title: Long-term rotational and emission variability of 17 radio pulsars

abstract:
  With the ever-increasing sensitivity and timing baselines of modern radio
telescopes, a growing number of pulsars are being shown to exhibit transitions
in their rotational and radio emission properties. In many of these cases, the
two are correlated with pulsars assuming a unique spin-down rate ($\dot{\nu}$)
for each of their specific emission states. In this work we revisit 17 radio
pulsars previously shown to exhibit spin-down rate variations. Using a Gaussian
process regression (GPR) method to model the timing residuals and the evolution
of the profile shape, we confirm the transitions already observed and reveal
new transitions in 8 years of extended monitoring with greater time resolution
and enhanced observing bandwidth. We confirm that 7 of these sources show
emission-correlated $\dot{\nu}$ transitions ($\Delta \dot{\nu}$) and we
characterise this correlation for one additional pulsar, PSR B1642$-$03. We
demonstrate that GPR is able to reveal extremely subtle profile variations
given sufficient data quality. We also corroborate the dependence of $\Delta
\dot{\nu}$ amplitude on $\dot{\nu}$ and pulsar characteristic age. Linking
$\Delta \dot{\nu}$ to changes in the global magnetospheric charge density
$\Delta \rho$, we speculate that $\dot{\nu}$ transitions associated with large
$\Delta \rho$ values may be exhibiting detectable profile changes with improved
data quality, in cases where they have not previously been observed.


title: nOS-V: Co-Executing HPC Applications Using System-Wide Task Scheduling

abstract:
  Future Exascale systems will feature massive parallelism, many-core
processors and heterogeneous architectures. In this scenario, it is
increasingly difficult for HPC applications to fully and efficiently utilize
the resources in system nodes. Moreover, the increased parallelism exacerbates
the effects of existing inefficiencies in current applications. Research has
shown that co-scheduling applications to share system nodes instead of
executing each application exclusively can increase resource utilization and
efficiency. Nevertheless, the current oversubscription and co-location
techniques to share nodes have several drawbacks which limit their
applicability and make them very application-dependent.
  This paper presents co-execution through system-wide scheduling. Co-execution
is a novel fine-grained technique to execute multiple HPC applications
simultaneously on the same node, outperforming current state-of-the-art
approaches. We implement this technique in nOS-V, a lightweight tasking library
that supports co-execution through system-wide task scheduling. Moreover, nOS-V
can be easily integrated with existing programming models, requiring no changes
to user applications. We showcase how co-execution with nOS-V significantly
reduces schedule makespan for several applications on single node and
distributed environments, outperforming prior node-sharing techniques.


title:
Density Functionals based on the mathematical structure of the
  strong-interaction limit of DFT

abstract:
  While in principle exact, Kohn-Sham density functional theory -- the
workhorse of computational chemistry -- must rely on approximations for the
exchange-correlation functional. Despite staggering successes, present-day
approximations still struggle when the effects of electron-electron correlation
play a prominent role. The limit in which the electronic Coulomb repulsion
completely dominates the exchange-correlation functional offers a well-defined
mathematical framework that provides insight for new approximations able to
deal with strong correlation. In particular, the mathematical structure of this
limit, which is now well-established thanks to its reformulation as an optimal
transport problem, points to the use of very different ingredients (or
features) with respect to the traditional ones used in present approximations.
We focus on strategies to use these new ingredients to build approximations for
computational chemistry and highlight future promising directions.


title:
Constructing dynamic residential energy lifestyles using Latent
  Dirichlet Allocation

abstract:
  The rapid expansion of Advanced Meter Infrastructure (AMI) has dramatically
altered the energy information landscape. However, our ability to use this
information to generate actionable insights about residential electricity
demand remains limited. In this research, we propose and test a new framework
for understanding residential electricity demand by using a dynamic energy
lifestyles approach that is iterative and highly extensible. To obtain energy
lifestyles, we develop a novel approach that applies Latent Dirichlet
Allocation (LDA), a method commonly used for inferring the latent topical
structure of text data, to extract a series of latent household energy
attributes. By doing so, we provide a new perspective on household electricity
consumption where each household is characterized by a mixture of energy
attributes that form the building blocks for identifying a sparse collection of
energy lifestyles. We examine this approach by running experiments on one year
of hourly smart meter data from 60,000 households and we extract six energy
attributes that describe general daily use patterns. We then use clustering
techniques to derive six distinct energy lifestyle profiles from energy
attribute proportions. Our lifestyle approach is also flexible to varying time
interval lengths, and we test our lifestyle approach seasonally (Autumn,
Winter, Spring, and Summer) to track energy lifestyle dynamics within and
across households and find that around 73% of households manifest multiple
lifestyles across a year. These energy lifestyles are then compared to
different energy use characteristics, and we discuss their practical
applications for demand response program design and lifestyle change analysis.


title:
Scintillation Event Localization in Hemi-Ellipsoid Detector for SPECT, a
  simulation study using Geant4 Monte-Carlo

abstract:
  A high sensitivity Cardiac SPECT system using curved crystals with pinhole
collimation was proposed previously (Dey, IEEE TNS 2012, Bhusal et al, Med.
Phys. 2019). Here, we hypothesize that a high curvature hemi-ellipsoid detector
results in measurable differences in light distribution from events at
different depths in the crystal. This was tested by analyzing the scintillation
light in hemi-ellipsoid detector using Monte-Carlo (Geant4) and evaluation of
both the localization error at detector and the back-projected errors in
object-space. We used Geant4 to simulate the propagation of scintillation light
in a monolithic hemi-ellipsoidal CsI crystal. A look-up table (LUT) was created
to map the points inside the crystal to the expected light pattern on the
crystal surface using Geant4. In thirteen zones across the crystal, gamma-rays
were simulated and the resulting scintillator light intensity on the surface
was captured, serving as our experimental interactions. A
Poisson-statistics-based algorithm was developed to limit the search of the
gamma-ray event locations into small regions of the LUT and fine-tuned by
interpolating between selected LUT points by comparing the light distribution
of the gamma interactions and LUT light patterns. The localized events were
individually back-projected to the object mid-plane, and the errors recorded.
Excluding some outliers (up to 2%), the localized errors averaged over all the
zones was 0.71 (+/-0.44) mm with a worse case of 1.36 (+/-0.67) mm at the apex.
When back-projected to the midplane of the object for Cardiac SPECT, the errors
were <1mm and average error was 0.4(+/-0.22) mm, due to the high system
magnification afforded by the detector. Thus, for our high sensitivity system,
we were also able to achieve high resolution, assuming perfect pinhole
collimator resolution recovery for Cardiac SPECT application.


title: Liouville theorems and optimal regularity in elliptic equations

abstract:
  The objective of this paper is the connection between the problem of optimal
regularity among solutions to elliptic divergence equations with measurable
coefficients with the Liouville property at infinity. Initially, we address the
two-dimensional case by proving an Alt-Caffarelli-Friedman type monotonicity
formula for non-negative subsolutions with disjoint supports, which allows to
prove optimal regularity and the Liouville property for multiphase problems. In
the higher-dimensional case we discuss the role of the monotonicity formula in
the characterization of the least growth at infinity and the exponent of
regularity as well. Finally, we establish this connection by combining blow-up
and $G$-convergence argument.


title:
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images
  of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of
  Convolution Neural Network

abstract:
  Two-dimensional (2D) fast spin echo (FSE) techniques play a central role in
the clinical magnetic resonance imaging (MRI) of knee joints. Moreover,
three-dimensional (3D) FSE provides high-isotropic-resolution magnetic
resonance (MR) images of knee joints, but it has a reduced signal-to-noise
ratio compared to 2D FSE. Deep-learning denoising methods are a promising
approach for denoising MR images, but they are often trained using synthetic
noise due to challenges in obtaining true noise distributions for MR images. In
this study, inherent true noise information from 2-NEX acquisition was used to
develop a deep-learning model based on residual learning of convolutional
neural network (CNN), and this model was used to suppress the noise in 3D FSE
MR images of knee joints. The proposed CNN used two-step residual learning over
parallel transporting and residual blocks and was designed to comprehensively
learn real noise features from 2-NEX training data. The results of an ablation
study validated the network design. The new method achieved improved denoising
performance of 3D FSE knee MR images compared with current state-of-the-art
methods, based on the peak signal-to-noise ratio and structural similarity
index measure. The improved image quality after denoising using the new method
was verified by radiological evaluation. A deep CNN using the inherent
spatial-varying noise information in 2-NEX acquisitions was developed. This
method showed promise for clinical MRI assessments of the knee, and has
potential applications for the assessment of other anatomical structures.


title:
High average power ultrafast laser technologies for driving future
  advanced accelerators

abstract:
  Large scale laser facilities are needed to advance the energy frontier in
high energy physics and accelerator physics. Laser plasma accelerators are core
to advanced accelerator concepts aimed at reaching TeV electron electron
colliders. In these facilities, intense laser pulses drive plasmas and are used
to accelerate electrons to high energies in remarkably short distances. A laser
plasma accelerator could in principle reach high energies with an accelerating
length that is 1000 times shorter than in conventional RF based accelerators.
Notionally, laser driven particle beam energies could scale beyond state of the
art conventional accelerators. LPAs have produced multi GeV electron beams in
about 20 cm with relative energy spread of about 2 percent, supported by highly
developed laser technology. This validates key elements of the US DOE strategy
for such accelerators to enable future colliders but extending best results to
date to a TeV collider will require lasers with higher average power. While the
per pulse energies envisioned for laser driven colliders are achievable with
current lasers, low laser repetition rates limit potential collider luminosity.
Applications will require rates of kHz to tens of kHz at Joules of energy and
high efficiency, and a collider would require about 100 such stages, a leap
from current Hz class LPAs. This represents a challenging 1000 fold increase in
laser repetition rates beyond current state of the art. This whitepaper
describes current research and outlook for candidate laser systems as well as
the accompanying broadband and high damage threshold optics needed for driving
future advanced accelerators.


title: Tur\'{a}n numbers and switching

abstract:
  Using a switching operation on tournaments we obtain some new lower bounds on
the Tur\'{a}n number of the $r$-graph on $r+1$ vertices with $3$ edges. For
$r=4$, extremal examples were constructed using Paley tournaments in previous
work. We show that these examples are unique (in a particular sense) using
Fourier analysis.
  A $3$-tournament is a `higher order' version of a tournament given by an
alternating function on triples of distinct vertices in a vertex set. We show
that $3$-tournaments also enjoy a switching operation and use this to give a
formula for the size of a switching class in terms of level permutations,
generalising a result of Babai--Cameron.


title:
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB
  Images

abstract:
  In this paper, we present a generalizable model-free 6-DoF object pose
estimator called Gen6D. Existing generalizable pose estimators either need
high-quality object models or require additional depth maps or object masks in
test time, which significantly limits their application scope. In contrast, our
pose estimator only requires some posed images of the unseen object and is able
to accurately predict the poses of the object in arbitrary environments. Gen6D
consists of an object detector, a viewpoint selector and a pose refiner, all of
which do not require the 3D object model and can generalize to unseen objects.
Experiments show that Gen6D achieves state-of-the-art results on two model-free
datasets: the MOPED dataset and a new GenMOP dataset collected by us. In
addition, on the LINEMOD dataset, Gen6D achieves competitive results compared
with instance-specific pose estimators. Project page:
https://liuyuan-pal.github.io/Gen6D/.


title:
ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in
  Parking Lots with CNN and Transformer

abstract:
  The problem of multimodal intent and trajectory prediction for human-driven
vehicles in parking lots is addressed in this paper. Using models designed with
CNN and Transformer networks, we extract temporal-spatial and contextual
information from trajectory history and local bird's eye view (BEV) semantic
images, and generate predictions about intent distribution and future
trajectory sequences. Our methods outperforms existing models in accuracy,
while allowing an arbitrary number of modes, encoding complex multi-agent
scenarios, and adapting to different parking maps. In addition, we present the
first public human driving dataset in parking lot with high resolution and rich
traffic scenarios for relevant research in this field.


title: Quantum interference measurement of the free fall of anti-hydrogen

abstract:
  We analyze a quantum measurement designed to improve the accuracy for the
free-fall acceleration of anti-hydrogen in the GBAR experiment. Including the
effect of photo-detachment recoil in the analysis and developing a full quantum
analysis of anti-matter wave propagation, we show that the accuracy is improved
by approximately three orders of magnitude with respect to the classical timing
technique planned for the current experiment.


title: Centralized Adversarial Learning for Robust Deep Hashing

abstract:
  Deep hashing has been extensively utilized in massive image retrieval because
of its efficiency and effectiveness. Recently, it becomes a hot issue to study
adversarial examples which poses a security challenge to deep hashing models.
However, there is still a critical bottleneck: how to find a superior and exact
semantic representative as the guide to further enhance the adversarial attack
and defense in deep hashing based retrieval. We, for the first time, attempt to
design an effective adversarial learning with the min-max paradigm to improve
the robustness of hashing networks by using the generated adversarial samples.
Specifically, we obtain the optimal solution (called center code) through a
proved Continuous Hash Center Method (CHCM), which preserves the semantic
similarity with positive samples and dissimilarity with negative samples. On
one hand, we propose the Deep Hashing Central Attack (DHCA) for efficient
attack on hashing retrieval by maximizing the Hamming distance between the hash
code of adversarial example and the center code. On the other hand, we present
the Deep Hashing Central Adversarial Training (DHCAT) to optimize the hashing
networks for defense, by minimizing the Hamming distance to the center code.
Extensive experiments on the benchmark datasets verify that our attack method
can achieve better performance than the state-of-the-arts, and our defense
algorithm can effectively mitigate the effects of adversarial perturbations.


title:
Anti-PT-symmetric harmonic oscillator and its relation to the inverted
  harmonic oscillator

abstract:
  We treat the quantum dynamics of a harmonic oscillator as well as its
inverted counterpart in the Schr\"odinger picture. Generally in the most papers
of the literature, the inverted harmonic oscillator is formally obtained from
the harmonic oscillator by the replacement of {\omega} to i{\omega}, this leads
to unbounded eigenvectors. This explicitly demonstrates that there are some
unclear points involved in redefining the variables in the harmonic oscillator
inversion. To remedy this situation, we introduce a scaling operator (Dyson
transformation) by connecting the inverted harmonic oscillator to an
anti-PT-symmetric harmonic oscillator, we obtain the standard quasi-Hermiticity
relation which would ensure the time invariance of the eigenfunction's norm. We
give a complete description for the eigenproblem. We show that the
wavefunctions for this system are normalized in the sense of the pseudo-scalar
product. A Gaussian wave packet of the inverted oscillator is investigated by
using the ladder operators method. This wave packet is found to be associated
with the generalized coherent state that can be crucially utilized for
investigating the mean values of the space and momentum operators. We find that
these mean values reproduce the classical motion.


title:
Self-similar random structures defined as fixed points of distributional
  equations

abstract:
  We consider fixed-point equations for probability distributions on isometry
classes of measured metric spaces. The construction is required to be recursive
and tree-like, but we allow loops for the geodesics between points in the
support of the measure: one can think of a $\beta$-stable looptree decomposed
around one loop. We study existence and uniqueness of solutions together with
the attractiveness of the fixed-points by iterating. We obtain bounds on the
Hausdorff and upper Minkowski dimension, which appear to be tight for the
studied models. This setup applies to formerly studied structures as the
$\beta$-stable trees and looptrees, of which we give a new characterization and
recover the fractal dimensions.


title:
On Feature Learning in Neural Networks with Global Convergence
  Guarantees

abstract:
  We study the optimization of wide neural networks (NNs) via gradient flow
(GF) in setups that allow feature learning while admitting non-asymptotic
global convergence guarantees. First, for wide shallow NNs under the mean-field
scaling and with a general class of activation functions, we prove that when
the input dimension is no less than the size of the training set, the training
loss converges to zero at a linear rate under GF. Building upon this analysis,
we study a model of wide multi-layer NNs whose second-to-last layer is trained
via GF, for which we also prove a linear-rate convergence of the training loss
to zero, but regardless of the input dimension. We also show empirically that,
unlike in the Neural Tangent Kernel (NTK) regime, our multi-layer model
exhibits feature learning and can achieve better generalization performance
than its NTK counterpart.


title:
Search for the charged-lepton-flavor-violating decay $Z\rightarrow e\mu$
  in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

abstract:
  A search for the charged-lepton-flavor-violating process $Z\rightarrow e\mu$
is presented, using 139 fb$^{-1}$ of $\sqrt{s}=13$ TeV $pp$ collision data
collected by the ATLAS experiment at the LHC. An excess in the $e\mu$ invariant
mass spectrum near the $Z$ boson mass would be a striking signature of new
physics. No excess is observed, and an upper limit ${\cal B}(Z\rightarrow e\mu)
< 2.62 \times 10^{-7}$ is placed on the branching fraction at 95% confidence
level, which is related to a ratio of the observed $e\mu$ yield to the average
of the observed yields of $ee$ and $\mu\mu$ events to significantly reduce
systematic uncertainties.


title: MCBeth: A Measurement Based Quantum Programming Language

abstract:
  Gate-based quantum programming languages are ubiquitous but measurement-based
languages currently exist only on paper. This work introduces MCBeth, a quantum
programming language which allows programmers to directly represent, program,
and simulate measurement-based and cluster state computation by building upon
the measurement calculus. While MCBeth programs are meant to be executed
directly on hardware, to take advantage of current machines we also provide a
compiler to gate-based instructions. We argue that there are clear advantages
to measurement-based quantum computation compared to gate-based when it comes
to implementing common quantum algorithms and distributed quantum computation.


title: Localizing Router Configuration Errors Using Minimal Correction Sets

abstract:
  Router configuration errors are unfortunately common and difficult to
localize using current network verifiers. We introduce a novel configuration
error localizer (CEL) that precisely identifies which configuration segments
contribute to the violation of forwarding requirements. In particular, CEL
generates a system of satisfiability modulo theories (SMT) constraints-which
encode a network's configurations, control logic, and forwarding
requirements-and uses a domain-specific minimal correction set (MCS)
enumeration algorithm to identify problematic configuration segments. CEL
efficiently locates several configuration errors in real university networks
and identifies all routing-related and at least half of all ACL-related errors
we introduce.


title: IBM quantum platforms: a quantum battery perspective

abstract:
  We characterize for the first time the performances of IBM quantum chips as
quantum batteries, specifically addressing the single-qubit Armonk processor.
By exploiting the Pulse access enabled to some of the IBM Quantum processors
via the Qiskit package, we investigate advantages and limitations of different
profiles for classical drives used to charge these miniaturized batteries,
establishing the optimal compromise between charging time and stored energy.
Moreover, we consider the role played by various possible initial conditions on
the functioning of the quantum batteries. As main result of our analysis, we
observe that unavoidable errors occurring in the initialization phase of the
qubit, which can be detrimental for quantum computing applications, only
marginally affects energy transfer and storage. This can lead
counter-intuitively to improvements of the performances. This is a strong
indication of the fact that IBM quantum devices are already in the proper range
of parameters to be considered as good and stable quantum batteries, comparable
to state of the art devices recently discussed in literature.


title: MNL-Bandits under Inventory and Limited Switches Constraints

abstract:
  Optimizing the assortment of products to display to customers is a key to
increasing revenue for both offline and online retailers. To trade-off between
exploring customers' preference and exploiting customers' choices learned from
data, in this paper, by adopting the Multi-Nomial Logit (MNL) choice model to
capture customers' choices over products, we study the problem of optimizing
assortments over a planning horizon $T$ for maximizing the profit of the
retailer. To make the problem setting more practical, we consider both the
inventory constraint and the limited switches constraint, where the retailer
cannot use up the resource inventory before time $T$ and is forbidden to switch
the assortment shown to customers too many times. Such a setting suits the case
when an online retailer wants to dynamically optimize the assortment selection
for a population of customers. We develop an efficient UCB-like algorithm to
optimize the assortments while learning customers' choices from data. We prove
that our algorithm can achieve a sub-linear regret bound
$\tilde{O}\left(T^{1-\alpha/2}\right)$ if $O(T^\alpha)$ switches are allowed.
%, and our regret bound is optimal with respect to $T$. Extensive numerical
experiments show that our algorithm outperforms baselines and the gap between
our algorithm's performance and the theoretical upper bound is small.


title:
Towards Accelerated Localization Performance Across Indoor Positioning
  Datasets

abstract:
  The localization speed and accuracy in the indoor scenario can greatly impact
the Quality of Experience of the user. While many individual machine learning
models can achieve comparable positioning performance, their prediction
mechanisms offer different complexity to the system. In this work, we propose a
fingerprinting positioning method for multi-building and multi-floor
deployments, composed of a cascade of three models for building classification,
floor classification, and 2D localization regression. We conduct an exhaustive
search for the optimally performing one in each step of the cascade while
validating on 14 different openly available datasets. As a result, we bring
forward the best-performing combination of models in terms of overall
positioning accuracy and processing speed and evaluate on independent sets of
samples. We reduce the mean prediction time by 71% while achieving comparable
positioning performance across all considered datasets. Moreover, in case of
voluminous training dataset, the prediction time is reduced down to 1% of the
benchmark's.


title: Verification of Locally Tight Programs

abstract:
  ANTHEM is a proof assistant that can be used for verifying the correctness of
tight programs in the input language of the answer set grounder GRINGO with
respect to specifications expressed by first-order formulas. We define the
concept of a locally tight program and prove that the verification process used
by ANTHEM is applicable in this more general setting. Unlike tightness, the
local tightness condition allows some forms of recursion. In particular, some
programs describing effects of actions are locally tight. Under consideration
for publication in Theory and Practice of Logic Programming


title:
Analogous Hawking radiation and quantum entanglement in two-component
  Bose-Einstein condensates: the gapped excitations

abstract:
  The condensates of cold atoms at zero temperature in the tunable binary
Bose-Einstein condensate system are studied with the Rabi transition between
atomic hyperfine states where the system can be represented by a coupled
two-field model of gapless excitations and gapped excitations. We set up the
configuration of the supersonic and subsonic regimes with the acoustic horizon
between them in the elongated two-component Bose-Einstein condensates, trying
to mimic Hawking radiations, in particular due to the gapped excitations. The
simplified step-like sound speed change is adopted for the subsonic-supersonic
transition so that the model can be analytically treatable. The effective
energy gap term in the dispersion relation of the gapped excitations introduces
the threshold frequency $\omega_\text{min}$ in the subsonic regime, below which
the propagating modes do not exist. Thus, the particle spectrum of the Hawking
modes significantly deviates from that of the gapless cases near the threshold
frequency due to the modified grey-body factor, which vanishes as the mode
frequency is below $\omega_\text{min}$. The influence from the gapped
excitations to the quantum entanglement of the Hawking mode and its partner of
the gapless excitations is also studied according to the Peres-Horodecki-Simon
(PHS) criterion. It is found that the presence of the gapped excitations will
deteriorate the quantumness of the pair modes of the gapless excitations when
the frequency of the pair modes in particular is around $\omega \sim
\omega_\text{min}$. On top of that, when the coupling constant between the
gapless and gapped excitations becomes large enough, the huge particle density
of the gapped excitations in the small $\omega$ regime will significantly
disentangle the pair modes of the gapless excitations. The detailed
time-dependent PHS criterion will be discussed.


title: Hexagonal Geometric Triangulations

abstract:
  It is well-known that the Euclidean plane has a standard 6-regular geodesic
triangulation , and the unit sphere has a 5-regular geodesic triangulation,
which is induced from the regular Dodecahedron, and the hyperbolic plane has an
n-regular geodesic triangulation for any n > 6. Here we constructed a 6-regular
geodesic triangulation of the hyperbolic plane.


title:
A correspondence between control and observation problems in
  decentralized discrete-event systems

abstract:
  This paper demonstrates a correspondence between control problems (with
partial observation) and observation problems in decentralized discrete-event
systems, namely, the two classes of problems are Turing equivalent, as one
class Turing reduces to the other. The correspondence allows decomposition of a
control problem into a collection of simpler control sub-problems. Since
observation problems in their most general formulation have been shown to be
undecidable in previous work, the correspondence produced in this paper
demonstrates that control problems -- in their most general form -- is also
undecidable.


title: Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions

abstract:
  The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm
that incorporates the gradient of the logarithm of the target density in its
proposal distribution. In an earlier joint work \cite{pill:stu:12}, the author
had extended the seminal work of \cite{Robe:Rose:98} and showed that in
stationarity, MALA applied to an $N$-dimensional approximation of the target
will take ${\cal O}(N^{\frac13})$ steps to explore its target measure. It was
also shown in \cite{Robe:Rose:98,pill:stu:12} that, as a consequence of the
diffusion limit, the MALA algorithm is optimized at an average acceptance
probability of $0.574$. In \cite{pere:16}, Pereyra introduced the proximal MALA
algorithm where the gradient of the log target density is replaced by the
proximal function (mainly aimed at implementing MALA non-differentiable target
densities). In this paper, we show that for a wide class of twice
differentiable target densities, the proximal MALA enjoys the same optimal
scaling as that of MALA in high dimensions and also has an average optimal
acceptance probability of $0.574$. The results of this paper thus give the
following practically useful guideline: for smooth target densities where it is
expensive to compute the gradient while implementing MALA, users may replace
the gradient with the corresponding proximal function (that can be often
computed relatively cheaply via convex optimization) \emph{without} losing any
efficiency. This confirms some of the empirical observations made in
\cite{pere:16}.


title: Operator residuation in orthomodular posets of finite height

abstract:
  We show that for every orthomodular poset P of finite height there can be
defined two operators forming an adjoint pair with respect to an order-like
relation defined on the power set of P. This enables us to introduce the
so-called operator residuated poset corresponding to P from which the original
orthomodular poset P can be recovered. Moreover, this correspondence is almost
one-to-one. We show that this construction of operators can be applied also to
so-called weakly orthomodular and dually weakly orthomodular posets. Examples
of such posets are included.


title:
An Empirical Review of Model-based Adaptive Sampling for Global
  Optimization of Expensive Black-box Functions

abstract:
  This paper reviews the state-of-the-art model-based adaptive sampling
approaches for single-objective black-box optimization (BBO). While BBO
literature includes various promising sampling techniques, there is still a
lack of comprehensive investigations of the existing research across the vast
scope of BBO problems. We first classify BBO problems into two categories:
engineering design and algorithm design optimization and discuss their
challenges. We then critically discuss and analyze the adaptive model-based
sampling techniques focusing on key acquisition functions. We elaborate on the
shortcomings of the variance-based sampling techniques for engineering design
problems. Moreover, we provide in-depth insights on the impact of the
discretization schemes on the performance of acquisition functions. We
emphasize the importance of dynamic discretization for distance-based
exploration and introduce EEPA+, an improved variant of a previously proposed
Pareto-based sampling technique. Our empirical analyses reveal the
effectiveness of variance-based techniques for algorithm design and
distance-based methods for engineering design optimization problems.


title: DACSR: Dual-Aggregation End-to-End Calibrated Sequential Recommendation

abstract:
  Recent years have witnessed the progress of sequential recommendation in
accurately predicting users' future behaviors. However, only persuading
accuracy leads to the risk of filter bubbles where recommenders only focus on
users' main interest areas. Different from other studies which improve
diversity or coverage, we investigate the calibration in sequential
recommendation. However, existing calibrated methods followed a post-processing
paradigm, which costs more computation time and sacrifices the recommendation
accuracy. To this end, we propose an end-to-end framework to provide both
accurate and calibrated recommendations. We propose a loss function to measure
the divergence of distributions between recommendation lists and historical
behaviors for sequential recommendation framework. In addition, we design a
dual-aggregation model which extracts information from two individual sequence
encoders with different objectives to further improve the recommendation.
Experiments on two benchmark datasets demonstrate the effectiveness and
efficiency of our model.


title:
Numerical properties of exceptional divisors of birational morphisms of
  smooth surfaces

abstract:
  We make a very detailed analysis of the numerical properties of effective
divisors whose support is contained in the exceptional locus of a birational
morphism of smooth projective surfaces. As an application we extend Miyaoka's
inequality on the number of canonical singularities on a minimal projective
normal surface with non-negative Kodaira dimension to the non minimal case.


title:
Frequency estimation under non-Markovian spatially correlated quantum
  noise: Restoring superclassical precision scaling

abstract:
  We study the estimation precision attainable by entanglement-enhanced Ramsey
interferometry in the presence of spatiotemporally correlated non-classical
noise. Our analysis relies on an exact expression of the reduced density matrix
of the qubit probes under general zero-mean Gaussian stationary dephasing,
which is established through cumulant-expansion techniques and may be of
independent interest in the context of non-Markovian open dynamics. By
continuing and expanding our previous work [Beaudoin et al., Phys. Rev. A 98,
020102(R) (2018)], we analyze the effects of a non-collective coupling regime
between the qubit probes and their environment, focusing on two limiting
scenarios where the couplings may take only two or a continuum of possible
values. In the paradigmatic case of spin-boson dephasing noise from a thermal
environment, we find that it is possible to suppress, on average, the effect of
spatial correlations by randomizing the location of the probes, as long as
enough configurations are sampled where noise correlations are negative. As a
result, superclassical precision scaling is asymptotically restored for initial
entangled states, including experimentally accessible one-axis spin-squeezed
states.


title:
On some simple geometric structure of affine Deligne-Lusztig varieties
  for $GL_n$

abstract:
  In this paper we study the geometric structure of affine Deligne-Lusztig
varieties for $GL_n$ and $b$ basic. We introduce a new condition on $\lambda$.
If this is satisfied, then the corresponding affine Deligne-Lusztig variety is
the disjoint union of classical Deligne-Lusztig varieties times
finite-dimensional affine spaces.


title:
Energy Dependent Model of Isotopic Production Cross Sections from
  Proton-$^{16}$O Interactions

abstract:
  Proton interactions with $^{16}$O nuclei are the most frequent nuclear
interaction leading to secondary radiation in tissues for space radiation and
cancer therapy with protons. In addition, $^{16}$O has the largest fluence of
galactic cosmic rays, and interacts with hydrogen in tissue or water and
polyethylene shielding. The fragmentation of oxygen produces a large number of
heavy ion (A>4) target fragments (TF) with high ionization density. Here we
develop an analytical model of energy dependent proton-$^{16}$O cross sections.
We introduce corrections to measurements of total charge changing cross
sections to extend data on nuclear absorption cross sections. Using
experimental data and a 2nd order optical model an accurate formula for the
p-$^{16}$O absorption cross section from <10 MeV/n to >10 GeV/N is obtained.
The energy dependence of the isotopic cross sections is modeled as
multiplicities scaled to absorption cross section resulting in an accurate
model over the full energy range.


title: On the Q-BOR-FDTD method

abstract:
  We discuss a recently proposed approach termed Q-BOR-FDTD method and develop
its main equations in a clearer and more rigorous way. We show that it is
unsuitable for the calculation of the eigenfunctions in the case of degenerate
states and propose an improvement to overcome such limitation.


title:
Masses of Heavy Flavour Mesons in a potential Model Approach with Wave
  Function containing Airy's Infinite Series

abstract:
  We report the masses of $B$ and $D$ sectors heavy-flavoured mesons obtained
by using our recently developed meson wave function employing potential model
approach with linear confinement term in potential as parent in the
perturbation method. As the wave-function involves infinite Airy's polynomial
series, in carrying out the mass calculation, to avoid divergences, we have
introduced some cut-off parameter for inter-quark separation. Our results for
ground state masses of heavy-flavoured $B$ and $D$ sector mesons are reasonably
closer to the PDG masses.


title:
Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object
  Detection

abstract:
  Despite the recent advances of deep neural networks, object detection for
adverse weather remains challenging due to the poor perception of some sensors
in adverse weather. Instead of relying on one single sensor, multimodal fusion
has been one promising approach to provide redundant detection information
based on multiple sensors. However, most existing multimodal fusion approaches
are ineffective in adjusting the focus of different sensors under varying
detection environments in dynamic adverse weather conditions. Moreover, it is
critical to simultaneously observe local and global information under complex
weather conditions, which has been neglected in most early or late-stage
multimodal fusion works. In view of these, this paper proposes a Global-Local
Attention (GLA) framework to adaptively fuse the multi-modality sensing
streams, i.e., camera, gated camera, and lidar data, at two fusion stages.
Specifically, GLA integrates an early-stage fusion via a local attention
network and a late-stage fusion via a global attention network to deal with
both local and global information, which automatically allocates higher weights
to the modality with better detection features at the late-stage fusion to cope
with the specific weather condition adaptively. Experimental results
demonstrate the superior performance of the proposed GLA compared with
state-of-the-art fusion approaches under various adverse weather conditions,
such as light fog, dense fog, and snow.


title: Inverted oscillator: pseudo hermiticity and coherent states

abstract:
  It is known that the standard and the inverted harmonic oscillator are
different. Replacing thus of {\omega} by i{\omega} in the regular oscillator is
necessary going to give the inverted oscillator H^{r}. This replacement would
lead to anti- PT-symmetric harmonic oscillator Hamiltonian (iH^{os}). The
pseudo-hermiticity relation has been used here to relate the anti-PT-symmetric
harmonic Hamiltonian to the inverted oscillator. By using a simple algebra, we
introduce the ladder operators describing the inverted harmonic oscillator to
reproduce the analytical solutions.We construct the inverted coherent states
which minimize the quantum mechanical uncertainty between the position and the
momentum.


title:
Revise and Resubmit: An Intertextual Model of Text-based Collaboration
  in Peer Review

abstract:
  Peer review is a key component of the publishing process in most fields of
science. The increasing submission rates put a strain on reviewing quality and
efficiency, motivating the development of applications to support the reviewing
and editorial work. While existing NLP studies focus on the analysis of
individual texts, editorial assistance often requires modeling interactions
between pairs of texts -- yet general frameworks and datasets to support this
scenario are missing. Relationships between texts are the core object of the
intertextuality theory -- a family of approaches in literary studies not yet
operationalized in NLP. Inspired by prior theoretical work, we propose the
first intertextual model of text-based collaboration, which encompasses three
major phenomena that make up a full iteration of the review-revise-and-resubmit
cycle: pragmatic tagging, linking and long-document version alignment. While
peer review is used across the fields of science and publication formats,
existing datasets solely focus on conference-style review in computer science.
Addressing this, we instantiate our proposed model in the first annotated
multi-domain corpus in journal-style post-publication open peer review, and
provide detailed insights into the practical aspects of intertextual
annotation. Our resource is a major step towards multi-domain, fine-grained
applications of NLP in editorial support for peer review, and our intertextual
framework paves the path for general-purpose modeling of text-based
collaboration.


title:
A Unifying Framework for Combining Complementary Strengths of Humans and
  ML toward Better Predictive Decision-Making

abstract:
  Hybrid human-ML systems are increasingly in charge of consequential decisions
in a wide range of domains. A growing body of work has advanced our
understanding of these systems by providing empirical and theoretical analyses.
However, existing empirical results are mixed, and theoretical proposals are
often incompatible with each other. Our goal in this work is to bring
much-needed organization to this field by offering a unifying framework for
understanding conditions under which combining complementary strengths of human
and ML leads to higher quality decisions than those produced by them
individually -- a state to which we refer to as human-ML complementarity. We
focus specifically on the context of human-ML predictive decision-making
systems and investigate optimal ways of combining human and ML-based predictive
decisions, accounting for the underlying causes of variation in their
judgments. Within this scope, we present two crucial contributions. First,
drawing upon prior literature in human psychology, machine learning, and
human-computer interaction, we introduce a taxonomy characterizing a wide
variety of criteria across which human and machine decision-making differ.
Building on our taxonomy, our second contribution presents a unifying
optimization-based framework for formalizing how human and ML predictive
decisions should be aggregated optimally. We show that our proposed framework
encompasses several existing models of human-ML complementarity as special
cases. Last but not least, the exploratory analysis of our framework offers a
critical piece of insight for future work in this area: the mechanism by which
we combine human-ML judgments should be informed by the underlying causes of
their diverging decisions.


title:
Predicting highway lane-changing maneuvers: A benchmark analysis of
  machine and ensemble learning algorithms

abstract:
  Understanding and predicting highway lane-change maneuvers is essential for
driving modeling and its automation. The development of data-based
lane-changing decision-making algorithms is nowadays in full expansion. We
compare empirically in this article different machine and ensemble learning
classification techniques to the MOBIL rule-based model using trajectory data
of European two-lane highways. The analysis relies on instantaneous
measurements of up to twenty-four spatial-temporal variables with the four
neighboring vehicles on current and adjacent lanes. Preliminary descriptive
investigations by principal component and logistic analyses allow identifying
main variables intending a driver to change lanes. We predict two types of
discretionary lane-change maneuvers: Overtaking (from slow to fast lane) and
fold-down (from fast to slow lane). The prediction accuracy is quantified using
total, lane-changing and lane-keeping errors and associated receiver operating
characteristic curves. The benchmark analysis includes logistic model, linear
discriminant, decision tree, na\"ive Bayes classifier, support vector machine,
neural network machine learning algorithms, and up to ten bagging and stacking
ensemble learning meta-heuristics. If the rule-based model provides limited
predicting accuracy, especially in case of fold-down, the data-based
algorithms, devoid of modeling bias, allow significant prediction improvements.
Cross validations show that selected neural networks and stacking algorithms
allow predicting from a single observation both fold-down and overtaking
maneuvers up to four seconds in advance with high accuracy.


title: Spinors in $\mathbb{K}$-Hilbert Spaces

abstract:
  We consider a structure of the $\mathbb{K}$-Hilbert space, where
$\mathbb{K}\simeq\mathbb{R}$ is a field of real numbers,
$\mathbb{K}\simeq\mathbb{C}$ is a field of complex numbers,
$\mathbb{K}\simeq\mathbb{H}$ is a quaternion algebra, within the framework of
division rings of Clifford algebras. The $\mathbb{K}$-Hilbert space is
generated by the Gelfand-Naimark-Segal construction, while the generating
$C^\ast$-algebra consists of the energy operator $H$ and the generators of the
group $SU(2,2)$ attached to $H$. The cyclic vectors of the $\mathbb{K}$-Hilbert
space corresponding to the tensor products of quaternionic algebras define the
pure separable states of the operator algebra. Depending on the division ring
$\mathbb{K}$, all states of the operator algebra are divided into three
classes: 1) charged states with $\mathbb{K}\simeq\mathbb{C}$; 2) neutral states
with $\mathbb{K}\simeq\mathbb{H}$; 3) truly neutral states with
$\mathbb{K}\simeq\mathbb{R}$. For pure separable states that define the
fermionic and bosonic states of the energy spectrum, the fusion, doubling
(complexification) and annihilation operations are determined.


title: Dynamical relaxation of cosmological constant

abstract:
  A special class of conformal gravity theories is proposed to solve the long
standing problem of the fine-tuned cosmological constant. In the proposed model
time evolution of the inflaton field leaves behind a nearly vanishing, but
finite value of dark energy density of order (a few meV)$^4$ to explain the
late-time accelerating universe. A multiple scalar inflaton field is assumed to
have a conformal coupling to the Ricci scalar curvature in the lagrantian,
which results in, after a Weyl rescaling to the Einstein metric frame,
modification of inflaton kinetic and potential terms along with its coupling to
Higgs fields in the standard model. One may define an effective cosmological
$\Lambda$ function in the Einstein metric frame, which controls, when the
potential is added, a slow-roll inflation and subsequent oscillation at the
potential minimum of a spontaneous symmetry breaking phase. The inflaton
oscillation accompanies particle production towards thermalized hot big-bang
universe. At the same time zero-point quantum fluctuation of second inflaton
field is generated,and its accumulated fluctuation gives rise to a symmetry
restoration, pushing back the inflaton field towards the infinity. This gives a
dynamical relaxation of vanishing effective cosmological constant. Both
inhomogeneous inflaton modes and their collapsed black holes of primordial
origin are good candidates of cold dark matter.


title: Learning to Scaffold: Optimizing Model Explanations for Teaching

abstract:
  Modern machine learning models are opaque, and as a result there is a
burgeoning academic subfield on methods that explain these models' behavior.
However, what is the precise goal of providing such explanations, and how can
we demonstrate that explanations achieve this goal? Some research argues that
explanations should help teach a student (either human or machine) to simulate
the model being explained, and that the quality of explanations can be measured
by the simulation accuracy of students on unexplained examples. In this work,
leveraging meta-learning techniques, we extend this idea to improve the quality
of the explanations themselves, specifically by optimizing explanations such
that student models more effectively learn to simulate the original model. We
train models on three natural language processing and computer vision tasks,
and find that students trained with explanations extracted with our framework
are able to simulate the teacher significantly more effectively than ones
produced with previous methods. Through human annotations and a user study, we
further find that these learned explanations more closely align with how humans
would explain the required decisions in these tasks. Our code is available at
https://github.com/coderpat/learning-scaffold


title: Indirect reciprocity in populations with group structure

abstract:
  Indirect reciprocity, whereby individuals cooperate with others of good
standing, is a powerful and parsimonious mechanism to sustain a high level of
cooperation. Studies of indirect reciprocity typically assume either that each
individual forms their own private assessment of the reputations of others or,
conversely, that everyone shares a consensus view of the reputations in the
population. Here we develop a mathematical framework for analyzing intermediate
cases between strictly private and fully public information. We introduce group
structure in information availability: the population is stratified into
disjoint groups, and reputation information is shared only within each group.
Even when social interactions and strategic imitations are well-mixed across
the entire population, the partitioning of reputation information into disjoint
groups weakens the fitness advantage of cooperative individuals, because it
makes it less likely that interacting players will agree about each other's
reputations. In the limit of a single group, or in the limit of a large number
of groups, we recover the classical cases of fully public, or completely
private, information. We analyze the effect of group structure on the stability
of behavioral strategies, and we identify critical values for the number of
groups required to support cooperation, under four different social norms of
reputation assessment.


title:
Partitioning qubits in hypergraph product codes to implement logical
  gates

abstract:
  The promise of high-rate low-density parity check (LDPC) codes to
substantially reduce the overhead of fault-tolerant quantum computation depends
on constructing efficient, fault-tolerant implementations of logical gates on
such codes. Transversal gates are the simplest type of fault-tolerant gate, but
the potential of transversal gates on LDPC codes has hitherto been largely
neglected. We investigate the transversal gates that can be implemented in
hypergraph product codes, a class of LDPC codes. Our analysis is aided by the
construction of a symplectic canonical basis for the logical operators of
hypergraph product codes, a result that may be of independent interest. We show
that in these codes transversal gates can implement Hadamard (up to logical
SWAP gates) and control-Z on all logical qubits. Moreover, we show that
sequences of transversal operations, interleaved with error correction, allow
implementation of entangling gates between arbitrary pairs of logical qubits in
the same code block. We thereby demonstrate that transversal gates can be used
as the basis for universal quantum computing on LDPC codes, when supplemented
with state injection.


title: Deep-tomography: iterative velocity model building with deep learning

abstract:
  The accurate and fast estimation of velocity models is crucial in seismic
imaging. Conventional methods, like Tomography and Full-Waveform Inversion
(FWI), obtain appropriate velocity models; however, they require intense and
specialized human supervision and consume much time and computational
resources. In recent years, some works investigated deep learning(DL)
algorithms to obtain the velocity model directly from shots or migrated angle
panels, obtaining encouraging predictions of synthetic models. This paper
proposes a new flow to increase the complexity of velocity models recovered
with DL. Inspired by the conventional geophysical velocity model building
methods, instead of predicting the entire model in one step, we predict the
velocity model iteratively. We implement the iterative nature of the process
when, for each iteration, we train the DL algorithm to determine the velocity
model with a certain level of precision/resolution for the next iteration; we
name this process as Deep-Tomography. Starting from an initial model that
roughly approaches the true model, the Deep-Tomography is able to predict an
appropriate final model, even in complete unseen data, like the Marmousi model.


title:
"Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic
  Decisions in Contentious Public Policy Domains

abstract:
  This position paper offers a framework to think about how to better involve
human influence in algorithmic decision-making of contentious public policy
issues. Drawing from insights in communication literature, we introduce a
"public(s)-in-the-loop" approach and enumerates three features that are central
to this approach: publics as plural political entities, collective
decision-making through deliberation, and the construction of publics. It
explores how these features might advance our understanding of stakeholder
participation in AI design in contentious public policy domains such as
recidivism prediction. Finally, it sketches out part of a research agenda for
the HCI community to support this work.


title:
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task
  Learning

abstract:
  Subword tokenization is a commonly used input pre-processing step in most
recent NLP models. However, it limits the models' ability to leverage
end-to-end task learning. Its frequency-based vocabulary creation compromises
tokenization in low-resource languages, leading models to produce suboptimal
representations. Additionally, the dependency on a fixed vocabulary limits the
subword models' adaptability across languages and domains. In this work, we
propose a vocabulary-free neural tokenizer by distilling segmentation
information from heuristic-based subword tokenization. We pre-train our
character-based tokenizer by processing unique words from multilingual corpus,
thereby extensively increasing word diversity across languages. Unlike the
predefined and fixed vocabularies in subword methods, our tokenizer allows
end-to-end task learning, resulting in optimal task-specific tokenization. The
experimental results show that replacing the subword tokenizer with our neural
tokenizer consistently improves performance on multilingual (NLI) and
code-switching (sentiment analysis) tasks, with larger gains in low-resource
languages. Additionally, our neural tokenizer exhibits a robust performance on
downstream tasks when adversarial noise is present (typos and misspelling),
further increasing the initial improvements over statistical subword
tokenizers.


title:
Propagation of a fast radio burst through a birefringent relativistic
  plasma

abstract:
  The study of impulsive astrophysical radio emission makes it possible to
probe the intervening plasma between the emission source and the Earth. In cold
electron-ion plasmas, the circular propagating wave modes primarily alter the
linear polarization plane that scales with the inverse-square of the emission
frequency. In relativistic plasmas, the wave modes are elliptically polarized,
and it is possible to convert linearly polarized emission into circular and
vice versa. Fast radio bursts (FRBs) enable the study of not only the
electron-ion plasma of the intergalactic medium but potentially the extreme
magneto-ionic medium in which these intense pulses are produced. Here we report
on the polarimetric analysis of a repeat burst from the FRB 20201124A source.
The burst displayed a significant frequency-dependent circularly polarized
component, unlike other bursts from this source or any other FRB found to date.
We model the frequency dependence of the circular polarization using a
phenomenological generalized Faraday rotation framework. From this, we
interpret the observed circular polarization in the burst as having been
induced by radiative propagation through a relativistic plasma within or close
to the magnetosphere of the progenitor.


title: Reward Reports for Reinforcement Learning

abstract:
  The desire to build good systems in the face of complex societal effects
requires a dynamic approach towards equity and access. Recent approaches to
machine learning (ML) documentation have demonstrated the promise of discursive
frameworks for deliberation about these complexities. However, these
developments have been grounded in a static ML paradigm, leaving the role of
feedback and post-deployment performance unexamined. Meanwhile, recent work in
reinforcement learning design has shown that the effects of optimization
objectives on the resultant system behavior can be wide-ranging and
unpredictable. In this paper we sketch a framework for documenting deployed
learning systems, which we call Reward Reports. Taking inspiration from various
contributions to the technical literature on reinforcement learning, we outline
Reward Reports as living documents that track updates to design choices and
assumptions behind what a particular automated system is optimizing for. They
are intended to track dynamic phenomena arising from system deployment, rather
than merely static properties of models or data. After presenting the elements
of a Reward Report, we provide three examples: DeepMind's MuZero, MovieLens,
and a hypothetical deployment of a Project Flow traffic control policy.


title: Convergence of the Riemannian Langevin Algorithm

abstract:
  We study the Riemannian Langevin Algorithm for the problem of sampling from a
distribution with density $\nu$ with respect to the natural measure on a
manifold with metric $g$. We assume that the target density satisfies a
log-Sobolev inequality with respect to the metric and prove that the manifold
generalization of the Unadjusted Langevin Algorithm converges rapidly to $\nu$
for Hessian manifolds. This allows us to reduce the problem of sampling
non-smooth (constrained) densities in ${\bf R}^n$ to sampling smooth densities
over appropriate manifolds, while needing access only to the gradient of the
log-density, and this, in turn, to sampling from the natural Brownian motion on
the manifold. Our main analytic tools are (1) an extension of self-concordance
to manifolds, and (2) a stochastic approach to bounding smoothness on
manifolds. A special case of our approach is sampling isoperimetric densities
restricted to polytopes by using the metric defined by the logarithmic barrier.


title:
Parameterized Sensitivity Oracles and Dynamic Algorithms using Exterior
  Algebras

abstract:
  We design the first efficient sensitivity oracles and dynamic algorithms for
a variety of parameterized problems. Our main approach is to modify the
algebraic coding technique from static parameterized algorithm design, which
had not previously been used in a dynamic context. We particularly build off of
the `extensor coding' method of Brand, Dell and Husfeldt [STOC'18], employing
properties of the exterior algebra over different fields.
  For the $k$-Path detection problem for directed graphs, it is known that no
efficient dynamic algorithm exists (under popular assumptions from fine-grained
complexity). We circumvent this by designing an efficient sensitivity oracle,
which preprocesses a directed graph on $n$ vertices in $2^k poly(k)
n^{\omega+o(1)}$ time, such that, given $\ell$ updates (mixing edge insertions
and deletions, and vertex deletions) to that input graph, it can decide in time
$\ell^2 2^kpoly(k)$ and with high probability, whether the updated graph
contains a path of length $k$. We also give a deterministic sensitivity oracle
requiring $4^k poly(k) n^{\omega+o(1)}$ preprocessing time and $\ell^2
2^{\omega k + o(k)}$ query time, and obtain a randomized sensitivity oracle for
the task of approximately counting the number of $k$-paths. For $k$-Path
detection in undirected graphs, we obtain a randomized sensitivity oracle with
$O(1.66^k n^3)$ preprocessing time and $O(\ell^3 1.66^k)$ query time, and a
better bound for undirected bipartite graphs.
  In addition, we present the first fully dynamic algorithms for a variety of
problems: $k$-Partial Cover, $m$-Set $k$-Packing, $t$-Dominating Set,
$d$-Dimensional $k$-Matching, and Exact $k$-Partial Cover. For example, for
$k$-Partial Cover we show a randomized dynamic algorithm with $2^k
poly(k)polylog(n)$ update time, and a deterministic dynamic algorithm with
$4^kpoly(k)polylog(n)$ update time.


title:
How causal machine learning can leverage marketing strategies: Assessing
  and improving the performance of a coupon campaign

abstract:
  We apply causal machine learning algorithms to assess the causal effect of a
marketing intervention, namely a coupon campaign, on the sales of a retail
company. Besides assessing the average impacts of different types of coupons,
we also investigate the heterogeneity of causal effects across subgroups of
customers, e.g. across clients with relatively high vs. low previous purchases.
Finally, we use optimal policy learning to learn (in a data-driven way) which
customer groups should be targeted by the coupon campaign in order to maximize
the marketing intervention's effectiveness in terms of sales. Our study
provides a use case for the application of causal machine learning in business
analytics, in order to evaluate the causal impact of specific firm policies
(like marketing campaigns) for decision support.


title: Routes to chaos in a class-B laser coupled to a neutral resonator

abstract:
  We study the dynamics of a class-B semiconductor microring laser coupled to a
neutral microring resonator for common fabrication parameters. For zero
detuning between the resonators, we identify five dynamical regions controlled
by the normalized pump rate of the laser and coupling strength between the
resonators. These regions show stable lasing with phase locking for either
sufficiently small or large coupling strength parameter values. Multistability
and chaos arise in between these stable regions where at least one fixed point
is attractive. The transition from stable to unstable lasing regions in the
parameter space phase diagram is a crossover.


title:
Robust and efficient primal-dual Newton-Krylov solvers for
  viscous-plastic sea-ice models

abstract:
  We present a Newton-Krylov solver for a viscous-plastic sea-ice model. This
constitutive relation is commonly used in climate models to describe the large
scale sea-ice motion. Due to the strong nonlinearity of the momentum equation,
the development of fast, robust and scalable solvers is still a substantial
challenge. We propose a novel primal-dual Newton linearization for the momentum
equation. In contrast to existing methods, it converges faster and more
robustly with respect to mesh refinement, and thus allows fully resolved
sea-ice simulations. Combined with an algebraic multigrid-preconditioned Krylov
method for the Newton linearized systems, which contain strongly varying
coefficients, the resulting solver scales well and can be used in parallel. We
present highly resolved benchmark solutions and solve problems with up to 8.4
million spatial unknowns.


title:
R-Drive: Resilient Data Storage and Sharing for Mobile Edge Computing
  Systems

abstract:
  Mobile edge computing (MEC) systems (in which intensive computation and data
storage tasks are performed locally, due to the absence of communication
infrastructure for connectivity to the cloud) are currently being developed for
disaster response applications and for tactical environments. MEC applications
for these scenarios generate and process significant mission-critical and
personal data that require resilient and secure storage and sharing. In this
paper, we present the design, implementation, and evaluation of R-Drive, a
resilient data storage and sharing framework for disaster response and tactical
MEC applications. R-Drive employs erasure coding and data encryption, ensuring
resilient and secure data storage against device failure. R-Drive adaptively
chooses erasure coding parameters to ensure the highest data availability with
a minimal storage cost. R-Drive's distributed directory service provides a
resilient and secure namespace for files with rigorous access control
management. R-Drive leverages opportunistic networking, allowing data storage
and sharing in mobile and loosely connected edge computing environments. We
implemented R-Drive on Android, and integrated it with existing MEC
applications. Performance evaluation results show that R-Drive enables
resilient and secure data storage and sharing.


title: Scalable symmetric Tucker tensor decomposition

abstract:
  We study the best low-rank Tucker decomposition of symmetric tensors,
advocating a straightforward projected gradient descent (PGD) method for its
computation. The main application of interest is in decomposing higher-order
multivariate moments, which are symmetric tensors. We develop scalable
adaptations of the basic PGD method and higher-order eigenvalue decomposition
(HOEVD) to decompose sample moment tensors. With the help of implicit and
streaming techniques, we evade the overhead cost of building and storing the
moment tensor. Such reductions make computing the Tucker decomposition
realizable for large data instances in high dimensions. Numerical experiments
demonstrate the efficiency of the algorithms and the applicability of moment
tensor decompositions to real-world datasets. Last, we study the convergence on
the Grassmannian manifold, and prove that the update sequence derived by the
PGD solver achieves first and second-order criticality.


title:
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional
  Characters with only a Few Utterances

abstract:
  In this paper, we consider mimicking fictional characters as a promising
direction for building engaging conversation models. To this end, we present a
new practical task where only a few utterances of each fictional character are
available to generate responses mimicking them. Furthermore, we propose a new
method named Pseudo Dialog Prompting (PDP) that generates responses by
leveraging the power of large-scale language models with prompts containing the
target character's utterances. To better reflect the style of the character,
PDP builds the prompts in the form of dialog that includes the character's
utterances as dialog history. Since only utterances of the characters are
available in the proposed task, PDP matches each utterance with an appropriate
pseudo-context from a predefined set of context candidates using a retrieval
model. Through human and automatic evaluation, we show that PDP generates
responses that better reflect the style of fictional characters than baseline
methods.


title:
A Fully-autonomous Framework of Unmanned Surface Vehicles in Maritime
  Environments using Gaussian Process Motion Planning

abstract:
  Unmanned surface vehicles (USVs) are of increasing importance to a growing
number of sectors in the maritime industry, including offshore exploration,
marine transportation and defence operations. A major factor in the growth in
use and deployment of USVs is the increased operational flexibility that is
offered through use of autonomous navigation systems that generate optimised
trajectories. Unlike path planning in terrestrial environments, planning in the
maritime environment is more demanding as there is need to assure mitigating
action is taken against the significant, random and often unpredictable
environmental influences from winds and ocean currents. With the focus of these
necessary requirements as the main basis of motivation, this paper proposes a
novel motion planner, denoted as GPMP2*, extending the application scope of the
fundamental GP-based motion planner, GPMP2, into complex maritime environments.
An interpolation strategy based on Monte-Carlo stochasticity has been
innovatively added to GPMP2* to produce a new algorithm named GPMP2* with
Monte-Carlo stochasticity (MC-GPMP2*), which can increase the diversity of the
paths generated. In parallel with algorithm design, a ROS based
fully-autonomous framework for an advanced unmanned surface vehicle, the WAM-V
20 USV, has been proposed. The practicability of the proposed motion planner as
well as the fully-autonomous framework have been functionally validated in a
simulated inspection missions for an offshore wind farm in ROS.


title: Chirality distributions inside baryons in ${\rm QCD_2}$

abstract:
  The connection between the spin distribution and the topological structure of
the baryon is an open and important problem. Here we address it using QCD in
$(1+1)$ space-time dimensions, which is exactly solvable at large number of
colors $N$. It is found that the distribution of chirality inside a baryon is
drastically different from a chirality distribution inside states with zero
baryon number, "mesons". Namely, the chirality distribution inside a baryon
peaks at small Bjorken $x$, whereas in a meson state it peaks at an
intermediate value of $x$ and vanishes in the small $x$ limit. This difference
is shown to arise from the topological structure of the baryon -- at large $N$,
all of the baryon's chirality is concentrated near $x=0$. Possible implications
for QCD in $(3+1)$ dimensions and for Deep Inelastic scattering experiments are
discussed.


title: Holographic RG flow and reparametrization invariance of Wilson Loops

abstract:
  We study the fate of reparametrization invariance of Wilson loops, also known
as 'zig-zag' symmetry, under the RG flow using some simple cases as guidance.
We restrict our analysis to large-$N$, strongly coupled CFTs and use the
holographic dual description of a Wilson loop as a fundamental string embedded
in asymptotically AdS spaces, at zero and nonzero temperature. We then
introduce a cutoff in the holographic radial direction and integrate out the
the section of the string closer to the AdS boundary in the spirit of
holographic Wilsonian renormalization. We make explicit the map between Wilson
loop reparametrizations and conformal transformation of the string worldsheet
and show that a cutoff anchored to the worldsheet breaks conformal invariance
and induces an effective defect action for reparametrizations at the cutoff
scale, in a way similar to nearly-$AdS_2$ gravity or SYK models. On the other
hand, a cutoff in the target space breaks worldsheet diffeomorphisms and Weyl
transformations but keeps conformal transformations unbroken and does not
generate a non-trivial action for reparametrizations.


title: Bayesian operator inference for data-driven reduced-order modeling

abstract:
  This work proposes a Bayesian inference method for the reduced-order modeling
of time-dependent systems. Informed by the structure of governing equations,
the task of learning a reduced-order model from data is posed as a Bayesian
inversion problem with Gaussian prior and likelihood. The operators defining
the reduced-order model, rather than being chosen deterministically, are
characterized probabilistically as posterior Gaussian distributions. This
embeds uncertainty into the reduced-order model, and hence the predictions
subsequently issued by the reduced-order model are endowed with uncertainty.
The learned reduced-order models are computationally efficient, which enables
Monte Carlo sampling over the posterior distributions of reduced-order
operators. Furthermore, the proposed Bayesian framework provides a statistical
interpretation of the Tikhonov regularization incorporated in the operator
inference, and the empirical Bayes approach of maximum marginal likelihood
suggests a selection algorithm for the regularization hyperparameters. The
proposed method is demonstrated by two examples: the compressible Euler
equations with noise-corrupted observations, and a single-injector combustion
process.


title: Memory Bounds for Continual Learning

abstract:
  Continual learning, or lifelong learning, is a formidable current challenge
to machine learning. It requires the learner to solve a sequence of $k$
different learning tasks, one after the other, while retaining its aptitude for
earlier tasks; the continual learner should scale better than the obvious
solution of developing and maintaining a separate learner for each of the $k$
tasks. We embark on a complexity-theoretic study of continual learning in the
PAC framework. We make novel uses of communication complexity to establish that
any continual learner, even an improper one, needs memory that grows linearly
with $k$, strongly suggesting that the problem is intractable. When
logarithmically many passes over the learning tasks are allowed, we provide an
algorithm based on multiplicative weights update whose memory requirement
scales well; we also establish that improper learning is necessary for such
performance. We conjecture that these results may lead to new promising
approaches to continual learning.


title: A note on graph drawings with star-shaped boundaries in the plane

abstract:
  In this note, we propose a straightforward method to produce an straight-line
embedding of a planar graph where one face of a graph is fixed in the plane as
a star-shaped polygon. It is based on minimizing discrete Dirichlet energies,
following the idea of Tutte's embedding theorem. We will call it a geodesic
triangulation of the star-shaped polygon. Moreover, we study the homotopy
property of spaces of all straight-line embeddings. We give a simple argument
to show that this space is contractible if the boundary is a non-convex
quadrilateral. We conjecture that the same statement holds for general
star-shaped polygons.


title: Longitudinal Variation of H$_2$O Ice Absorption on Miranda

abstract:
  Many tidally locked icy satellites in the outer Solar System show
leading/trailing hemispherical asymmetries in the strength of near-infrared
(NIR) H$_2$O ice absorption bands, in which the absorption bands are stronger
on the leading hemisphere. This is often attributed to a combination of
magnetospheric irradiation effects and impact gardening, which can modify grain
size, expose fresh ice, and produce dark contaminating compounds that reduce
the strength of absorption features. Previous research identified this
leading/trailing asymmetry on the four largest classical Uranian satellites but
did not find a clear leading/trailing asymmetry on Miranda, the smallest and
innermost classical moon. We undertook an extensive observational campaign to
investigate variations of the NIR spectral signature of H$_2$O ice with
longitude on Miranda's northern hemisphere. We acquired 22 new spectra with the
TripleSpec spectrograph on the ARC 3.5m telescope and 4 new spectra with GNIRS
on Gemini North. Our analysis also includes 3 unpublished and 7 previously
published spectra taken with SpeX on the 3m IRTF. We confirm that Miranda has
no substantial leading/trailing hemispherical asymmetry in the strength of its
H$_2$O ice absorption features. We additionally find evidence for an
anti-Uranus/sub-Uranus asymmetry in the strength of the 1.5-$\mu$m H$_2$O ice
band that is not seen on the other Uranian satellites, suggesting that
additional endogenic or exogenic processes influence the longitudinal
distribution of H$_2$O ice band strengths on Miranda.


title: Spaces of Geodesic Triangulations Are Cells

abstract:
  It has been shown that spaces of geodesic triangulations of surfaces with
negative curvature are contractible. Here we propose an approach aiming to
prove that the spaces of geodesic triangulations of a surface with negative
curvature are homeomorphic to Euclidean spaces $\mathbb{R}^n$.


title: Learning for Spatial Branching: An Algorithm Selection Approach

abstract:
  The use of machine learning techniques to improve the performance of
branch-and-bound optimization algorithms is a very active area in the context
of mixed integer linear problems, but little has been done for non-linear
optimization. To bridge this gap, we develop a learning framework for spatial
branching and show its efficacy in the context of the
Reformulation-Linearization Technique for polynomial optimization problems. The
proposed learning is performed offline, based on instance-specific features and
with no computational overhead when solving new instances. Novel graph-based
features are introduced, which turn out to play an important role for the
learning. Experiments on different benchmark instances from the literature show
that the learning-based branching rule significantly outperforms the standard
rules.


title: The impact of time-dependent stellar activity on exoplanet atmospheres

abstract:
  M-dwarfs are thought to be hostile environments for exoplanets. Stellar
events are very common on such stars. These events might cause the atmospheres
of exoplanets to change significantly over time. It is not only the major
stellar flare events that contribute to this disequilibrium, but the smaller
flares might also affect the atmospheres in an accumulating manner. In this
study, we aim to investigate the effects of time-dependent stellar activity on
the atmospheres of known exoplanets. We simulate the chemistry of GJ876c,
GJ581c, and GJ832c that go from H$_2$-dominated to N$_2$-dominated atmospheres
using observed stellar spectra from the MUSCLES-collaboration. We make use of
the chemical kinetics code VULCAN and implement a flaring routine that
stochastically generates synthetic flares based on observed flare statistics.
Using the radiative transfer code petitRADTrans we also simulate the evolution
of emission and transmission spectra. We investigate the effect of recurring
flares for a total of 11 days covering 515 flares. Results show a significant
change in abundance for some relevant species such as H, OH, and CH$_4$, with
factors going up to 3 orders of magnitude difference with respect to the
preflare abundances. We find a maximum change of $\sim$12 ppm for CH$_4$ in
transmission spectra on GJ876c. These changes in the spectra remain too small
to observe. We also find that the change in abundance and spectra of the
planets accumulate throughout time, causing permanent changes in the chemistry.
We conclude this small but gradual change in chemistry arises due to the
recurring flares.


title: Federated Learning Enables Big Data for Rare Cancer Boundary Detection

abstract:
  Although machine learning (ML) has shown promise in numerous domains, there
are concerns about generalizability to out-of-sample data. This is currently
addressed by centrally sharing ample, and importantly diverse, data from
multiple sites. However, such centralization is challenging to scale (or even
not feasible) due to various limitations. Federated ML (FL) provides an
alternative to train accurate and generalizable ML models, by only sharing
numerical model updates. Here we present findings from the largest FL study
to-date, involving data from 71 healthcare institutions across 6 continents, to
generate an automatic tumor boundary detector for the rare disease of
glioblastoma, utilizing the largest dataset of such patients ever used in the
literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33%
improvement over a publicly trained model to delineate the surgically
targetable tumor, and 23% improvement over the tumor's entire extent. We
anticipate our study to: 1) enable more studies in healthcare informed by large
and diverse data, ensuring meaningful results for rare diseases and
underrepresented populations, 2) facilitate further quantitative analyses for
glioblastoma via performance optimization of our consensus model for eventual
public release, and 3) demonstrate the effectiveness of FL at such scale and
task complexity as a paradigm shift for multi-site collaborations, alleviating
the need for data sharing.


title: Morse matching method for conformal cohomologies

abstract:
  We apply discrete algebraic Morse theory to the computation of Hochschild
cohomologies of associative conformal algebras. As an example, we evaluate the
dimensions of the universal associative conformal envelope $U(3)$ of the
Virasoro Lie conformal algebra relative to the associative locality $N=3$ on
the generator with scalar coefficients.


title:
S2AMP: A High-Coverage Dataset of Scholarly Mentorship Inferred from
  Publications

abstract:
  Mentorship is a critical component of academia, but is not as visible as
publications, citations, grants, and awards. Despite the importance of studying
the quality and impact of mentorship, there are few large representative
mentorship datasets available. We contribute two datasets to the study of
mentorship. The first has over 300,000 ground truth academic mentor-mentee
pairs obtained from multiple diverse, manually-curated sources, and linked to
the Semantic Scholar (S2) knowledge graph. We use this dataset to train an
accurate classifier for predicting mentorship relations from bibliographic
features, achieving a held-out area under the ROC curve of 0.96. Our second
dataset is formed by applying the classifier to the complete co-authorship
graph of S2. The result is an inferred graph with 137 million weighted
mentorship edges among 24 million nodes. We release this first-of-its-kind
dataset to the community to help accelerate the study of scholarly mentorship:
\url{https://github.com/allenai/S2AMP-data}


title: How Sampling Impacts the Robustness of Stochastic Neural Networks

abstract:
  Stochastic neural networks (SNNs) are random functions and predictions are
gained by averaging over multiple realizations of this random function.
Consequently, an adversarial attack is calculated based on one set of samples
and applied to the prediction defined by another set of samples. In this paper
we analyze robustness in this setting by deriving a sufficient condition for
the given prediction process to be robust against the calculated attack. This
allows us to identify the factors that lead to an increased robustness of SNNs
and helps to explain the impact of the variance and the amount of samples.
Among other things, our theoretical analysis gives insights into (i) why
increasing the amount of samples drawn for the estimation of adversarial
examples increases the attack's strength, (ii) why decreasing sample size
during inference hardly influences the robustness, and (iii) why a higher
prediction variance between realizations relates to a higher robustness. We
verify the validity of our theoretical findings by an extensive empirical
analysis.


title:
Probabilistic Methods in the Study of Topological Indices on Random
  Spider Trees

abstract:
  In this paper, we characterize the structure and topological indices of a
class of random spider trees (RSTs) such as degree-based Gini index,
degree-based Hoover index, generalized Zagreb index and other indices
associated with these. We obtain the exact and asymptotic distributions of the
number of leaves via probabilistic methods. Moreover, we relate this model to
the class of RSTs that evolves in a preferential attachment manner.


title:
Detecting early signs of depression in the conversational domain: The
  role of transfer learning in low-resource scenarios

abstract:
  The high prevalence of depression in society has given rise to the need for
new digital tools to assist in its early detection. To this end, existing
research has mainly focused on detecting depression in the domain of social
media, where there is a sufficient amount of data. However, with the rise of
conversational agents like Siri or Alexa, the conversational domain is becoming
more critical. Unfortunately, there is a lack of data in the conversational
domain. We perform a study focusing on domain adaptation from social media to
the conversational domain. Our approach mainly exploits the linguistic
information preserved in the vector representation of text. We describe
transfer learning techniques to classify users who suffer from early signs of
depression with high recall. We achieve state-of-the-art results on a commonly
used conversational dataset, and we highlight how the method can easily be used
in conversational agents. We publicly release all source code.


title: Transient phenomena in the axion assisted Schwinger effect

abstract:
  Particle production induced by a time-dependent background is well understood
as the projection of the time-evolved initial state onto a set of final states.
While the asymptotic initial and final states are well defined in the usual
way, the definition of particles and antiparticles at intermediate times in the
presence of external fields is ambiguous. These external fields moreover induce
divergences which require regularization. In this paper we clarify some
subtleties in the computation of transient effects in physical quantities for
fermions in a homogeneous axion background, including Schwinger production in
background electromagnetic fields. The presence of the axion requires
particular care as well as knowledge of the UV theory when regulating the
theory and computing the vacuum contribution to the fermion energy.


title:
Optical trapping of high-aspect-ratio NaYF hexagonal prisms for kHz-MHz
  gravitational wave detectors

abstract:
  We present experimental results on optical trapping of Yb-doped $\beta-$NaYF
sub-wavelength-thickness high-aspect-ratio hexagonal prisms with a micron-scale
radius. The prisms are trapped in vacuum using an optical standing wave,
oriented with the normal vector to their face along the beam propagation
direction, and exhibit characteristic modes of three translational and two
torsional degrees of freedom. The measured motional spectra are compared with
numerical simulations. This plate-like geometry simultaneously enables trapping
with low photon-recoil-heating, high mass, and high trap frequency, potentially
leading to advances in high frequency gravitational wave searches in the
Levitated Sensor Detector (LSD), currently under construction. The material
used here has previously been shown to exhibit internal cooling via laser
refrigeration when optically trapped and illuminated with light of suitable
wavelength. Employing such laser refrigeration methods in the context of our
work may enable higher trapping intensity thus and higher trap frequencies for
gravitational wave searches approaching the several hundred kHz range.


title: Emerging research landscape of altermagnetism

abstract:
  Magnetism is one of the largest, most fundamental, and technologically most
relevant fields of condensed-matter physics. Traditionally, two basic magnetic
phases have been considered -- ferromagnetism and antiferromagnetism. The
breaking of the time-reversal symmetry and spin splitting of the electronic
states by the magnetization in ferromagnets underpins a range of macroscopic
responses in this extensively explored and exploited type of magnets. By
comparison, antiferromagnets have vanishing net magnetization. This Perspective
reflects on recent observations of materials hosting an intriguing
ferromagnetic-antiferromagnetic dichotomy, in which spin-split spectra and
macroscopic observables, akin to ferromagnets, are accompanied by antiparallel
magnetic order with vanishing magnetization, typical of antiferromagnets. An
unconventional non-relativistic symmetry-group formalism offers a resolution of
this apparent contradiction by delimiting a third basic magnetic phase, dubbed
altermagnetism. Our Perspective starts with an overview of the still emerging
unique phenomenology of the phase, and of the wide array of altermagnetic
material candidates. In the main part of the article, we illustrate how
altermagnetism can enrich our understanding of overarching condensed-matter
physics concepts, and have impact on prominent condensed-matter research areas.


title: Unraveling the $0\nu\beta\beta$ Decay Mechanisms

abstract:
  We discuss the possibilities of distinguishing among different mechanisms of
neutrinoless double beta decay arising in the effective field theory framework.
Following the review and detailed investigation of the particular ways of
discrimination, we conclude that the 32 different low-energy effective
operators can be split into multiple groups that are in principle
distinguishable from each other by measurements of the phase-space observables
and by comparison of the decay rates obtained using different isotopes. This
would require not only a substantial experimental precision but necessarily
also a considerable improvement of the current theoretical knowledge of the
underlying nuclear physics. Specifically, the limiting aspect in our approach
turns out to be the currently unknown or uncertain values of low-energy
constants. Besides the study adopting the effective field theory language we
also look into several typical UV models.


title:
Self-Supervised Video Object Segmentation via Cutout Prediction and
  Tagging

abstract:
  We propose a novel self-supervised Video Object Segmentation (VOS) approach
that strives to achieve better object-background discriminability for accurate
object segmentation. Distinct from previous self-supervised VOS methods, our
approach is based on a discriminative learning loss formulation that takes into
account both object and background information to ensure object-background
discriminability, rather than using only object appearance. The discriminative
learning loss comprises cutout-based reconstruction (cutout region represents
part of a frame, whose pixels are replaced with some constant values) and tag
prediction loss terms. The cutout-based reconstruction term utilizes a simple
cutout scheme to learn the pixel-wise correspondence between the current and
previous frames in order to reconstruct the original current frame with added
cutout region in it. The introduced cutout patch guides the model to focus as
much on the significant features of the object of interest as the less
significant ones, thereby implicitly equipping the model to address
occlusion-based scenarios. Next, the tag prediction term encourages
object-background separability by grouping tags of all pixels in the cutout
region that are similar, while separating them from the tags of the rest of the
reconstructed frame pixels. Additionally, we introduce a zoom-in scheme that
addresses the problem of small object segmentation by capturing fine structural
information at multiple scales. Our proposed approach, termed CT-VOS, achieves
state-of-the-art results on two challenging benchmarks: DAVIS-2017 and
Youtube-VOS. A detailed ablation showcases the importance of the proposed loss
formulation to effectively capture object-background discriminability and the
impact of our zoom-in scheme to accurately segment small-sized objects.


title: Controlling the spontaneous firing behavior of a neuron with astrocyte

abstract:
  Mounting evidence in recent years suggests that astrocytes, a sub-type of
glial cells, not only serve metabolic and structural support for neurons and
synapses but also play critical roles in regulation of proper functioning of
the nervous system. In this work, we investigate the effect of astrocyte on the
spontaneous firing activity of a neuron through a combined model which includes
a neuron-astrocyte pair. First, we show that an astrocyte may provide a kind of
multistability in neuron dynamics by inducing different firing modes such as
random and bursty spiking. Then, we identify the underlying mechanism of this
behavior and search for the astrocytic factors that may have regulatory roles
in different firing regimes. More specifically, we explore how an astrocyte can
participate in occurrence and control of spontaneous irregular spiking activity
of a neuron in random spiking mode. Additionally, we systematically investigate
the bursty firing regime dynamics of the neuron under the variation of
biophysical facts related to the intracellular environment of the astrocyte. It
is found that an astrocyte coupled to a neuron can provide a control mechanism
for both spontaneous firing irregularity and burst firing statistics, i.e.,
burst regularity and size.


title: MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes

abstract:
  Recent advances in the visualization of continuous multimodal multi-objective
optimization (MMMOO) landscapes brought a new perspective to their search
dynamics. Locally efficient (LE) sets, often considered as traps for local
search, are rarely isolated in the decision space. Rather, intersections by
superposing attraction basins lead to further solution sets that at least
partially contain better solutions. The Multi-Objective Gradient Sliding
Algorithm (MOGSA) is an algorithmic concept developed to exploit these
superpositions. While it has promising performance on many MMMOO problems with
linear LE sets, closer analysis of MOGSA revealed that it does not sufficiently
generalize to a wider set of test problems. Based on a detailed analysis of
shortcomings of MOGSA, we propose a new algorithm, the Multi-Objective
Landscape Explorer (MOLE). It is able to efficiently model and exploit LE sets
in MMMOO problems. An implementation of MOLE is presented for the bi-objective
case, and the practicality of the approach is shown in a benchmarking
experiment on the Bi-Objective BBOB testbed.


title: Metric Learning and Adaptive Boundary for Out-of-Domain Detection

abstract:
  Conversational agents are usually designed for closed-world environments.
Unfortunately, users can behave unexpectedly. Based on the open-world
environment, we often encounter the situation that the training and test data
are sampled from different distributions. Then, data from different
distributions are called out-of-domain (OOD). A robust conversational agent
needs to react to these OOD utterances adequately. Thus, the importance of
robust OOD detection is emphasized. Unfortunately, collecting OOD data is a
challenging task. We have designed an OOD detection algorithm independent of
OOD data that outperforms a wide range of current state-of-the-art algorithms
on publicly available datasets. Our algorithm is based on a simple but
efficient approach of combining metric learning with adaptive decision
boundary. Furthermore, compared to other algorithms, we have found that our
proposed algorithm has significantly improved OOD performance in a scenario
with a lower number of classes while preserving the accuracy for in-domain
(IND) classes.


title:
Control-NeRF: Editable Feature Volumes for Scene Rendering and
  Manipulation

abstract:
  We present a novel method for performing flexible, 3D-aware image content
manipulation while enabling high-quality novel view synthesis. While NeRF-based
approaches are effective for novel view synthesis, such models memorize the
radiance for every point in a scene within a neural network. Since these models
are scene-specific and lack a 3D scene representation, classical editing such
as shape manipulation, or combining scenes is not possible. Hence, editing and
combining NeRF-based scenes has not been demonstrated. With the aim of
obtaining interpretable and controllable scene representations, our model
couples learnt scene-specific feature volumes with a scene agnostic neural
rendering network. With this hybrid representation, we decouple neural
rendering from scene-specific geometry and appearance. We can generalize to
novel scenes by optimizing only the scene-specific 3D feature representation,
while keeping the parameters of the rendering network fixed. The rendering
function learnt during the initial training stage can thus be easily applied to
new scenes, making our approach more flexible. More importantly, since the
feature volumes are independent of the rendering model, we can manipulate and
combine scenes by editing their corresponding feature volumes. The edited
volume can then be plugged into the rendering model to synthesize high-quality
novel views. We demonstrate various scene manipulations, including mixing
scenes, deforming objects and inserting objects into scenes, while still
producing photo-realistic results.