title: Diffusion of photo-excited holes in viscous electron fluid

abstract:
  The diffusion of photo-generated holes is studied in a high-mobility
mesoscopic GaAs\ channel where electrons exhibit hydrodynamic properties. It is
shown that the injection of holes into such an electron system leads to the
formation of a hydrodynamic three-component mixture consisted of electrons and
photo-generated heavy and light holes. The obtained results are analyzed within
the framework of ambipolar diffusion, which reveals characteristics of a
viscous flow. Both hole types exhibit similar hydrodynamic characteristics. In
such a way the diffusion lengths, ambipolar diffusion coefficient and the
effective viscosity of the electron-hole system are determined.


title:
Efficient algorithms for computing ground states of the 2D random-field
  Ising model

abstract:
  We investigate the application of graph-cut methods for the study of the
critical behaviour of the two-dimensional random-field Ising model. We focus on
exact ground-state calculations, crossing the phase boundary of the model at
zero temperature and varying the disorder strength. For this purpose we employ
two different minimum-cut--maximum-flow algorithms, one of augmenting-path and
another of push-relabel style. We implement these approaches for the square and
triangular lattice problems and compare their computational efficiency.


title: Efficient Active Learning with Abstention

abstract:
  The goal of active learning is to achieve the same accuracy achievable by
passive learning, while using much fewer labels. Exponential savings in label
complexity are provably guaranteed in very special cases, but fundamental lower
bounds show that such improvements are impossible in general. This suggests a
need to explore alternative goals for active learning. Learning with abstention
is one such alternative. In this setting, the active learning algorithm may
abstain from prediction in certain cases and incur an error that is marginally
smaller than $\frac{1}{2}$. We develop the first computationally efficient
active learning algorithm with abstention. Furthermore, the algorithm is
guaranteed to only abstain on hard examples (where the true label distribution
is close to a fair coin), a novel property we term "proper abstention" that
also leads to a host of other desirable characteristics. The option to abstain
reduces the label complexity by an exponential factor, with no assumptions on
the distribution, relative to passive learning algorithms and/or active
learning that are not allowed to abstain. A key feature of the algorithm is
that it avoids the undesirable "noise-seeking" behavior often seen in active
learning. We also explore extensions that achieve constant label complexity and
deal with model misspecification.


title: Corrections to scaling in geometrical clusters of the 2D Ising model

abstract:
  We study the scaling of the average cluster size and percolation strength of
geometrical clusters for the two-dimensional Ising model. By means of Monte
Carlo simulations and a finite-size scaling analysis we discuss the appearance
of corrections to scaling for different definitions of cluster sets. We find
that including all percolating clusters, or excluding only clusters that
percolate in one but not the other direction, leads to smaller corrections to
scaling for the average cluster size as compared to the other definitions
considered. The percolation strength is less sensitive to the definitions used.


title: dS$\boldsymbol{_2}$ as excitation of AdS$\boldsymbol{_2}$

abstract:
  We introduce a family of 2D dilaton gravity models with state-dependent
constant curvature so that dS$_2$ emerges as an excitation of AdS$_2$.
Curiously, the strong coupling region corresponds to the asymptotic region
geometrically. Apart from these key differences, many features resemble the
Almheiri--Polchinski model. We discuss perturbative and non-perturbative
thermodynamical stability, bubble nucleation through matter shockwaves, and
semiclassical backreaction effects. In some of these models, we find that low
temperatures are dominated by AdS$_2$ but high temperatures are dominated by
dS$_2$, concurrent with a recent proposal by Susskind.


title:
Geometric numerical methods for Lie systems and their application in
  optimal control

abstract:
  A Lie system is a non-autonomous system of first-order ordinary differential
equations whose general solution can be written via an autonomous function, a
so-called (nonlinear) superposition rule of a finite number of particular
solutions and some parameters to be related to initial conditions. Even if the
superposition rules for some Lie systems are known, the explicit analytic
expression of their solutions frequently is not. This is why this article
focuses on a novel geometric attempt to integrate Lie systems analytically and
numerically. We focus on two families of methods: those based on Magnus
expansions and the Runge-Kutta-Munthe-Kaas method, which are here adapted to
the geometric properties of Lie systems. To illustrate the accuracy of our
techniques we propose examples based on the SL$(n,\mathbb{R})$ Lie group, which
plays a very relevant role in mechanics. In particular, we depict an optimal
control problem for a vehicle with quadratic cost function. Particular
numerical solutions of the studied examples are given.


title:
Multicanonical simulations of the 2D spin-$1$ Baxter-Wu model in a
  crystal field

abstract:
  We investigate aspects of universality in the two-dimensional (2D) spin-$1$
Baxter-Wu model in a crystal field $\Delta$ using a parallel version of the
multicanonical algorithm employed at constant temperature $T$. A detailed
finite-size scaling analysis in the continuous regime of the $\Delta-T$ phase
diagram of the model indicates that the transition belongs to the universality
class of the $4$-state Potts model. The presence of first-order-like
finite-size effects that become more pronounced as one approaches the
pentacritical point of the model is highlighted and discussed.


title: Improving Biomarker Based HIV Incidence Estimation in the Treatment Era

abstract:
  Estimating HIV-1 incidence using biomarker assays in cross-sectional surveys
is important for understanding the HIV pandemic. However, the utility of these
estimates has been limited by uncertainty about what input parameters to use
for False Recency Rate (FRR) and Mean Duration of Recent Infection (MDRI) after
applying recent infection testing algorithm (RITA). This article shows how
testing and diagnosis in a population reduce both FRR and MDRI compared to a
treatment-naive population. Using self-reported testing history, a new method
is proposed for calculating appropriate context-specific estimates of FRR and
MDRI. The result of this is a new formula for incidence that depends only on
reference FRR and MDRI parameters derived in an undiagnosed, treatment-naive,
non-elite controller, non-AIDS-progressed population.


title: AKF-SR: Adaptive Kalman Filtering-based Successor Representation

abstract:
  Recent studies in neuroscience suggest that Successor Representation
(SR)-based models provide adaptation to changes in the goal locations or reward
function faster than model-free algorithms, together with lower computational
cost compared to that of model-based algorithms. However, it is not known how
such representation might help animals to manage uncertainty in their
decision-making. Existing methods for SR learning do not capture uncertainty
about the estimated SR. In order to address this issue, the paper presents a
Kalman filter-based SR framework, referred to as Adaptive Kalman
Filtering-based Successor Representation (AKF-SR). First, Kalman temporal
difference approach, which is a combination of the Kalman filter and the
temporal difference method, is used within the AKF-SR framework to cast the SR
learning procedure into a filtering problem to benefit from the uncertainty
estimation of the SR, and also decreases in memory requirement and sensitivity
to model's parameters in comparison to deep neural network-based algorithms. An
adaptive Kalman filtering approach is then applied within the proposed AKF-SR
framework in order to tune the measurement noise covariance and measurement
mapping function of Kalman filter as the most important parameters affecting
the filter's performance. Moreover, an active learning method that exploits the
estimated uncertainty of the SR to form the behaviour policy leading to more
visits to less certain values is proposed to improve the overall performance of
an agent in terms of received rewards while interacting with its environment.


title:
Leakage Localization in Water Distribution Networks: A Model-Based
  Approach

abstract:
  The paper studies the problem of leakage localization in water distribution
networks. For the case of a single pipe that suffers from a single leak, by
taking recourse to pressure and flow measurements, and assuming those are
noiseless, we provide a closed-form expression for leak localization, leak
exponent and leak constant. For the aforementioned setting, but with noisy
pressure and flow measurements, an expression for estimating the location of
the leak is provided. Finally, assuming the existence of a single leak, for a
network comprising of more than one pipe and assuming that the network has a
tree structure, we provide a systematic procedure for determining the leak
location, the leak exponent, and the leak constant


title:
Phase Field Crystal model for particles with n-fold rotational symmetry
  in two dimensions

abstract:
  We introduce a Phase Field Crystal (PFC) model for particles with n-fold
rotational symmetry in two dimensions. Our approach is based on a free energy
functional that depends on the reduced one-particle density, the strength of
the orientation, and the direction of the orientation, where all these order
parameters depend on the position. The functional is constructed such that for
particles with axial symmetry (i. e. n = 2) the PFC model for liquid crystals
as introduced by H. L\"owen [J. Phys.: Condens. Matter 22, 364105 (2010)] is
recovered. We discuss the stability of the functional and explore phases that
occur for $1 \leq n \leq 6$. In addition to isotropic, nematic, stripe, and
triangular order, we also observe cluster crystals with square, rhombic,
honeycomb, and even quasicrystalline symmetry. The n-fold symmetry of the
particles corresponds to the one that can be realized for colloids with
symmetrically arranged patches. We explain how both, repulsive as well as
attractive patches, are described in our model.


title: Digitizing Historical Balance Sheet Data: A Practitioner's Guide

abstract:
  This paper discusses how to successfully digitize large-scale historical
micro-data by augmenting optical character recognition (OCR) engines with pre-
and post-processing methods. Although OCR software has improved dramatically in
recent years due to improvements in machine learning, off-the-shelf OCR
applications still present high error rates which limits their applications for
accurate extraction of structured information. Complementing OCR with
additional methods can however dramatically increase its success rate, making
it a powerful and cost-efficient tool for economic historians. This paper
showcases these methods and explains why they are useful. We apply them against
two large balance sheet datasets and introduce "quipucamayoc", a Python package
containing these methods in a unified framework.


title:
Ultra-high terahertz index in deep subwavelength coupled bi-layer
  free-standing flexible metamaterials

abstract:
  We report extensive enhancement in the refractive index of artificially
designed metamaterials by exploiting the deep subwavelength coupling in a
free-standing, thin-film metal-dielectric-metal checkboard structure. A record
high refractive index of 77.02+43.22i is obtained at terahertz frequencies. The
detailed investigations reveal that the enhancement of the effective refractive
index of the structure via deep subwavelength coupling in a bilayer design is
governed by a power law, which is an effective and simpler approach to design
high index metamaterial. The approach relies on deep subwavelength coupling to
obtain extremely high refractive index values that can lead to many practical
applications in the field of imaging, lithography, design of delay lines and
interferometers.


title:
Distributed Robust Geocast Multicast Routing for Inter-Vehicle
  Communication

abstract:
  Numerous protocols for geocast have been proposed in literature. It has been
shown that explicit route setup approaches perform poorly with VANETs due to
limited route lifetime and frequent network fragmentation. The broadcast based
approaches have considerable redundancy and add significantly to the overhead
of the protocol. A completely distributed and robust geocast approach is
presented in this paper, that is resilient to frequent topology changes and
network fragmentation. A distance-based backoff algorithm is used to reduce the
number of hops and a novel mechanism to reduce redundant broadcasts is
introduced. The performance of the proposed protocol is evaluated for various
scenarios and compared with simple flooding and a protocol based on explicit
route setup.


title:
Inequalities for higher order differences of the logarithm of the
  overpartition function and a problem of Wang-Xie-Zhang

abstract:
  Let $\overline{p}(n)$ denote the overpartition function. In this paper, our
primary goal is to study the asymptotic behavior of the finite differences of
the logarithm of the overpartition function, i.e., $(-1)^{r-1}\Delta^r \log
\p(n)$, by studying the inequality of the following form $$\log
\Bigl(1+\dfrac{C(r)}{n^{r-1/2}}-\dfrac{C_1(r)}{n^{r}}\Bigr)<(-1)^{r-1}\Delta^r
\log \p(n) <\log \Bigl(1+\dfrac{C(r)}{n^{r-1/2}}\Bigr)\ \text{for}\ n \geq
N(r),$$ where $C(r), C_1(r), \text{and}\ N(r)$ are computable constants
depending on the positive integer $r$, determined explicitly. This solves a
problem posed by Wang, Xie and Zhang in the context of searching for a better
lower bound of $(-1)^{r-1}\Delta^r \log \p(n)$ than $0$. By settling the
problem, we are able to show that \begin{equation*} \lim_{n\rightarrow
\infty}(-1)^{r-1}\Delta^r \log \p(n)
=\dfrac{\pi}{2}\Bigl(\dfrac{1}{2}\Bigr)_{r-1}n^{\frac{1}{2}-r}. \end{equation*}


title: SELFIES and the future of molecular string representations

abstract:
  Artificial intelligence (AI) and machine learning (ML) are expanding in
popularity for broad applications to challenging tasks in chemistry and
materials science. Examples include the prediction of properties, the discovery
of new reaction pathways, or the design of new molecules. The machine needs to
read and write fluently in a chemical language for each of these tasks. Strings
are a common tool to represent molecular graphs, and the most popular molecular
string representation, SMILES, has powered cheminformatics since the late
1980s. However, in the context of AI and ML in chemistry, SMILES has several
shortcomings -- most pertinently, most combinations of symbols lead to invalid
results with no valid chemical interpretation. To overcome this issue, a new
language for molecules was introduced in 2020 that guarantees 100\% robustness:
SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and
enabled numerous new applications in chemistry. In this manuscript, we look to
the future and discuss molecular string representations, along with their
respective opportunities and challenges. We propose 16 concrete Future Projects
for robust molecular representations. These involve the extension toward new
chemical domains, exciting questions at the interface of AI and robust
languages and interpretability for both humans and machines. We hope that these
proposals will inspire several follow-up works exploiting the full potential of
molecular string representations for the future of AI in chemistry and
materials science.


title:
ElectAnon: A Blockchain-Based, Anonymous, Robust and Scalable
  Ranked-Choice Voting Protocol

abstract:
  Remote voting has become more critical in recent years, especially after the
Covid-19 outbreak. Blockchain technology and its benefits like
decentralization, security, and transparency have encouraged remote voting
systems to use blockchains. Analysis of existing solutions reveals that
anonymity, robustness, and scalability are common problems in blockchain-based
election systems. In this work, we propose ElectAnon, a blockchain-based,
ranked-choice election protocol focusing on anonymity, robustness, and
scalability. ElectAnon achieves anonymity by enabling voters to cast their
votes via zero-knowledge proofs anonymously. Robustness is realized by removing
the direct control of the authorities in the voting process by using
timed-state machines. Results show that ElectAnon is scalable amongst existing
works as it reduces the gas consumption up to 89% compared to previous works.
The proposed protocol includes a candidate proposal system and swappable
tallying libraries. An extension is also proposed to minimize the trust
assumption on election authorities. Our code is available on
https://github.com/ceyonur/electanon.


title: The Multilinear Spherical Maximal Function in one dimension

abstract:
  In dimension $n=1$ we obtain $L^{p_1}(\mathbb R) \times\dots\times
L^{p_m}(\mathbb R)$ to $L^p(\mathbb R)$ boundedness for the multilinear
spherical maximal function in the largest possible open set of indices and we
provide counterexamples that indicate the optimality of our results.


title:
Lyapunov based Stochastic Stability of Human-Machine Interaction: A
  Quantum Decision System Approach

abstract:
  In mathematical psychology, decision makers are modeled using the Lindbladian
equations from quantum mechanics to capture important human-centric features
such as order effects and violation of the sure thing principle. We consider
human-machine interaction involving a quantum decision maker (human) and a
controller (machine). Given a sequence of human decisions over time, how can
the controller dynamically provide input messages to adapt these decisions so
as to converge to a specific decision? We show via novel stochastic Lyapunov
arguments how the Lindbladian dynamics of the quantum decision maker can be
controlled to converge to a specific decision asymptotically. Our methodology
yields a useful mathematical framework for human-sensor decision making. The
stochastic Lyapunov results are also of independent interest as they generalize
recent results in the literature.


title: Rogue waves in discrete-time quantum walks

abstract:
  Rogue waves are rapid and unpredictable events of exceptional amplitude
reported in various fields, such as oceanography and optics, with much of the
interest being targeted towards their physical origins and likelihood of
occurrence. Here, we use the all-round framework of discrete-time quantum walks
to study the onset of those events due to a random phase modulation, unveiling
its long-tailed statistics, distribution profile, and dependence upon the
degree of randomness. We find that those rogue waves belong the Gumbel family
of extreme value distributions.


title: Data-augmented cross-lingual synthesis in a teacher-student framework

abstract:
  Cross-lingual synthesis can be defined as the task of letting a speaker
generate fluent synthetic speech in another language. This is a challenging
task, and resulting speech can suffer from reduced naturalness, accented
speech, and/or loss of essential voice characteristics. Previous research shows
that many models appear to have insufficient generalization capabilities to
perform well on every of these cross-lingual aspects. To overcome these
generalization problems, we propose to apply the teacher-student paradigm to
cross-lingual synthesis. While a teacher model is commonly used to produce
teacher forced data, we propose to also use it to produce augmented data of
unseen speaker-language pairs, where the aim is to retain essential speaker
characteristics. Both sets of data are then used for student model training,
which is trained to retain the naturalness and prosodic variation present in
the teacher forced data, while learning the speaker identity from the augmented
data. Some modifications to the student model are proposed to make the
separation of teacher forced and augmented data more straightforward. Results
show that the proposed approach improves the retention of speaker
characteristics in the speech, while managing to retain high levels of
naturalness and prosodic variation.


title: SimPO: Simultaneous Prediction and Optimization

abstract:
  Many machine learning (ML) models are integrated within the context of a
larger system as part of a key component for decision making processes.
Concretely, predictive models are often employed in estimating the parameters
for the input values that are utilized for optimization models as isolated
processes. Traditionally, the predictive models are built first, then the model
outputs are used to generate decision values separately. However, it is often
the case that the prediction values that are trained independently of the
optimization process produce sub-optimal solutions. In this paper, we propose a
formulation for the Simultaneous Prediction and Optimization (SimPO) framework.
This framework introduces the use of a joint weighted loss of a decision-driven
predictive ML model and an optimization objective function, which is optimized
end-to-end directly through gradient-based methods.


title: On the Generalised Ricci Solitons and Sasakian Manifolds

abstract:
  In this note, we find a necessary condition on odd-dimensional Riemannian
manifolds under which both of Sasakian structure and the generalised Ricci
soliton equation are satisfied, and we give some examples.


title:
Simplistic approach for Determining Center Shift of M\"ossbauer
  Spectrum: exemplified on two cases

abstract:
  A very simple method for determining the center (isomer) shift, CS, of a
M\"ossbauer spectrum is outlined. Its applicability is demonstrated on two
examples viz. pyrite and a ternary sigma-phase Fe-Cr-Ni compound. Sets of the
spectra recorded in the temperature interval of 78-295 K for the former, and
5-293 K for the latter were analyzed with the simple and a commonly used
methods. CS(T)-values obtained with both ways of the fitting procedures were
analyzed in terms of the Debye model. The determined therefrom values of the
Debye temperatures agree within the error limit with each other proving thereby
that this very simple method gives correct values of CS.


title:
Importance of Different Temporal Modulations of Speech: A Tale of Two
  Perspectives

abstract:
  How important are different temporal speech modulations for speech
recognition? We answer this question from two complementary perspectives.
Firstly, we quantify the amount of phonetic information in the modulation
spectrum of speech by computing the mutual information between temporal
modulations with frame-wise phoneme labels. Looking from another perspective,
we ask - which speech modulations does an Automatic Speech Recognition (ASR)
system prefer for its operation. Data-driven weights are learnt over the
modulation spectrum and optimized for an end-to-end ASR task. Both methods
unanimously agree that speech information is mostly contained in slow
modulation. Maximum mutual information occurs around 3-6 Hz which also happens
to be the range of modulations most preferred by the ASR. In addition, we show
that incorporation of this knowledge into ASRs significantly reduces its
dependency on the amount of training data.


title: Jordan types of triangular matrices over a finite field

abstract:
  Let $\lambda$ be a partition of an integer $n$ and ${\mathbb F}_q$ be a
finite field of order $q$. Let $P_\lambda(q)$ be the number of strictly upper
triangular $n\times n$ matrices of the Jordan type $\lambda$. It is known that
the polynomial $P_\lambda$ has a tendency to be divisible by high powers of $q$
and $Q=q-1$, and we put
$P_\lambda(q)=q^{d(\lambda)}Q^{e(\lambda)}R_\lambda(q)$, where
$R_\lambda(0)\neq0$ and $R_\lambda(1)\neq0$. In this article, we study the
polynomials $P_\lambda(q)$ and $R_\lambda(q)$. Our main results: an explicit
formula for $d(\lambda)$ (an explicit formula for $e(\lambda)$ is known, see
Proposition 3.3 below), a recursive formula for $R_\lambda(q)$ (a similar
formula for $P_\lambda(q)$ is known, see Proposition 3.1 below), the
stabilization of $R_\lambda$ with respect to extending $\lambda$ by adding
strings of 1's, and an explicit formula for the limit series
$R_{\lambda1^\infty}$. Our studies are motivated by projected applications to
the orbit method in the representation theory of nilpotent algebraic groups
over finite fields.


title:
rfPhen2Gen: A machine learning based association study of brain imaging
  phenotypes to genotypes

abstract:
  Imaging genetic studies aim to find associations between genetic variants and
imaging quantitative traits. Traditional genome-wide association studies (GWAS)
are based on univariate statistical tests, but when multiple traits are
analyzed together they suffer from a multiple-testing problem and from not
taking into account correlations among the traits. An alternative approach to
multi-trait GWAS is to reverse the functional relation between genotypes and
traits, by fitting a multivariate regression model to predict genotypes from
multiple traits simultaneously. However, current reverse genotype prediction
approaches are mostly based on linear models. Here, we evaluated random forest
regression (RFR) as a method to predict SNPs from imaging QTs and identify
biologically relevant associations. We learned machine learning models to
predict 518,484 SNPs using 56 brain imaging QTs. We observed that genotype
regression error is a better indicator of permutation p-value significance than
genotype classification accuracy. SNPs within the known Alzheimer disease (AD)
risk gene APOE had lowest RMSE for lasso and random forest, but not ridge
regression. Moreover, random forests identified additional SNPs that were not
prioritized by the linear models but are known to be associated with
brain-related disorders. Feature selection identified well-known brain regions
associated with AD,like the hippocampus and amygdala, as important predictors
of the most significant SNPs. In summary, our results indicate that non-linear
methods like random forests may offer additional insights into
phenotype-genotype associations compared to traditional linear multi-variate
GWAS methods.


title:
Automatic Classification of Alzheimer's Disease using brain MRI data and
  deep Convolutional Neural Networks

abstract:
  Alzheimer's disease (AD) is one of the most common public health issues the
world is facing today. This disease has a high prevalence primarily in the
elderly accompanying memory loss and cognitive decline. AD detection is a
challenging task which many authors have developed numerous computerized
automatic diagnosis systems utilizing neuroimaging and other clinical data. MRI
scans provide high-intensity visible features, making these scans the most
widely used brain imaging technique. In recent years deep learning has achieved
leading success in medical image analysis. But a relatively little
investigation has been done to apply deep learning techniques for the brain MRI
classification. This paper explores the construction of several deep learning
architectures evaluated on brain MRI images and segmented images. The idea
behind segmented images investigates the influence of image segmentation step
on deep learning classification. The image processing presented a pipeline
consisting of pre-processing to enhance the MRI scans and post-processing
consisting of a segmentation method for segmenting the brain tissues. The
results show that the processed images achieved a better accuracy in the binary
classification of AD vs. CN (Cognitively Normal) across four different
architectures. ResNet architecture resulted in the highest prediction accuracy
amongst the other architectures (90.83% for the original brain images and
93.50% for the processed images).


title:
Scale Separated Approaches to the Interaction of Oceanic Internal Waves,
  part II: A one-dimensional numerical model

abstract:
  We present an idealized representation of high frequency oceanic internal
waves propagating in a background field of inertial waves. This report follows
a companion paper in which we present derivations for transport equations using
both wave turbulence theory and path integral techniques. These differ, with
the path integral transport equation explicitly acknowledging a mean drift of
wave packets to smaller scales. Neither prediction for the net downscale
transport of energy compares well with observations. Here we compare ray
tracing numerical results with metrics derived using a wave turbulence
derivation for the kinetic equation and using path integral techniques. At
small inertial wave amplitudes, all three provide consistent descriptions for
the mean drift and dispersion about that mean drift. At oceanic amplitudes,
however, the numerics support diminished transport and dispersion. This
coincides with the mean drift time scale becoming similar to the correlation
time scale, which we parse as the signature of a transition to a non-Markovian
process. espite this decrease, estimates of downscale energy transfer are still
significantly larger than expected from oceanic derived metrics. We offer the
hypothesis that differences between the ray-path formulation and observations
results from an unwarranted discard of Bragg scattering resonances that reduce
the mean drift.


title:
KPZ universality in a classical single random walker under continuous
  measurement

abstract:
  We introduce and study a new model consisting of a single classical random
walker undergoing continuous monitoring at rate $\gamma$ on a discrete lattice.
Although such a continuous measurement cannot affect physical observables, it
has a non-trivial effect on the probability distribution of the random walker.
When $\gamma$ is small, we show analytically that the time-evolution of the
latter can be mapped to the Stochastic Heat Equation (SHE). In this limit, the
width of the log probability thus follows a Family-Vicsek scaling law,
$N^{\alpha}f(t/N^{\alpha/\beta})$, with growth exponent $\beta=1/3$ and
roughness exponent $\alpha=0.5$ corresponding to the Kardar-Parisi-Zhang (KPZ)
universality class. When $\gamma$ is further increased outside this regime, we
find numerically that, while $\beta$ remains constant, $\alpha$ monotonously
grows before saturating at a value $\alpha=1$ at high monitoring rates.
Furthermore, beyond the initial power-law characterized by $\beta$ and whenever
the measurement rate exceeds a finite threshold, we observe the emergence of a
second growth regime at intermediate times which could be the landmark of a
measurement-induced transition.


title:
Numerical Solution of the Steady-State Network Flow Equations for a
  Non-Ideal Gas

abstract:
  We formulate a steady-state network flow problem for non-ideal gas that
relates injection rates and nodal pressures in the network to flows in pipes.
For this problem, we present and prove a theorem on uniqueness of generalized
solution for a broad class of non-ideal pressure-density relations that satisfy
a monotonicity property. Further, we develop a Newton-Raphson algorithm for
numerical solution of the steady-state problem, which is made possible by a
systematic non-dimensionalization of the equations. The developed algorithm has
been extensively tested on benchmark instances and shown to converge robustly
to a generalized solution. Previous results indicate that the steady-state
network flow equations for an ideal gas are difficult to solve by the
Newton-Raphson method because of its extreme sensitivity to the initial guess.
In contrast, we find that non-dimensionalization of the steady-state problem is
key to robust convergence of the Newton-Raphson method. We identify criteria
based on the uniqueness of solutions under which the existence of a
non-physical generalized solution found by a non-linear solver implies
non-existence of a physical solution, i.e., infeasibility of the problem.
Finally, we compare pressure and flow solutions based on ideal and non-ideal
equations of state to demonstrate the need to apply the latter in practice. The
solver developed in this article is open-source and is made available for both
the academic and research communities as well as the industry.


title: Cyclic independence: Boolean and monotone

abstract:
  The present paper introduces a modified version of cyclic-monotone
independence which originally arose in the context of random matrices, and also
introduces its natural analogy called cyclic-Boolean independence. We
investigate formulas for convolutions, limit theorems for sums of independent
random variables, and also classify infinitely divisible distributions with
respect to cyclic-Boolean convolution. Finally, we provide applications to the
eigenvalues of the adjacency matrices of iterated star products of graphs and
also iterated comb products of graphs.


title: On-line Estimation of Stability and Passivity Metrics

abstract:
  We consider the problem of on-line evaluation of critical characteristic
parameters such as the L_2-gain (L2G), input feedforward passivity index (IFP)
and output feedback passivity index (OFP) of non-linear systems using their
input-output data. Typically, having an accurate measure of such "system
indices" enables the application of systematic control design techniques.
Moreover, if such system indices can efficiently be evaluated on-line, they can
be exploited to device intelligent controller reconfiguration and
fault-tolerant control techniques. However, the existing estimation methods of
such system indices (i.e., L2G, IFP and OFP) are predominantly off-line,
computationally inefficient, and require a large amount of actual or
synthetically generated input-output trajectory data under some specific
initial/terminal conditions. On the other hand, the existing on-line estimation
methods take an averaging-based approach, which may be sub-optimal,
computationally inefficient and susceptible to estimate saturation. In this
paper, to overcome these challenges (in the on-line estimation of system
indices), we establish and exploit several interesting theoretical results on a
particular class of fractional function optimization problems. For comparison
purposes, the details of an existing averaging-based approach are provided for
the same on-line estimation problem. Finally, several numerical examples are
discussed to demonstrate the proposed on-line estimation approach and to
highlight our contributions.


title:
When Artificial Parameter Evolution Gets Real: Particle Filtering for
  Time-Varying Parameter Estimation in Deterministic Dynamical Systems

abstract:
  Estimating and quantifying uncertainty in unknown system parameters from
limited data remains a challenging inverse problem in a variety of real-world
applications. While many approaches focus on estimating constant parameters, a
subset of these problems includes time-varying parameters with unknown
evolution models that often cannot be directly observed. This work develops a
systematic particle filtering approach that reframes the idea behind artificial
parameter evolution to estimate time-varying parameters in nonstationary
inverse problems arising from deterministic dynamical systems. Focusing on
systems modeled by ordinary differential equations, we present two particle
filter algorithms for time-varying parameter estimation: one that relies on a
fixed value for the noise variance of a parameter random walk; another that
employs online estimation of the parameter evolution noise variance along with
the time-varying parameter of interest. Several computed examples demonstrate
the capability of the proposed algorithms in estimating time-varying parameters
with different underlying functional forms and different relationships with the
system states (i.e., additive vs. multiplicative).


title: The holographic entropy cone from marginal independence

abstract:
  The holographic entropy cone characterizes the relations between entanglement
entropies for a spatial partitioning of the boundary spacetime of a holographic
CFT in any state describing a classical bulk geometry. We argue that the
holographic entropy cone, for an arbitrary number of parties, can be
reconstructed from more fundamental data determined solely by subadditivity of
quantum entropy. We formulate certain conjectures about graph models of
holographic entanglement, for which we provide strong evidence, and rigorously
prove that they all imply that such a reconstruction is possible. Our
conjectures (except only for the weakest) further imply that the necessary data
is remarkably simple. In essence, all one needs to know to reconstruct the
holographic entropy cone, is a certain subset of the extreme rays of this
simpler "subadditivity cone", namely those which can be realized in holography.
This recasting of the bewildering entanglement structure of geometric states
into primal building blocks paves the way to distilling the essence of
holography for the emergence of a classical bulk spacetime.


title: Reachability Logic for Low-Level Programs

abstract:
  Automatic exploit generation is a relatively new area of research. Work in
this area aims to automate the manual and labor intensive task of finding
exploits in software. In this paper we present a novel program logic to support
automatic exploit generation. We develop a program logic called Reachability
Logic, which formally defines the relation between reachability of an assertion
and the preconditions which allow them to occur. This relation is then used to
calculate the search space of preconditions. We show that Reachability Logic is
a powerful tool in automatically finding evidence that an assertion is
reachable. We verify that the system works for small litmus tests, as well as
real-world algorithms. An implementation has been developed, and the entire
system is proven to be sound and complete in a theorem prover. This work
represents an important step towards formally verified automatic exploit
generation.


title: Efficient Maximal Coding Rate Reduction by Variational Forms

abstract:
  The principle of Maximal Coding Rate Reduction (MCR$^2$) has recently been
proposed as a training objective for learning discriminative low-dimensional
structures intrinsic to high-dimensional data to allow for more robust training
than standard approaches, such as cross-entropy minimization. However, despite
the advantages that have been shown for MCR$^2$ training, MCR$^2$ suffers from
a significant computational cost due to the need to evaluate and differentiate
a significant number of log-determinant terms that grows linearly with the
number of classes. By taking advantage of variational forms of spectral
functions of a matrix, we reformulate the MCR$^2$ objective to a form that can
scale significantly without compromising training accuracy. Experiments in
image classification demonstrate that our proposed formulation results in a
significant speed up over optimizing the original MCR$^2$ objective directly
and often results in higher quality learned representations. Further, our
approach may be of independent interest in other models that require
computation of log-determinant forms, such as in system identification or
normalizing flow models.


title: The DESC Stellarator Code Suite Part III: Quasi-symmetry optimization

abstract:
  The DESC stellarator optimization code takes advantage of advanced numerical
methods to search the full parameter space much faster than conventional tools.
Only a single equilibrium solution is needed at each optimization step thanks
to automatic differentiation, which efficiently provides exact derivative
information. A Gauss-Newton trust-region optimization method uses second-order
derivative information to take large steps in parameter space and converges
rapidly. With just-in-time compilation and GPU portability, high-dimensional
stellarator optimization runs take orders of magnitude less computation time
with DESC compared to other approaches. This paper presents the theory of the
DESC fixed-boundary local optimization algorithm along with demonstrations of
how to easily implement it in the code. Example quasi-symmetry optimizations
are shown and compared to results from conventional tools. Three different
forms of quasi-symmetry objectives are available in DESC, and their relative
advantages are discussed in detail. In the examples presented, the triple
product formulation yields the best optimization results in terms of minimized
computation time and particle transport. This paper concludes with an
explanation of how the modular code suite can be extended to accommodate other
types of optimization problems.


title:
Sensitive label-free and compact ultrasonic sensor based on double
  silicon-on-insulator slot micro-ring resonators

abstract:
  We propose a new label-free ultrasonic sensor, which comprises a slot
wave-guide and double silicon-on-insulator (SOI) slot micro-ring resonators.
The all-optical sensors do not suffer from electromagnetic interference. We
choose to integrate a silicon slot double micro-ring (SDMR) resonators in an
acoustically resonant membrane. Optimization of the several key structural
parameters is investigated to achieve the mode-field distributions of
transmission spectrum based on Comsol Multiphysics software. Our numerical
studies show that the proposed ultrasonic sensor offers higher sensitivity and
a larger detection frequency range than conventional piezoelectric based
ultrasound transducer. For a SDMR system with an area of 15um*30um, sensitivity
as high as 2453.7mv/kpa, and over a bandwidth range of 1-150MHz. The
sensitivity value is 36 times higher than that of single slot micro-ring
ultrasonic sensor. The theoretical-factor of the SDMR can be approximately
1.24*10^6 with bending radius of 5um. The investigation on the SDMR system is a
valuable exploration of the photo-acoustic microscopy for the ultra-high Q
factor and large frequency range.


title: 4Weed Dataset: Annotated Imagery Weeds Dataset

abstract:
  Weeds are a major threat to crops and are responsible for reducing crop yield
worldwide. To mitigate their negative effect, it is advantageous to accurately
identify them early in the season to prevent their spread throughout the field.
Traditionally, farmers rely on manually scouting fields and applying herbicides
for different weeds. However, it is easy to confuse between crops with weeds
during the early growth stages. Recently, deep learning-based weed
identification has become popular as deep learning relies on convolutional
neural networks that are capable of learning important distinguishable features
between weeds and crops. However, training robust deep learning models requires
access to large imagery datasets. Therefore, an early-season weeds dataset was
acquired under field conditions. The dataset consists of 159 Cocklebur images,
139 Foxtail images, 170 Redroot Pigweed images and 150 Giant Ragweed images
corresponding to four common weed species found in corn and soybean production
systems.. Bounding box annotations were created for each image to prepare the
dataset for training both image classification and object detection deep
learning networks capable of accurately locating and identifying weeds within
corn and soybean fields. (https://osf.io/w9v3j/)


title:
Support-vector-machine with Bayesian optimization for lithofacies
  classification using elastic properties

abstract:
  We investigate an applicability of Bayesian-optimization (BO) to optimize
hyperparameters associated with support-vector-machine (SVM) in order to
classify facies using elastic properties derived from well data in the East
Central Graben, UKCS. The cross-plot products of the field dataset appear to be
successfully classified with non-linear boundaries. Although there are a few
factors to be predetermined in the BO scheme such as an iteration number to
deal with a trade-off between the prediction accuracy and the computational
cost, this approach effectively reduces possible human subjectivity connected
to the architecture of the SVM. Our proposed workflow might be beneficial in
resource-exploration and development in terms of subsurface objective technical
evaluations.


title:
Quantitative analysis of diaphragm motion during fluoroscopic sniff test
  to assist in diagnosis of hemidiaphragm paralysis

abstract:
  The current imaging gold standard for detecting paradoxical diaphragm motion
and diagnosing hemidiaphragm paralysis is to perform the fluoroscopic sniff
test. The images are visually examined by an experienced radiologist, and if
one hemidiaphragm ascends while the other descends, then it is described as
paradoxical motion, which is highly suggestive of hemidiaphragm paralysis.
However, diagnosis can be challenging because diaphragm motion during sniffing
is fast, paradoxical motion can be subtle, and the analysis is based on a
2-dimensional projection of a 3-dimensional surface. This paper presents a case
of chronic left hemidiaphragm elevation that was initially reported as mild
paradoxical motion on fluoroscopy. After measuring the elevations of the
diaphragms and modeling their temporal correlation using Gaussian process
regression, the systematic trend of the hemidiaphragmatic motion along with its
stochastic properties was determined. When analyzing the trajectories of the
hemidiaphragms, no statistically significant paradoxical motion was detected.
This could potentially change the prognosis if the patient was to consider
diaphragm plication as treatment. The presented method provides a more
objective analysis of hemidiaphragm motions and can potentially improve
diagnostic accuracy.


title: Irreducibility of Chebyshev-Lissajous polynomials

abstract:
  We study certain kind of polynomials associated with Lissajous curves, called
Chebyshev-Lissajous polynomials. We investigate their irreducibilities over the
real numbers and complex numbers, thus comfirming two conjectures proposed by
Merino.


title:
Evolution of barchan dune interactions investigated by a downscaled
  water tunnel experiment: the temporal characteristics and a soliton-like
  behavior

abstract:
  This paper reports a downscaled water tunnel experiment to study the temporal
characteristics of a double dune interaction system and the new pattern of dune
interaction when the initial mass ratio of the two dunes is large. These topics
are useful for a comprehensive understanding of the dune interaction system but
were rarely covered before. The turnover time scale under dune interaction is
defined, and its time averaged value is found to have a nonmonotonic
relationship with the initial mass ratio. A nonmonotonic relationship is also
found between the convexity of the downstream dune tip and the initial mass
ratio. The stationary points of the two nonmonotonic curves above correspond to
the same dune interaction pattern named 'exchange-chasing', which is considered
indispensable in the classification map of dune interactions. The upstream dune
acts as an energy transmitter between fluid flow and the downstream dune. A
soliton-like behavior occurs when the downstream dune enlarges, where a small
dune is detached from the downstream dune tip and gets passed by the upstream
dune approximately without mass exchange. The activity of such temporary
soliton is found to be negatively related with the initial dune spacing and
positively related with the initial mass ratio.


title:
Bifurcation of limit cycles in piecewise quadratic differential systems
  with an invariant straight line

abstract:
  We solve the center-focus problem in a class of piecewise quadratic
polynomial differential systems with an invariant straight line. The separation
curve is also a straight line which is not invariant. We provide families
having at the origin a weak-foci of maximal order. In the continuous class, the
cyclicity problem is also solved, being $3$ such maximal number. Moreover, for
the discontinuous class but without sliding segment, we prove the existence of
$7$ limit cycles of small amplitude.


title: Discoveries and Timing of Pulsars in NGC 6440

abstract:
  Using the MeerKAT radio telescope, a series of observations have been
conducted to time the known pulsars and search for new pulsars in the globular
cluster NGC 6440. As a result, two pulsars have been discovered, NGC 6440G and
NGC 6440H, one of which is isolated and the other a non-eclipsing (at
frequencies above 962 MHz) "Black Widow", with a very low mass companion
(M$_{\rm c}$ > 0.006 M$_{\odot}$). It joins the other binary pulsars discovered
so far in this cluster which all have low companion masses (M$_{\rm c}$ < 0.30
M$_{\odot}$). We present the results of long-term timing solutions obtained
using data from both Green Bank and MeerKAT telescopes for these two new
pulsars and an analysis of the pulsars NGC 6440C and NGC 6440D. For the
isolated pulsar NGC 6440C, we searched for planets using a Markov Chain Monte
Carlo technique. We find evidence for significant unmodelled variations but
they cannot be well modelled as planets nor as part of a power-law red-noise
process. Studies of the eclipses of the "Redback" pulsar NGC 6440D at two
different frequency bands reveal a frequency dependence with longer and
asymmetric eclipses at lower frequencies (962-1283 MHz).


title:
A quantum learning approach based on Hidden Markov Models for failure
  scenarios generation

abstract:
  Finding the failure scenarios of a system is a very complex problem in the
field of Probabilistic Safety Assessment (PSA). In order to solve this problem
we will use the Hidden Quantum Markov Models (HQMMs) to create a generative
model. Therefore, in this paper, we will study and compare the results of HQMMs
and classical Hidden Markov Models HMM on a real datasets generated from real
small systems in the field of PSA. As a quality metric we will use Description
accuracy DA and we will show that the quantum approach gives better results
compared with the classical approach, and we will give a strategy to identify
the probable and no-probable failure scenarios of a system.


title:
Speech and the n-Back task as a lens into depression. How combining both
  may allow us to isolate different core symptoms of depression

abstract:
  Embedded in any speech signal is a rich combination of cognitive,
neuromuscular and physiological information. This richness makes speech a
powerful signal in relation to a range of different health conditions,
including major depressive disorders (MDD). One pivotal issue in
speech-depression research is the assumption that depressive severity is the
dominant measurable effect. However, given the heterogeneous clinical profile
of MDD, it may actually be the case that speech alterations are more strongly
associated with subsets of key depression symptoms. This paper presents strong
evidence in support of this argument. First, we present a novel large,
cross-sectional, multi-modal dataset collected at Thymia. We then present a set
of machine learning experiments that demonstrate that combining speech with
features from an n-Back working memory assessment improves classifier
performance when predicting the popular eight-item Patient Health Questionnaire
depression scale (PHQ-8). Finally, we present a set of experiments that
highlight the association between different speech and n-Back markers at the
PHQ-8 item level. Specifically, we observe that somatic and psychomotor
symptoms are more strongly associated with n-Back performance scores, whilst
the other items: anhedonia, depressed mood, change in appetite, feelings of
worthlessness and trouble concentrating are more strongly associated with
speech changes.


title: Investigating Top-$k$ White-Box and Transferable Black-box Attack

abstract:
  Existing works have identified the limitation of top-$1$ attack success rate
(ASR) as a metric to evaluate the attack strength but exclusively investigated
it in the white-box setting, while our work extends it to a more practical
black-box setting: transferable attack. It is widely reported that stronger
I-FGSM transfers worse than simple FGSM, leading to a popular belief that
transferability is at odds with the white-box attack strength. Our work
challenges this belief with empirical finding that stronger attack actually
transfers better for the general top-$k$ ASR indicated by the interest class
rank (ICR) after attack. For increasing the attack strength, with an intuitive
interpretation of the logit gradient from the geometric perspective, we
identify that the weakness of the commonly used losses lie in prioritizing the
speed to fool the network instead of maximizing its strength. To this end, we
propose a new normalized CE loss that guides the logit to be updated in the
direction of implicitly maximizing its rank distance from the ground-truth
class. Extensive results in various settings have verified that our proposed
new loss is simple yet effective for top-$k$ attack. Code is available at:
\url{https://bit.ly/3uCiomP}


title:
Gravitational Waves in ECSK theory: Robustness of mergers as standard
  sirens and nonvanishing torsion

abstract:
  The amplitude propagation of gravitational waves in an
Einstein-Cartan-Sciamma-Kibble (ECSK) theory is studied by assuming a dark
matter spin tensor sourcing for spacetime torsion at cosmological scales. The
analysis focuses on a weak-torsion regime, such that gravitational wave
emission, at leading and subleading orders, does not deviate from standard
General Relativity. We show that, in principle, the background torsion induced
by an eventual dark matter spin component could lead to an anomalous dampening
or amplification of the gravitational wave amplitude, after going across a long
cosmological distance. The importance of this torsion-induced anomalous
propagation of amplitude for binary black hole mergers is assessed. For
realistic late-universe astrophysical scenarios, the effect is tiny and falls
below detection thresholds, even for near-future interferometers such as LISA.
To detect this effect may not be impossible, but it is still beyond our
technological capabilities.

categories: gr-qc astro-ph.CO hep-th math-ph math.MP


title:
Atomic-scale origin of the low grain-boundary resistance in perovskite
  solid electrolytes

abstract:
  Oxide solid electrolytes (OSEs) have the potential to achieve improved safety
and energy density for lithium-ion batteries, but their high grain-boundary
(GB) resistance is a general bottleneck. In the most well studied perovskite
OSE, Li3xLa2/3-xTiO3 (LLTO), the ionic conductivity of GBs is about three
orders of magnitude lower than that of the bulk. In contrast, the related
Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75) perovskite exhibits low GB resistance
for reasons yet unknown. Here, we used aberration-corrected scanning
transmission electron microscopy and spectroscopy, along with an active
learning moment tensor potential, to reveal the atomic scale structure and
composition of LSTZ0.75 GBs. Vibrational electron energy loss spectroscopy is
applied for the first time to characterize the otherwise unmeasurable Li
distribution in GBs of LSTZ0.75. We found that Li depletion, which is a major
reason for the low GB ionic conductivity of LLTO, is absent for the GBs of
LSTZ0.75. Instead, the low GB resistivity of LSTZ0.75 is attributed to the
formation of a unique defective cubic perovskite interfacial structure that
contained abundant vacancies. Our study provides insights into the atomic scale
mechanisms of low GB resistivity and sheds light on possible paths for
designing OSEs with high total ionic conductivity.


title:
Machine learning for a finite size correction in periodic coupled
  cluster theory calculations

abstract:
  We introduce a straightforward Gaussian process regression (GPR) model for
the transition structure factor of metal periodic coupled cluster singles and
doubles (CCSD) calculations. This is inspired by the method introduced by Liao
and Gr\"uneis for interpolating over the transition structure factor to obtain
a finite size correction for CCSD [J. Chem. Phys. 145, 141102 (2016)], and by
our own prior work using the transition structure factor to efficiently
converge CCSD for metals to the thermodynamic limit [Nat. Comput. Sci. 1, 801
(2021)]. In our CCSD-FS-GPR method to correct for finite size errors, we fit
the structure factor to a 1D function in the momentum transfer, $G$. We then
integrate over this function by projecting it onto a k-point mesh to obtain
comparisons with extrapolated results. Results are shown for lithium, sodium,
and the uniform electron gas.


title: The Spectrum of Triangle-free Graphs

abstract:
  Denote by $q_n(G)$ the smallest eigenvalue of the signless Laplacian matrix
of an $n$-vertex graph $G$. Brandt conjectured in 1997 that for regular
triangle-free graphs $q_n(G) \leq \frac{4n}{25}$. We prove a stronger result:
If $G$ is a triangle-free graph then $q_n(G) \leq \frac{15n}{94}<
\frac{4n}{25}$. Brandt's conjecture is a subproblem of two famous conjectures
of Erd\H{o}s:
  (1) Sparse-Half-Conjecture: Every $n$-vertex triangle-free graph has a subset
of vertices of size $\lceil\frac{n}{2}\rceil$ spanning at most $n^2/50$ edges.
  (2) Every $n$-vertex triangle-free graph can be made bipartite by removing at
most $n^2/25$ edges.
  In our proof we use linear algebraic methods to upper bound $q_n(G)$ by the
ratio between the number of induced paths with 3 and 4 vertices. We give an
upper bound on this ratio via the method of flag algebras.


title: Perceptive, non-linear Speech Processing and Spiking Neural Networks

abstract:
  Source separation and speech recognition are very difficult in the context of
noisy and corrupted speech. Most conventional techniques need huge databases to
estimate speech (or noise) density probabilities to perform separation or
recognition. We discuss the potential of perceptive speech analysis and
processing in combination with biologically plausible neural network
processors. We illustrate the potential of such non-linear processing of speech
on a source separation system inspired by an Auditory Scene Analysis paradigm.
We also discuss a potential application in speech recognition.


title:
Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets
  and Morphological Processing

abstract:
  Automatic teeth segmentation in panoramic x-ray images is an important
research subject of the image analysis in dentistry. In this study, we propose
a post-processing stage to obtain a segmentation map in which the objects in
the image are separated, and apply this technique to tooth instance
segmentation with U-Net network. The post-processing consists of grayscale
morphological and filtering operations, which are applied to the sigmoid output
of the network before binarization. A dice overlap score of 95.4 - 0.3% is
obtained in overall teeth segmentation. The proposed post-processing stages
reduce the mean error of tooth count to 6.15%, whereas the error without
post-processing is 26.81%. The performances of both segmentation and tooth
counting are the highest in the literature, to our knowledge. Moreover, this is
achieved by using a relatively small training dataset, which consists of 105
images. Although the aim in this study is to segment tooth instances, the
presented method is applicable to similar problems in other domains, such as
separating the cell instances


title:
Iterative Reconstruction of the Electron Density and Effective Atomic
  Number using a Non-Linear Forward Model

abstract:
  For material identification, characterization, and quantification, it is
useful to estimate system-independent material properties that do not depend on
the detailed specifications of the X-ray computed tomography (CT) system such
as spectral response. System independent rho-e and Z-e (SIRZ) refers to a suite
of methods for estimating the system independent material properties of
electron density, rho-e, and effective atomic number, Z-e, of an object scanned
using dual-energy X-ray CT (DECT). The current state-of-the-art approach,
SIRZ-2, makes certain approximations that lead to inaccurate estimates for
large atomic numbered (Z-e) materials. In this paper, we present an extension,
SIRZ-3, which iteratively reconstructs the unknown rho-e and Z-e while avoiding
the limiting approximations made by SIRZ-2. Unlike SIRZ-2, this allows SIRZ-3
to accurately reconstruct rho-e and Z-e even at large values of Z-e. SIRZ-3
relies on the use of a new non-linear differentiable forward measurement model
that expresses the DECT measurement data as a direct analytical function of
rho-e and Z-e. Leveraging this new forward model, we use an iterative
optimization algorithm to reconstruct (or solve for) rho-e and Z-e directly
from the DECT data. Compared to SIRZ-2, we show that the magnitude of
performance improvement using SIRZ-3 increases with increasing values for Z-e.


title:
TransGeo: Transformer Is All You Need for Cross-view Image
  Geo-localization

abstract:
  The dominant CNN-based methods for cross-view image geo-localization rely on
polar transform and fail to model global correlation. We propose a pure
transformer-based approach (TransGeo) to address these limitations from a
different perspective. TransGeo takes full advantage of the strengths of
transformer related to global information modeling and explicit position
information encoding. We further leverage the flexibility of transformer input
and propose an attention-guided non-uniform cropping method, so that
uninformative image patches are removed with negligible drop on performance to
reduce computation cost. The saved computation can be reallocated to increase
resolution only for informative patches, resulting in performance improvement
with no additional computation cost. This "attend and zoom-in" strategy is
highly similar to human behavior when observing images. Remarkably, TransGeo
achieves state-of-the-art results on both urban and rural datasets, with
significantly less computation cost than CNN-based methods. It does not rely on
polar transform and infers faster than CNN-based methods. Code is available at
https://github.com/Jeff-Zilence/TransGeo2022.


title: Readout for Calorimetry at Future Colliders: A Snowmass 2021 White Paper

abstract:
  Calorimeters will provide critical measurements at future collider detectors.
As the traditional challenge of high dynamic range, high precision, and high
readout rates for signal amplitudes is compounded by increasing granularity and
precision timing the readout systems will become increasingly complex. This
white paper reviews the challenges and opportunities in calorimeter readout at
future collider detectors.


title: Decidability bounds for Presburger arithmetic extended by sine

abstract:
  We consider Presburger arithmetic extended by the sine function, call this
extension sine-Presburger arithmetic ($\sin$-PA), and systematically study
decision problems for sets of sentences in $\sin$-PA. In particular, we detail
a decision algorithm for existential $\sin$-PA sentences under assumption of
Schanuel's conjecture. This procedure reduces decisions to the theory of the
ordered additive group of real numbers extended by sine, which is decidable
under Schanuel's conjecture. On the other hand, we prove that four alternating
quantifier blocks suffice for undecidability of $\sin$-PA sentences. To do so,
we explicitly interpret the weak monadic second-order theory of the grid, which
is undecidable, in $\sin$-PA.


title:
Distributed Stochastic Nash Equilibrium Learning in Locally Coupled
  Network Games with Unknown Parameters

abstract:
  In stochastic Nash equilibrium problems (SNEPs), it is natural for players to
be uncertain about their complex environments and have multi-dimensional
unknown parameters in their models. Among various SNEPs, this paper focuses on
locally coupled network games where the objective of each rational player is
subject to the aggregate influence of its neighbors. We propose a distributed
learning algorithm based on the proximal-point iteration and ordinary
least-square estimator, where each player repeatedly updates the local
estimates of neighboring decisions, makes its augmented best-response decisions
given the current estimated parameters, receives the realized objective values,
and learns the unknown parameters. Leveraging the Robbins-Siegmund theorem and
the law of large deviations for M-estimators, we establish the almost sure
convergence of the proposed algorithm to solutions of SNEPs when the updating
step sizes decay at a proper rate.


title: Higher-order magnetohydrodynamic numerics

abstract:
  In this chapter, we aim at presenting the basic techniques necessary to go
beyond the widely accepted paradigm of second-order numerics. We specifically
focus on finite-volume schemes for hyperbolic conservation laws occuring in
fluid approximations such as the equations of ideal magnetohydrodynamics or the
Euler equations of gas dynamics. For the sake of clarity, a simple fourth-order
ideal magnetohydrodynamic (MHD) solver which allows to simulate strongly
shocked systems serves as an instructive example. Issues that only or mainly
arise in the world of higher-order numerics are given specific focus.
Alternative algorithms as well as refinements and improvements are dicussed and
are referenced to in the literature. As an example of application, some results
on decaying compressible turbulence are presented.

categories: math.NA cs.NA physics.comp-ph physics.flu-dyn physics.plasm-ph physics.space-ph


title: Dynamic Multimodal Fusion

abstract:
  Deep multimodal learning has achieved great progress in recent years.
However, current fusion approaches are static in nature, i.e., they process and
fuse multimodal inputs with identical computation, without accounting for
diverse computational demands of different multimodal data. In this work, we
propose dynamic multimodal fusion (DynMM), a new approach that adaptively fuses
multimodal data and generates data-dependent forward paths during inference.
DynMM can reduce redundant computations for "easy" multimodal inputs (that can
be predicted correctly using only one modality or simple fusion techniques) and
retain representation power for "hard" samples by adopting all modalities and
complex fusion operations for prediction. Results on various multimodal tasks
demonstrate the efficiency and wide applicability of our approach. For
instance, DynMM can reduce the computation cost by 46.5% with a negligible
accuracy loss on CMU-MOSEI sentiment analysis. For RGB-D semantic segmentation
on NYU Depth data, DynMM achieves a +0.7% mIoU improvement with over 21%
reductions for the depth encoder when compared with strong baselines. We
believe this opens a novel direction towards dynamic multimodal network design,
with applications to a wide range of multimodal tasks.


title: Scalable Whitebox Attacks on Tree-based Models

abstract:
  Adversarial robustness is one of the essential safety criteria for
guaranteeing the reliability of machine learning models. While various
adversarial robustness testing approaches were introduced in the last decade,
we note that most of them are incompatible with non-differentiable models such
as tree ensembles. Since tree ensembles are widely used in industry, this
reveals a crucial gap between adversarial robustness research and practical
applications. This paper proposes a novel whitebox adversarial robustness
testing approach for tree ensemble models. Concretely, the proposed approach
smooths the tree ensembles through temperature controlled sigmoid functions,
which enables gradient descent-based adversarial attacks. By leveraging
sampling and the log-derivative trick, the proposed approach can scale up to
testing tasks that were previously unmanageable. We compare the approach
against both random perturbations and blackbox approaches on multiple public
datasets (and corresponding models). Our results show that the proposed method
can 1) successfully reveal the adversarial vulnerability of tree ensemble
models without causing computational pressure for testing and 2) flexibly
balance the search performance and time complexity to meet various testing
criteria.


title:
Observing Particle Energization above the Nyquist Frequency: An
  Application of the Field-Particle Correlation Technique

abstract:
  The field-particle correlation technique utilizes single-point measurements
to uncover signatures of various particle energization mechanisms in turbulent
space plasmas. The signature of Landau damping by electrons has been found in
both simulations and observations from Earth's magnetosheath using this
technique, but instrumental limitations of spacecraft sampling rates present a
challenge to discovering the full extent of the presence of Landau damping in
the solar wind. Theory predicts that field-particle correlations can recover
velocity-space energization signatures even from data that is undersampled with
respect to the characteristic frequencies at which the wave damping occurs. To
test this hypothesis, we perform a high-resoluation gyrokinetic simulation of
space plasma turbulence, confirm that it contains signatures of electron Landau
damping, and then systematically reduce the time resolution of the data to
identify the point at which the signatures become impossible to recover. We
find results in support of our theoretical prediction and look for a rule of
thumb that can be compared with the measurement capabilities of spacecraft
missions to inform the process of applying field-particle correlations to low
time resolution data.


title: Refinements of Beck-type partition identities

abstract:
  Franklin's identity generalizes Euler's identity and states that the number
of partitions of $n$ with $j$ different parts divisible by $r$ equals the
number of partitions of $n$ with $j$ repeated parts. In this article, we give a
refinement of Franklin's identity when $j=1$. We prove Franklin's identity when
$j=1$, $r=2$ for partitions with fixed perimeter, i.e., fixed largest hook. We
also derive a Beck-type identity for partitions with fixed perimeter: the
excess in the number of parts in all partitions into odd parts with perimeter
$M$ over the number of parts in all partitions into distinct parts with
perimeter $M$ equals the number of partitions with perimeter $M$ whose set of
even parts is a singleton. We provide analytic and combinatorial proofs of our
results.


title: A Survey of Robust 3D Object Detection Methods in Point Clouds

abstract:
  The purpose of this work is to review the state-of-the-art LiDAR-based 3D
object detection methods, datasets, and challenges. We describe novel data
augmentation methods, sampling strategies, activation functions, attention
mechanisms, and regularization methods. Furthermore, we list recently
introduced normalization methods, learning rate schedules and loss functions.
Moreover, we also cover advantages and limitations of 10 novel autonomous
driving datasets. We evaluate novel 3D object detectors on the KITTI, nuScenes,
and Waymo dataset and show their accuracy, speed, and robustness. Finally, we
mention the current challenges in 3D object detection in LiDAR point clouds and
list some open issues.


title: Comments on Comments: Where Code Review and Documentation Meet

abstract:
  A central function of code review is to increase understanding; helping
reviewers understand a code change aids in knowledge transfer and finding bugs.
Comments in code largely serve a similar purpose, helping future readers
understand the program. It is thus natural to study what happens when these two
forms of understanding collide. We ask: what documentation-related comments do
reviewers make and how do they affect understanding of the contribution? We
analyze ca.700K review comments on 2,000 (Java and Python) GitHub projects, and
propose several filters to identify which comments are likely to be either in
response to a change in documentation and/or call for such a change. We
identify 65K such cases. We next develop a taxonomy of the reviewer intents
behind such "comments on comments". We find that achieving a shared
understanding of the code is key: reviewer comments most often focused on
clarification, followed by pointing out issues to fix, such as typos and
outdated comments. Curiously, clarifying comments were frequently suggested
(often verbatim) by the reviewer, indicating a desire to persist their
understanding acquired during code review. We conclude with a discussion of
implications of our comments-on-comments dataset for research on improving code
review, including the potential benefits for automating code review.


title: QUIP: Query-driven Missing Value Imputation

abstract:
  Missing values widely exist in real-world data sets, and failure to clean the
missing data may result in the poor quality of answers to queries.
\yiming{Traditionally, missing value imputation has been studied as an offline
process as part of preparing data for analysis.} This paper studies query-time
missing value imputation and proposes QUIP, which only imputes minimal missing
values to answer the query. Specifically, by taking a reasonable good query
plan as input, QUIP tries to minimize the missing value imputation cost and
query processing overhead. QUIP proposes a new implementation of outer join to
preserve missing values in query processing and a bloom filter based index
structure to optimize the space and runtime overhead. QUIP also designs a
cost-based decision function to automatically guide each operator to impute
missing values now or delay imputations. Efficient optimizations are proposed
to speed-up aggregate operations in QUIP, such as MAX/MIN operator. Extensive
experiments on both real and synthetic data sets demonstrates the effectiveness
and efficiency of QUIP, which outperforms the state-of-the-art ImputeDB by 2 to
10 times on different query sets and data sets, and achieves the
order-of-magnitudes improvement over the offline approach.


title: Rigidity and Parallelism in the spacetime

abstract:
  The effect of the linear-fractional transformations on the parallel lines in
the spacetime has been studied. Fock-Lorentz transformations maps a line to a
line, from which one can obtain the combinations rule for the velocities in the
Fock-Lorentz transformations. Rigidity is defined as a consequences of holding
parallelism under the transformations. The Fock-Lorentz transformations do not
preserve rigidity, which leads to some novel results such as growing distances
alongside with advancing time. Also, it is shown that the time coordinates of
events will come closer to each other in the transformed coordinates by going
back in time


title:
Does This Apply to Me? An Empirical Study of Technical Context in Stack
  Overflow

abstract:
  Stack Overflow has become an essential technical resource for developers.
However, given the vast amount of knowledge available on Stack Overflow,
finding the right information that is relevant for a given task is still
challenging, especially when a developer is looking for a solution that applies
to their specific requirements or technology stack. Clearly marking answers
with their technical context, i.e., the information that characterizes the
technologies and assumptions needed for this answer, is potentially one way to
improve navigation. However, there is no information about how often such
context is mentioned, and what kind of information it might offer. In this
paper, we conduct an empirical study to understand the occurrence of technical
context in Stack Overflow answers and comments, using tags as a proxy for
technical context. We specifically focus on additional context, where
answers/comments mention information that is not already discussed in the
question. Our results show that nearly half of our studied threads contain at
least one additional context. We find that almost 50% of the additional context
are either a library/framework, a programming language, a tool/application, an
API, or a database. Overall, our findings show the promise of using additional
context as navigational cues.


title:
Estimation and inference for high-dimensional nonparametric additive
  instrumental-variables regression

abstract:
  The method of instrumental variables provides a fundamental and practical
tool for causal inference in many empirical studies where unmeasured
confounding between the treatments and the outcome is present. Modern data such
as the genetical genomics data from these studies are often high-dimensional.
The high-dimensional linear instrumental-variables regression has been
considered in the literature due to its simplicity albeit a true nonlinear
relationship may exist. We propose a more data-driven approach by considering
the nonparametric additive models between the instruments and the treatments
while keeping a linear model between the treatments and the outcome so that the
coefficients therein can directly bear causal interpretation. We provide a
two-stage framework for estimation and inference under this more general setup.
The group lasso regularization is first employed to select optimal instruments
from the high-dimensional additive models, and the outcome variable is then
regressed on the fitted values from the additive models to identify and
estimate important treatment effects. We provide non-asymptotic analysis of the
estimation error of the proposed estimator. A debiasing procedure is further
employed to yield valid inference. Extensive numerical experiments show that
our method can rival or outperform existing approaches in the literature. We
finally analyze the mouse obesity data and discuss new findings from our
method.


title:
Gallium Oxide Heterojunction Diodes for Improved High-Temperature
  Performance

abstract:
  ${\beta}$-Ga${_2}$O${_3}$ based semiconductor devices are expected to have
significantly improved high-power and high-temperature performance due to its
ultra-wide bandgap of close to 5 eV. However, the high-temperature operation of
these ultra-wide-bandgap devices is usually limited by the relatively low 1-2
eV built-in potential at the Schottky barrier with most high-work-function
metals. Here, we report heterojunction p-NiO/n-${\beta}$-Ga${_2}$O${_3}$ diodes
fabrication and optimization for high-temperature device applications,
demonstrating a current rectification ratio of more than 10${^6}$ at
410{\deg}C. The NiO heterojunction diode can achieve higher turn-on voltage and
lower reverse leakage current compared to the Ni-based Schottky diode
fabricated on the same single crystal ${\beta}$-Ga${_2}$O${_3}$ substrate,
despite charge transport dominated by interfacial recombination. Electrical
characterization and device modeling show that these advantages are due to a
higher built-in potential and additional band offset. These results suggest
that heterojunction p-n diodes based on ${\beta}$-Ga${_2}$O${_3}$ can
significantly improve high-temperature electronic device and sensor
performance.


title:
Catapulting of topological defects through elasticity bands in active
  nematics

abstract:
  Active materials are those in which individual, uncoordinated local stresses
drive the material out of equilibrium on a global scale. Examples of such
assemblies can be seen across scales from schools of fish to the cellular
cytoskeleton and underpin many important biological processes. Synthetic
experiments that recapitulate the essential features of such active systems
have been the object of study for decades as their simple rules allow us to
elucidate the physical underpinnings of collective motion. One system of
particular interest has been active nematic liquid crystals (LCs). Because of
their well understood passive physics, LCs provide a rich platform to
interrogate the effects of active stress. The flows and steady state structures
that emerge in an active LCs have been understood to result from a competition
between nematic elasticity and the local activity. However most investigations
of such phenomena consider only the magnitude of the elastic resistance and not
its peculiarities. Here we investigate a nematic liquid crystal and selectively
change the ratio of the material's splay and bend elasticities. We show that
increases in the nematic's bend elasticity specifically drives the material
into an exotic steady state where elongated regions of acute bend distortion or
"elasticity bands" dominate the structure and dynamics. We show that these
bands strongly influence defect dynamics, including the rapid motion or
"catapulting" along the disintegration of one of these bands thus converting
bend distortion into defect transport. Thus, we report a novel dynamical state
resultant from the competition between nematic elasticity and active stress.


title: Generalized virtual polytopes and quasitoric manifolds

abstract:
  In this paper we develop a theory of volume polynomials of generalized
virtual polytopes based on the study of topology of affine subspace
arrangements in a real Euclidean space. We apply this theory to obtain a
topological version of the BKK Theorem, the Stanley-Reisner and
Pukhlikov-Khovanskii type descriptions for cohomology rings of generalized
quasitoric manifolds.


title:
An Inverse Spectral Problem for Hankel Operators: the Case of Compact
  Operators with Non-Simple Singular Values

abstract:
  In the author's previous paper, we have discussed what type of spectral data
can uniquely determine a compact Hankel operator with simple singular values.
Especially for the compact case, we have shown that given two sequences $\{
\lambda_n \}_{n=1}^{\infty}$, $\{ \mu_k \}_{k=1}^{\infty}$ satisfying an
intertwining relationship
  \begin{align*}
  \lambda_1 > \mu_1 > ...> \lambda_n > \mu_n ...\to 0,
  \end{align*} then there exists a self-adjoint Hankel operator $\Gamma$, such
that $\Gamma$ and $\Gamma_1 := \Gamma S$ have simple eigenvalues as $\{
\lambda_n \}_{n=1}^{\infty}$, $\{ \mu_k \}_{k=1}^{\infty}$ respectively. In
this paper, we consider a general Hankel operator $\Gamma$ as a complex
symmetric operator. Especifically, we have shown that a compact Hankel operator
$\Gamma$ with non-simple singular values can be uniquely determined by two
sequences of complex numbers satisfying an intertwining relation, and two
sequences of discrete probability measure. Here the two sequences of complex
numbers give all the non-zero singular values of $\Gamma$ and $\Gamma_1$; and
the two sequences of probability measures is the scalar spectral measure of
some unitary operators on some eigenspace of $|\Gamma|$ and $|\Gamma_1|$.


title:
Giant terahertz pulling force within an evanescent field propelled by
  wave coupling into radiation and bound modes

abstract:
  Manipulation of subwavelength objects by engineering the electromagnetic
waves in the environment medium is pivotal for several particle handling
techniques. In this letter, we theoretically demonstrate the possibility of
engineering a compact and tunable plasmon-based terahertz tweezer using a
graphene monolayer that is deposited on a high-index substrate. Under
total-internal-reflection illumination, such device is shown to be capable of
inducing an enhanced rotating polarizability thus enabling directional
near-field coupling into the graphene plasmon mode and radiation modes in the
substrate. As a result of the total momentum conservation, the net force
exerted on the particle points in a direction opposite to the pushing force of
the exciting evanescent field. Our results can contribute to novel realizations
of photonic devices based on polarization dependent interactions between
nanoparticles and electromagnetic mode fields.


title:
Visual-Tactile Multimodality for Following Deformable Linear Objects
  Using Reinforcement Learning

abstract:
  Manipulation of deformable objects is a challenging task for a robot. It will
be problematic to use a single sensory input to track the behaviour of such
objects: vision can be subjected to occlusions, whereas tactile inputs cannot
capture the global information that is useful for the task. In this paper, we
study the problem of using vision and tactile inputs together to complete the
task of following deformable linear objects, for the first time. We create a
Reinforcement Learning agent using different sensing modalities and investigate
how its behaviour can be boosted using visual-tactile fusion, compared to using
a single sensing modality. To this end, we developed a benchmark in simulation
for manipulating the deformable linear objects using multimodal sensing inputs.
The policy of the agent uses distilled information, e.g., the pose of the
object in both visual and tactile perspectives, instead of the raw sensing
signals, so that it can be directly transferred to real environments. In this
way, we disentangle the perception system and the learned control policy. Our
extensive experiments show that the use of both vision and tactile inputs,
together with proprioception, allows the agent to complete the task in up to
92% of cases, compared to 77% when only one of the signals is given. Our
results can provide valuable insights for the future design of tactile sensors
and for deformable objects manipulation.


title:
Designing for emotion regulation interventions: an agenda for HCI theory
  and research

abstract:
  There is a growing interest in HCI to envision, design, and evaluate
technology-enabled interventions that support users' emotion regulation. This
interest stems in part from increased recognition that the ability to regulate
emotions is critical to mental health, and that a lack of effective emotion
regulation is a transdiagnostic factor for mental illness. However, the
potential to combine innovative HCI designs with the theoretical grounding and
state-of-art interventions from psychology has yet to be fully realised. In
this paper, we synthesise HCI work on emotion regulation interventions and
propose a three-part framework to guide technology designers in making: (i)
theory-informed decisions about intervention targets; (ii) strategic decisions
regarding the technology-enabled intervention mechanisms to be included in the
system; and (iii) practical decisions around previous implementations of the
selected intervention components. We show how this framework can both
systematise HCI work to date and suggest a research agenda for future work.


title:
Dynamical analysis of the covarying coupling constants in scalar-tensor
  gravity

abstract:
  We consider a scalar-tensor theory of gravity wherein the scalar field $\phi$
includes the gravitational coupling $G$ and the speed of light $c$, both of
which are allowed to be functions of the spacetime coordinates. The
cosmological coupling $\Lambda$ turns out to be an emergent phenomenon from a
possible behavior of $\phi$. The dynamics of $\phi$ is analysed in the phase
space where we describe gravity from a precisely specified form of the action.
For reasonable assumptions on the potential $V\left(\phi\right)$ and the
matter-energy content, we show that an attractor point can be quickly reached
within a period where the Hubble parameter becomes essentially constant. When
the system gets to the stable point, the dynamics of $\phi$ ceases and the
constraint $\dot{G}/G=\sigma\left(\dot{c}/c\right)$ with $\sigma=3$ must be
satisfied for the rest of the cosmic evolution. Our findings thus provide a
foundation for the phenomenological model used recently to interpret
cosmological and astrophysical data. It is shown that cosmic evolution after
the equilibrium accommodates radiation- and matter-dominated eras that
naturally evolve to an accelerated expansion typical of dark energy. The
generalized Brans-Dicke approach developed in this paper results in equations
that can be readily tested with observational data.


title: Joint Power and Subcarrier Allocation in Multi-Cell Multi-Carrier NOMA

abstract:
  Non-orthogonal multiple access (NOMA) is a technology proposed for next
generation cellular networks because of its high spectral efficiency and
enhanced user connectivity. However, in the literature the optimal joint power
and sub-carrier allocation for NOMA has been proposed for single cell only.
Consequently, a global optimal algorithm for the joint power and sub-carrier
allocation for NOMA system in multi-cell scenario is still an open problem. In
this work, we propose a polyblock optimization based algorithm for obtaining a
global optimal solution. It has reduced complexity due to a necessary and
sufficient condition for feasible successive interference cancellation (SIC).
Besides, we can adjust its optimization approximation parameter to serve as
benchmark solution or to offer suitable practical solution for multi-cell
multi-carrier NOMA systems. Numerical studies have shown its effectiveness.


title:
An MPSoC-based on-line Edge Infrastructure for Embedded Neuromorphic
  Robotic Controllers

abstract:
  In this work, an all-in-one neuromorphic controller system with reduced
latency and power consumption for a robotic arm is presented. Biological muscle
movement consists of stretching and shrinking fibres via spike-commanded
signals that come from motor neurons, which in turn are connected to a central
pattern generator neural structure. In addition, biological systems are able to
respond to diverse stimuli rather fast and efficiently, and this is based on
the way information is coded within neural processes. As opposed to
human-created encoding systems, neural ones use neurons and spikes to process
the information and make weighted decisions based on a continuous learning
process. The Event-Driven Scorbot platform (ED-Scorbot) consists of a 6 Degrees
of Freedom (DoF) robotic arm whose controller implements a Spiking
Proportional-Integrative- Derivative algorithm, mimicking in this way the
previously commented biological systems. In this paper, we present an
infrastructure upgrade to the ED-Scorbot platform, replacing the controller
hardware, which was comprised of two Spartan Field Programmable Gate Arrays
(FPGAs) and a barebone computer, with an edge device, the Xilinx Zynq-7000 SoC
(System on Chip) which reduces the response time, power consumption and overall
complexity.


title: Synthesis of Stabilizing Recurrent Equilibrium Network Controllers

abstract:
  We propose a parameterization of a nonlinear dynamic controller based on the
recurrent equilibrium network, a generalization of the recurrent neural
network. We derive constraints on the parameterization under which the
controller guarantees exponential stability of a partially observed dynamical
system with sector-bounded nonlinearities. Finally, we present a method to
synthesize this controller using projected policy gradient methods to maximize
a reward function with arbitrary structure. The projection step involves the
solution of convex optimization problems. We demonstrate the proposed method
with simulated examples of controlling nonlinear plants, including plants
modeled with neural networks.


title: Geometrising the Micro-Cosmos on a Supermanifold

abstract:
  For more than half a century, covariant and differential geometric methods
have been playing a central role in the development of Quantum Field Theory
(QFT). After a brief historic overview of the major scientific achievements
using these methods, we will focus on the covariant and differential geometric
formalism originally proposed by Vilkovisky and DeWitt (VDW). We discuss recent
developments made in addressing the uniqueness of the path-integral measure of
the VDW effective action, and so address the problem of quantum frame
dependence in cosmologically relevant scalar-tensor theories beyond the
classical approximation. Particular attention will be drawn to a long-standing
problem concerning the obstacles that the VDW formalism was facing from its
original conception in describing generic QFTs that include fermions. We show
how in addition to bosons the VDW effective action can be extended to
supermanifolds to include fermions. The so-extended formulation appears to be
very promising for a complete geometrisation of realistic theories of
micro-cosmos, such as the Standard Model and its gravitational sector.


title: Co-accretion + giant impact origin of the Uranus system: Tilting Impact

abstract:
  The origin of the Uranian satellite system remains uncertain. The four major
satellites have nearly circular, co-planar orbits and the ratio of the
satellite system and planetary mass resembles Jupiter's satellite system,
suggesting the Uranian system was similarly formed within a disk produced by
gas co-accretion. However, Uranus is a retrograde rotator with a high
obliquity. The satellites orbit in its highly tilted equatorial plane in the
same sense as the planet's retrograde rotation, a configuration that cannot be
explained by co-accretion alone. In this work we investigate the first stages
of the co-accretion + giant impact scenario proposed by Morbidelli et al.
(2012) for the origin of the Uranian system. In this model, a satellite system
formed by co-accretion is destabilized by a giant impact that tilts the planet.
The primordial satellites collide and disrupt, creating an outer debris disk
that can re-orient to the planet's new equatorial plane and accrete into
Uranus' 4 major satellites. The needed reorientation out to distances
comparable to outermost Oberon requires that the impact creates an inner disk
with $\ge 1\%$ of Uranus' mass. We here simulate giant impacts that
appropriately tilt the planet and leave the system with an angular momentum
comparable to that of the current system. We find that such impacts do not
produce inner debris disks massive enough to realign the outer debris disk to
the post-impact equatorial plane. Although our results are inconsistent with
the apparent requirements of a co-accretion + giant impact model, we suggest
alternatives that merit further exploration.


title: GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing

abstract:
  Compositing-aware object search aims to find the most compatible objects for
compositing given a background image and a query bounding box. Previous works
focus on learning compatibility between the foreground object and background,
but fail to learn other important factors from large-scale data, i.e. geometry
and lighting. To move a step further, this paper proposes GALA
(Geometry-and-Lighting-Aware), a generic foreground object search method with
discriminative modeling on geometry and lighting compatibility for open-world
image compositing. Remarkably, it achieves state-of-the-art results on the CAIS
dataset and generalizes well on large-scale open-world datasets, i.e. Pixabay
and Open Images. In addition, our method can effectively handle non-box
scenarios, where users only provide background images without any input
bounding box. A web demo (see supplementary materials) is built to showcase
applications of the proposed method for compositing-aware search and automatic
location/scale prediction for the foreground object.


title: On site occupancy models with heterogeneity

abstract:
  Site occupancy models are routinely used to estimate the probability of
species presence from either abundance or presence-absence data collected
across sites with repeated sampling occasions. In the last two decades, a broad
class of occupancy models has been developed, but little attention has been
given to examining the effects of heterogeneity in parameter estimation. This
study focuses on occupancy models where heterogeneity is present in detection
intensity and the presence probability. We show that the presence probability
will be underestimated if detection heterogeneity is ignored. On the other
hand, the behavior is different if heterogeneity in the presence probability is
ignored; notably, an estimate of the average presence probability may be
unbiased or over- or under-estimated depending on the relationship between
detection and presence probabilities. In addition, when heterogeneity in the
detection intensity is related to covariates, we propose a conditional
likelihood approach to estimate the detection intensity parameters. This
alternative method shares an optimal estimating function property and it
ensures robustness against model specification on the presence probability. We
then propose a consistent estimator for the average presence probability,
provided that the detection intensity component model is correctly specified.
We illustrate the bias effects and estimator performance in simulation studies
and real data analysis.


title: Intersection Crossing with Future-Focused Control Barrier Functions

abstract:
  In this paper, we introduce a future-focused control barrier function
(ff-CBF) approach to an unsignaled four-way intersection crossing problem for a
collection of communicating automobiles. Our novel ff-CBF encodes that vehicles
take control actions that avoid predicted collisions over an arbitrarily long
future time horizon, thereby defining a virtual barrier. We then propose a
relaxed-virtual CBF (rv-CBF) that inherits the predictive power of the ff-CBF
while allowing relaxations of the virtual barrier away from the physical
barrier between vehicles. We study the efficacy of the ff-CBF and rv-CBF based
controllers via a series of simulated trials of the intersection scenario and
highlight how the rv-CBF based controller empirically outperforms a benchmark
controller from the literature by improving intersection throughput while
preserving safety and feasibility properties.


title: Perceptual Quality Assessment of UGC Gaming Videos

abstract:
  In recent years, with the vigorous development of the video game industry,
the proportion of gaming videos on major video websites like YouTube has
dramatically increased. However, relatively little research has been done on
the automatic quality prediction of gaming videos, especially on those that
fall in the category of "User-Generated-Content" (UGC). Since current leading
general-purpose Video Quality Assessment (VQA) models do not perform well on
this type of gaming videos, we have created a new VQA model specifically
designed to succeed on UGC gaming videos, which we call the Gaming Video
Quality Predictor (GAME-VQP). GAME-VQP successfully predicts the unique
statistical characteristics of gaming videos by drawing upon features designed
under modified natural scene statistics models, combined with gaming specific
features learned by a Convolution Neural Network. We study the performance of
GAME-VQP on a very recent large UGC gaming video database called
LIVE-YT-Gaming, and find that it both outperforms other mainstream general VQA
models as well as VQA models specifically designed for gaming videos. The new
model will be made public after paper being accepted.