract:
  Minimizing risk with fairness constraints is one of the popular approaches to
learning a fair classifier. Recent works showed that this approach yields an
unfair classifier if the training set is corrupted. In this work, we study the
minimum amount of data corruption required for a successful flipping attack.
First, we find lower/upper bounds on this quantity and show that these bounds
are tight when the target model is the unique unconstrained risk minimizer.
Second, we propose a computationally efficient data poisoning attack algorithm
that can compromise the performance of fair learning algorithms.


title:
Strange stars in $f(\mathcal{R})$ gravity Palatini formalism and
  gravitational wave echoes from them

abstract:
  The compact stars are promising candidates associated with the generation of
gravitational waves (GWs). In this work, we study a special type of compact
stars known as strange stars in the $f(\mathcal{R})$ gravity Palatini
formalism. Here we consider three promising $f(\mathcal{R})$ gravity models
viz., Starobinsky, Hu-Sawicki and Gogoi-Goswami models in the domain of MIT Bag
model and linear equations of state (EoSs). We compute the stellar structures
numerically and constrained the $f(\mathcal{R})$ model parameters with a set of
probable strange star candidates. The study shows that the consideration of
stiffer MIT Bag model and linear EoSs within a favourable set of
$f(\mathcal{R})$ gravity model parameters may result in strange stars with
sufficient compactness to produce echoes of GWs. Thus, we have computed the GWs
echo frequencies and characteristic echo times for such stars. It is found that
in compliance with the experimentally obtained possible strange star
candidates, the obtained GW echo frequencies for all the models are in the
range of $65 - 85$ kHz.


title:
Multidimensional quantum calculation of the infrared spectra under
  polaritonic vibrational strong and ultrastrong coupling

abstract:
  Recent experiments and theory demonstrate that the the ground state
properties and chemical reactivity of molecules can be modified inside an
optical cavity. The vibrational strong or ultrastrong coupling results in the
formation of vibrational polaritons which are usually observed through infrared
spectra (IR). Here, we provide a theoretical framework to conduct
multidimensional quantum simulations of the infrared spectra when the molecule
is interacting with cavity modes. Taking single water molecule as an example,
combing with accurate potential energy and dipole moment surfaces, our
implemented cavity vibrational self-consistent field/virtual state
configuration interaction (cVSCF/VCI) is shown to be able to provide
quantitative predictions of the IR spectra when the molecule is inside or
outside the cavity. The spectral signatures of resonance splittings and
blue/red shift of certain bands are found to be highly related with the
frequency and polarization direction of the cavity modes. Further analyses of
the simulated spectra shows that polaritonic strong vibrational coupling
greatly induce the coupling between molecule's vibrational modes, indicating
the intramolecular vibrational energy transfer may be significantly accelerated
by the cavity.


title: About the Infinite Windy Firebreak Location problem

abstract:
  The severity of wildfires can be mitigated adopting preventive measures like
the construction of firebreaks that are strips of land from which the
vegetation is completely removed. In this paper, we model the problem of
wildfire containment as an optimization problem on infinite graphs called
Infinite Windy Firebreak Location. A land of unknown extension is modeled as an
infinite undirected graph in which the vertices correspond to areas subject to
fire and edges represent the propagation of fire from an area to another. The
construction of a firebreak on the territory is modeled as the removal of edges
in both directions between two vertices. The number of firebreaks that can be
installed depends on budget constraints. We assume that fire ignites in a
subset of vertices and propagates to the neighbours. The goal is to select a
subset of edges to remove in order to contain the fire and avoid burning an
infinite part of the graph. We prove that Infinite Windy Firebreak Location is
coNP-complete in restricted cases and we address some polynomial cases. We show
that Infinite Windy Firebreak Location polynomially reduces to Min Cut for
certain classes of graphs like infinite grid graphs and polyomino-grids.


title:
Accurate Discharge Coefficient Prediction of Streamlined Weirs by
  Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit

abstract:
  Streamlined weirs which are a nature-inspired type of weir have gained
tremendous attention among hydraulic engineers, mainly owing to their
established performance with high discharge coefficients. Computational fluid
dynamics (CFD) is considered as a robust tool to predict the discharge
coefficient. To bypass the computational cost of CFD-based assessment, the
present study proposes data-driven modeling techniques, as an alternative to
CFD simulation, to predict the discharge coefficient based on an experimental
dataset. To this end, after splitting the dataset using a k fold cross
validation technique, the performance assessment of classical and hybrid
machine learning deep learning (ML DL) algorithms is undertaken. Among ML
techniques linear regression (LR) random forest (RF) support vector machine
(SVM) k-nearest neighbor (KNN) and decision tree (DT) algorithms are studied.
In the context of DL, long short-term memory (LSTM) convolutional neural
network (CNN) and gated recurrent unit (GRU) and their hybrid forms such as
LSTM GRU, CNN LSTM and CNN GRU techniques, are compared using different error
metrics. It is found that the proposed three layer hierarchical DL algorithm
consisting of a convolutional layer coupled with two subsequent GRU levels,
which is also hybridized with the LR method, leads to lower error metrics. This
paper paves the way for data-driven modeling of streamlined weirs.


title: Deep Normed Embeddings for Patient Representation

abstract:
  We introduce a novel contrastive representation learning objective and a
training scheme for clinical time series. Specifically, we project high
dimensional E.H.R. data to a closed unit ball of low dimension, encoding
geometric priors so that the origin represents an idealized perfect health
state and the euclidean norm is associated with the patient's mortality risk.
Moreover, using septic patients as an example, we show how we could learn to
associate the angle between two vectors with the different organ system
failures, thereby, learning a compact representation which is indicative of
both mortality risk and specific organ failure. We show how the learned
embedding can be used for online patient monitoring, supplement clinicians and
improve performance of downstream machine learning tasks. This work was
partially motivated from the desire and the need to introduce a systematic way
of defining intermediate rewards for Reinforcement Learning in critical care
medicine. Hence, we also show how such a design in terms of the learned
embedding can result in qualitatively different policies and value
distributions, as compared with using only terminal rewards.


title:
Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in
  Child Abuse Hotline Screening

abstract:
  Algorithmic risk assessment tools are now commonplace in public sector
domains such as criminal justice and human services. These tools are intended
to aid decision makers in systematically using rich and complex data captured
in administrative systems. In this study we investigate sources of
heterogeneity in the alignment between worker decisions and algorithmic risk
scores in the context of a real world child abuse hotline screening use case.
Specifically, we focus on heterogeneity related to worker experience. We find
that senior workers are far more likely to screen in referrals for
investigation, even after we control for the observed algorithmic risk score
and other case characteristics. We also observe that the decisions of
less-experienced workers are more closely aligned with algorithmic risk scores
than those of senior workers who had decision-making experience prior to the
tool being introduced. While screening decisions vary across child race, we do
not find evidence of racial differences in the relationship between worker
experience and screening decisions. Our findings indicate that it is important
for agencies and system designers to consider ways of preserving institutional
knowledge when introducing algorithms into high employee turnover settings such
as child welfare call screening.


title:
The Number of Possible CETIs within Our Galaxy and the Communication
  Probability among These CETIs

abstract:
  As the only known intelligent civilization, human beings are always curious
about the existence of other communicating extraterrestrial intelligent
civilizations (CETIs). Based on the latest astrophysical information, we carry
out Monte Carlo simulations to estimate the number of possible CETIs within our
Galaxy and the communication probability among them. Two poorly known
parameters have a great impact on the results. One is the probability of life
appearing on terrestrial planets and eventually evolving a into CETI ($f_c$),
and the other determines at what stage of their host star's evolution CETIs
would be born ($F$). In order to ensure the completeness of the simulation, we
consider a variety of combinations of $f_c$ and $F$. Our results indicate that
for optimistic situations (e.g. $F=25\%$ and $f_c=0.1\%$), there could be
$42777_{-369}^{+267}$ CETIs and they need to survive for $3_{-2}^{+17}$ yr
($2000_{-1400}^{+2000}$ yr) to achieve one-way communication (two-way
communication). In this case, human beings need to survive
$0.3_{-0.298}^{+0.6}$ Myr to receive one alien signal. For pessimistic
situations (e.g. $F=75\%$ and $f_c=0.001\%$), only $111_{-17}^{+28}$ CETIs
could be born and they need to survive for $0.8_{-0.796}^{+1.2}$ Myr
($0.9_{-0.88}^{+4.1}$ Myr) to achieve one-way communication (two-way
communication). In this case, human beings need to survive $50_{-49.6}^{+250}$
Myr to receive one signal from other CETIs. Our results may quantitatively
explain why we have not detected any alien signals so far. The uncertainty of
the results has been discussed in detail and would be alleviated with the
further improvement of our astronomical observation ability in the future.


title: Maximum Entropy Density Estimation from Tabulated Summary Data

abstract:
  Existing nonparametric density estimators typically require individual-level
data. For administrative data, individual-level information is difficult to
access, but tabulated summaries are often publicly available. In this light, we
propose a novel method of maximum entropy density estimation from tabulated
summary data. We establish the strong uniform consistency property for this
nonparametric estimator. Applying the proposed method to the tabulated summary
data of the U.S. tax returns, we estimate the national income distribution.