title: Stochastic Block Smooth Graphon Model

abstract:
  The paper proposes the combination of stochastic blockmodels with smooth
graphon models. The first allow for partitioning the set of individuals in a
network into blocks which represent groups of nodes that presumably connect
stochastically equivalently, therefore often also called communities. Smooth
graphon models instead assume that the network's nodes can be arranged on a
one-dimensional scale such that closeness implies a similar connectivity
behavior. Both models belong to the model class of node-specific latent
variables, entailing a natural relationship. While these model strands have
developed more or less completely independently, this paper proposes their
generalization towards stochastic block smooth graphon models. This approach
enables to exploit the advantages of both worlds. We pursue a general EM-type
algorithm for estimation and demonstrate the usability by applying the model to
both simulated and real-world examples.


title:
Snowmass2021 White Paper: Collider Physics Opportunities of Extended
  Warped Extra-Dimensional Models

abstract:
  While the warped extra-dimensional models provide an attractive solution to
both the gauge and the flavor hierarchy problems, the mass scale of new
particles predicted by the minimal models would be beyond the reach of the LHC.
Models of extended warped extra dimensions have been proposed to evade these
issues and their collider implications have been investigated for the last
decade. This white paper summarizes the recent developments in the context of
collider phenomenology. The strategies and lessons are broad, and provide a
template to extend the experimental program, to cover a wider class of signals
in other new physics scenarios as well.


title: Even anisotropic-flow harmonics are from Venus, odd ones are from Mars

abstract:
  We test the ability of the "escape mechanism" to create the anisotropic flow
observed in high-energy nuclear collisions. We compare the flow harmonics $v_n$
in the few-rescatterings regime from two types of transport simulations, with
$2\to 2$ and $2\to 0$ collision kernels respectively, and from analytical
calculations neglecting the gain term of the Boltzmann equation. We find that
the even flow harmonics are similar in the three approaches, while the odd
harmonics differ significantly.


title:
Tackling Online One-Class Incremental Learning by Removing Negative
  Contrasts

abstract:
  Recent work studies the supervised online continual learning setting where a
learner receives a stream of data whose class distribution changes over time.
Distinct from other continual learning settings the learner is presented new
samples only once and must distinguish between all seen classes. A number of
successful methods in this setting focus on storing and replaying a subset of
samples alongside incoming data in a computationally efficient manner. One
recent proposal ER-AML achieved strong performance in this setting by applying
an asymmetric loss based on contrastive learning to the incoming data and
replayed data. However, a key ingredient of the proposed method is avoiding
contrasts between incoming data and stored data, which makes it impractical for
the setting where only one new class is introduced in each phase of the stream.
In this work we adapt a recently proposed approach (\textit{BYOL}) from
self-supervised learning to the supervised learning setting, unlocking the
constraint on contrasts. We then show that supplementing this with additional
regularization on class prototypes yields a new method that achieves strong
performance in the one-class incremental learning setting and is competitive
with the top performing methods in the multi-class incremental setting.


title: Verifiable Access Control for Augmented Reality Localization and Mapping

abstract:
  Localization and mapping is a key technology for bridging the virtual and
physical worlds in augmented reality (AR). Localization and mapping works by
creating and querying maps made of anchor points that enable the overlay of
these two worlds. As a result, information about the physical world is captured
in the map and naturally gives rise to concerns around who can map physical
spaces as well as who can access or modify the virtual ones. This paper
discusses how we can provide access controls over virtual maps as a basic
building block to enhance security and privacy of AR systems. In particular, we
propose VACMaps: an access control system for localization and mapping using
formal methods. VACMaps defines a domain-specific language that enables users
to specify access control policies for virtual spaces. Access requests to
virtual spaces are then evaluated against relevant policies in a way that
preserves confidentiality and integrity of virtual spaces owned by the users.
The precise semantics of the policies are defined by SMT formulas, which allow
VACMaps to reason about properties of access policies automatically. An
evaluation of VACMaps is provided using an AR testbed of a single-family home.
We show that VACMaps is scalable in that it can run at practical speeds and
that it can also reason about access control policies automatically to detect
potential policy misconfigurations.


title:
Weakly-Supervised Online Action Segmentation in Multi-View Instructional
  Videos

abstract:
  This paper addresses a new problem of weakly-supervised online action
segmentation in instructional videos. We present a framework to segment
streaming videos online at test time using Dynamic Programming and show its
advantages over greedy sliding window approach. We improve our framework by
introducing the Online-Offline Discrepancy Loss (OODL) to encourage the
segmentation results to have a higher temporal consistency. Furthermore, only
during training, we exploit frame-wise correspondence between multiple views as
supervision for training weakly-labeled instructional videos. In particular, we
investigate three different multi-view inference techniques to generate more
accurate frame-wise pseudo ground-truth with no additional annotation cost. We
present results and ablation studies on two benchmark multi-view datasets,
Breakfast and IKEA ASM. Experimental results show efficacy of the proposed
methods both qualitatively and quantitatively in two domains of cooking and
assembly.


title: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection

abstract:
  Monocular 3D object detection has long been a challenging task in autonomous
driving, which requires to decode 3D predictions solely from a single 2D image.
Most existing methods follow conventional 2D object detectors to first localize
objects by their centers, and then predict 3D attributes using
center-neighboring local features. However, such center-based pipeline views 3D
prediction as a subordinate task and lacks inter-object depth interactions with
global spatial clues. In this paper, we introduce a simple framework for
Monocular DEtection with depth-aware TRansformer, named MonoDETR. We enable the
vanilla transformer to be depth-aware and enforce the whole detection process
guided by depth. Specifically, we represent 3D object candidates as a set of
queries and produce non-local depth embeddings of the input image by a
lightweight depth predictor and an attention-based depth encoder. Then, we
propose a depth-aware decoder to conduct both inter-query and query-scene depth
feature communication. In this way, each object estimates its 3D attributes
adaptively from the depth-informative regions on the image, not limited by
center-around features. With minimal handcrafted designs, MonoDETR is an
end-to-end framework without additional data, anchors or NMS and achieves
competitive performance on KITTI benchmark among state-of-the-art center-based
networks. Extensive ablation studies demonstrate the effectiveness of our
approach and its potential to serve as a transformer baseline for future
monocular research. Code is available at
https://github.com/ZrrSkywalker/MonoDETR.git.


title: Multi-wavelength Variability of Sagittarius A* in July 2019

abstract:
  We report timing analysis of near-infrared (NIR), X-ray, and sub-millimeter
(submm) data during a three-day coordinated campaign observing Sagittarius A*.
Data were collected at 4.5 micron with the Spitzer Space Telescope, 2-8 keV
with the Chandra X-ray Observatory, 3-70 keV with NuSTAR, 340 GHz with ALMA,
and at 2.2 micron with the GRAVITY instrument on the Very Large Telescope
Interferometer. Two dates show moderate variability with no significant lags
between the submm and the infrared at 99% confidence. July 18 captured a
moderately bright NIR flare (F_K ~ 15 mJy) simultaneous with an X-ray flare (F
~ 0.1 cts/s) that most likely preceded bright submm flux (F ~ 5.5 Jy) by about
+34 (+14 -33) minutes at 99% confidence. The uncertainty in this lag is
dominated by the fact that we did not observe the peak of the submm emission. A
synchrotron source cooled through adiabatic expansion can describe a rise in
the submm once the synchrotron-self-Compton NIR and X-ray peaks have faded.
This model predicts high GHz and THz fluxes at the time of the NIR/X-ray peak
and electron densities well above those implied from average accretion rates
for Sgr A*. However, the higher electron density postulated in this scenario
would be in agreement with the idea that 2019 was an extraordinary epoch with a
heightened accretion rate. Since the NIR and X-ray peaks can also be fit by a
non-thermal synchrotron source with lower electron densities, we cannot rule
out an unrelated chance coincidence of this bright submm flare with the
NIR/X-ray emission.


title:
SharpContour: A Contour-based Boundary Refinement Approach for Efficient
  and Accurate Instance Segmentation

abstract:
  Excellent performance has been achieved on instance segmentation but the
quality on the boundary area remains unsatisfactory, which leads to a rising
attention on boundary refinement. For practical use, an ideal post-processing
refinement scheme are required to be accurate, generic and efficient. However,
most of existing approaches propose pixel-wise refinement, which either
introduce a massive computation cost or design specifically for different
backbone models. Contour-based models are efficient and generic to be
incorporated with any existing segmentation methods, but they often generate
over-smoothed contour and tend to fail on corner areas. In this paper, we
propose an efficient contour-based boundary refinement approach, named
SharpContour, to tackle the segmentation of boundary area. We design a novel
contour evolution process together with an Instance-aware Point Classifier. Our
method deforms the contour iteratively by updating offsets in a discrete
manner. Differing from existing contour evolution methods, SharpContour
estimates each offset more independently so that it predicts much sharper and
accurate contours. Notably, our method is generic to seamlessly work with
diverse existing models with a small computational cost. Experiments show that
SharpContour achieves competitive gains whilst preserving high efficiency


title:
Deep learning for laboratory earthquake prediction and autoregressive
  forecasting of fault zone stress

abstract:
  Earthquake forecasting and prediction have long and in some cases sordid
histories but recent work has rekindled interest based on advances in early
warning, hazard assessment for induced seismicity and successful prediction of
laboratory earthquakes. In the lab, frictional stick-slip events provide an
analog for earthquakes and the seismic cycle. Labquakes are ideal targets for
machine learning (ML) because they can be produced in long sequences under
controlled conditions. Recent works show that ML can predict several aspects of
labquakes using fault zone acoustic emissions. Here, we generalize these
results and explore deep learning (DL) methods for labquake prediction and
autoregressive (AR) forecasting. DL improves existing ML methods of labquake
prediction. AR methods allow forecasting at future horizons via iterative
predictions. We demonstrate that DL models based on Long-Short Term Memory
(LSTM) and Convolution Neural Networks predict labquakes under several
conditions, and that fault zone stress can be predicted with fidelity,
confirming that acoustic energy is a fingerprint of fault zone stress. We
predict also time to start of failure (TTsF) and time to the end of Failure
(TTeF) for labquakes. Interestingly, TTeF is successfully predicted in all
seismic cycles, while the TTsF prediction varies with the amount of preseismic
fault creep. We report AR methods to forecast the evolution of fault stress
using three sequence modeling frameworks: LSTM, Temporal Convolution Network
and Transformer Network. AR forecasting is distinct from existing predictive
models, which predict only a target variable at a specific time. The results
for forecasting beyond a single seismic cycle are limited but encouraging. Our
ML/DL models outperform the state-of-the-art and our autoregressive model
represents a novel framework that could enhance current methods of earthquake
forecasting.


title:
Investigating saturation effects and the virtual pion in leading neutron
  events at HERA with the dipole model

abstract:
  We investigate events with very forward neutrons in $ep$ collisions at HERA
using impact parameter dependent colour dipole models bSat and bNonSat, with
and without saturation. This is the first study of the leading neutron process
deploying these models. The models prediction are compared with the available
HERA measurements for $6<Q^2<100~GeV^2$ , $70<W<245~GeV$. Our analysis shows
that the models exhibit Feynman scaling, independent of $Q^2$. Our results
demonstrate that the $W$ and $Q^2$ dependence of the cross section is
independent of the presence of a forward neutron as predicted by limiting
fragmentation hypothesis, which is a consequence of Feynman scaling itself.
Thus, we infer that the HERA leading neutron production inclusive data is
insensitive to saturation physics and these cross sections may not be able to
distinguish gluon saturation effects in future $ep$ colliders. We further
demonstrate the validity of the hypothesis that the hadronic cross section is
universal between pions and protons at small $x$, up to normalisation. We
provide a good description of the pion structure function $F_2^{LN}$ at small
$x$. We also show that the observables in the exclusive diffractive
measurements with a vector meson in the final state are more sensitive to
saturation physics at small $x$ than inclusive measurements. At last we provide
a prediction for the $\hat t$ spectrum in exclusive vector meson production in
the dipole model using Yukawa theory to model the virtual pion's spatial wave
function.


title: Two more characterisations of nearly pseudocompact space

abstract:
  In this paper we have obtained two more characterizations of nearly
pseudocompact spaces.


title: Immersive Visual Analysis of Cello Bow Movements

abstract:
  We propose a 3D immersive visualization environment for analyzing the right
hand movements of a cello player. To achieve this, we track the position and
orientation of the cello bow and record audio. As movements mostly occur in a
shallow volume and the motion is therefore mostly two-dimensional, we use the
third dimension to encode time. Our concept further explores various mappings
from motion and audio data to spatial and other visual attributes. We work in
close cooperation with a cellist and plan to evaluate our prototype through a
user study with a group of cellists in the near future.


title: Human Gait Recognition Using Bag of Words Feature Representation Method

abstract:
  In this paper, we propose a novel gait recognition method based on a
bag-of-words feature representation method. The algorithm is trained, tested
and evaluated on a unique human gait data consisting of 93 individuals who
walked with comfortable pace between two end points during two different
sessions. To evaluate the effectiveness of the proposed model, the results are
compared with the outputs of the classification using extracted features. As it
is presented, the proposed method results in significant improvement accuracy
compared to using common statistical features, in all the used classifiers.


title: NPBG++: Accelerating Neural Point-Based Graphics

abstract:
  We present a new system (NPBG++) for the novel view synthesis (NVS) task that
achieves high rendering realism with low scene fitting time. Our method
efficiently leverages the multiview observations and the point cloud of a
static scene to predict a neural descriptor for each point, improving upon the
pipeline of Neural Point-Based Graphics in several important ways. By
predicting the descriptors with a single pass through the source images, we
lift the requirement of per-scene optimization while also making the neural
descriptors view-dependent and more suitable for scenes with strong
non-Lambertian effects. In our comparisons, the proposed system outperforms
previous NVS approaches in terms of fitting and rendering runtimes while
producing images of similar quality.


title:
Remember and Forget Experience Replay for Multi-Agent Reinforcement
  Learning

abstract:
  We present the extension of the Remember and Forget for Experience Replay
(ReF-ER) algorithm to Multi-Agent Reinforcement Learning (MARL). {ReF-ER} was
shown to outperform state of the art algorithms for continuous control in
problems ranging from the OpenAI Gym to complex fluid flows. In MARL, the
dependencies between the agents are included in the state-value estimator and
the environment dynamics are modeled via the importance weights used by ReF-ER.
In collaborative environments, we find the best performance when the value is
estimated using individual rewards and we ignore the effects of other actions
on the transition map. We benchmark the performance of ReF-ER MARL on the
Stanford Intelligent Systems Laboratory (SISL) environments. We find that
employing a single feed-forward neural network for the policy and the value
function in ReF-ER MARL, outperforms state of the art algorithms that rely on
complex neural network architectures.


title: Data-Driven Visual Reflection on Music Instrument Practice

abstract:
  We propose a data-driven approach to music instrument practice that allows
studying patterns and long-term trends through visualization. Inspired by life
logging and fitness tracking, we imagine musicians to record their practice
sessions over the span of months or years. The resulting data in the form of
MIDI or audio recordings can then be analyzed sporadically to track progress
and guide decisions. Toward this vision, we started exploring various
visualization designs together with a group of nine guitarists, who provided us
with data and feedback over the course of three months.


title:
Addressing Client Drift in Federated Continual Learning with Adaptive
  Optimization

abstract:
  Federated learning has been extensively studied and is the prevalent method
for privacy-preserving distributed learning in edge devices. Correspondingly,
continual learning is an emerging field targeted towards learning multiple
tasks sequentially. However, there is little attention towards additional
challenges emerging when federated aggregation is performed in a continual
learning system. We identify \textit{client drift} as one of the key weaknesses
that arise when vanilla federated averaging is applied in such a system,
especially since each client can independently have different order of tasks.
We outline a framework for performing Federated Continual Learning (FCL) by
using NetTailor as a candidate continual learning approach and show the extent
of the problem of client drift. We show that adaptive federated optimization
can reduce the adverse impact of client drift and showcase its effectiveness on
CIFAR100, MiniImagenet, and Decathlon benchmarks. Further, we provide an
empirical analysis highlighting the interplay between different hyperparameters
such as client and server learning rates, the number of local training
iterations, and communication rounds. Finally, we evaluate our framework on
useful characteristics of federated learning systems such as scalability,
robustness to the skewness in clients' data distribution, and stragglers.


title: Timing Properties of Blue-emitting Scintillating Fibers

abstract:
  Scintillating fibers are widely used in particle detectors, mainly for
tracking. We have studied the properties and have compared the performance of
different blue-emitting 250~$\mu$m diameter round double-clad scintillating
fibers from Kuraray and Saint-Gobain. Here we focus on the properties of the
fibers over short lengths ($< 50~{\rm cm}$), in particular on their timing
performances. We report on the light yield and attenuation, scintillation light
decay time and the achievable time resolution.


title: A Schwarzian on the Stretched Horizon

abstract:
  It is well known that the Euclidean black hole action has a boundary term at
the horizon proportional to the area. I show that if the horizon is replaced by
a stretched horizon with appropriate boundary conditions, a new boundary term
appears, described by a Schwarzian action similar to the recently discovered
boundary actions in "nearly anti-de Sitter" gravity.


title:
Resilient Execution of Data-triggered Applications on Edge, Fog and
  Cloud Resources

abstract:
  Internet of Things (IoT) is leading to the pervasive availability of
streaming data about the physical world, coupled with edge computing
infrastructure deployed as part of smart cities and 5G rollout. These
constrained, less reliable but cheap resources are complemented by fog
resources that offer federated management and accelerated computing, and
pay-as-you-go cloud resources. There is a lack of intuitive means to deploy
application pipelines to consume such diverse streams, and to execute them
reliably on edge and fog resources. We propose an innovative application model
to declaratively specify queries to match streams of micro-batch data from
stream sources and trigger the distributed execution of data pipelines. We also
design a resilient scheduling strategy using advanced reservation on reliable
fogs to guarantee dataflow completion within a deadline while minimizing the
execution cost. Our detailed experiments on over 100 virtual IoT resources and
for $\approx 10k$ task executions, with comparison against baseline scheduling
strategies, illustrates the cost-effectiveness, resilience and scalability of
our framework.


title:
The inverse scattering transform for weak Wigner-von Neumann type
  potentials

abstract:
  In the context of the Cauchy problem for the Korteweg-de Vries equation we
extend the inverse scattering transform to initial data that behave at plus
infinity like a sum of Wigner-von Neumann type potentials with small coupling
constants. Our arguments are based on the theory of Hankel operators.


title:
Thermal and electrical cross-plane conductivity at the nanoscale in
  poly(3,4-ethylenedioxythiophene):trifuoromethanesulfonate thin films

abstract:
  Cross-plane electrical and thermal transport in thin films of a conducting
polymer (poly(3,4-ethylenedioxythiophene), PEDOT) stabilized with
trifluoromethanesulfonate (OTf) is investigated in this study. We explore their
electrical properties by conductive atomic force microscopy (C-AFM), which
reveals the presence of highly conductive nano-domains. Thermal conductivity in
cross-plane direction is measured with Null-Point scanning thermal microscopy
(NP-SThM): PEDOT:OTf indeed demonstrates non-negligible electronic contribution
to the thermal transport. We further investigate the correlation between
electrical and thermal conductivity by applying posttreatment: chemical
reduction (de-doping) for the purpose of lowering charge carrier concentration
and hence, electrical conductivity and acid treatment (over-doping) to increase
the latter. From our measurements, we find a vibrational thermal conductivity
of 0.34 (+/- 0.04) Wm-1 K-1. From the linear dependence or the electronic
contribution of thermal conductivity vs. the electronic conductivity
(Widemann-Franz law), we infer a Lorenz number 6 times larger than the
classical Sommerfeld value as also observed in many organic materials for
in-plane thermal transport. Applying the recently proposed molecular
Widemann-Franz law, we deduced a reorganization energy of 0.53 (+/- 0.06) eV.


title:
Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint
  Localization and Channel Estimation at mmWave

abstract:
  RIS-aided millimeter wave wireless systems benefit from robustness to
blockage and enhanced coverage. In this paper, we study the ability of RIS to
also provide enhanced localization capabilities as a by-product of
communication. We consider sparse reconstruction algorithms to obtain high
resolution channel estimates that are mapped to position information. In
RIS-aided mmWave systems, the complexity of sparse recovery becomes a
bottleneck, given the large number of elements of the RIS and the large
communication arrays. We propose to exploit a multidimensional orthogonal
matching pursuit strategy for compressive channel estimation in a RIS-aided
millimeter wave system. We show how this algorithm, based on computing the
projections on a set of independent dictionaries instead of a single large
dictionary, enables high accuracy channel estimation at reduced complexity. We
also combine this strategy with a localization approach which does not rely on
the absolute time of arrival of the LoS path. Localization results in a
realistic 3D indoor scenario show that RIS-aided wireless system can also
benefit from a significant improvement in localization accuracy.


title:
Apparent anomalous diffusion and non-Gaussian distributions in a simple
  mobile-immobile transport model with Poissonian switching

abstract:
  We analyse mobile-immobile transport of particles that switch between the
mobile and immobile phases with finite rates. Despite this seemingly simple
assumption of Poissonian switching we unveil a rich transport dynamics
including significant transient anomalous diffusion and non-Gaussian
displacement distributions. Our discussion is based on experimental parameters
for tau proteins in neuronal cells, but the results obtained here are expected
to be of relevance for a broad class of processes in complex systems.
Concretely, we obtain that when the mean binding time is significantly longer
than the mean mobile time, transient anomalous diffusion is observed at short
and intermediate time scales, with a strong dependence on the fraction of
initially mobile and immobile particles. We unveil a Laplace distribution of
particle displacements at relevant intermediate time scales. For any initial
fraction of mobile particles, the respective mean squared displacement displays
a plateau. Moreover, we demonstrate a short-time cubic time dependence of the
mean squared displacement for immobile tracers when initially all particles are
immobile.


title:
Mutual Inclination of Ultra-Short-Period Planets with Time Varying
  Stellar J2-moment

abstract:
  Systems with ultra-short-period planets (USPs) tend to possess larger mutual
inclinations compared to those with planets located farther from their host
stars. This could be explained due to precession caused by stellar oblateness
at early times when the host star was rapidly spinning. However, stellar
oblateness reduces over time due to the decrease in the stellar rotation rate,
and this may further shape the planetary mutual inclinations. In this work, we
investigate in detail how the final mutual inclination varies under the effect
of a decreasing $J_2$. We find that different initial parameters (e.g., the
magnitude of $J_2$ and planetary inclinations) will contribute to different
final mutual inclinations, providing a constraint on the formation mechanisms
of USPs. In general, if the inner planets start in the same plane as the
stellar equator (or co-planar while misaligned with the stellar spin-axis), the
mutual inclination decreases (or increases then decreases) over time due to the
decay of the $J_2$ moment. This is because the inner orbit typically possesses
less orbital angular momentum than the outer ones. However, if the outer planet
is initially aligned with the stellar spin while the inner one is misaligned,
the mutual inclination nearly stays the same. Overall, our results suggest that
either the USP planets formed early and acquired significant inclinations
(e.g., $\gtrsim30^\circ$ with its companion or $\gtrsim10^\circ$ with its host
star spin-axis for Kepler-653c) or they formed late ($\gtrsim$Gyr) when their
host stars rotate slower.


title: Effect of vacancy defects on frustrated magnets and quantum spin liquids

abstract:
  Quenched disorder may prevent the formation of the widely sought
quantum-spin-liquid states (QSLs) or mask their signatures by inducing a
spin-glass state, which is why considerable experimental efforts are directed
at purifying materials that may host QSLs. However, in geometrically frustrated
(GF) magnets, the largest class of materials in which QSLs are sought, the
glass-transition temperature $T_g$ grows with decreasing the density of vacancy
defects, accompanied by a simultaneous growth of the magnetic susceptibility.
In this paper, we develop a phenomenological theory of glass transitions and
magnetic susceptibility in 3D GF magnetic materials. We consider a model of a
GF magnet in which the glass transition occurs in the absence of vacancies,
e.g., due to other types of quenched disorder. We show that disorder that
creates weak local perturbations, e.g. weak random strain, leads to the growth
of the transition temperature $T_g$. By contrast, vacancies reduce $T_g$ for
small vacancy concentrations. Another consequence of the presence of vacancies
is the creation of quasispins, effective magnetic moments localised near the
vacancies, that contribute to the magnetic susceptibility of the system
together with the bulk spins. We show that increasing the vacancy density leads
to an increase of the total magnetic susceptibility.

categories: cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech cond-mat.str-el


title:
Prespecification of Structure for Optimizing Data Collection and
  Research Transparency by Leveraging Conditional Independencies

abstract:
  Data collection and research methodology represents a critical part of the
research pipeline. On the one hand, it is important that we collect data in a
way that maximises the validity of what we are measuring, which may involve the
use of long scales with many items. On the other hand, collecting a large
number of items across multiple scales results in participant fatigue, and
expensive and time consuming data collection. It is therefore important that we
use the available resources optimally. In this work, we consider how a
consideration for theory and the associated causal/structural model can help us
to streamline data collection procedures by not wasting time collecting data
for variables which are not causally critical for subsequent analysis. This not
only saves time and enables us to redirect resources to attend to other
variables which are more important, but also increases research transparency
and the reliability of theory testing. In order to achieve this streamlined
data collection, we leverage structural models, and Markov conditional
independency structures implicit in these models to identify the substructures
which are critical for answering a particular research question. In this work,
we review the relevant concepts and present a number of didactic examples with
the hope that psychologists can use these techniques to streamline their data
collection process without invalidating the subsequent analysis. We provide a
number of simulation results to demonstrate the limited analytical impact of
this streamlining.


title: Counterexamples in 4-manifold topology

abstract:
  We consider several of the most commonly studied equivalence relations on
4-manifolds and how they are related to one another. We explain implications
e.g. that $h$-cobordant manifolds are stably homeomorphic, and we provide
examples illustrating the failure of other potential implications. The
information is conveniently organised in a flowchart and a table.


title: Text to Mesh Without 3D Supervision Using Limit Subdivision

abstract:
  We present a technique for zero-shot generation of a 3D model using only a
target text prompt. Without a generative model or any 3D supervision our method
deforms a control shape of a limit subdivided surface along with a texture map
and normal map to obtain a 3D model asset that matches the input text prompt
and can be deployed into games or modeling applications. We rely only on a
pre-trained CLIP model that compares the input text prompt with differentiably
rendered images of our 3D model. While previous works have focused on
stylization or required training of generative models we perform optimization
on mesh parameters directly to generate shape and texture. To improve the
quality of results we also introduce a set of techniques such as render
augmentations, primitive selection, prompt augmentation that guide the mesh
towards a suitable result.


title: Studying Interstellar Turbulence Driving Scales using the Bispectrum

abstract:
  We demonstrate the utility of the bispectrum, the Fourier three-point
correlation function, for studying driving scales of magnetohydrodynamic (MHD)
turbulence in the interstellar medium. We calculate the bispectrum by
implementing a parallelized Monte Carlo direct measurement method, which we
have made publicly available. In previous works, the bispectrum has been used
to identify non-linear scaling correlations and break degeneracies in
lower-order statistics like the power spectrum. We find that the bicoherence, a
related statistic which measures phase coupling of Fourier modes, identifies
turbulence driving scales using density and column density fields. In
particular, it shows that the driving scale is phase-coupled to scales present
in the turbulent cascade. We also find that the presence of an ordered magnetic
field at large-scales enhances phase coupling as compared to a pure
hydrodynamic case. We therefore suggest the bispectrum and bicoherence as tools
for searching for non-locality for wave interactions in MHD turbulence.


title:
Adverse Health Correlates of Intimate Partner Violence against Older
  Women: Mining Electronic Health Records

abstract:
  Intimate partner violence (IPV) is often studied as a problem that
predominantly affects younger women. However, studies show that older women are
also frequently victims of abuse even though the physical effects of abuse are
harder to detect. In this study, we mined the electronic health records (EHR)
available through IBM Explorys to identify health correlates of IPV that are
specific to older women. Our analyses suggested that diagnostic terms that are
co-morbid with IPV in older women are dominated by substance abuse and
associated toxicities. When we considered differential co-morbidity, i.e.,
terms that are significantly more associated with IPV in older women compared
to younger women, we identified terms spanning mental health issues,
musculoskeletal issues, neoplasms, and disorders of various organ systems
including skin, ears, nose and throat. Our findings provide pointers for
further investigation in understanding the health effects of IPV among older
women, as well as potential markers that can be used for screening IPV.


title: Long time well-posedness of Whitham-Boussinesq systems

abstract:
  Consideration is given to three different full dispersion Boussinesq systems
arising as asymptotic models in the bi-directional propagation of weakly
nonlinear surface waves in shallow water. We prove that, under a non-cavitation
condition on the initial data, these three systems are well-posed on a time
scale of order $\mathcal{O}(\frac1\varepsilon)$, where $\varepsilon$ is a small
parameter measuring the weak non-linearity of the waves. This result seems new
for one of these systems, even for short time. The two other systems involve
surface tension, and for one of them, the non-cavitation condition has to be
sharpened when the surface tension is small. The proof relies on suitable
symmetrizers and the classical theory of hyperbolic systems. However, we have
to track the small parameters carefully in the commutator estimates to get the
long time well-posedness.
  Finally, combining our results with the recent ones of Emerald provide a full
justification of these systems as water wave models in a larger range of
regimes than the classical $(a,b,c,d)$-Boussinesq systems.


title:
Electrical Programmable Low-loss high cyclable Nonvolatile Photonic
  Random-Access Memory

abstract:
  The retention-of-state functionality provided by memories is fundamental to
any Turing machine and neural network, hence is critical for any information
system today. While emerging optical machine learning accelerators and photonic
neuromorphic computing paradigms provide promising signal processing and
computing performance, the lack of a photon-photon force in the universe makes
storing optical information challenging. Fortunately, phase change materials
provide such a missing memristive nonvolatile function via their reconfigurable
crystalline structure and allow for rapid optical READ paradigms. However,
demonstrations of photonic memory are limited by high optical loss, low
state-cyclability, and rely on cumbersome non-CMOS like optical
programmability. To overcome all three shortcomings and unlock the full
potential of optical information storage and access, here we introduce a
photonic random-access memory featuring vanishing low optical loss, demonstrate
more than half a million switching cycles, a 100x improvement over
state-of-art, and realize electrical programmability on-chip. The exceedingly
low optical absorption (0.0015 dB/{\mu}m) is achieved via a novel broadband
transparent phase change material, Ge2Sb2Se5 integrated atop a nanophotonic
waveguide of a silicon chip. We show a highly efficient signal modulation (0.2
dB/{\mu}m) achieved by realizing a newly designed paired micro-heaters along
both sides of waveguide, which allows for electronic-standard programmability
of these photonic memories. When interrogated by an optical beam, they offer
picosecond-short memory READ latency. Furthermore, we demonstrate a partial
amorphization scheme realizing multi-state memory levels on a single heater
enhancing footprint efficiency.


title: Adsorption of Lattice Polymers with Quenched Topologies

abstract:
  We introduce a framework for adsorption of a single polymer in which the
topology of the polymer is quenched before adsorption, in contrast to more
standard adsorption models having annealed topology. Our "topology" refers
either to the precise branching structure of a branched polymer (in any
dimension), or else to the knot type of a ring polymer in three dimensions. The
quenched topology is chosen uniformly at random from all lattice polymers of a
given size in one of four classes (lattice animals, trees, combs, or rings),
and we then consider adsorption of the subclass of configurations that have the
quenched topology. When the polymer-surface attraction increases without bound,
the quenched topological structure keeps a macroscopic fraction of monomers off
the surface, in contrast with annealed models that asymptotically have 100% of
monomers in the surface. We prove properties of the limiting free energy and
the critical point in each model, although important open questions remain. We
pay special attention to the class of comb polymers, which admit some rigorous
answers to questions that otherwise remain open. Since the class of all combs
was not previously examined rigorously in full generality, we also prove the
existence of its growth constant and its limiting free energy for annealed
adsorption.


title:
Leveraging unsupervised and weakly-supervised data to improve direct
  speech-to-speech translation

abstract:
  End-to-end speech-to-speech translation (S2ST) without relying on
intermediate text representations is a rapidly emerging frontier of research.
Recent works have demonstrated that the performance of such direct S2ST systems
is approaching that of conventional cascade S2ST when trained on comparable
datasets. However, in practice, the performance of direct S2ST is bounded by
the availability of paired S2ST training data. In this work, we explore
multiple approaches for leveraging much more widely available unsupervised and
weakly-supervised speech and text data to improve the performance of direct
S2ST based on Translatotron 2. With our most effective approaches, the average
translation quality of direct S2ST on 21 language pairs on the CVSS-C corpus is
improved by +13.6 BLEU (or +113% relatively), as compared to the previous
state-of-the-art trained without additional data. The improvements on
low-resource language are even more significant (+398% relatively on average).
Our comparative studies suggest future research directions for S2ST and speech
representation learning.


title: Bubbles or Sound Waves -- How do Super-Massive Black Holes heat the ICM?

abstract:
  Galaxy clusters are promising probes of precision cosmology. Their ability to
deliver precise, unbiased, results depends on a better understanding of the
intracluster medium (ICM). Active galactic nuclei (AGN) powered by the central
Super-Massive Black Holes (SMBHs) play a major role in modifying the thermal
properties of the ICM. Therefore, understanding the AGN feedback mechanism is
essential for cluster cosmology. In this work, we implement two AGN heating
models: (i) by buoyant cavities rising through stratified ICM (Effervescent
model) and, (ii) by viscous and conductive dissipation of sound waves (Acoustic
model). Our aim is to determine whether these heating models are consistent
with ICM observables and if one is preferred over the other. We study the
evolution of ICM thermal profiles with effervescent and acoustic models of AGN
heating. We assume an initial entropy profile of ICM expected from the purely
gravitational infall of the gas in the potential of the dark matter halo. We
then incorporate heating, radiative cooling, and thermal conduction over the
age of the clusters. Our results are: (i) We find that both heating processes
match well with observations, with some tuning of relevant parameters. (ii)
Thermal conduction is crucially important, even at the level of 10\% of the
Spitzer values, in transferring the injected energy beyond the central regions,
and without which the temperature/entropy profiles do not match with
observations. (iii) We show that the required injected AGN power scales with
cluster mass as $M_{\rm vir}^{1.5}$ for both models. (iv) Moreover, the
required AGN luminosity is comparable with the observed radio jet power,
reinforcing the idea that AGNs provide the most dominant heating sources in
clusters. (v) Finally, we estimate that the fraction of the total AGN
luminosity available as the AGN mechanical luminosity at $0.02r_{500}$ is less
than 0.05\%.


title:
Using Active Matter to Introduce Spatial Heterogeneity to the
  Susceptible-Infected-Recovered Model of Epidemic Spreading

abstract:
  The widely used susceptible-infected-recovered (S-I-R) epidemic model assumes
a uniform, well-mixed population, and incorporation of spatial heterogeneities
remains a major challenge. Understanding failures of the mixing assumption is
important for designing effective disease mitigation approaches. We combine a
run-and-tumble self-propelled active matter system with an S-I-R model to
capture the effects of spatial disorder. Working in the motility-induced phase
separation regime both with and without quenched disorder, we find two epidemic
regimes. For low transmissibility, quenched disorder lowers the frequency of
epidemics and increases their average duration. For high transmissibility, the
epidemic spreads as a front and the epidemic curves are less sensitive to
quenched disorder; however, within this regime it is possible for quenched
disorder to enhance the contagion by creating regions of higher particle
densities. We discuss how this system could be realized using artificial
swimmers with mobile optical traps operated on a feedback loop.


title:
Information is Physical: Cross-Perspective Links in Relational Quantum
  Mechanics

abstract:
  Relational quantum mechanics (RQM) is an interpretation of quantum mechanics
based on the idea that quantum states describe not an absolute property of a
system but rather a relationship between systems. In this article, we observe
that there is a tension between RQM's naturalistic emphasis on the physicality
of information and the inaccessibility of certain sorts of information in
current formulations of RQM. Therefore we propose a new postulate for RQM which
requires that all of the information possessed by a certain observer is stored
in physical variables of that observer and thus accessible by measurement to
other observers, so observers can reach intersubjective agreement about quantum
events which have occurred in the past. Based on this postulate, we suggest an
ontology for RQM which upholds the principle that quantum states are always
relational, but which also postulates a set of quantum events which are not
strictly relational. We show that the new postulate helps address some existing
objections to RQM and finally we address the Frauchiger-Renner experiment in
the context of RQM.


title: On some questions around Berest's conjecture

abstract:
  Let $K$ be a field of characteristic zero, let $A_1=K[x][\partial ]$ be the
first Weyl algebra. In this paper we prove the following result.
  Assume there exists a non-zero polynomial $f(X,Y)\in K[X,Y]$, which has a
non-trivial solution $(P,Q)\in A_{1}^{2}$ (which implies $[P,Q]=0$), and the
number of orbits under the group action of $Aut(A_1)$ on solutions of $f$ in
$A_{1}^{2}$ is finite. Then the Dixmier conjecture holds, i.e $\forall
\varphi\in End(A_{1})-\{0\}$, $\varphi$ is an automorphism.


title: Linking Emergent and Natural Languages via Corpus Transfer

abstract:
  The study of language emergence aims to understand how human languages are
shaped by perceptual grounding and communicative intent. Computational
approaches to emergent communication (EC) predominantly consider referential
games in limited domains and analyze the learned protocol within the game
framework. As a result, it remains unclear how the emergent languages from
these settings connect to natural languages or provide benefits in real-world
language processing tasks, where statistical models trained on large text
corpora dominate. In this work, we propose a novel way to establish such a link
by corpus transfer, i.e. pretraining on a corpus of emergent language for
downstream natural language tasks, which is in contrast to prior work that
directly transfers speaker and listener parameters. Our approach showcases
non-trivial transfer benefits for two different tasks -- language modeling and
image captioning. For example, in a low-resource setup (modeling 2 million
natural language tokens), pre-training on an emergent language corpus with just
2 million tokens reduces model perplexity by $24.6\%$ on average across ten
natural languages. We also introduce a novel metric to predict the
transferability of an emergent language by translating emergent messages to
natural language captions grounded on the same images. We find that our
translation-based metric highly correlates with the downstream performance on
modeling natural languages (for instance $\rho=0.83$ on Hebrew), while
topographic similarity, a popular metric in previous work, shows surprisingly
low correlation ($\rho=0.003$), hinting that simple properties like attribute
disentanglement from synthetic domains might not capture the full complexities
of natural language. Our findings also indicate potential benefits of moving
language emergence forward with natural language resources and models.


title: Infrared free-electron laser irradiation of carbon dioxide ice

abstract:
  Interstellar ice grains are believed to play a key role in the formation of
many of the simple and complex organic species detected in space. However, many
fundamental questions on the physicochemical processes linked to the formation
and survival of species in ice grains remain unanswered. Field work at
large-scale facilities such as free-electron lasers (FELs) can aid the
investigation of the composition and morphology of ice grains by providing
novel tools to the laboratory astrophysics community. We combined the high
tunability, wide infrared spectral range and intensity of the FEL beam line
FELIX-2 at the HFML-FELIX Laboratory in the Netherlands with the
characteristics of the ultrahigh vacuum LISA end station to perform
wavelength-dependent mid-IR irradiation experiments of space-relevant pure
carbon dioxide (CO2) ice at 20 K. We used the intense monochromatic radiation
of FELIX to inject vibrational energy at selected frequencies into the CO2 ice
to study ice restructuring effects in situ by Fourier Transform
Reflection-Absorption Infrared (FT-RAIR) spectroscopy. This work improves our
understanding of how vibrational energy introduced by external triggers such as
photons, electrons, cosmic rays, and thermal heating coming from a nascent
protostar or field stars is dissipated in an interstellar icy dust grain in
space. Moreover, it adds to the current literature debate concerning the
amorphous and polycrystalline structure of CO2 ice observed upon deposition at
low temperatures, showing that, under our experimental conditions, CO2 ice
presents amorphous characteristics when deposited at 20 K and is unambiguously
crystalline if deposited at 75 K.


title: Shape analysis via gradient flows on diffeomorphism groups

abstract:
  We study a gradient flow on Sobolev diffeomorphisms for the problem of image
registration. The energy functional quantifies the effect of transforming a
template to a target, while also penalizing deformation of the metric tensor.
The main result is well-posedness of the flow. We also give a geometric
description of the gradient in terms of the momentum map.


title:
Multi-modal multi-objective model-based genetic programming to find
  multiple diverse high-quality models

abstract:
  Explainable artificial intelligence (XAI) is an important and rapidly
expanding research topic. The goal of XAI is to gain trust in a machine
learning (ML) model through clear insights into how the model arrives at its
predictions. Genetic programming (GP) is often cited as being uniquely
well-suited to contribute to XAI because of its capacity to learn (small)
symbolic models that have the potential to be interpreted. Nevertheless, like
many ML algorithms, GP typically results in a single best model. However, in
practice, the best model in terms of training error may well not be the most
suitable one as judged by a domain expert for various reasons, including
overfitting, multiple different models existing that have similar accuracy, and
unwanted errors on particular data points due to typical accuracy measures like
mean squared error. Hence, to increase chances that domain experts deem a
resulting model plausible, it becomes important to be able to explicitly search
for multiple, diverse, high-quality models that trade-off different meanings of
accuracy. In this paper, we achieve exactly this with a novel multi-modal
multi-tree multi-objective GP approach that extends a modern model-based GP
algorithm known as GP-GOMEA that is already effective at searching for small
expressions.


title:
On the choosability with separation of planar graphs and its
  correspondence colouring analogue

abstract:
  A list assignment $L$ for a graph $G$ is an $(\ell,k)$-list assignment if
$|L(v)|\geq \ell$ for each $v \in V(G)$ and $|L(u) \cap L(v)| \leq k$ for each
$uv \in E(G)$. We say $G$ is $(\ell,k)$-choosable if it admits an $L$-colouring
for every $(\ell, k)$-list assignment $L$. We prove that if $G$ is a planar
graph with $(4,2)$-list assignment $L$ and for every triangle $T \subseteq G$
we have that $|\bigcap_{v \in V(T)} L(v)| \neq 2$, then $G$ is $L$-colourable.
In fact, we prove a slightly stronger result: if $G$ contains a clique $H$ such
that $V(H) \cap V(T) \neq \emptyset$ for every triangle $T \subseteq G$ with
$|\bigcap_{v \in V(T)} L(v)| = 2$, then $G$ is $L$-colourable. Additionally, we
give a counterexample to the correspondence colouring analogue of
$(4,2)$-choosability for planar graphs.


title: Occluded Human Mesh Recovery

abstract:
  Top-down methods for monocular human mesh recovery have two stages: (1)
detect human bounding boxes; (2) treat each bounding box as an independent
single-human mesh recovery task. Unfortunately, the single-human assumption
does not hold in images with multi-human occlusion and crowding. Consequently,
top-down methods have difficulties in recovering accurate 3D human meshes under
severe person-person occlusion. To address this, we present Occluded Human Mesh
Recovery (OCHMR) - a novel top-down mesh recovery approach that incorporates
image spatial context to overcome the limitations of the single-human
assumption. The approach is conceptually simple and can be applied to any
existing top-down architecture. Along with the input image, we condition the
top-down model on spatial context from the image in the form of body-center
heatmaps. To reason from the predicted body centermaps, we introduce Contextual
Normalization (CoNorm) blocks to adaptively modulate intermediate features of
the top-down model. The contextual conditioning helps our model disambiguate
between two severely overlapping human bounding-boxes, making it robust to
multi-person occlusion. Compared with state-of-the-art methods, OCHMR achieves
superior performance on challenging multi-person benchmarks like 3DPW,
CrowdPose and OCHuman. Specifically, our proposed contextual reasoning
architecture applied to the SPIN model with ResNet-50 backbone results in 75.2
PMPJPE on 3DPW-PC, 23.6 AP on CrowdPose and 37.7 AP on OCHuman datasets, a
significant improvement of 6.9 mm, 6.4 AP and 20.8 AP respectively over the
baseline. Code and models will be released.


title: Encoding quantum bits in bound electronic states of a graphene nanotorus

abstract:
  We propose to use the quantum states of an electron trapped on the inner
surface of a graphene nanotorus to realize as a new kind of physical quantum
bit, which can be used to encode quantum information. Fundamental tasks for
quantum information processing, such as the qubit initialization and the
implementation of arbitrary single qubit gates, can be performed using external
magnetic and electric fields. We also analyze the robustness of the device
again systematic errors, which can be suppressed by a suitable choice of the
external control fields. These findings open new prospects for the development
an alternative platform for quantum computing, for which the scalability
remains to be determined.


title: Predicting Personas Using Mechanic Frequencies and Game State Traces

abstract:
  We investigate how to efficiently predict play personas based on playtraces.
Play personas can be computed by calculating the action agreement ratio between
a player and a generative model of playing behavior, a so-called procedural
persona. But this is computationally expensive and assumes that appropriate
procedural personas are readily available. We present two methods for
estimating player persona, one using regular supervised learning and aggregate
measures of game mechanics initiated, and another based on sequence learning on
a trace of closely cropped gameplay observations. While both of these methods
achieve high accuracy when predicting play personas defined by agreement with
procedural personas, they utterly fail to predict play style as defined by the
players themselves using a questionnaire. This interesting result highlights
the value of using computational methods in defining play personas.


title: Does human speech follow Benford's Law?

abstract:
  Researchers have observed that the frequencies of leading digits in many
man-made and naturally occurring datasets follow a logarithmic curve, with
digits that start with the number 1 accounting for $\sim 30\%$ of all numbers
in the dataset and digits that start with the number 9 accounting for $\sim
5\%$ of all numbers in the dataset. This phenomenon, known as Benford's Law, is
highly repeatable and appears in lists of numbers from electricity bills, stock
prices, tax returns, house prices, death rates, lengths of rivers, and
naturally occurring images. In this paper we demonstrate that human speech
spectra also follow Benford's Law. We use this observation to motivate a new
set of features that can be efficiently extracted from speech and demonstrate
that these features can be used to classify between human speech and synthetic
speech.


title:
On index divisors and monogenity of certain number fields defined by
  trinomials

abstract:
  Let $K$ be a number field generated by a root $\th$ of a monic irreducible
trinomial $F(x) = x^n+ax^{m}+b \in \Z[x]$. In this paper, we study the problem
of $K$. More precisely, we provide some explicit conditions on $a$, $b$, $n$,
and $m$ for which $K$ is not monogenic. As applications, we show that there are
infinite families of non-monogenic number fields defined by trinomials of
degree $n=2^r\cdot3^k$ with $r$ and $k$ two positive integers. We also give
infinite families of non-monogenic sextic number fields defined by trinomials.
Some illustrating examples are giving at the end of this paper.


title:
A Method for Generating Closely Packed Orbital Shells and the
  Implication on Orbital Capacity

abstract:
  Shell-wise orbital slotting in Low Earth Orbit (LEO) can improve space
safety, simplify space traffic coordination and management, and optimize
orbital capacity. This paper describes two methods to generate 2D Lattice
Flower Constellations (2D-LFCs) that are defined with respect to either an
arbitrary degree or an arbitrary degree and order Earth geopotential. By
generating shells that are quasi-periodic and frozen with respect to the Earth
geopotential, it is possible to safely stack shells with vertical separation
distances smaller than the osculating variation in semi-major axis of each
shell or a corresponding Keplerian 2D-LFC propagated under an aspherical
geopotential. This helps mitigate the single inclination per shell requirement
in prior work by admitting more shells for a given orbital volume while
retaining self-safe phasing in each shell. These methods exploit previous work
on the Time Distribution Constellation formulation and designs of closed
2D-LFCs under arbitrary Earth geopotentials using repeating ground track
orbits. Factors that influence the widths and shapes of these frozen shells are
identified. Simplified formulas for estimating shell geometry and thickness are
presented. It is shown that sequencing shells to group similar or ascending
inclinations improves capacity versus arbitrary inclination ordering.


title: Gating-Induced Mott Transition in NiS$_2$

abstract:
  NiS$_2$ has been widely regarded as a model system to study the
bandwidth-controlled Mott transition, as enabled by isovalent Se chemical
substitution on the S sites. Motivated by advances in electrolyte gating, we
theoretically investigate the filling-controlled Mott transition induced by
gating, which has the advantage of avoiding dopant disorder and stoichiometric
changes. We use combined Density Functional Theory (DFT) and Dynamical Mean
Field Theory (DMFT) to study such a filling-controlled transition and compare
it with the case of bandwidth control. We draw a temperature-filling phase
diagram and find that the Mott-insulator to metal transition occurs with modest
added electron concentrations, well within the capabilities of existing
electrolyte gating experiments. We find that there is significant incoherent
weight at the Fermi level in the metallic phase when the transition is induced
by gating. In contrast, the spectral weight remains rather coherent in the case
of the bandwidth-controlled transition.


title:
Shadowing, topological entropy and recurrence of induced Morse-Smale
  diffeomorphisms

abstract:
  Let $f : M \rightarrow M$ be a Morse-Smale diffeomorphism defined on a
compact and connected manifold without boundary. Let $C(M)$ denote the
hyperspace of all subcontinua of M endowed with the Hausdorff metric and $C(f)
: C(M) \rightarrow C(M)$ denote the induced homeomorphism of $f$. We show in
this paper that if $M$ is the unit circle $S^1$ then the induced map $C(f)$ has
not the shadowing property. Also we show that the topological entropy of $C(f)$
has only two possible values: $0$ or $\infty$. In particular, we show that the
entropy of $C(f)$ is $0$ when $M$ is the unit circle $S^1$ and it is $\infty$
if the dimension of the manifold $M$ is greater than two. Furthermore, we study
the recurrence of the induced maps $2^f$ and $C(f)$ and sufficient conditions
to obtain infinite topological entropy in the hyperspace.


title:
One Country, 700+ Languages: NLP Challenges for Underrepresented
  Languages and Dialects in Indonesia

abstract:
  NLP research is impeded by a lack of resources and awareness of the
challenges presented by underrepresented languages and dialects. Focusing on
the languages spoken in Indonesia, the second most linguistically diverse and
the fourth most populous nation of the world, we provide an overview of the
current state of NLP research for Indonesia's 700+ languages. We highlight
challenges in Indonesian NLP and how these affect the performance of current
NLP systems. Finally, we provide general recommendations to help develop NLP
technology not only for languages of Indonesia but also other underrepresented
languages.


title:
Energy effective mass dependence of electron tunneling through CdS/CdSe,
  AlxGa1-xAs/GaAs and AlSb/InAs Multiple Quantum Barriers

abstract:
  Tunneling of electrons through the barriers in heterostructures devices is
investigated by using the unified Transfer Matrix Method. The effect of barrier
width on electron transmission coefficients has also been examined for
different pairs of semiconductor devices of significant research interest in
current years. Such Pairs involve AlxGa1-xAs/GaAs, AlSb/InAs, and CdS/CdSe
quantum barriers with varying dimensions reduced from 20 nm to 5nm to observe
how tunneling properties are affected by scaling. The effective electron masses
in the well and barrier regions typically vary with constituent materials. It
has been shown that the transmission coefficients are significantly changed due
to the coupling. The effective mass-dependent transmission coefficients for
electron energy have been evaluated in terms of the mass discontinuity metrics.
The electron transmission coefficients for each pair of quantum structures are
plotted with the variation of its electron energy, normalized to its potential
energy. The resonant state obtained here will be beneficial for designing
detectors, optical filters, photonic-switching devices and other optoelectronic
and photonic devices.


title:
Generalized Dynamic Junction Theory to Resolve the Mechanism of Direct
  Current Generation in Liquid-Solid Interfaces

abstract:
  Despite the unsettled mechanism of electricity generation from the continuous
flow of liquids on a surface, the charge-discharge theory has been widely
accepted for alternating current (AC) generation from a moving droplet. It has
been recently extended to rationalize direct current (DC) generation across a
droplet moving between two different materials. By designing a reconfigurable
contact between a metal wire and a water droplet moving on graphene, we show
that the charge-discharge theory cannot explain the reversal of current when
water-metal interfaces switch from dynamic to static. All experiments can be
described after we distinguish a dynamic from a static interface and generalize
the photovoltaic-like effect to all dynamic junctions: excited electrons and
holes in a moving interface will be separated and swept under the built-in
electrical field, leading to a DC response. This generalized theory will lead
to an understanding and the design of efficient electricity generation based on
interfacial charge transfer.


title:
Datta-Das transistor for atomtronic circuits using artificial gauge
  fields

abstract:
  Spintronics have emerged in the late 1980s mainly triggered by A. Fert and P.
Gr\"ungerg's discovery of the giant magnetoresistivity. Since, the
spin-dependent electrical injection has found useful applications in storage
devices, but fully operational spin-dependent semiconductor electronics remain
a challenging task because of weak spin-orbit couplings and/or strong spin
relaxations. These limitations are lifted considering atoms instead of
electrons or holes as spin carriers. In this emerging field of atomtronics, we
demonstrate the equivalent of a Datta-Das transistor using a degenerate fermi
gas of strontium atoms as spin carriers in interaction with a tripod
laser-beams scheme. We explore the dependence of spin rotation, and we identify
two key control parameters which we interpret as equivalent to the gate-source
and drain-source voltages of a field effect transistor. Our finding broadens
the spectrum of atomtronics devices for implementation of operational
spin-sensitive circuits.


title: Phonon Chirality Induced by Vibronic-Orbital Coupling

abstract:
  The notion that phonons can carry pseudo-angular momentum has become popular
in the last decade, with recent research efforts highlighting phonon chirality,
Berry curvature of phonon band structure, and the phonon Hall effect. When a
phonon is resonantly coupled to a crystal electric field excitation, a
so-called vibronic bound state forms. Here, we observe angular momentum
transfer of $\delta$Jz = $\pm$1$\hbar$ between phonons and an orbital state in
a vibronic bound state of a candidate quantum spin liquid. This observation has
profound implications for the engineering of phonon band structure topology
through chiral quasiparticle interactions.


title: The transient route to resonance

abstract:
  The periodically driven harmonic oscillator with damping is one of the most
elementary and trusted models in physics and normally applied in its steady
state, disregarding specific initial conditions and associated transients. For
example, steady state solutions are already sufficient to describe resonance.
In some applications transient solutions might play a role though, and a review
of these might be useful. In order to characterize the transients of the
harmonic oscillator, its winding number is graphed in dependence of initial
conditions. These visualizations show a complexity of the transients of the
harmonic oscillator that are not readily evident from the inspection of the
model alone.


title: Diverse surface waves supported by bianisotropic metasurfaces

abstract:
  Surface waves supported by structured metallic surfaces, i.e. metasurfaces,
have drawn wide attention recently. They are promising for various applications
ranging from integrated photonic circuits to imaging and bio-sensing in various
frequency regimes. In this work, we show that surface states with diverse
polarization configurations can be supported by a metasurface consisting of a
single layer of bianisotropic metamaterial elements. The structure possesses
D2d symmetry, which includes mirror symmetry in the xz and yz plane, and C2
rotational symmetry along y = +-x axis. Due to this unique symmetry, the
metasuface supports both transverse electric (TE) and transverse magnetic (TM)
waves along kx and ky directions, while a purely longitudinal mode and an
elliptically polarized transverse electromagnetic (TEM) mode along ky = +-kx
directions. The versatility of the surface modes on the metasurface may lead to
new surface wave phenomena and device applications.


title: Calibration Error for Heterogeneous Treatment Effects

abstract:
  Recently, many researchers have advanced data-driven methods for modeling
heterogeneous treatment effects (HTEs). Even still, estimation of HTEs is a
difficult task -- these methods frequently over- or under-estimate the
treatment effects, leading to poor calibration of the resulting models.
However, while many methods exist for evaluating the calibration of prediction
and classification models, formal approaches to assess the calibration of HTE
models are limited to the calibration slope. In this paper, we define an
analogue of the \smash{($\ell_2$)} expected calibration error for HTEs, and
propose a robust estimator. Our approach is motivated by doubly robust
treatment effect estimators, making it unbiased, and resilient to confounding,
overfitting, and high-dimensionality issues. Furthermore, our method is
straightforward to adapt to many structures under which treatment effects can
be identified, including randomized trials, observational studies, and survival
analysis. We illustrate how to use our proposed metric to evaluate the
calibration of learned HTE models through the application to the CRITEO-UPLIFT
Trial.


title:
Comprehensive Analyses of the Neutrino-Process in the Core-collapsing
  Supernova

abstract:
  We investigate the neutrino flavor change effects due to neutrino
self-interaction, shock wave propagation as well as matter effect on the
neutrino process of the core-collapsing supernova. For the hydrodynamics, we
use two models: a simple thermal bomb model and a specified hydrodynamic model
for SN1987A. As a pre-supernova model, we take an updated model adjusted to
explain the SN1987A employing recent development of the $(n,\gamma)$ reaction
rates for nuclei near the stability line $(A \sim 100)$. As for the neutrino
luminosity, we adopt two different models: equivalent neutrino luminosity and
non-equivalent luminosity models. The latter is taken from the synthetic
analyses of the CCSN simulation data which involved quantitatively the results
obtained by various neutrino transport models. Relevant neutrino-induced
reaction rates are calculated by a shell model for light nuclei and a
quasi-particle random phase approximation model for heavy nuclei. For each
model, we present abundances of the light nuclei ($^7$Li, $^7$Be, $^{11}$B and
$^{11}$C) and heavy nuclei ($^{92}$Nb, $^{98}$Tc, $^{138}$La and $^{180}$Ta)
produced by the neutrino-process. The light nuclei abundances turn out to be
sensitive to the Mikheyev-Smirnov-Wolfenstein region around ONeMg region while
the heavy nuclei are mainly produced prior to the MSW region. Through the
detailed analyses of the numerical abundances, we find that neutrino
self-interaction becomes a key ingredient in addition to the MSW effect for
understanding the neutrino process and the relevant nuclear abundances.
However, the whole results are shown to depend on the adopted neutrino
luminosity scheme. Detailed evaluations of the nuclear abundances for the two
possible neutrino mass hierarchies are performed with the comparison to the
available meteorite analyses data. The normal mass hierarchy is shown to be
more compatible with the meteoritic data.


title:
Recommendation as Language Processing (RLP): A Unified Pretrain,
  Personalized Prompt & Predict Paradigm (P5)

abstract:
  For a long period, different recommendation tasks typically require designing
task-specific architectures and training objectives. As a result, it is hard to
transfer the learned knowledge and representations from one task to another,
thus restricting the generalization ability of existing recommendation
approaches, e.g., a sequential recommendation model can hardly be applied or
transferred to a review generation method. To deal with such issues,
considering that language grounding is a powerful medium to describe and
represent various problems or tasks, we present a flexible and unified
text-to-text paradigm called "Pretrain, Personalized Prompt, and Predict
Paradigm" (P5) for recommendation, which unifies various recommendation tasks
in a shared framework. In P5, all data such as user-item interactions, item
metadata, and user reviews are converted to a common format -- natural language
sequences. The rich information from natural language assist P5 to capture
deeper semantics for recommendation. P5 learns different tasks with the same
language modeling objective during pretraining. Thus, it possesses the
potential to serve as the foundation model for downstream recommendation tasks,
allows easy integration with other modalities, and enables instruction-based
recommendation, which will revolutionize the technical form of recommender
system towards universal recommendation engine. With adaptive personalized
prompt for different users, P5 is able to make predictions in a zero-shot or
few-shot manner and largely reduces the necessity for extensive fine-tuning. On
several recommendation benchmarks, we conduct experiments to show the
effectiveness of our generative approach. We will release our prompts and
pretrained P5 language model to help advance future research on Recommendation
as Language Processing (RLP) and Personalized Foundation Models.


title:
Efficiency of higher-order algorithms for minimizing general composite
  optimization

abstract:
  Composite minimization involves a collection of functions which are
aggregated in a nonsmooth manner. It covers, as a particular case, optimization
problems with functional constraints, minimization of max-type functions, and
simple composite minimization problems, where the objective function has a
nonsmooth component. We design a higher-order majorization algorithmic
framework for fully composite problems (possibly nonconvex). Our framework
replaces each component with a higher-order surrogate such that the
corresponding error function has a higher-order Lipschitz continuous
derivative. We present convergence guarantees for our method for composite
optimization problems with (non)convex and (non)smooth objective function. In
particular, we prove stationary point convergence guarantees for general
nonconvex (possibly nonsmooth) problems and under Kurdyka-Lojasiewicz (KL)
property of the objective function we derive improved rates depending on the KL
parameter. For convex (possibly nonsmooth) problems we also provide sublinear
convergence rates.


title: Gravitational Wave luminosity distance in viscous cosmological models

abstract:
  We study the so-called Gravitational Wave luminosity distance-redshift
relation $d_L^{\,GW}(z)$ during cosmological eras driven by non-perfect fluids.
In particular, we show that the presence of a shear viscosity in the energy
momentum tensor turns out to be the most relevant effect. Within this scenario,
a constant shear viscosity imprints the gravitational wave propagation through
a friction term $\delta(z)$ with a uniquely given redshift dependence. This
peculiar evolution predicts a specific shape for the ratio
$d_{L}^{GW}/d_{L}^{EM}$ which tends to a constant value when the sources are at
$z\gtrsim 1$, whereas scales linearly with the shear viscosity at lower
redshifts, regardless of the value of $\Omega_{m0}$. According to our final
discussion, the predicted redshift dependence $\delta(z)$ provided by a shear
viscosity could be tested by upcoming surveys of multi-messenger sources
against analogous scenarios provided by some widely studied theories of
modified gravity.


title: Gender and Racial Stereotype Detection in Legal Opinion Word Embeddings

abstract:
  Studies have shown that some Natural Language Processing (NLP) systems encode
and replicate harmful biases with potential adverse ethical effects in our
society. In this article, we propose an approach for identifying gender and
racial stereotypes in word embeddings trained on judicial opinions from U.S.
case law. Embeddings containing stereotype information may cause harm when used
by downstream systems for classification, information extraction, question
answering, or other machine learning systems used to build legal research
tools. We first explain how previously proposed methods for identifying these
biases are not well suited for use with word embeddings trained on legal
opinion text. We then propose a domain adapted method for identifying gender
and racial biases in the legal domain. Our analyses using these methods suggest
that racial and gender biases are encoded into word embeddings trained on legal
opinions. These biases are not mitigated by exclusion of historical data, and
appear across multiple large topical areas of the law. Implications for
downstream systems that use legal opinion word embeddings and suggestions for
potential mitigation strategies based on our observations are also discussed.


title: Upper Bounds For Families Without Weak Delta-Systems

abstract:
  For $k\geq3$, a collection of $k$ sets is said to form a \emph{weak
$\Delta$-system} if the intersection of any two sets from the collection has
the same size. Erd\H{o}s and Szemer\'{e}di asked about the size of the largest
family $\mathcal{F}$ of subsets of $\{1,\dots,n\}$ that does not contain a weak
$\Delta$-system. In this note we improve upon the best upper bound from
arXiv:1606.09575 and show that \[
|\mathcal{F}|\leq\left(\frac{2}{3}\Theta(C)+o(1)\right)^{n} \] where
$\Theta(C)$ is the capset capacity. In particular, this shows that \[
|\mathcal{F}|\leq(1.8367\dots+o(1))^{n}. \]


title:
FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot
  Video Understanding Tasks

abstract:
  Large-scale pretrained image-text models have shown incredible zero-shot
performance in a handful of tasks, including video ones such as action
recognition and text-to-video retrieval. However, these models haven't been
adapted to video, mainly because they don't account for the time dimension but
also because video frames are different from the typical images (e.g.,
containing motion blur, less sharpness). In this paper, we present a
fine-tuning strategy to refine these large-scale pretrained image-text models
for zero-shot video understanding tasks. We show that by carefully adapting
these models we obtain considerable improvements on two zero-shot Action
Recognition tasks and three zero-shot Text-to-video Retrieval tasks. The code
is available at https://github.com/bryant1410/fitclip


title:
Predicting Solar Wind Streams from the Inner-Heliosphere to Earth via
  Shifted Operator Inference

abstract:
  Solar wind conditions are predominantly predicted via three-dimensional
numerical magnetohydrodynamic (MHD) models. Despite their ability to produce
highly accurate predictions, MHD models require computationally intensive
high-dimensional simulations. This renders them inadequate for making
time-sensitive predictions and for large-ensemble analysis required in
uncertainty quantification. This paper presents a new data-driven reduced-order
model (ROM) capability for forecasting heliospheric solar wind speeds.
Traditional model reduction methods based on Galerkin projection have
difficulties with advection-dominated systems -- such as solar winds -- since
they require a large number of basis functions and can become unstable. A core
contribution of this work addresses this challenge by extending the
non-intrusive operator inference ROM framework to exploit the translational
symmetries present in the solar wind caused by the Sun's rotation. The
numerical results show that our method can adequately emulate the MHD
simulations and outperforms a reduced-physics surrogate model, the Heliospheric
Upwind Extrapolation model.


title:
Pickl's Proof of the Quantum Mean-Field Limit and Quantum Klimontovich
  Solutions

abstract:
  This paper discusses the mean-field limit for the quantum dynamics of $N$
identical bosons in $\mathbf R^3$ interacting via a binary potential with
Coulomb type singularity. Our approach is based on the theory of quantum
Klimontovich solutions defined in [F. Golse, T. Paul, Commun. Math. Phys. 369
(2019), 1021-1053]. Our first main result is a definition of the interaction
nonlinearity in the equation governing the dynamics of quantum Klimontovich
solutions for a class of interaction potentials slightly less general than
those considered in [T. Kato, Trans. Amer. Math. Soc. 70 (1951), 195-211]. Our
second main result is a new operator inequality satisfied by the quantum
Klimontovich solution in the case of an interaction potential with Coulomb type
singularity. When evaluated on an initial bosonic pure state, this operator
inequality reduces to a Gronwall inequality for a functional introduced in [P.
Pickl, Lett. Math. Phys. 97 (2011), 151-164], resulting in a convergence rate
estimate for the quantum mean-field limit leading to the time-dependent Hartree
equation.


title:
The X-ray Luminosity Function Evolution of Quasars and the Correlation
  Between the X-ray and Ultraviolet Luminosities

abstract:
  We explore the evolution of the X-ray luminosity function of quasars and the
intrinsic correlation between the X-ray and 2500 Angstrom ultraviolet
luminosities, utilizing techniques verified in previous works and a sample of
over 4000 quasars detected with Chandra and XMM-Newton in the range $0<z<5$. We
find that quasars have undergone significantly less evolution with redshift in
their total X-ray luminosity than in other wavebands. We then determine that
the best fit intrinsic power law correlation between the X-ray and ultraviolet
luminosities, of the form $L'_{\rm X} \propto ({L'_{\rm UV}})^{\gamma}$, is
$\gamma=0.28\pm$0.03, and we derive the luminosity function and density
evolution in the X-ray band. We discuss the implications of these results for
models of quasar systems.


title:
Deep reinforcement learning for optimal well control in subsurface
  systems with uncertain geology

abstract:
  A general control policy framework based on deep reinforcement learning (DRL)
is introduced for closed-loop decision making in subsurface flow settings.
Traditional closed-loop modeling workflows in this context involve the repeated
application of data assimilation/history matching and robust optimization
steps. Data assimilation can be particularly challenging in cases where both
the geological style (scenario) and individual model realizations are
uncertain. The closed-loop reservoir management (CLRM) problem is formulated
here as a partially observable Markov decision process, with the associated
optimization problem solved using a proximal policy optimization algorithm.
This provides a control policy that instantaneously maps flow data observed at
wells (as are available in practice) to optimal well pressure settings. The
policy is represented by a temporal convolution and gated transformer blocks.
Training is performed in a preprocessing step with an ensemble of prior
geological models, which can be drawn from multiple geological scenarios.
Example cases involving the production of oil via water injection, with both 2D
and 3D geological models, are presented. The DRL-based methodology is shown to
result in an NPV increase of 15% (for the 2D cases) and 33% (3D cases) relative
to robust optimization over prior models, and to an average improvement of 4%
in NPV relative to traditional CLRM. The solutions from the control policy are
found to be comparable to those from deterministic optimization, in which the
geological model is assumed to be known, even when multiple geological
scenarios are considered. The control policy approach results in a 76% decrease
in computational cost relative to traditional CLRM with the algorithms and
parameter settings considered in this work.


title:
Five Decades of Chromospheric Activity in 59 Sun-like Stars and New
  Maunder Minimum Candidate HD 166620

abstract:
  We present five decades of chromospheric activity measurements in 59 Sun-like
stars as time series. These include and extend the 35 yr of stellar
chromospheric activity observations by the Mount Wilson Survey (1966--2001),
and continued observations at Keck by the California Planet Search (1996--).
The Mount Wilson Survey was studied closely in 1995, and revealed periodic
activity cycles similar to the Sun's 11 yr cycle. The California Planet Search
provides more than five decades of measurements, significantly improving our
understanding of these stars' activity behavior. We have curated the activity
measurements in order to create contiguous time series, and have classified the
stellar sample according to a predetermined system. We have analyzed 29 stars
with periodic cycles using the Lomb-Scargle periodogram, and present best-fit
sinusoids to their activity time series. We report the best-fit periods for
each cycling star, along with stellar parameters (T$_{eff}$, log(g), v$sin(i)$,
etc.) for the entire sample. As a first application of these data, we offer a
possible Maunder minimum candidate, HD 166620.


title:
Statistic Selection and MCMC for Differentially Private Bayesian
  Estimation

abstract:
  This paper concerns differentially private Bayesian estimation of the
parameters of a population distribution, when a statistic of a sample from that
population is shared in noise to provide differential privacy.
  This work mainly addresses two problems: (1) What statistic of the sample
should be shared privately? For the first question, i.e., the one about
statistic selection, we promote using the Fisher information. We find out that,
the statistic that is most informative in a non-privacy setting may not be the
optimal choice under the privacy restrictions. We provide several examples to
support that point. We consider several types of data sharing settings and
propose several Monte Carlo-based numerical estimation methods for calculating
the Fisher information for those settings. The second question concerns
inference: (2) Based on the shared statistics, how could we perform effective
Bayesian inference? We propose several Markov chain Monte Carlo (MCMC)
algorithms for sampling from the posterior distribution of the parameter given
the noisy statistic. The proposed MCMC algorithms can be preferred over one
another depending on the problem. For example, when the shared statistics is
additive and added Gaussian noise, a simple Metropolis-Hasting algorithm that
utilizes the central limit theorem is a decent choice. We propose more advanced
MCMC algorithms for several other cases of practical relevance.
  Our numerical examples involve comparing several candidate statistics to be
shared privately. For each statistic, we perform Bayesian estimation based on
the posterior distribution conditional on the privatized version of that
statistic. We demonstrate that, the relative performance of a statistic, in
terms of the mean squared error of the Bayesian estimator based on the
corresponding privatized statistic, is adequately predicted by the Fisher
information of the privatized statistic.


title:
Magnetic Impurity as a Local Probe of the U(1) Quantum Spin Liquid with
  Spinon Fermi Surface

abstract:
  We solve the problem of a magnetic impurity coupled to a U(1) quantum spin
liquid with spinon Fermi surface and compute the impurity spectral function.
Using the slave rotor mean field approach combined with gauge field
fluctuations, we find that a peak located at the top of the lower Hubbard band
and one at the bottom of the upper Hubbard band can emerge in the impurity
spectral function. The peaks at the Hubbard band edges arise from the gauge
field fluctuations induced spinon chargon binding inside the spinon Kondo
screening cloud of the magnetic impurity. For a magnetic impurity embedded in a
Mott insulator, our findings suggest that the emergence of a pair of peaks at
the Hubbard band edges in the impurity density of states spectra provides
strong evidence that the host Mott insulator is a U(1) quantum spin liquid with
spinon Fermi surface.


title: Spread approximations for forbidden intersections problems

abstract:
  We develop a new approach to approximate families of sets, complementing the
existing `$\Delta$-system method' and 'junta approximations method'. The
approach, which we refer to as `spread approximations method', is based on the
notion of $r$-spread families and builds on the recent breakthrough result of
Alweiss, Lovett, Wu and Zhang for the Erd\H{o}s--Rado `Sunflower Conjecture'.
Our approach can work in a variety of sparse settings.
  To demonstrate the versatility and strength of the approach, we present
several of its applications to forbidden intersection problems, including
bounds on the size of regular intersecting families, the resolution of the
Erd\H{o}s--S\'os problem for sets in a new range and, most notably, the
resolution of the $t$-intersection and Erd\H{o}s--S\'os problems for
permutations in a new range. Specifically, we show that any collection of
permutations of an $n$-element set with no two permutations intersecting in at
most (exactly) $t-1$ elements has size at most $(n-t)!$, provided $t\le
n^{1-\epsilon}$ ($t \le n^{\frac{1}{3}-\epsilon}$) for an arbitrary
$\epsilon>0$ and $n>n_0(\epsilon)$. Previous results for these problems only
dealt with the case of fixed $t$.


title: Email Summarization to Assist Users in Phishing Identification

abstract:
  Cyber-phishing attacks recently became more precise, targeted, and tailored
by training data to activate only in the presence of specific information or
cues. They are adaptable to a much greater extent than traditional phishing
detection. Hence, automated detection systems cannot always be 100% accurate,
increasing the uncertainty around expected behavior when faced with a potential
phishing email. On the other hand, human-centric defence approaches focus
extensively on user training but face the difficulty of keeping users up to
date with continuously emerging patterns. Therefore, advances in analyzing the
content of an email in novel ways along with summarizing the most pertinent
content to the recipients of emails is a prospective gateway to furthering how
to combat these threats. Addressing this gap, this work leverages
transformer-based machine learning to (i) analyze prospective psychological
triggers, to (ii) detect possible malicious intent, and (iii) create
representative summaries of emails. We then amalgamate this information and
present it to the user to allow them to (i) easily decide whether the email is
"phishy" and (ii) self-learn advanced malicious patterns.


title:
Probing Representation Forgetting in Supervised and Unsupervised
  Continual Learning

abstract:
  Continual Learning research typically focuses on tackling the phenomenon of
catastrophic forgetting in neural networks. Catastrophic forgetting is
associated with an abrupt loss of knowledge previously learned by a model when
the task, or more broadly the data distribution, being trained on changes. In
supervised learning problems this forgetting, resulting from a change in the
model's representation, is typically measured or observed by evaluating the
decrease in old task performance. However, a model's representation can change
without losing knowledge about prior tasks. In this work we consider the
concept of representation forgetting, observed by using the difference in
performance of an optimal linear classifier before and after a new task is
introduced. Using this tool we revisit a number of standard continual learning
benchmarks and observe that, through this lens, model representations trained
without any explicit control for forgetting often experience small
representation forgetting and can sometimes be comparable to methods which
explicitly control for forgetting, especially in longer task sequences. We also
show that representation forgetting can lead to new insights on the effect of
model capacity and loss function used in continual learning. Based on our
results, we show that a simple yet competitive approach is to learn
representations continually with standard supervised contrastive learning while
constructing prototypes of class samples when queried on old samples.


title:
Fast multigrid reduction-in-time for advection via modified
  semi-Lagrangian coarse-grid operators

abstract:
  Many iterative parallel-in-time algorithms have been shown to be highly
efficient for diffusion-dominated partial differential equations (PDEs), but
are inefficient or even divergent when applied to advection-dominated PDEs. We
consider the application of the multigrid reduction-in-time (MGRIT) algorithm
to linear advection PDEs. The key to efficient time integration with this
method is using a coarse-grid operator that provides a sufficiently accurate
approximation to the the so-called ideal coarse-grid operator. For certain
classes of semi-Lagrangian discretizations, we present a novel
semi-Lagrangian-based coarse-grid operator that leads to fast and scalable
multilevel time integration of linear advection PDEs. The coarse-grid operator
is composed of a semi-Lagrangian discretization followed by a correction term,
with the correction designed so that the leading-order truncation error of the
composite operator is approximately equal to that of the ideal coarse-grid
operator. Parallel results show substantial speed-ups over sequential time
integration for variable-wave-speed advection problems in one and two spatial
dimensions, and using high-order discretizations up to order five. The proposed
approach establishes the first practical method that provides small and
scalable MGRIT iteration counts for advection problems.


title: Accurate Diffusion Coefficients for Dense White Dwarf Plasma Mixtures

abstract:
  Diffusion coefficients are essential microphysics input for modeling white
dwarf evolution, as they impact phase separation at crystallization and
sedimentary heat sources. Present schemes for computing diffusion coefficients
are accurate at weak coupling ($\Gamma \ll 1$), but they have errors as large
as a factor of two in the strongly coupled liquid regime ($1 \lesssim \Gamma
\lesssim 200$). With modern molecular dynamics codes it is possible to
accurately determine diffusion coefficients in select systems with
percent-level precision. In this work, we develop a theoretically motivated law
for diffusion coefficients which works across the wide range of parameters
typical for white dwarf interiors. We perform molecular dynamics simulations of
pure systems and two mixtures that respectively model a typical-mass C/O white
dwarf and a higher-mass O/Ne white dwarf, and resolve diffusion coefficients
for several trace neutron-rich nuclides. We fit the model to the pure systems
and propose a physically motivated generalization for mixtures. We show that
this model is accurate to roughly 15% when compared to molecular dynamics for
many individual elements under conditions typical of white dwarfs, and is
straightforward to implement in stellar evolution codes.


title:
Supercurrent-enabled Andreev reflection in a chiral quantum Hall edge
  state

abstract:
  A chiral quantum Hall (QH) edge state placed in proximity to an s-wave
superconductor experiences induced superconducting correlations. Recent
experiments have observed the effect of proximity-coupling in QH edge states
through signatures of the mediating process of Andreev reflection. We present
the microscopic theory behind this effect by modeling the system with a
many-body Hamiltonian, consisting of an s-wave superconductor, subject to
spin-orbit coupling and a magnetic field, which is coupled by electron
tunneling to a QH edge state. By integrating out the superconductor we obtain
an effective pairing Hamiltonian in the QH edge state. We clarify the
qualitative appearance of nonlocal superconducting correlations in a chiral
edge state and analytically predict the suppression of electron-hole conversion
at low energies (Pauli blocking) and negative resistance as experimental
signatures of Andreev reflection in this setup. In particular, we show how two
surface phenomena of the superconductor, namely Rashba spin-orbit coupling and
a supercurrent due to the Meissner effect, are essential for the Andreev
reflection. Our work provides a promising pathway to the realization of
Majorana zero-modes and their parafermionic generalizations.


title: A singular Yamabe problem on manifolds with solid cones

abstract:
  We study the existence of conformal metrics on non-compact Riemannian
manifolds with non-compact boundary, which are complete as metric spaces and
have negative constant scalar curvature in the interior and negative constant
mean curvature on the boundary. These metrics are constructed on smooth
manifolds obtained by removing d-dimensional submanifolds from certain
n-dimensional compact spaces locally modelled on generalized solid cones. We
prove the existence of such metrics if and only if d>(n-2)/2. Our main theorem
is inspired by the classical results by Aviles-McOwen and Loewner-Nirenberg
known in the literature as the "singular Yamabe problem".


title:
Discrete analytic functions, structured matrices and a new family of
  moment problems

abstract:
  Using Zeilberger generating function formula for the values of a discrete
analytic function in a quadrant we make connections with the theory of
structured reproducing kernel spaces, structured matrices and a generalized
moment problem. An important role is played by a Krein space realization result
of Dijksma, Langer and de Snoo for functions analytic in a neighborhood of the
origin. A key observation is that, using a simple Moebius transform, one can
reduce the study of discrete analytic functions in the upper right quadrant to
problems of function theory in the open unit disk. As an example, we associate
to each finite positive measure on $[0,2\pi]$ a discrete analytic function on
the right-upper quarter plane with values on the lattice defining a positive
definite function. Emphasis is put on the rational case, both when an
underlying Carath\'eodory function is rational and when, in the positive case,
the spectral function is rational. The rational case and the general case are
linked via the existence of a unitary dilation, possibly in a Krein space