title:
Fast Bayesian analysis of individual binaries in pulsar timing array
  data

abstract:
  Searching for gravitational waves in pulsar timing array data is
computationally intensive. The data is unevenly sampled, and the noise is
heteroscedastic, necessitating the use of a time-domain likelihood function
with attendant expensive matrix operations. The computational cost is
exacerbated when searching for individual supermassive black hole binaries,
which have a large parameter space due to the additional pulsar distance, phase
offset and noise model parameters needed for each pulsar. We introduce a new
formulation of the likelihood function which can be used to make the Bayesian
analysis significantly faster. We divide the parameters into projection and
shape parameters. We then accelerate the exploration of the projection
parameters by more than four orders of magnitude by precomputing the expensive
inner products for each set of shape parameters. The projection parameters
include nuisance parameters such as the gravitational wave phase offset at each
pulsar. In the new scheme, these troublesome nuisance parameters are
efficiently marginalized over using multiple-try Markov chain Monte Carlo
sampling as part of a Metropolis-within-Gibbs scheme. The acceleration provided
by our method will become increasingly important as pulsar timing datasets
rapidly grow. Our method also makes sophisticated analyses more tractable, such
as searches for multiple binaries, or binaries with non-negligible
eccentricities.


title: Spatio-temporally graded causality: a model

abstract:
  In this work a model is proposed, according to which causality can be
considered to be spatio-temporally graded. It is outlined how this can be
described using the formalism of fuzzy sets theory, with the degree of
causality varying between 1, that is no separation between causes and effects,
and 0, that is perfect separation between causes and their effects as in
classical 'billiard balls' models of physical systems. The model posits that
subjective moments of time are like fuzzy sets, with their extension determined
by local degrees of causality, resulting from information integration processes
extended gradually in space and time. This, we argue, is a way in which a
notion of causality could be, to a certain degree, spared and reconciled with a
variant of Bergsonian duration theory as formulated in the theory of continuous
change as the dimension of consciousness.


title:
Spatio-Temporal Analysis of Transformer based Architecture for Attention
  Estimation from EEG

abstract:
  For many years now, understanding the brain mechanism has been a great
research subject in many different fields. Brain signal processing and
especially electroencephalogram (EEG) has recently known a growing interest
both in academia and industry. One of the main examples is the increasing
number of Brain-Computer Interfaces (BCI) aiming to link brains and computers.
In this paper, we present a novel framework allowing us to retrieve the
attention state, i.e degree of attention given to a specific task, from EEG
signals. While previous methods often consider the spatial relationship in EEG
through electrodes and process them in recurrent or convolutional based
architecture, we propose here to also exploit the spatial and temporal
information with a transformer-based network that has already shown its
supremacy in many machine-learning (ML) related studies, e.g. machine
translation. In addition to this novel architecture, an extensive study on the
feature extraction methods, frequential bands and temporal windows length has
also been carried out. The proposed network has been trained and validated on
two public datasets and achieves higher results compared to state-of-the-art
models. As well as proposing better results, the framework could be used in
real applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD)
symptoms or vigilance during a driving assessment.


title:
Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural
  Networks

abstract:
  An open problem in neuroscience is to explain the functional role of
oscillations in neural networks, contributing, for example, to perception,
attention, and memory. Cross-frequency coupling (CFC) is associated with
information integration across populations of neurons. Impaired CFC is linked
to neurological disease. It is unclear what role CFC has in information
processing and brain functional connectivity. We construct a model of CFC which
predicts a computational role for observed $\theta - \gamma$ oscillatory
circuits in the hippocampus and cortex. Our model predicts that the complex
dynamics in recurrent and feedforward networks of coupled oscillators performs
robust information storage and pattern retrieval. Based on phasor associative
memories (PAM), we present a novel oscillator neural network (ONN) model that
includes subharmonic injection locking (SHIL) and which reproduces experimental
observations of CFC. We show that the presence of CFC increases the memory
capacity of a population of neurons connected by plastic synapses. CFC enables
error-free pattern retrieval whereas pattern retrieval fails without CFC. In
addition, the trade-offs between sparse connectivity, capacity, and information
per connection are identified. The associative memory is based on a
complex-valued neural network, or phasor neural network (PNN). We show that for
values of $Q$ which are the same as the ratio of $\gamma$ to $\theta$
oscillations observed in the hippocampus and the cortex, the associative memory
achieves greater capacity and information storage than previous models. The
novel contributions of this work are providing a computational framework based
on oscillator dynamics which predicts the functional role of neural
oscillations and connecting concepts in neural network theory and dynamical
system theory.


title:
A Higher Dimension of Consciousness: Constructing an empirically
  falsifiable panpsychist model of consciousness

abstract:
  Panpsychism is a solution to the mind-body problem that presumes that
consciousness is a fundamental aspect of reality instead of a product or
consequence of physical processes (i.e., brain activity). Panpsychism is an
elegant solution to the mind-body problem: it effectively rids itself of the
explanatory gap materialist theories of consciousness suffer from. However,
many theorists and experimentalists doubt panpsychism can ever be successful as
a scientific theory, as it cannot be empirically verified or falsified. In this
paper, I present a panpsychist model based on the controversial idea that
consciousness may be a so called higher physical dimension. Although this
notion seems outrageous, I show that the idea has surprising explanatory power,
even though the model (as most models) is most likely wrong. Most importantly,
though, it results in a panpsychist model that yields predictions that can be
empirically verified or falsified. As such, the model's main purpose is to
serve as an example how a metaphysical model of consciousness can be specified
in such a way that they can be tested in a scientifically rigorous way.


title: Power graphs of Moufang loops

abstract:
  Power graphs of both groups and semigroups have been widely studied. While
the power graph of a quasigroup can be defined analogously to that of a group,
power graphs of quasigroups and loops have thus far been little studied. In
this paper we begin transferring results on the power graphs of groups to the
context of loops by addressing a question posed by Peter Cameron: if two
Moufang loops have isomorphic undirected power graphs, must they have
isomorphic directed power graphs? It is known that two groups with isomorphic
undirected power graphs must have isomorphic directed power graphs. We are able
to extend that result to Moufang loops by showing that Moufang loops with
isomorphic undirected power graphs have isomorphic directed power graphs. Along
the way we investigate a class of Moufang loops which we term the generalized
octonion loops and behave similarly to the generalized quaternion groups.


title: Simulated assessment of light transport through ischaemic skin flaps

abstract:
  Currently, free flaps and pedicled flaps are assessed for reperfusion in
post-operative care using colour, capillary refill, temperature, texture and
Doppler signal (if available). While these techniques are effective, they are
prone to error due to their qualitative nature. In this research, we explore
using different wavelengths of light to quantify the response of ischaemic
tissue. The assessment provides us with indicators that are key to our goal of
developing a point-of-care diagnostics device, capable of observing reduced
perfusion quantitatively. We set up a detailed optical model of the layers of
the skin. The layers of the model are given appropriate optical properties of
the tissue, with due consideration of melanin and haemoglobin concentrations.
We simulate 24 models of healthy, perfused tissue and perfusion-deprived tissue
to assess the responses when illuminated with visible and near-infrared
wavelengths of light. In addition to detailed fluence maps of photon
propagation, we propose a simple mathematical model to assess the differential
propagation of photons in tissue; the optical reperfusion factor (ORF). Our
results show clear advantages of using light at longer wavelengths (red,
near-infrared) and the inferences drawn from the simulations hold significant
clinical relevance. The simulated scenarios and results consolidate the belief
of a multi-wavelength, point-of-care diagnostics device and inform its design
for quantifying blood flow in transplanted tissue. The modelling approach is
applicable beyond the current research, wherein other medical conditions that
can be mathematically represented in the skin can be investigated. Through
these, additional inferences and approaches to other point-of-care devices can
be realised.

categories: physics.med-ph physics.app-ph physics.bio-ph physics.optics


title: Towards Porting Operating Systems with Program Synthesis

abstract:
  The end of Moore's Law has ushered in a diversity of hardware not seen in
decades. Operating system (and system software) portability is accordingly
becoming increasingly critical. Simultaneously, there has been tremendous
progress in program synthesis. We set out to explore the feasibility of using
modern program synthesis to generate the machine-dependent parts of an
operating system. Our ultimate goal is to generate new ports automatically from
descriptions of new machines. One of the issues involved is writing
specifications, both for machine-dependent operating system functionality and
for instruction set architectures. We designed two domain-specific languages:
Alewife for machine-independent specifications of machine-dependent operating
system functionality, and Cassiopea for describing instruction set architecture
semantics. Automated porting also requires an implementation. We developed a
toolchain that, given an Alewife specification and a Cassiopea machine
description, specializes the machine-independent specification to the target
instruction set architecture and synthesizes an implementation in assembly
language. Using this approach, we demonstrate successful synthesis of a total
of 140 OS components from two pre-existing OSes for four real hardware
platforms. We also developed several optimization methods for OS-related
assembly synthesis to improve scalability. The effectiveness of our languages
and ability to synthesize code for all 140 specifications is evidence of the
feasibility of program synthesis for machine-dependent OS code. However, many
research challenges remain; we also discuss the benefits and limitations of our
synthesis-based approach to automated OS porting.


title: The Extremal Function for Apex Graphs

abstract:
  McCarty and Thomas conjectured that a linklessly embeddable graph with $n\ge
7 $ vertices and $t$ triangles has at most $3n-9 +\frac{t}{3}$ edges. Thomas
and Yoo proved this to be true for apex graphs. We give a shorter and simpler
proof for the apex case.


title:
Formation of Low-mass Black Holes and Single Millisecond Pulsars in
  Globular Clusters

abstract:
  Close encounters between neutron stars and main-sequence stars occur in
globular clusters and may lead to various outcomes. Here we study encounters
resulting in tidal disruption of the star. Using $N$-body models, we predict
the typical stellar masses in these disruptions and the dependence of the event
rate on host cluster properties. We find that tidal disruption events occur
most frequently in core-collapsed globular clusters and that roughly $25\%$ of
the disrupted stars are merger products (i.e., blue straggler stars). Using
hydrodynamic simulations, we model the tidal disruptions themselves (over
timescales of days) to determine the mass bound to the neutron star and the
properties of the remnant disks formed. Depending on the impact parameter and
mass of the disrupted star, we find disk masses ranging from roughly
$0.1-1\,M_{\odot}$ and typical radii of $\rm{a\,few}\,\it{R}_{\odot}$.
Additionally, we find that neutron stars receive impulsive kicks of up to about
$20\,$km/s as a result of the asymmetry of unbound ejecta; these kicks place
these neutron stars on elongated orbits within their host cluster, with
apocenter distances well outside the cluster core. Finally, we model the
evolution of the (hypercritical) accretion disks on longer timescales (days to
years after disruption) to estimate the accretion rate onto the neutron stars
and accompanying spin-up. As long as $\gtrsim1\%$ of the bound mass accretes
onto the neutron star, millisecond spin periods can be attained. We argue the
growing numbers of {\em isolated\/} millisecond pulsars observed in globular
clusters may have formed, at least in part, through this mechanism. In the case
of significant mass growth, some of these neutron stars may collapse to form
low-mass ($\lesssim3\,M_{\odot}$) black holes.


title: Linearizing the BPS Equations with Vector and Tensor Multiplets

abstract:
  We analyse the BPS equations of $\mathcal{N} = (1,0)$ supergravity theory in
six dimensions coupled to a vector and tensor multiplet. We show how these BPS
equations can be reduced to a set of linear differential equations. This system
is triangular in that each layer of equations, while linear, is quadratically
sourced by the solutions of the previous layers. We examine several explicit
examples and discuss the construction of new families of microstate geometries.
We expect that the result presented here will open up new branches of
superstrata in which the momentum is encoded in a new class of charge carriers.


title: Holography and the KKLT Scenario

abstract:
  The KKLT scenario, one of the few ideas to realize dS vacua in string theory,
consists of two steps: the first involves the construction of a supersymmetric
AdS vacuum with a small negative cosmological constant, and the second involves
breaking supersymmetry and uplifting the energy to achieve dS. In this paper we
use conventional holography to argue why it is not possible to complete the
first step. We obtain this by putting a bound on the central charge of the dual
theory which involves branes wrapping special Lagrangian cycles in CY 4-folds.
We find that $l_{\rm AdS}^2 \lesssim \chi(CY_4)$. Since $l_{\rm
species}^2\gtrsim \chi(CY_4)$ this leads to $l_{\rm AdS}/l_{\rm
species}\lesssim 1$ leading at best to a highly curved AdS which is beyond the
validity of the EFT.


title: Diagnosing and Fixing Manifold Overfitting in Deep Generative Models

abstract:
  Likelihood-based, or explicit, deep generative models use neural networks to
construct flexible high-dimensional densities. This formulation directly
contradicts the manifold hypothesis, which states that observed data lies on a
low-dimensional manifold embedded in high-dimensional ambient space. In this
paper we investigate the pathologies of maximum-likelihood training in the
presence of this dimensionality mismatch. We formally prove that degenerate
optima are achieved wherein the manifold itself is learned but not the
distribution on it, a phenomenon we call manifold overfitting. We propose a
class of two-step procedures consisting of a dimensionality reduction step
followed by maximum-likelihood density estimation, and prove that they recover
the data-generating distribution in the nonparametric regime, thus avoiding
manifold overfitting. We also show that these procedures enable density
estimation on the manifolds learned by implicit models, such as generative
adversarial networks, hence addressing a major shortcoming of these models.
Several recently proposed methods are instances of our two-step procedures; we
thus unify, extend, and theoretically justify a large class of models.


title:
Implications of the Milky Way travel velocity for dynamical mass
  estimates of the Local Group

abstract:
  The total mass of the Local Group (LG) is a fundamental quantity that enables
interpreting the orbits of its constituent galaxies and placing the LG in a
cosmological context. One of the few methods that allows inferring the total
mass directly is the "Timing Argument," which models the relative orbit of the
Milky Way (MW) and M31. However, the MW itself is not in equilibrium, a
byproduct of its merger history and the recent pericentric passage of the
LMC/SMC. As a result, recent work has found that the MW disk is moving with a
lower bound "travel velocity" of $\sim 32~{\rm km}~{\rm s}^{-1}$ with respect
to the outer stellar halo (Petersen & Pe\~{n}arrubia 2021), thus biasing past
Timing Argument measurements that do not account for this motion. We measure
the total LG mass using a Timing Argument model that incorporates this measured
travel velocity of the MW disk using several different compilations of recent
kinematic measurements of M31. We find that incorporating the measured travel
velocity lowers the inferred LG mass by 10-20% compared to a static MW halo,
and find an updated total mass of either $4.0^{+0.5}_{-0.3}\times 10^{12}\rm
M_{\odot}$ or $4.5^{+0.8}_{-0.6}\times 10^{12}\rm M_{\odot}$ depending on the
adopted dataset. Measurements of the travel velocity with more distant tracers
could yield even larger values, which would further decrease the inferred LG
mass. Therefore, the newly measured travel velocity directly implies a lower LG
mass than from a model with a static MW halo and must be considered in future
dynamical studies of the Local Volume.


title:
Investigating the source of hysteresis in the Soil-Water Characteristic
  Curve using the multiphase lattice Boltzmann method

abstract:
  The soil-water characteristic curve (SWCC) is the most fundamental
relationship in unsaturated soil mechanics, relating the amount of water in the
soil to the corresponding matric suction. From experimental evidence, it is
known that SWCC exhibits hysteresis (i.e. wetting/drying path dependence).
Various factors have been proposed as contributors to SWCC hysteresis,
including air entrapment, contact angle hysteresis, ink-bottle effect, and
change of soil fabric due to swelling and shrinkage, however, the significance
of their contribution is debated. From our pore-scale numerical simulations,
using the multiphase lattice Boltzmann method, we see that even when
controlling for all these factors SWCC hysteresis still occurs, indicating that
there is some underlying source that is not accounted for in these factors. We
find this underlying source by comparing the liquid/gas phase distributions for
simulated wetting and drying experiments of 2D and 3D granular packings. We see
that during wetting (i.e. pore filling) many liquid bridges expand
simultaneously and join together to fill the pores from the smallest to the
largest, allowing menisci with larger radii of curvature (lower matric
suction). Whereas, during drying (i.e. pore emptying), only the limited
existing gas clusters can expand, which become constrained by the size of the
pore openings surrounding them and result in menisci with smaller radii of
curvature (higher matric suction).


title: Fundamentals of Compositional Rewriting Theory

abstract:
  A foundational theory of compositional categorical rewriting theory is
presented, based on a collection of fibration-like properties that collectively
induce and structure intrinsically the large collection of lemmata used in the
proofs of theorems such as concurrency and associativity. The resulting highly
generic proofs of these theorems are given; it is noteworthy that the proof of
the concurrency theorem takes only a few lines and, while that of associativity
remains somewhat longer, it would be unreadably long if written directly in
terms of the basic lemmata. In addition to improving, or even enabling, the
readability of human-written proofs, we anticipate that this more generic and
modular style of writing proofs should organize and inform the production of
formalized proofs in a proof assistant such as Coq or Isabelle. A curated list
of known instances of our framework is used to conclude the paper with a
detailed discussion of the conditions under which the Double Pushout and
Sesqui-Pushout semantics of graph transformation are compositional.


title:
A collaborative decomposition-based evolutionary algorithm integrating
  normal and penalty-based boundary intersection for many-objective
  optimization

abstract:
  Decomposition-based evolutionary algorithms have become fairly popular for
many-objective optimization in recent years. However, the existing
decomposition methods still are quite sensitive to the various shapes of
frontiers of many-objective optimization problems (MaOPs). On the one hand, the
cone decomposition methods such as the penalty-based boundary intersection
(PBI) are incapable of acquiring uniform frontiers for MaOPs with very convex
frontiers. On the other hand, the parallel reference lines of the parallel
decomposition methods including the normal boundary intersection (NBI) might
result in poor diversity because of under-sampling near the boundaries for
MaOPs with concave frontiers. In this paper, a collaborative decomposition
method is first proposed to integrate the advantages of parallel decomposition
and cone decomposition to overcome their respective disadvantages. This method
inherits the NBI-style Tchebycheff function as a convergence measure to
heighten the convergence and uniformity of distribution of the PBI method.
Moreover, this method also adaptively tunes the extent of rotating an NBI
reference line towards a PBI reference line for every subproblem to enhance the
diversity of distribution of the NBI method. Furthermore, a collaborative
decomposition-based evolutionary algorithm (CoDEA) is presented for
many-objective optimization. A collaborative decomposition-based environmental
selection mechanism is primarily designed in CoDEA to rank all the individuals
associated with the same PBI reference line in the boundary layer and pick out
the best ranks. CoDEA is compared with several popular algorithms on 85
benchmark test instances. The experimental results show that CoDEA achieves
high competitiveness benefiting from the collaborative decomposition
maintaining a good balance among the convergence, uniformity, and diversity of
distribution.


title:
Active Learning for Regression and Classification by Inverse Distance
  Weighting

abstract:
  This paper proposes an active learning algorithm for solving regression and
classification problems based on inverse-distance weighting functions for
selecting the feature vectors to query. The algorithm has the following
features: (i) supports both pool-based and population-based sampling; (ii) is
independent of the type of predictor used; (iii) can handle known and unknown
constraints on the queryable feature vectors; and (iv) can run either
sequentially, or in batch mode, depending on how often the predictor is
retrained. The method's potential is shown in numerical tests on illustrative
synthetic problems and real-world regression and classification datasets from
the UCI repository. A Python implementation of the algorithm that we call IDEAL
(Inverse-Distance based Exploration for Active Learning), is available at
\url{http://cse.lab.imtlucca.it/~bemporad/ideal}.


title: Relaxing Equivariance Constraints with Non-stationary Continuous Filters

abstract:
  Equivariances provide useful inductive biases in neural network modeling,
with the translation equivariance of convolutional neural networks being a
canonical example. Equivariances can be embedded in architectures through
weight-sharing and place symmetry constraints on the functions a neural network
can represent. The type of symmetry is typically fixed and has to be chosen in
advance. Although some tasks are inherently equivariant, many tasks do not
strictly follow such symmetries. In such cases, equivariance constraints can be
overly restrictive. In this work, we propose a parameter-efficient relaxation
of equivariance that can effectively interpolate between a (i) non-equivariant
linear product, (ii) a strict-equivariant convolution, and (iii) a
strictly-invariant mapping. The proposed parameterization can be thought of as
a building block to allow adjustable symmetry structure in neural networks.
Compared to non-equivariant or strict-equivariant baselines, we experimentally
verify that soft equivariance leads to improved performance in terms of test
accuracy on CIFAR-10 and CIFAR-100 image classification tasks.


title:
ADAPT-VQE is insensitive to rough parameter landscapes and barren
  plateaus

abstract:
  Variational quantum eigensolvers (VQEs) represent a powerful class of hybrid
quantum-classical algorithms for computing molecular energies. Various
numerical issues exist for these methods, however, including barren plateaus
and large numbers of local minima. In this work, we consider Adaptive,
Problem-Tailored (ADAPT)-VQE ans\"atze, and examine how they are impacted by
these local minima. We find that while ADAPT-VQE does not remove local minima,
the gradient-informed, one-operator-at-a-time circuit construction seems to
accomplish two things: First, it provides an initialization strategy that is
dramatically better than random initialization, and which is applicable in
situations where chemical intuition cannot help with initialization, i.e., when
Hartree-Fock is a poor approximation to the ground state. Second, even if an
ADAPT-VQE iteration converges to a local trap at one step, it can still
"burrow" toward the exact solution by adding more operators, which
preferentially deepens the occupied trap. This same mechanism helps highlight a
surprising feature of ADAPT-VQE: It should not suffer optimization problems due
to "barren plateaus". Even if barren plateaus appear in the parameter
landscape, our analysis and simulations reveal that ADAPT-VQE avoids such
regions by design.


title: Visible Energy Alternative to Dark Energy

abstract:
  Quantum gravitational effects usually are assumed to be important on small
scale (Planck scale), but actually these effects are also very significant on
large (cosmological) scales. It is recognized that in curved spacetime, the
existence of a minimal measurable momentum is inevitable. In this paper, we
study thermodynamic properties of the late time universe in the presence of a
minimal measurable momentum cutoff that encodes infra-red modification of the
underlying field theory. In this regard, we consider a non-relativistic regime
and show that the existence of a minimal measurable momentum in the very
essence of the theory leads to accelerating expansion of the universe, which
can be interpreted as an alternative to Dark Energy. The universe in this model
has experienced the phantom line crossing in the near past.


title: Cumulants of conserved charges in hydrodynamic simulations

abstract:
  We introduce a fast and simple method of computing cumulants of net-proton or
net-charge fluctuations in event-by-event hydrodynamic simulations of heavy-ion
collisions. One evaluates the mean numbers of particles in every hydrodynamic
event. Cumulants are then expressed as a function of these mean numbers. We
implement the corrections due to global conservation laws. The method is tested
using ideal hydrodynamic simulations of Au+Au collisions at $\sqrt{s_{NN}}=200$
AGeV with the NeXSPheRIO code. Results are in good agreement with experimental
data on net-proton and net-charge fluctuations by the STAR collaboration.


title:
Brazilian Court Documents Clustered by Similarity Together Using Natural
  Language Processing Approaches with Transformers

abstract:
  Recent advances in Artificial intelligence (AI) have leveraged promising
results in solving complex problems in the area of Natural Language Processing
(NLP), being an important tool to help in the expeditious resolution of
judicial proceedings in the legal area. In this context, this work targets the
problem of detecting the degree of similarity between judicial documents that
can be achieved in the inference group, by applying six NLP techniques based on
transformers, namely BERT, GPT-2 and RoBERTa pre-trained in the Brazilian
Portuguese language and the same specialized using 210,000 legal proceedings.
Documents were pre-processed and had their content transformed into a vector
representation using these NLP techniques. Unsupervised learning was used to
cluster the lawsuits, calculating the quality of the model based on the cosine
of the distance between the elements of the group to its centroid. We noticed
that models based on transformers present better performance when compared to
previous research, highlighting the RoBERTa model specialized in the Brazilian
Portuguese language, making it possible to advance in the current state of the
art in the area of NLP applied to the legal sector.


title: Interactive Object Segmentation in 3D Point Clouds

abstract:
  Deep learning depends on large amounts of labeled training data. Manual
labeling is expensive and represents a bottleneck, especially for tasks such as
segmentation, where labels must be assigned down to the level of individual
points. That challenge is even more daunting for 3D data: 3D point clouds
contain millions of points per scene, and their accurate annotation is markedly
more time-consuming. The situation is further aggravated by the added
complexity of user interfaces for 3D point clouds, which slows down annotation
even more. For the case of 2D image segmentation, interactive techniques have
become common, where user feedback in the form of a few clicks guides a
segmentation algorithm -- nowadays usually a neural network -- to achieve an
accurate labeling with minimal effort. Surprisingly, interactive segmentation
of 3D scenes has not been explored much. Previous work has attempted to obtain
accurate 3D segmentation masks using human feedback from the 2D domain, which
is only possible if correctly aligned images are available together with the 3D
point cloud, and it involves switching between the 2D and 3D domains. Here, we
present an interactive 3D object segmentation method in which the user
interacts directly with the 3D point cloud. Importantly, our model does not
require training data from the target domain: when trained on ScanNet, it
performs well on several other datasets with different data characteristics as
well as different object classes. Moreover, our method is orthogonal to
supervised (instance) segmentation methods and can be combined with them to
refine automatic segmentations with minimal human effort.


title: Separating the World and Ego Models for Self-Driving

abstract:
  Training self-driving systems to be robust to the long-tail of driving
scenarios is a critical problem. Model-based approaches leverage simulation to
emulate a wide range of scenarios without putting users at risk in the real
world. One promising path to faithful simulation is to train a forward model of
the world to predict the future states of both the environment and the
ego-vehicle given past states and a sequence of actions. In this paper, we
argue that it is beneficial to model the state of the ego-vehicle, which often
has simple, predictable and deterministic behavior, separately from the rest of
the environment, which is much more complex and highly multimodal. We propose
to model the ego-vehicle using a simple and differentiable kinematic model,
while training a stochastic convolutional forward model on raster
representations of the state to predict the behavior of the rest of the
environment. We explore several configurations of such decoupled models, and
evaluate their performance both with Model Predictive Control (MPC) and direct
policy learning. We test our methods on the task of highway driving and
demonstrate lower crash rates and better stability. The code is available at
https://github.com/vladisai/pytorch-PPUU/tree/ICLR2022.


title: This is the Moment for Probabilistic Loops

abstract:
  We present a novel static analysis technique to derive higher moments for
program variables for a large class of probabilistic loops with potentially
uncountable state spaces. Our approach is fully automatic, meaning it does not
rely on externally provided invariants or templates. We employ algebraic
techniques based on linear recurrences and introduce program transformations to
simplify probabilistic programs while preserving their statistical properties.
We develop power reduction techniques to further simplify the polynomial
arithmetic of probabilistic programs and define the theory of moment-computable
probabilistic loops for which higher moments can precisely be computed. Our
work has applications towards recovering probability distributions of random
variables and computing tail probabilities. The empirical evaluation of our
results demonstrates the applicability of our work on many challenging
examples.


title:
Optimal quadratic binding for relational reasoning in vector symbolic
  neural architectures

abstract:
  Binding operation is fundamental to many cognitive processes, such as
cognitive map formation, relational reasoning, and language comprehension. In
these processes, two different modalities, such as location and objects, events
and their contextual cues, and words and their roles, need to be bound
together, but little is known about the underlying neural mechanisms. Previous
works introduced a binding model based on quadratic functions of bound pairs,
followed by vector summation of multiple pairs. Based on this framework, we
address following questions: Which classes of quadratic matrices are optimal
for decoding relational structures? And what is the resultant accuracy? We
introduce a new class of binding matrices based on a matrix representation of
octonion algebra, an eight-dimensional extension of complex numbers. We show
that these matrices enable a more accurate unbinding than previously known
methods when a small number of pairs are present. Moreover, numerical
optimization of a binding operator converges to this octonion binding. We also
show that when there are a large number of bound pairs, however, a random
quadratic binding performs as well as the octonion and previously-proposed
binding methods. This study thus provides new insight into potential neural
mechanisms of binding operations in the brain.


title:
ALMA Lensing Cluster Survey: ALMA-Herschel Joint Study of Lensed Dusty
  Star-Forming Galaxies across $z\simeq0.5-6$

abstract:
  We present an ALMA-Herschel joint analysis of sources detected by the ALMA
Lensing Cluster Survey (ALCS) at 1.15 mm. Herschel/PACS and SPIRE data at
100-500 $\mu$m are deblended for 180 ALMA sources in 33 lensing cluster fields
that are either detected securely (141 sources; in our main sample) or
tentatively at S/N$\geq$4 with cross-matched HST/Spitzer counterparts, down to
a delensed 1.15-mm flux density of $\sim0.02$ mJy. We performed far-infrared
spectral energy distribution modeling and derived the physical properties of
dusty star formation for 125 sources (109 independently) that are detected at
$>2\sigma$ in at least one Herschel band. 27 secure ALCS sources are not
detected in any Herschel bands, including 17 optical/near-IR-dark sources that
likely reside at $z=4.2\pm1.2$. The 16-50-84 percentiles of the redshift
distribution are 1.15-2.08-3.59 for ALCS sources in the main sample, suggesting
an increasing fraction of $z\simeq1-2$ galaxies among fainter millimeter
sources ($f_{1150}\sim 0.1$ mJy). With a median lensing magnification factor of
$\mu = 2.6_{-0.8}^{+2.6}$, ALCS sources in the main sample exhibit a median
intrinsic star-formation rate of
$94_{-54}^{+84}\,\mathrm{M}_\odot\,\mathrm{yr}^{-1}$, lower than that of
conventional submillimeter galaxies at similar redshifts by a factor of
$\sim$3. Our study suggests weak or no redshift evolution of dust temperature
with $L_\mathrm{IR}<10^{12}\,\mathrm{L}_\odot$ galaxies within our sample at $z
\simeq 0 - 2$. At $L_\mathrm{IR}>10^{12}\,\mathrm{L}_\odot$, the dust
temperatures show no evolution across $z \simeq 1 -4$ while being lower than
those in the local Universe. For the highest-redshift source in our sample
($z=6.07$), we can rule out an extreme dust temperature ($>$80 K) that was
reported for MACS0416 Y1 at $z=8.31$.


title: Flexible Marginal Models for Dependent Data

abstract:
  Models for dependent data are distinguished by their targets of inference.
Marginal models are useful when interest lies in quantifying associations
averaged across a population of clusters. When the functional form of a
covariate-outcome association is unknown, flexible regression methods are
needed to allow for potentially non-linear relationships. We propose a novel
marginal additive model (MAM) for modelling cluster-correlated data with
non-linear population-averaged associations. The proposed MAM is a unified
framework for estimation and uncertainty quantification of a marginal mean
model, combined with inference for between-cluster variability and
cluster-specific prediction. We propose a fitting algorithm that enables
efficient computation of standard errors and corrects for estimation of penalty
terms. We demonstrate the proposed methods in simulations and in application to
(i) a longitudinal study of beaver foraging behaviour, and (ii) a spatial
analysis of Loaloa infection in West Africa. R code for implementing the
proposed methodology is available at https://github.com/awstringer1/mam.


title:
Peeling away neutron skin in ultracentral collisions of relativistic
  nuclei

abstract:
  In heavy nuclei the ratio between local densities of neutrons and protons
increases towards the nuclear periphery. This phenomenon is known as the
neutron skin (NS), and it is characterized by a subtle difference ($<0.5$ fm)
between the r.m.s radii of the distributions of neutrons and protons. We show
that the existence of NS in $^{208}$Pb translates into the excess of spectator
neutrons in ultracentral $^{208}$Pb-$^{208}$Pb collisions. The yields of
spectator neutrons and protons in such collisions at the CERN SPS and LHC were
calculated within a new version of Abrasion-Ablation Monte Carlo for Colliders
model (AAMCC-MST) taking into account NS and pre-equilibrium clustering of
spectator matter. The average numbers of spectator neutrons and protons in
ultracentral collisions vary insignificantly in calculations with different
parameterizations of neutron and proton densities. In contrast, the cross
sections of emission of certain numbers of spectrator neutrons in events with
0,1,...5 spectator protons are changed by 50-250 % depending on the thickness
of NS and relations between the half-density radii of neutron and proton
density distributions and diffuseness parameter for neutrons. We argue that the
measurements of such cross sections make it possible to restrict the existing
variety of neutron density parameterizations in $^{208}$Pb. These cross
sections can be proposed for measurements by the ALICE experiment at the LHC by
means of ALICE Zero Degree Calorimeters.


title: Measuring Compositional Consistency for Video Question Answering

abstract:
  Recent video question answering benchmarks indicate that state-of-the-art
models struggle to answer compositional questions. However, it remains unclear
which types of compositional reasoning cause models to mispredict. Furthermore,
it is difficult to discern whether models arrive at answers using compositional
reasoning or by leveraging data biases. In this paper, we develop a question
decomposition engine that programmatically deconstructs a compositional
question into a directed acyclic graph of sub-questions. The graph is designed
such that each parent question is a composition of its children. We present
AGQA-Decomp, a benchmark containing $2.3M$ question graphs, with an average of
$11.49$ sub-questions per graph, and $4.55M$ total new sub-questions. Using
question graphs, we evaluate three state-of-the-art models with a suite of
novel compositional consistency metrics. We find that models either cannot
reason correctly through most compositions or are reliant on incorrect
reasoning to reach answers, frequently contradicting themselves or achieving
high accuracies when failing at intermediate reasoning steps.


title: Inference from Selectively Disclosed Data

abstract:
  This paper considers the disclosure problem of a sender who wants to use hard
evidence to persuade a receiver towards higher actions. When the receiver hopes
to make inferences based on the distribution of the data, the sender has an
incentive to drop observations to mimic the distributions observed under better
states. We find that, in the limit when datasets are large, it is optimal for
senders to play an imitation strategy, under which they submit evidence
imitating the natural distribution under some desirable target state. The
volume of data that the sender can submit must meet a certain standard, a
"burden of proof", before the receiver can be persuaded to take a high action.
The outcome exhibits partial pooling: senders are honest when either they have
little data or the state is good, but they try to deceive the receiver when
they have access to a lot of data and the state is bad.


title:
Multi-step two-copy distillation of squeezed states via two photon
  subtraction

abstract:
  Squeezed states of light have been improving the sensitivity of
gravitational-wave observatories and are nonclassical resources of quantum
cryptography and envisioned photonic quantum computers. The higher the squeeze
factor is, the higher is the quantum advantage. Almost all applications of
squeezed light require multi-path optical interference, whose unavoidable
imperfections introduce optical loss, degrade the squeeze factor, as well as
the quantum advantage. Here, for the first time, we experimentally demonstrate
the distillation of Gaussian squeezed states that suffered from Gaussian photon
loss. Our demonstration already involves two distillation steps. The first step
improved the squeeze factor from 2.4 dB to 2.8 dB by the subtraction of two
photons. The second step improved the value from 2.8 dB to 3.4 dB by a
Gaussification protocol. It was realised on data measured at different times
via an 8-port balanced homodyne detector and via data post-processing. The
number of distillation steps can be increased by longer data sampling times,
without additional hardware. We propose and discuss the application to quantum
cryptography and photonic quantum computers.


title:
Dimensional reduction of Courant sigma models and Lie theory of Poisson
  groupoids

abstract:
  We show that the 2d Poisson Sigma Model on a Poisson groupoid arises as an
effective theory of the 3d Courant Sigma Model associated to the double of the
underlying Lie bialgebroid. This field-theoretic result follows from a
Lie-theoretic one involving a coisotropic reduction of the odd cotangent bundle
by a generalized space of algebroid paths. We also provide several examples,
including the case of symplectic groupoids in which we relate the symplectic
realization construction of Crainic-Marcut to a particular gauge fixing of the
3d theory.


title: Spectral Form Factor in Sparse SYK Models

abstract:
  We investigate the spectral form factor of the sparse Sachdev-Ye-Kitaev
model. We use numerical methods to establish that at intermediate times the
connected part of the spectral form factor is the dominant one. These connected
contributions arise from fluctuations around the disconnected geometry, not
from a new saddle point. A similar effect was previously conjectured in SYK but
required a value of $N$ out of reach of current numerical simulations.


title: Hierarchical Control of Smart Particle Swarms

abstract:
  We present a method for the control of robot swarms which allows the shaping
and the translation of patterns of simple robots ("smart particles"), using two
types of devices. These two types represent a hierarchy: a larger group of
simple, oblivious robots (which we call the workers) that is governed by simple
local attraction forces, and a smaller group (the guides) with sufficient
mission knowledge to create and maintain a desired pattern by operating on the
local forces of the former. This framework exploits the knowledge of the
guides, which coordinate to shape the workers like smart particles by changing
their interaction parameters. We study the approach with a large scale
simulation experiment in a physics based simulator with up to 1000 robots
forming three different patterns. Our experiments reveal that the approach
scales well with increasing robot numbers, and presents little pattern
distortion for a set of target moving shapes. We evaluate the approach on a
physical swarm of robots that use visual inertial odometry to compute their
relative positions and obtain results that are comparable with simulation. This
work lays foundation for designing and coordinating configurable smart
particles, with applications in smart materials and nanomedicine.


title: Testing distributional assumptions of learning algorithms

abstract:
  There are many important high dimensional function classes that have fast
agnostic learning algorithms when strong assumptions on the distribution of
examples can be made, such as Gaussianity or uniformity over the domain. But
how can one be sufficiently confident that the data indeed satisfies the
distributional assumption, so that one can trust in the output quality of the
agnostic learning algorithm? We propose a model by which to systematically
study the design of tester-learner pairs $(\mathcal{A},\mathcal{T})$, such that
if the distribution on examples in the data passes the tester $\mathcal{T}$
then one can safely trust the output of the agnostic learner $\mathcal{A}$ on
the data.
  To demonstrate the power of the model, we apply it to the classical problem
of agnostically learning halfspaces under the standard Gaussian distribution
and present a tester-learner pair with a combined run-time of
$n^{\tilde{O}(1/\epsilon^4)}$. This qualitatively matches that of the best
known ordinary agnostic learning algorithms for this task. In contrast, finite
sample Gaussian distribution testers do not exist for the $L_1$ and EMD
distance measures. A key step in the analysis is a novel characterization of
concentration and anti-concentration properties of a distribution whose
low-degree moments approximately match those of a Gaussian. We also use tools
from polynomial approximation theory.
  In contrast, we show strong lower bounds on the combined run-times of
tester-learner pairs for the problems of agnostically learning convex sets
under the Gaussian distribution and for monotone Boolean functions under the
uniform distribution over $\{0,1\}^n$. Through these lower bounds we exhibit
natural problems where there is a dramatic gap between standard agnostic
learning run-time and the run-time of the best tester-learner pair.


title: RobustScaler: QoS-Aware Autoscaling for Complex Workloads

abstract:
  Autoscaling is a critical component for efficient resource utilization with
satisfactory quality of service (QoS) in cloud computing. This paper
investigates proactive autoscaling for widely-used scaling-per-query
applications where scaling is required for each query, such as container
registry and function-as-a-service (FaaS). In these scenarios, the workload
often exhibits high uncertainty with complex temporal patterns like
periodicity, noises and outliers. Conservative strategies that scale out
unnecessarily many instances lead to high resource costs whereas aggressive
strategies may result in poor QoS. We present RobustScaler to achieve superior
trade-off between cost and QoS. Specifically, we design a novel autoscaling
framework based on non-homogeneous Poisson processes (NHPP) modeling and
stochastically constrained optimization. Furthermore, we develop a specialized
alternating direction method of multipliers (ADMM) to efficiently train the
NHPP model, and rigorously prove the QoS guarantees delivered by our
optimization-based proactive strategies. Extensive experiments show that
RobustScaler outperforms common baseline autoscaling strategies in various
real-world traces, with large margins for complex workload patterns.


title: RIS-Assisted Visible Light Communication Systems: A Tutorial

abstract:
  Recent development of the fifth-generation (5G) of cellular networks has led
to their deployment worldwide. As part of the implementation, one of the
challenges that must be addressed is the skip-zone problem, which occurs when
objects obstruct the transmission of signals. A signal obstruction can
significantly reduce the signal-to-noise ratio in radio frequency (RF) and
indoor visible light communications (VLC) systems, whereas the obstruction can
completely disrupt data transmission in free-space optical (FSO) systems.
Therefore, the skip-zone dilemma must be resolved to ensure the efficient
operation of 5G and beyond networks. In recent years, reconfigurable
intelligent surfaces (RISs) that are more efficient than relays have become
widely accepted as a method of mitigating skip-zones and providing
reconfigurable radio environments. However, there have been limited studies on
RISs for optical wireless communication (OWC) systems. This paper aims to
provide a comprehensive tutorial on indoor VLC systems utilizing RISs
technology. The article discusses the basics of VLC and RISs and reintroduces
RISs for OWC systems, focusing on RIS-assisted indoor VLC systems. We also
provide a comprehensive overview of optical RISs and examine the differences
between optical RISs, RF-RISs, and optical relays. Furthermore, we discuss in
detail how RISs can be used to overcome line-of-sight blockages and device
orientation issue in VLC systems while revealing key challenges such as RIS
element orientation design, RIS elements to access point/user assignment
design, and RIS array positioning design problems that need to be studied.
Moreover, we discuss and propose several research problems on integrating
optical RISs with other emerging technologies and highlight other important
research directions for RIS-assisted VLC systems.


title: Ear Wearable (Earable) User Authentication via Acoustic Toothprint

abstract:
  Earables (ear wearables) is rapidly emerging as a new platform encompassing a
diverse range of personal applications. The traditional authentication methods
hence become less applicable and inconvenient for earables due to their limited
input interface. Nevertheless, earables often feature rich around-the-head
sensing capability that can be leveraged to capture new types of biometrics. In
this work, we proposeToothSonic which leverages the toothprint-induced sonic
effect produced by users performing teeth gestures for earable authentication.
In particular, we design representative teeth gestures that can produce
effective sonic waves carrying the information of the toothprint. To reliably
capture the acoustic toothprint, it leverages the occlusion effect of the ear
canal and the inward-facing microphone of the earables. It then extracts
multi-level acoustic features to reflect the intrinsic toothprint information
for authentication. The key advantages of ToothSonic are that it is suitable
for earables and is resistant to various spoofing attacks as the acoustic
toothprint is captured via the user's private teeth-ear channel that modulates
and encrypts the sonic waves. Our experiment studies with 25 participants show
that ToothSonic achieves up to 95% accuracy with only one of the users' tooth
gestures.


title:
Concurrent kilovoltage CBCT imaging and megavoltage beam delivery:
  Suppression of cross-scatter with 2D antiscatter grids and grid-based scatter
  sampling

abstract:
  The concept of using kilovoltage (kV) and megavoltage (MV) beams concurrently
has potential applications in cone beam computed tomography (CBCT) guided
radiation therapy, such as single breath hold scans, metal artifact reduction,
and simultaneous imaging during MV treatment delivery. However, MV
cross-scatter generated during MV beam delivery degrades CBCT image quality. To
address this, a 2D antiscatter grid and cross scatter correction method were
investigated. a 3D printed, tungsten 2D antiscatter grid prototype was utilized
to reduce MV cross-scatter fluence in kV projections during concurrent MV beam
delivery. Remaining cross-scatter was corrected by using the 2D grid itself as
a cross-scatter intensity sampling device, referred as Grid-based Scatter
Sampling. To test this approach, kV CBCT acquisitions were performed while
delivering 6 and 10 MV beams, mimicking high dose rate treatment delivery
scenarios. MV cross-scatter suppression performance of the proposed approach
was evaluated in projections and CBCT images of phantoms. 2D grid reduced the
intensity of MV cross-scatter in kV projections by a factor of 3 on the
average. Remaining MV cross-scatter estimated by Grid-based Scatter Sampling
was within 7% of measured reference intensity values. CBCT image quality was
improved substantially during concurrent kV-MV beam delivery. Median Hounsfield
Unit (HU) inaccuracy was up to 191 HU without our methods, and it was reduced
to 3 HU with our 2D grid and scatter correction approach. Our methods provided
a factor of 2-6 improvement in contrast-to-ratio. Results indicate that our
approach can successfully minimize the effects of high energy cross-scatter in
concurrent kV CBCT imaging and megavoltage treatment delivery.


title: Stability for QED in d=3: an overview

abstract:
  We report on a result on quantum electrodynamics on a three dimensional
Euclidean spacetime. The model is formulated on a toroidal lattice with unit
volume and variable lattice spacing. The result is that the renormalized
partition function is bounded above and below uniformly in the lattice spacing.
This is a first step toward showing that the partition function and correlation
functions have limits as the lattice spacing goes to zero.


title:
Simple coplanar waveguide resonator mask targeting metal-substrate
  interface

abstract:
  This white paper presents a single-layer mask, found at
https://github.com/Boulder-Cryogenic-Quantum-Testbed/simple-resonator-mask. It
is designed for fabrication of superconducting microwave resonators towards 1:1
comparisons of dielectric losses from the metal-substrate interface.
Finite-element electromagnetic simulations are used to determine participation
ratios of the four major regions of the on-chip devices, as well as to confirm
lack of crosstalk between neighboring devices and demonstrate coupling
tunability over three orders of magnitude. This mask is intended as an
open-source community resource for facilitating precise and accurate
comparisons of materials in the single-photon, millikelvin regime.


title: EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs

abstract:
  Machine learning models that learn from dynamic graphs face nontrivial
challenges in learning and inference as both nodes and edges change over time.
The existing large-scale graph benchmark datasets that are widely used by the
community primarily focus on homogeneous node and edge attributes and are
static. In this work, we present a variety of large scale, dynamic
heterogeneous academic graphs to test the effectiveness of models developed for
multi-step graph forecasting tasks. Our novel datasets cover both context and
content information extracted from scientific publications across two
communities: Artificial Intelligence (AI) and Nuclear Nonproliferation (NN). In
addition, we propose a systematic approach to improve the existing evaluation
procedures used in the graph forecasting models.


title:
Test-Time Training Can Close the Natural Distribution Shift Performance
  Gap in Deep Learning Based Compressed Sensing

abstract:
  Deep learning based image reconstruction methods outperform traditional
methods in accuracy and runtime. However, neural networks suffer from a
performance drop when applied to images from a different distribution than the
training images. For example, a model trained for reconstructing knees in
accelerated magnetic resonance imaging (MRI) does not reconstruct brains well,
even though the same network trained on brains reconstructs brains perfectly
well. Thus there is a distribution shift performance gap for a given neural
network, defined as the difference in performance when training on a
distribution $P$ and training on another distribution $Q$, and evaluating both
models on $Q$. In this work, we propose a domain adaptation method for deep
learning based compressive sensing that relies on self-supervision during
training paired with test-time training at inference. We show that for four
natural distribution shifts, this method essentially closes the distribution
shift performance gap for state-of-the-art architectures for accelerated MRI.


title: Expanding the Reach of Research Computing: A Landscape Study

abstract:
  Research-computing continues to play an ever increasing role in academia.
Access to computing resources, however, varies greatly between institutions.
Sustaining the growing need for computing skills and access to advanced
cyberinfrastructure requires that computing resources be available to students
at all levels of scholarship, including community colleges. The National
Science Foundation-funded Building Research Innovation in Community Colleges
(BRICCs) community set out to understand the challenges faced by
administrators, researchers and faculty in building a sustainable research
computing continuum that extends to smaller and two-year terminal degree
granting institutions. BRICCs purpose is to address the technology gaps, and
encourage the development of curriculum needed to grow a computationally
proficient research workforce. Toward addressing these goals, we performed a
landscape study that culminated with a community workshop. Here, we present our
key findings from workshop discussions and identify next steps to be taken by
BRICCs, funding agencies, and the broader cyberinfrastructure community.


title:
Spectroscopic Confirmation of Two Luminous Post-AGB Stars in the
  Globular Cluster M19

abstract:
  The visually brightest stars in globular clusters (GCs) are the ones evolving
off the asymptotic giant branch (AGB) and passing through spectral types F and
A--the "yellow" post-AGB (yPAGB) stars. yPAGB stars are potentially excellent
"Population II" standard candles for measuring extragalactic distances. A
recent survey of the Galactic GC system, using uBVI photometry to identify
stars of low surface gravities with large Balmer discontinuities, discovered a
candidate luminous yPAGB star in the GC M19 (NGC 6273), designated ZNG 4. The
same survey also identified a bright, hotter candidate blue PAGB star, ZNG 2,
lying near the top of the white-dwarf cooling sequence. Both PAGB candidates
have proper motions and parallaxes in the recent Gaia Early Data Release 3
consistent with cluster membership, but they still lacked spectroscopic
verification. Here we present moderate-resolution spectra of both stars,
confirming them as low-gravity objects that are extremely likely to be cluster
members. Through comparison with a library of synthetic spectra, we made
approximate estimates of the stars' atmospheric parameters. We find that the
yPAGB star ZNG 4 has an effective temperature of Teff ~ 6500 K, a surface
gravity of log g ~ 1, and a metallicity of [Fe/H] ~ -1.5, similar to that of
the host cluster. The blue PAGB star ZNG 2 has Teff ~ 18000 K, log g ~ 3, and
an apparently low metallicity in the range of [Fe/H] ~ -2.0 to -2.5. Both stars
are bright (V=12.5 and 13.3, respectively). We urge high-dispersion
spectroscopic follow-up to determine detailed atmospheric parameters and
chemical compositions, and to monitor radial velocities.


title: Hierarchical Embedded Bayesian Additive Regression Trees

abstract:
  We propose a simple yet powerful extension of Bayesian Additive Regression
Trees which we name Hierarchical Embedded BART (HE-BART). The model allows for
random effects to be included at the terminal node level of a set of regression
trees, making HE-BART a non-parametric alternative to mixed effects models
which avoids the need for the user to specify the structure of the random
effects in the model, whilst maintaining the prediction and uncertainty
calibration properties of standard BART. Using simulated and real-world
examples, we demonstrate that this new extension yields superior predictions
for many of the standard mixed effects models' example data sets, and yet still
provides consistent estimates of the random effect variances. In a future
version of this paper, we outline its use in larger, more advanced data sets
and structures.


title:
Alternating Mahalanobis Distance Minimization for Stable and Accurate CP
  Decomposition

abstract:
  CP decomposition (CPD) is prevalent in chemometrics, signal processing, data
mining and many more fields. While many algorithms have been proposed to
compute the CPD, alternating least squares (ALS) remains one of the most widely
used algorithm for computing the decomposition. Recent works have introduced
the notion of eigenvalues and singular values of a tensor and explored
applications of eigenvectors and singular vectors in areas like signal
processing, data analytics and in various other fields. We introduce a new
formulation for deriving singular values and vectors of a tensor by considering
the critical points of a function different from what is used in the previous
work. Computing these critical points in an alternating manner motivates an
alternating optimization algorithm which corresponds to alternating least
squares algorithm in the matrix case. However, for tensors with order greater
than equal to $3$, it minimizes an objective function which is different from
the commonly used least squares loss. Alternating optimization of this new
objective leads to simple updates to the factor matrices with the same
asymptotic computational cost as ALS. We show that a subsweep of this algorithm
can achieve a superlinear convergence rate for exact CPD with known rank and
verify it experimentally. We then view the algorithm as optimizing a
Mahalanobis distance with respect to each factor with ground metric dependent
on the other factors. This perspective allows us to generalize our approach to
interpolate between updates corresponding to the ALS and the new algorithm to
manage the tradeoff between stability and fitness of the decomposition. Our
experimental results show that for approximating synthetic and real-world
tensors, this algorithm and its variants converge to a better conditioned
decomposition with comparable and sometimes better fitness as compared to the
ALS algorithm.


title:
Development of a technology for manufacturing a heat-shielding structure
  on nitrogen cryocontainers, excluding heat transfer through gas

abstract:
  One of the important stages in the creation of the scientific and technical
foundations for the calculation, design and manufacturing technology of the
lowest heat-conductivity thermal protection from screen-vacuum thermal
insulation (SVTI) is the development of a process for achieving the optimal
vacuum in the SVTI layers, since at this pressure, thermal conductivity through
the SVTI is carried out only due to the radiant and contact-conductive
components. It is proposed to obtain such a pressure in thermal insulation by
using cushioning material in it, which was previously degassed in a separate
vacuum chamber at 370-380 K for 12 hours in order to remove water molecules
from its structure and then replace them with dry nitrogen molecules. These
molecules have 3-4 times less heat of adsorption; therefore they are pumped out
faster. As a result, it becomes possible to accelerate (by 20 hours) to achieve
optimal vacuum in thermal insulation, as well as 11% lower effective thermal
conductivity. The analysis carried out (according to the developed methodology)
showed that the achieved optimal effective thermal conductivity of thermal
insulation in a cryocontainers is determined by 33% of radiant thermal
conductivity and 67% of the contact-conductive component.


title: A Case for Microservices Orchestration Using Workflow Engines

abstract:
  Microservices have become the de-facto software architecture for cloud-native
applications. A contentious architectural decision in microservices is to
compose them using choreography or orchestration. In choreography, every
service works independently, whereas, in orchestration, there is a controller
that coordinates service interactions. This paper makes a case for
orchestration. The promise of microservices is that each microservice can be
independently developed, deployed, tested, upgraded, and scaled. This makes
them suitable for systems running on cloud infrastructures. However,
microservice-based systems become complicated due to the complex interactions
of various services, concurrent events, failing components, developers' lack of
global view, and configurations of the environment. This makes maintaining and
debugging such systems very challenging. We hypothesize that orchestrated
services are easier to debug and to test this we ported the largest publicly
available microservices' benchmark TrainTicket, which is implemented using
choreography, to a fault-oblivious stateful workflow framework Temporal. We
report our experience in porting the code from traditional choreographed
microservice architecture to one orchestrated by Temporal and present our
initial findings of time to debug the 22 bugs present in the benchmark. Our
findings suggest that an effort towards making a transition to orchestrated
approach is worthwhile, making the ported code easier to debug.


title: Analysis of Workflow Schedulers in Simulated Distributed Environments

abstract:
  Task graphs provide a simple way to describe scientific workflows (sets of
tasks with dependencies) that can be executed on both HPC clusters and in the
cloud. An important aspect of executing such graphs is the used scheduling
algorithm. Many scheduling heuristics have been proposed in existing works;
nevertheless, they are often tested in oversimplified environments. We provide
an extensible simulation environment designed for prototyping and benchmarking
task schedulers, which contains implementations of various scheduling
algorithms and is open-sourced, in order to be fully reproducible. We use this
environment to perform a comprehensive analysis of workflow scheduling
algorithms with a focus on quantifying the effect of scheduling challenges that
have so far been mostly neglected, such as delays between scheduler invocations
or partially unknown task durations. Our results indicate that network models
used by many previous works might produce results that are off by an order of
magnitude in comparison to a more realistic model. Additionally, we show that
certain implementation details of scheduling algorithms which are often
neglected can have a large effect on the scheduler's performance, and they
should thus be described in great detail to enable proper evaluation.


title:
Reputation and Audit Bit Based Distributed Detection in the Presence of
  Byzantine

abstract:
  In this paper, two reputation based algorithms called Reputation and audit
based clustering (RAC) algorithm and Reputation and audit based clustering with
auxiliary anchor node (RACA) algorithm are proposed to defend against Byzantine
attacks in distributed detection networks when the fusion center (FC) has no
prior knowledge of the attacking strategy of Byzantine nodes. By updating the
reputation index of the sensors in cluster-based networks, the system can
accurately identify Byzantine nodes. The simulation results show that both
proposed algorithms have superior detection performance compared with other
algorithms. The proposed RACA algorithm works well even when the number of
Byzantine nodes exceeds half of the total number of sensors in the network.
Furthermore, the robustness of our proposed algorithms is evaluated in a
dynamically changing scenario, where the attacking parameters change over time.
We show that our algorithms can still achieve superior detection performance.


title: Polar Liquids at Charged Interfaces: A Dipolar Shell Theory

abstract:
  The structure of polar liquids and electrolytic solutions, such as water and
aqueous electrolytes, at interfaces underlies numerous phenomena in physics,
chemistry, biology, and engineering. In this work, we develop a continuum
theory that captures the essential features of dielectric screening by polar
liquids at charged interfaces, including oscillations in charge and mass,
starting from the molecular properties of the solvent. The theory predicts an
anisotropic dielectric tensor of interfacial polar liquids previously studied
in molecular dynamics simulations. We explore the effect of the interfacial
polar liquid properties on the capacitance of the electrode/electrolyte
interface and on hydration forces between two plane-parallel polarized
surfaces. In the linear response approximation, we obtain simple formulas for
the characteristic decay lengths of molecular and ionic profiles at the
interface.


title:
Observation of non-trivial topological electronic structure of
  orthorhombic SnSe

abstract:
  Topological electronic structures are key to the topological classification
of quantum materials and play an important role in their physical properties
and applications. Recently, SnSe has attracted great research interests due to
its superior thermoelectric performance. However, it's topological nature has
long been ignored. In this work, by combining synchrotron-based angle-resolved
photoemission spectroscopy and ab-initio calculations, we systematically
investigated the topological electronic structure of orthorhombic SnSe. By
identifying the continuous gap in the valence bands due to the band inversion
and the topological surface states on its (001) surface, we establish SnSe as a
strong topological insulator. Furthermore, we studied the evolution of the
topological electronic structure and propose the topological phase diagram in
SnSe1-xTex. Our work reveals the topological non-trivial nature of SnSe and
provides new understandings of its intriguing transport properties.


title: An advection-diffusion process with proportional resetting

abstract:
  This paper presents a diffusion process with a novel resetting mechanism in
which the amplitude of the process is instantaneously converted to a proportion
of its value at random times. This model is described by a Langevin equation
with both additive Gaussian white noise and multiplicative Poisson shot noise
terms. The distribution function obeys a pantograph equation, a functional
partial differential equation evaluated at two amplitudes simultaneously. From
this equation the exact statistical moments and steady-state distribution of
the process are calculated. The distribution interpolates between exponential
and Gaussian extremes depending on the proportion of the amplitude lost in each
reset. These results will be useful for applications in which stochastic
quantities are suddenly reduced in proportion to their values due to random
events.


title:
Phased Array With Improved Beamforming Capability via Use of Double
  Phase Shifters

abstract:
  The passive electronically scanned array (PESA) is widely used due to its
simple structure and low cost. {Its antenna weights have unit modulus and thus,
only the weights phases can be controlled. PESA has limited degrees of freedom
for beampattern design, where only the direction of the main beam can be
controlled.} In this paper we propose a novel way to improve the beamforming
capability of PESA by endowing it with more degrees of freedom via the use of
double phase shifters (DPS). By doing so, both the magnitude and the phase of
the antenna weights can be controlled, allowing for more flexibility in
beampattern design. We also take into account the physical resolution
limitation of phase shifters, and propose a method to approximate a given
complex beamformer using DPS. Simulation results indicate significant
beamforming improvement even at low phase resolution.


title: Analytical evaluation of cosmological correlation functions

abstract:
  Using the Schwinger-Keldysh-formalism, reformulated in arXiv:2108.01695 as an
effective field theory in Euclidean anti-de Sitter, we evaluate the one-loop
cosmological four-point function of a conformally coupled interacting scalar
field in de Sitter. Recasting the Witten cosmological correlator as flat space
Feynman integrals, we evaluate the one-loop cosmological four-point functions
in de Sitter space in terms of single-valued multiple polylogarithms. From it
we derive anomalous dimensions and OPE coefficients of the dual conformal field
theory at space-like, future infinity. In particular, we find an interesting
degeneracy in the anomalous dimensions relating operators of neighboring spins.


title: Number of partitions of n with a given parity of the smallest part

abstract:
  We obtain a combinatorial proof of a surprising weighted partition equality
of Berkovich and Uncu. Our proof naturally leads to a formula for the number of
partitions with a given parity of the smallest part, in terms of S(i), the
number of partitions of i into distinct parts with even rank minus the number
with odd rank, for which there is an almost closed formula by Andrews, Dyson
and Hickerson. This method of calculating the number of partitions of n with a
given parity of the smallest part is practical and efficient.


title: A combinatorial proof of the Burdzy-Pitman conjecture

abstract:
  We prove a sharp upper bound for the number of high degree differences in
bipartite graphs: let $ (U, V, E)$ be a bipartite graph with $U=\{u_1, u_2,
\dots, u_n\}$ and $V=\{v_1, v_2, \dots, v_n\}$; for $n\ge k>\frac{n}{2}$ we
show that $\sum_{1\le i,j \le n} 1 {\Big\{|\text{deg}(u_i)-\text{deg}(v_j)|\ge
k}\Big\} \le 2k(n-k).$ As a direct application we show a slightly stronger,
probabilistic version of this theorem and thus confirm the Burdzy-Pitman
conjecture about the maximal spread of coherent and independent distributions.


title: Nonparametric Analysis of Dynamic Random Utility Models

abstract:
  We study a dynamic generalization of stochastic rationality in consumer
behavior, the Dynamic Random Utility Model (DRUM). Under DRUM, a consumer draws
a utility function from a stochastic utility process and maximizes this utility
subject to her budget constraint in each time period. Utility is random, with
unrestricted correlation across time periods and unrestricted heterogeneity in
a cross-section. We provide a revealed preference characterization of DRUM when
we observe a panel of choices from budgets. This characterization is amenable
to statistical testing. Our result unifies Afriat's (1967) theorem that works
with time-series data and the static random utility framework of
McFadden-Richter (1990) that works with cross-sections of choice.


title:
Causal Disentanglement with Network Information for Debiased
  Recommendations

abstract:
  Recommender systems aim to recommend new items to users by learning user and
item representations. In practice, these representations are highly entangled
as they consist of information about multiple factors, including user's
interests, item attributes along with confounding factors such as user
conformity, and item popularity. Considering these entangled representations
for inferring user preference may lead to biased recommendations (e.g., when
the recommender model recommends popular items even if they do not align with
the user's interests).
  Recent research proposes to debias by modeling a recommender system from a
causal perspective. The exposure and the ratings are analogous to the treatment
and the outcome in the causal inference framework, respectively. The critical
challenge in this setting is accounting for the hidden confounders. These
confounders are unobserved, making it hard to measure them. On the other hand,
since these confounders affect both the exposure and the ratings, it is
essential to account for them in generating debiased recommendations. To better
approximate hidden confounders, we propose to leverage network information
(i.e., user-social and user-item networks), which are shown to influence how
users discover and interact with an item. Aside from the user conformity,
aspects of confounding such as item popularity present in the network
information is also captured in our method with the aid of \textit{causal
disentanglement} which unravels the learned representations into independent
factors that are responsible for (a) modeling the exposure of an item to the
user, (b) predicting the ratings, and (c) controlling the hidden confounders.
Experiments on real-world datasets validate the effectiveness of the proposed
model for debiasing recommender systems.


title: Effective Dynamics of Extended Fermi Gases in the High-Density Regime

abstract:
  We study the quantum evolution of many-body Fermi gases in three dimensions,
in arbitrarily large domains. We focus on the high-density regime, in the
semiclassical scaling. For a class of initial data describing zero-temperature
states we prove that, as the density goes to infinity, and for short
macroscopic times, the many-body evolution of the reduced one-particle density
matrix converges to the solution of the time-dependent Hartree equation. With
respect to previous work, the rate of convergence does not depend on the total
number of particles, but only on the density: in particular, our result allows
us to study the quantum dynamics of extensive many-body Fermi gases.


title: On Scheduling Mechanisms Beyond the Worst Case

abstract:
  The problem of scheduling unrelated machines has been studied since the
inception of algorithmic mechanism design \cite{NR99}. It is a resource
allocation problem that entails assigning $m$ tasks to $n$ machines for
execution. Machines are regarded as strategic agents who may lie about their
execution costs so as to minimize their allocated workload. To address the
situation when monetary payment is not an option to compensate the machines'
costs, \citeauthor{DBLP:journals/mst/Koutsoupias14} [2014] devised two
\textit{truthful} mechanisms, K and P respectively, that achieve an
approximation ratio of $\frac{n+1}{2}$ and $n$, for social cost minimization.
In addition, no truthful mechanism can achieve an approximation ratio better
than $\frac{n+1}{2}$. Hence, mechanism K is optimal. While approximation ratio
provides a strong worst-case guarantee, it also limits us to a comprehensive
understanding of mechanism performance on various inputs. This paper
investigates these two scheduling mechanisms beyond the worst case. We first
show that mechanism K achieves a smaller social cost than mechanism P on every
input. That is, mechanism K is pointwise better than mechanism P. Next, for
each task $j$, when machines' execution costs $t_i^j$ are independent and
identically drawn from a task-specific distribution $F^j(t)$, we show that the
average-case approximation ratio of mechanism K converges to a constant. This
bound is tight for mechanism K. For a better understanding of this distribution
dependent constant, on the one hand, we estimate its value by plugging in a few
common distributions; on the other, we show that this converging bound improves
a known bound \cite{DBLP:conf/aaai/Zhang18} which only captures the single-task
setting. Last, we find that the average-case approximation ratio of mechanism P
converges to the same constant.


title:
Observation of breakdown wave mechanism in avalanche ionization produced
  atmospheric plasma generated by a picosecond CO$_2$ laser

abstract:
  Understanding the formation and long-timescale evolution of atmospheric
plasmas produced by ultrashort, long wavelength IR (LWIR) pulses is an
important but partially understood problem. Of particular interest are plasmas
produced in air with a peak laser intensity $\sim$10$^{12}$ W/cm$^2$, the
so-called clamping intensity observed in LWIR atmospheric guiding experiments
where tunneling and multi-photon ionization operative at near-IR or shorter
wavelengths are inoperative. We find that avalanche breakdown on the surface of
aerosol (dust) particles can act to seed the breakdown of air observed above
the 200 GW/cm$^2$ threshold when a train of 3 ps 10.6 $\mu$m laser pulses
separated by 18 ps are used. The breakdown first appears at the best focus but
propagates backwards towards the focusing optic as the plasma density
approaches critical density and makes forward propagation impossible. The
velocity of the backward propagating breakdown can be as high as 10$^9$ cm/s,
an order of magnitude greater than measured with ns pulse-produced breakdown
and can be explained rather well by the so-called breakdown wave mechanism.
Transverse plasma expansion with a similar velocity is assisted by UV
photoionization and is observed as a secondary longitudinal breakdown mechanism
in roughly 10 percent of the shots. When a cm size, TW power beam is
propagated, interception of aerosol particles is guaranteed and several (40
cm$^{-3}$) breakdown sites appear, each initially producing a near critical
density plasma. On a 10 ns-1 $\mu$s timescale, shockwaves from each site expand
radially and coalesce to produce a large hot gas channel. The radial velocity
of the expansion agrees well with the prediction of the blast wave theory
developed for ultrafast atmospheric detonations.


title:
MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with
  Iterative Self-Supervision

abstract:
  Machine programming (MP) is concerned with automating software development.
According to studies, software engineers spend upwards of 50% of their
development time debugging software. To help accelerate debugging, we present
MP-CodeCheck (MPCC). MPCC is an MP system that attempts to identify anomalous
code patterns within logical program expressions. In designing MPCC, we
developed two novel programming language representations, the formations of
which are critical in its ability to exhaustively and efficiently process the
billions of lines of code that are used in its self-supervised training. To
quantify MPCC's performance, we compare it against ControlFlag, a
state-of-the-art self-supervised code anomaly detection system; we find that
MPCC is more spatially and temporally efficient. We demonstrate MPCC's
anomalous code detection capabilities by exercising it on a variety of
open-source GitHub repositories and one proprietary code base. We also provide
a brief qualitative study on some of the different classes of code anomalies
that MPCC can detect to provide an abbreviated insight into its capabilities.


title: DC-coupled resistive silicon detectors for 4-D tracking

abstract:
  In this work, we introduce a new design concept: the DC-Coupled Resistive
Silicon Detectors, based on the LGAD technology. This new approach intends to
address a few known features of the first generation of AC-Coupled Resistive
Silicon Detectors (RSD). Our simulation exploits a fast hybrid approach based
on a combination of two packages, Weightfield2 and LTSpice. It demonstrates
that the key features of the RSD design are maintained, yielding excellent
timing and spatial resolutions: a few tens of ps and a few microns. In the
presentation, we will outline the optimization methodology and the results of
the simulation. We will present detailed studies on the effect of changing the
ratio between the n+ layer resistivity and the low-resistivity ring and on the
achievable temporal and spatial resolution.


title: A deep first-order system least squares method for solving elliptic PDEs

abstract:
  We propose a First-Order System Least Squares (FOSLS) method based on
deep-learning for numerically solving second-order elliptic PDEs. The method we
propose is capable of dealing with either variational and non-variational
problems, and because of its meshless nature, it can also deal with problems
posed in high-dimensional domains. We prove the $\Gamma$-convergence of the
neural network approximation towards the solution of the continuous problem,
and extend the convergence proof to some well-known related methods. Finally,
we present several numerical examples illustrating the performance of our
discretization.


title:
Applying Feature Underspecified Lexicon Phonological Features in
  Multilingual Text-to-Speech

abstract:
  This study investigates whether the phonological features derived from the
Featurally Underspecified Lexicon model can be applied in text-to-speech
systems to generate native and non-native speech in English and Mandarin. We
present a mapping of ARPABET/pinyin to SAMPA/SAMPA-SC and then to phonological
features. This mapping was tested for whether it could lead to the successful
generation of native, non-native, and code-switched speech in the two
languages. We ran two experiments, one with a small dataset and one with a
larger dataset. The results supported that phonological features could be used
as a feasible input system for languages in or not in the train data, although
further investigation is needed to improve model performance. The results lend
support to FUL by presenting successfully synthesised output, and by having the
output carrying a source-language accent when synthesising a language not in
the training data. The TTS process stimulated human second language acquisition
process and thus also confirm FUL's ability to account for acquisition.


title:
Automatic Fake News Detection: Are current models "fact-checking" or
  "gut-checking"?

abstract:
  Automatic fake news detection models are ostensibly based on logic, where the
truth of a claim made in a headline can be determined by supporting or refuting
evidence found in a resulting web query. These models are believed to be
reasoning in some way; however, it has been shown that these same results, or
better, can be achieved without considering the claim at all -- only the
evidence. This implies that other signals are contained within the examined
evidence, and could be based on manipulable factors such as emotion, sentiment,
or part-of-speech (POS) frequencies, which are vulnerable to adversarial
inputs. We neutralize some of these signals through multiple forms of both
neural and non-neural pre-processing and style transfer, and find that this
flattening of extraneous indicators can induce the models to actually require
both claims and evidence to perform well. We conclude with the construction of
a model using emotion vectors built off a lexicon and passed through an
"emotional attention" mechanism to appropriately weight certain emotions. We
provide quantifiable results that prove our hypothesis that manipulable
features are being used for fact-checking.


title:
Learning two-phase microstructure evolution using neural operators and
  autoencoder architectures

abstract:
  Phase-field modeling is an effective mesoscale method for capturing the
evolution dynamics of materials, e.g., in spinodal decomposition of a two-phase
mixture. However, the accuracy of high-fidelity phase field models comes at a
substantial computational cost. Hence, fast and generalizable surrogate models
are needed to alleviate the cost in computationally taxing processes such as in
optimization and design of materials. The intrinsic discontinuous nature of the
physical phenomena incurred by the presence of sharp phase boundaries makes the
training of the surrogate model cumbersome. We develop a new framework that
integrates a convolutional autoencoder architecture with a deep neural operator
(DeepONet) to learn the dynamic evolution of a two-phase mixture. We utilize
the convolutional autoencoder to provide a compact representation of the
microstructure data in a low-dimensional latent space. DeepONet, which consists
of two sub-networks, one for encoding the input function at a fixed number of
sensors locations (branch net) and another for encoding the locations for the
output functions (trunk net), learns the mesoscale dynamics of the
microstructure evolution in the latent space. The decoder part of the
convolutional autoencoder can then reconstruct the time-evolved microstructure
from the DeepONet predictions. The result is an efficient and accurate
accelerated phase-field framework that outperforms other neural-network-based
approaches while at the same time being robust to noisy inputs.


title: Periodic strings: a mechanical analogy to photonic and phononic crystals

abstract:
  We study a periodic vibrating string composed of a finite sequence of string
segments connected periodically, with each segment characterized by a constant
linear mass density. The main purpose is to provide a configuration that can
mimic the properties of photonic or phononic crystals and could be implemented
in undergraduate physics laboratories. We demonstrate that this configuration
displays frequency intervals for which wave propagation is not allowed
(frequency bandgaps), in close analogy to photonic and phononic crystals. We
discuss the behavior of these bandgaps when varying physical parameters, such
as the values of the linear mass densities, the oscillation frequency, and the
number of strings constituting the entire system. Some analogies with the
propagation of electronic waves through a crystal lattice in condensed matter
physics are also explored.


title: Hidden symmetries in acoustic wave systems

abstract:
  Mirror symmetry of a wave system imposes corresponding even or odd parity on
its eigenmodes. For a discrete system, eigenmode parity on a specific subset of
sites may also originate from so-called latent symmetry. This symmetry is
hidden, but can be revealed in an effective model upon reduction of the
original system onto the latently symmetric sites. Here we show how latent
symmetries can be leveraged for continuous wave setups in the form of acoustic
networks. These are systematically designed to have point-wise amplitude parity
between selected waveguide junctions for all low frequency eigenmodes. We
further develop a modular principle: latently symmetric networks can be
interconnected to feature multiple latently symmetric junction pairs, allowing
the design of arbitrarily large latently symmetric networks. By connecting such
networks to a mirror symmetric subsystem, we design asymmetric setups featuring
eigenmodes with domain-wise parity. Bridging the gap between discrete and
continuous models, our work takes a pivotal step towards exploiting hidden
geometrical symmetries in realistic wave setups.


title: How Different are Pre-trained Transformers for Text Ranking?

abstract:
  In recent years, large pre-trained transformers have led to substantial gains
in performance over traditional retrieval models and feedback approaches.
However, these results are primarily based on the MS Marco/TREC Deep Learning
Track setup, with its very particular setup, and our understanding of why and
how these models work better is fragmented at best. We analyze effective
BERT-based cross-encoders versus traditional BM25 ranking for the passage
retrieval task where the largest gains have been observed, and investigate two
main questions. On the one hand, what is similar? To what extent does the
neural ranker already encompass the capacity of traditional rankers? Is the
gain in performance due to a better ranking of the same documents (prioritizing
precision)? On the other hand, what is different? Can it retrieve effectively
documents missed by traditional systems (prioritizing recall)? We discover
substantial differences in the notion of relevance identifying strengths and
weaknesses of BERT that may inspire research for future improvement. Our
results contribute to our understanding of (black-box) neural rankers relative
to (well-understood) traditional rankers, help understand the particular
experimental setting of MS-Marco-based test collections.


title:
Physics-Aware Recurrent Convolutional (PARC) Neural Networks to
  Assimilate Meso-scale Reactive Mechanics of Energetic Materials

abstract:
  The thermomechanical properties of energetic materials (EM) are known to be a
function of their microscopic structures, i.e., morphological configurations of
crystals and pores. This microstructural dependency has motivated vigorous
research in the EM community, seeking to engineer material microstructures with
targeted properties and performance under the materials-by-design paradigm.
However, establishing the complex structure-property-performance (SPP)
relationships of EMs demands extensive experimental and simulation efforts, and
assimilating and encapsulating these relationships in usable models is a
challenge. Here, we present a novel deep learning method, Physics-Aware
Recurrent Convolutional (PARC) Neural Network, that can "learn" the mesoscale
thermo-mechanics of EM microstructures during the shock-to-detonation
transition (SDT). We show that this new approach can produce accurate
high-fidelity predictions of time-evolving temperature and pressure fields of
the same quality as the state-of-the-art direct numerical simulations (DNS),
despite the dramatic reduction of computing time, from hours and days on a
high-performance computing cluster (HPC) to a little more than a second on a
commodity laptop. We also demonstrate that PARC can provide physical insights,
i.e., the artificial neurons can illuminate the underlying physics by
identifying which microstructural features led to critical hotspots and what
are the characteristics of "critical" versus "non-critical" microstructures.
This new knowledge generated alongside the capacity to conduct high-throughput
experiments will broaden our theoretical understanding of the initiation
mechanisms of EM detonation, as a step towards engineering EMs with specific
properties.


title:
Harnessing Interpretable Machine Learning for Origami Feature Design and
  Pattern Selection

abstract:
  Engineering design of origami systems is challenging because comparing
different origami patterns requires using categorical features and evaluating
multi-physics behavior targets introduces multi-objective problems. This work
shows that a decision tree machine learning method is particularly suitable for
the inverse design of origami. This interpretable machine learning method can
reveal complex interactions between categorical features and continuous
features for comparing different origami patterns, can tackle multi-objective
problems for designing active origami with multi-physics performance targets,
and can extend existing origami shape fitting algorithms to further consider
non-geometrical performances of origami systems. The proposed framework shows a
holistic way of designing active origami systems for various applications such
as metamaterials, deployable structures, soft robots, biomedical devices, and
many more.


title: A* shortest string decoding for non-idempotent semirings

abstract:
  The single shortest path algorithm is undefined for weighted finite-state
automata over non-idempotent semirings because such semirings do not guarantee
the existence of a shortest path. However, in non-idempotent semirings
admitting an order satisfying a monotonicity condition (such as the plus-times
or log semirings), the notion of shortest string is well-defined. We describe
an algorithm which finds the shortest string for a weighted non-deterministic
automaton over such semirings using the backwards shortest distance of an
equivalent deterministic automaton (DFA) as a heuristic for A* search performed
over a companion idempotent semiring, which is proven to return the shortest
string. While there may be exponentially more states in the DFA, this algorithm
needs to visit only a small fraction of them if determinization is performed
"on the fly".


title:
Constructing Open Cloze Tests Using Generation and Discrimination
  Capabilities of Transformers

abstract:
  This paper presents the first multi-objective transformer model for
constructing open cloze tests that exploits generation and discrimination
capabilities to improve performance. Our model is further enhanced by tweaking
its loss function and applying a post-processing re-ranking algorithm that
improves overall test structure. Experiments using automatic and human
evaluation show that our approach can achieve up to 82% accuracy according to
experts, outperforming previous work and baselines. We also release a
collection of high-quality open cloze tests along with sample system output and
human annotations that can serve as a future benchmark.


title: Micro magnet location using spin waves

abstract:
  In this work, we present experimental data demonstrating the feasibility of
magnetic object location using spin waves. The test structure includes a
Y$_3$Fe$_2$(FeO$_4$)$_3$) (YIG) film with four micro-antennas placed on the
edges. A constant in-plane bias magnetic field is provided by NdFeB permanent
magnet. Two antennas are used for spin wave excitation while the other two are
used for the inductive voltage measurement. There are nine selected places for
the magnet on the film. The magnet was subsequently placed in all nine
positions and spin wave transmission and reflection were measured. The obtained
experimental data show the difference in the output signal amplitude depending
on the magnet position. All nine locations can be identified by the frequency
and the amplitude of the absolute minimum in the output power. All experiments
are accomplished at room temperature. Potentially, spin waves can be utilized
for remote magnetic bit read-out. The disadvantages and physical constraints of
this approach are also discussed.