title: Defect absorption and emission for $p$-atic liquid crystals on cones

abstract:
  We investigate the ground state configurations of $p$-atic liquid crystals on
fixed curved surfaces. We focus on the intrinsic geometry and show that
isothermal coordinates are particularly convenient as they explicitly encode a
geometric contribution to the elastic potential. In the special case of a cone
with half-angle $\beta$, the apex develops an effective topological charge of
$-\chi$, where $2\pi\chi = 2\pi(1-\sin\beta)$ is the deficit angle of the cone,
and a topological defect of charge $\sigma$ behaves as if it had an effective
topological charge $Q_\mathrm{eff} = (\sigma - \sigma^2/2)$ when interacting
with the apex. The effective charge of the apex leads to defect absorption and
emission at the cone apex as the deficit angle of the cone is varied.
  For total topological defect charge 1, e.g. imposed by tangential boundary
conditions at the edge, we find that for a disk the ground state configuration
consists of $p$ defects each of charge $+1/p$ lying equally spaced on a
concentric ring of radius $d = (\frac{p-1}{3p-1})^{\frac{1}{2p}} R$, where $R$
is the radius of the disk. In the case of a cone with tangential boundary
conditions at the base, we find three types of ground state configurations as a
function of cone angle: (1) for sharp cones, all of the $+1/p$ defects are
absorbed by the apex; (2) at intermediate cone angles, some of the $+1/p$
defects are absorbed by the apex and the rest lie equally spaced along a
concentric ring on the flank; and (3) for nearly flat cones, all of the $+1/p$
defects lie equally spaced along a concentric ring on the flank. Here the
defect positions and the absorption transitions depend intricately on $p$ and
the deficit angle which we analytically compute. We check these results with
numerical simulations for a set of commensurate cone angles and find excellent
agreement.


title:
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors
  on LiDAR Data

abstract:
  While 3D object detection in LiDAR point clouds is well-established in
academia and industry, the explainability of these models is a largely
unexplored field. In this paper, we propose a method to generate attribution
maps for the detected objects in order to better understand the behavior of
such models. These maps indicate the importance of each 3D point in predicting
the specific objects. Our method works with black-box models: We do not require
any prior knowledge of the architecture nor access to the model's internals,
like parameters, activations or gradients. Our efficient perturbation-based
approach empirically estimates the importance of each point by testing the
model with randomly generated subsets of the input point cloud. Our
sub-sampling strategy takes into account the special characteristics of LiDAR
data, such as the depth-dependent point density. We show a detailed evaluation
of the attribution maps and demonstrate that they are interpretable and highly
informative. Furthermore, we compare the attribution maps of recent 3D object
detection architectures to provide insights into their decision-making
processes.


title: Long-term evolution of multimass rotating star clusters

abstract:
  We investigate the long-term dynamical evolution of the internal kinematics
of multimass rotating star clusters. We have performed a set of N-body
simulations to follow the internal evolution of clusters with different degrees
of initial rotation and have explored the evolution of the rotational velocity,
the degree of energy equipartition, and anisotropy in the velocity
distribution. Our simulations show that: 1) as the cluster evolves, the
rotational velocity develops a dependence on the stellar mass with more massive
stars characterised by a more rapid rotation and a peak in the rotation curve
closer to the cluster centre than low-mass stars; 2) the degree of energy
equipartition in the cluster's intermediate and outer regions depends on the
component of the velocity dispersion measured; for more rapidly rotating
clusters, the evolution towards energy equipartition is more rapid in the
direction of the rotational velocity; 3) the anisotropy in the velocity
distribution is stronger for massive stars; 4) both the degree of mass
segregation and energy equipartition are characterised by spatial anisotropy;
they have a dependence on both $R$ and $z$, correlated with the flattening in
the spatial variation of the cluster's density and velocity dispersion, as
shown by 2D maps of the mass segregation and energy equipartition on the
($R$-$z$) meridional plane.


title: Non-local spin entanglement in a fermionic chain

abstract:
  An effective two-spin density matrix (TSDM) for a pair of spin-$1/2$ degree
of freedom, residing at a distance of $R$ in a spinful Fermi sea, can be
obtained from the two-electron density matrix following the framework
prescribed in Phys. Rev. A 69, 054305 (2004). We note that the single spin
density matrix (SSDM) obtained from this TSDM for generic spin-degenerate
systems of free fermions is always pinned to the maximally mixed state $i.e.$
$(1/2) \ \mathbb{I}$, independent of the distance $R$ while the TSDM confirms
to the form for the set of maximally entangled mixed state (the so called
"X-state") at finite $R$. The X-state reduces to a pure state (a singlet) in
the $R\rightarrow 0$ limit while it saturates to an X-state with largest
allowed value of von-Neumann entropy of $2 \ln2$ as $R \rightarrow \infty$
independent of the value of chemical potential. However, once an external
magnetic field is applied to lift the spin-degeneracy, we find that the
von-Neumann entropy of SSDM becomes a function of the distance $R$ between the
two spins. We also show that the von-Neumann entropy of TSDM in the
$R\rightarrow \infty$ limit becomes a function of the chemical potential and it
saturate to $2 \ln2$ only when the band in completely filled unlike the
spin-degenerate case. Finally we extend our study to include spin-orbit
coupling and show that it does effect these asymptotic results. Our findings
are in sharp contrast with previous works which were based on continuum models
owing to physics which stem from the lattice model.


title:
Illumination-Invariant Active Camera Relocalization for Fine-Grained
  Change Detection in the Wild

abstract:
  Active camera relocalization (ACR) is a new problem in computer vision that
significantly reduces the false alarm caused by image distortions due to camera
pose misalignment in fine-grained change detection (FGCD). Despite the fruitful
achievements that ACR can support, it still remains a challenging problem
caused by the unstable results of relative pose estimation, especially for
outdoor scenes, where the lighting condition is out of control, i.e., the twice
observations may have highly varied illuminations. This paper studies an
illumination-invariant active camera relocalization method, it improves both in
relative pose estimation and scale estimation. We use plane segments as an
intermediate representation to facilitate feature matching, thus further
boosting pose estimation robustness and reliability under lighting variances.
Moreover, we construct a linear system to obtain the absolute scale in each ACR
iteration by minimizing the image warping error, thus, significantly reduce the
time consume of ACR process, it is nearly $1.6$ times faster than the
state-of-the-art ACR strategy. Our work greatly expands the feasibility of
real-world fine-grained change monitoring tasks for cultural heritages.
Extensive experiments tests and real-world applications verify the
effectiveness and robustness of the proposed pose estimation method using for
ACR tasks.


title: Anatomy of a top-down approach to discrete and modular flavor symmetry

abstract:
  The framework of compactified heterotic string theory offers consistent
ultraviolet (UV) completions of the Standard Model (SM) of particle physics. In
this approach, the existence of flavor symmetries beyond the SM is imperative
and the flavor symmetries can be derived from the top-down. Such a derivation
uncovers a unified origin of traditional discrete flavor symmetries, discrete
modular flavor symmetries, discrete R symmetries of supersymmetry, as well as
CP symmetry - altogether called the eclectic flavor symmetry. I will show a
specific example of such a top-down derived eclectic flavor symmetry, discuss
the different sources of breaking of the eclectic flavor symmetry, as well as
the possible lessons for bottom-up flavor model building.


title:
Forecasting cosmological parameter constraints using multiple sparsity
  measurements as tracers of the mass profiles of dark matter haloes

abstract:
  The dark matter halo sparsity, i.e. the ratio between spherical halo masses
enclosing two different overdensities, provides a non-parametric proxy of the
halo mass distribution which has been shown to be a sensitive probe of the
cosmological imprint encoded in the mass profile of haloes hosting galaxy
clusters. Mass estimations at several overdensities would allow for multiple
sparsity measurements, that can potentially retrieve the entirety of the
cosmological information imprinted on the halo profile. Here, we investigate
the impact of multiple sparsity measurements on the cosmological model
parameter inference. For this purpose, we analyse N-body halo catalogues from
the Raygal and M2Csims simulations and evaluate the correlations among six
different sparsities from Spherical Overdensity halo masses at
$\Delta=200,500,1000$ and $2500$ (in units of the critical density).
Remarkably, sparsities associated to distinct halo mass shells are not highly
correlated. This is not the case for sparsities obtained using halo masses
estimated from the Navarro-Frenk-White (NFW) best-fit profile, that
artificially correlates different sparsities to order one. This implies that
there is additional information in the mass profile beyond the NFW
parametrization and that it can be exploited with multiple sparsities. In
particular, from a likelihood analysis of synthetic average sparsity data, we
show that cosmological parameter constraints significantly improve when
increasing the number of sparsity combinations, though the constraints saturate
beyond four sparsity estimates. We forecast constraints for the CHEX-MATE
cluster sample and find that systematic mass bias errors mildly impact the
parameter inference, though more studies are needed in this direction.


title: Hawking radiation on the lattice as universal (Floquet) quench dynamics

abstract:
  We construct two free fermion lattice models exhibiting Hawking pair
creation. Specifically, we consider the simplest case of a d=1+1 massless Dirac
fermion, for which the Hawking effect can be understood in terms of a quench of
the uniform vacuum state with a non-uniform Hamiltonian that interfaces modes
with opposite chirality. For both our models we find that additional modes
arising from the lattice discretization play a crucial role, as they provide
the bulk reservoir for the Hawking radiation: the Hawking pairs emerge from
fermions deep inside the Fermi sea scattering off the effective black hole
horizon. Our first model combines local hopping dynamics with a translation
over one lattice site, and we find the resulting Floquet dynamics to realize a
causal horizon, with fermions scattering from the region outside the horizon.
For our second model, which relies on a purely local hopping Hamiltonian, we
find the fermions to scatter from the inside. In both cases, for Hawking
temperatures up to the inverse lattice spacing we numerically find the
resulting Hawking spectrum to be in perfect agreement with the Fermi-Dirac
quantum field theory prediction.

categories: quant-ph cond-mat.other cond-mat.quant-gas gr-qc hep-lat hep-th


title:
A Distant Supervision Corpus for Extracting Biomedical Relationships
  Between Chemicals, Diseases and Genes

abstract:
  We introduce ChemDisGene, a new dataset for training and evaluating
multi-class multi-label document-level biomedical relation extraction models.
Our dataset contains 80k biomedical research abstracts labeled with mentions of
chemicals, diseases, and genes, portions of which human experts labeled with 18
types of biomedical relationships between these entities (intended for
evaluation), and the remainder of which (intended for training) has been
distantly labeled via the CTD database with approximately 78\% accuracy. In
comparison to similar preexisting datasets, ours is both substantially larger
and cleaner; it also includes annotations linking mentions to their entities.
We also provide three baseline deep neural network relation extraction models
trained and evaluated on our new dataset.


title:
On the generality of symmetry breaking and dissipative freezing in
  quantum trajectories

abstract:
  Recently, several studies involving open quantum systems which possess a
strong symmetry have observed that every individual trajectory in the Monte
Carlo unravelling of the master equation will dynamically select a specific
symmetry sector to `freeze' into in the long-time limit. This phenomenon has
been termed `dissipative freezing', and in this paper we demonstrate how it is
a general consequence of the presence of a strong symmetry in an open system
and independent of any microscopic details. Specifically, through several
simple, plausible, mathematical arguments, we detail how this phenomenon will
happen in most setups with only a few exceptions. Using a number of example
systems we illustrate these arguments, highlighting the relationship between
the spectral properties of the Liouvillian in off-diagonal symmetry sectors and
the time it takes for freezing to occur. In the limiting case that eigenmodes
with purely imaginary eigenvalues are manifest in these sectors, freezing fails
to occur. Such modes indicate the preservation of information and coherences
between symmetry sectors of the system and are associated with phenomena such
as non-stationarity and synchronisation. The absence of freezing at the level
of a single quantum trajectory provides a simple, computationally efficient way
of identifying these traceless modes.


title:
Tunable Confinement-Deconfinement Transition in an Ultracold Atom
  Quantum Simulator

abstract:
  The one-dimensional lattice Schwinger model has recently been realized by
using bosons in optical lattices. This model contains both confinement and
deconfinement phases, whose phase diagram is controlled by the mass of the
matter field and the topological angle. Since varying the mass of matter field
is straightforward experimentally, we propose how to tune the topological
angle, allowing accessing the entire phase diagram. We propose that direct
experimental evidence of confinement and deconfinement can be obtained by
measuring whether a physical charge is localized around a fixed gauge charge to
screen it. We also discuss the PXP model realized in the Rydberg atoms array,
which is equivalent to the lattice Schwinger model when all local gauge charges
are fixed as zero. Although the gauge charges are fixed, we can alternatively
probe the confinement and the deconfinement in the PXP model by studying the
relative motion of a pair of a physical charge and an anti-charge. Our scheme
can be directly implemented in these two relevant experimental platforms of
ultracold atom quantum simulators.

categories: cond-mat.quant-gas cond-mat.stat-mech cond-mat.str-el hep-lat hep-th


title: Odd moduli of disordered odd elastic lattices

abstract:
  We study the effects of bond disorder on triangular and honeycomb lattices
where each spring has a probability p to be odd elastic. Using an effective
medium theory and numerical simulations, we uncover the behavior of odd moduli
in the presence of disorder, which we interpret as a crossover between the
affine response of the passive elastic backbone, and a rigidity percolation
transition in the odd elastic components. Though oddness is generally robust
against disorder even at low p, we find that fine-tuned features of an odd
elastic honeycomb lattice are not robust against disorder.


title:
Pollution from Freight Trucks in the Contiguous United States: Public
  Health Damages and Implications for Environmental Justice

abstract:
  PM2.5 produced by freight trucking has significant adverse impacts on human
health. Here we explore the spatial distribution of freight trucking emissions
and demonstrate that public health impacts due to freight trucking
disproportionately affect certain racial and ethnic groups. Based on the US
federal government data, we build an emissions inventory to quantify
heterogeneity of trucking emissions and find that ~10% of NOx and ~12% of CO2
emissions from all sources in the US come from freight trucks. The costs to
human health and the environment due to NOx, PM2.5, SO2, and CO2 from freight
trucking in the US are estimated respectively to be $11B, $5.5B, $100M, and
$30B (social cost of carbon of $51 per ton). We demonstrate that more freight
pollution occurs in counties and census tracts with a higher proportion of
Black and Hispanic residents. Counties with a higher proportion of Black and
Hispanic residents are also more likely to be net importers of pollution
damages from other counties.


title: The boundaries of 2+1D fermionic topological orders

abstract:
  $2+1$D bosonic topological orders can be characterized by the $S,T$ matrices
that encode the statistics of topological excitations. In particular, the $S,T$
matrices can be used to systematically obtain the gapped boundaries of bosonic
topological orders. Such an approach, however, does not naively apply to
fermionic topological orders (FTOs). In this work, we propose a systematic
approach to obtain the gapped boundaries of $2+1$D abelian FTOs. The main trick
is to construct a bosonic extension in which the fermionic excitation is
"condensed" to form the associated FTOs. Here we choose the parent bosonic
topological order to be the $\mathbb{Z}_2$ topological order, which indeed has
a fermionic excitation. Such a construction allows us to find an explicit
correspondence between abelian FTOs (described by odd $K$-matrix $K_F$) and the
"fermion-" condensed $\mathbb{Z}_2$ topological orders (described by even
$K$-matrix $K_B$). This provides a systematic algorithm to obtain the modular
covariant boundary partition functions as well as the boundary topological
excitations of abelian FTOs. For example, the $\nu=1-\frac{1}{m}$ Laughlin's
states have exactly one type of gapped boundary when $m$ is a square, whose
boundary excitations form a $\mathbb{Z}_{2}\times\mathbb{Z}_{\sqrt{m}}$ fusion
ring. Our approach can be easily generalized to obtain gapped and gapless
boundaries of non-abelian fermionic topological orders.


title: Gradient regularity in mixed local and nonlocal problems

abstract:
  Minimizers of functionals of mixed local and nonlocal type are locally
$C^{1,\beta}$-regular.


title: Precision cosmology from large-scale structure of the Universe

abstract:
  Large scale structure of the Universe becomes a leading source of precision
cosmological information. We present two particular tools that can be used in
cosmological analyses of the redshift space galaxy clustering data: a new
open-source code CLASS-PT and the theoretical error approach. CLASS-PT computes
one-loop power auto- and cross-power spectra for matter fields and biased
tracers in real and redshift spaces. We show that the code meets the precision
standards set by the upcoming high-precision large-scale structure surveys. The
theoretical error likelihood approach allows one to analyze galaxy clustering
data without having to measure the scale cut $k_{\rm max}$. This approach takes
into account that theoretical uncertainties affect parameter estimation
gradually, which helps optimize data analysis and ensures that all available
cosmological information is extracted.


title:
Fluctuation bounds for first-passage percolation on the square, tube,
  and torus

abstract:
  In first-passage percolation, one assigns i.i.d. nonnegative weights $(t_e)$
to the edges of $\mathbb{Z}^d$ and studies the induced distance (passage time)
$T(x,y)$ between vertices $x$ and $y$. It is known that for $d=2$, the
fluctuations of $T(x,y)$ are at least order $\sqrt{\log |x-y|}$ under mild
assumptions on $t_e$. We study the question of fluctuation lower bounds for
$T_n$, the minimal passage time between two opposite sides of an $n$ by $n$
square. The main result is that, under a curvature assumption, this quantity
has fluctuations at least of order $n^{1/8-\epsilon}$ for any $\epsilon>0$ when
the $t_e$ are exponentially distributed. As previous arguments to bound the
fluctuations of $T(x,y)$ only give a constant lower bound for those of $T_n$
(even assuming curvature), a different argument, representing $T_n$ as a
minimum of cylinder passage times, and deriving more detailed information about
the distribution of cylinder times using the Markov property, is developed. As
a corollary, we obtain the first polynomial lower bounds on higher central
moments of the discrete torus passage time, under the same curvature
assumption.


title:
Large deviations and the emergence of a logarithmic delay in a nonlocal
  Fisher-KPP equation

abstract:
  We study a variant of the Fisher-KPP equation with nonlocal dispersal. Using
the theory of large deviations, we show the emergence of a "Bramson-like"
logarithmic delay for the linearised equation with step-like initial data. We
conclude that the logarithmic delay emerges also for the solutions of the
nonlinear equation.


title:
A stopping criterion for iterative proton CT image reconstruction based
  on correlated noise properties

abstract:
  Background: Filtered back projection algorithms for voxel-based CT image
reconstruction have noise properties defined by the filter. Iterative
algorithms must stop at some point in their convergence and do not necessarily
produce consistent noise properties for images with different degrees of
heterogeneity. Purpose: A least-squares iterative algorithm for pCT image
reconstruction converges toward a unique solution that optimally fits the
protons. We present a stopping criterion that delivers solutions with the
property that correlations of RSP noise between voxels are relatively low. This
provides a method to produce pCT images with consistent noise properties useful
for proton therapy treatment planning, which relies on summing RSP along
columns of voxels. Consistent noise properties will also be useful for future
studies of image quality using metrics such as contrast to noise ratio, and to
compare RSP noise and dose of pCT with other modalities such as dual-energy CT.
With simulated and real images with varying heterogeneity from a prototype
clinical proton imaging system, we calculate average RSP correlations between
voxel pairs in uniform regions-of-interest versus distance between voxels. We
define a parameter r, the remaining distance to the unique solution relative to
estimated RSP noise, and our stopping criterion is based on r falling below a
chosen value. Results: We find large correlations between voxels for larger
values of r, and anticorrelations for smaller values. For r in the range of 0.5
to 1, voxels are relatively uncorrelated, and compared to smaller values of r
have lower noise with only slight loss of spatial resolution. Conclusions: A
least-squares iterative algorithm with an r-based stopping criterion defines a
pCT image reconstruction method with consistent statistical properties optimal
for clinical use, including for treatment planning with pCT images.


title: Geometric phase assisted enhancement of non-inertial cavity-QED effects

abstract:
  The state of a quantum system acquires a phase factor, called the geometric
phase, when taken around a closed trajectory in the parameter space, which
depends only on the geometry of the parameter space. Due to its sensitive
nature, the geometric phase is instrumental in capturing weak effects such as
the acceleration-induced non-inertial quantum field theoretic effects. In this
paper, we study the geometric phase response of a circularly rotating detector
inside an electromagnetic cavity. Using the cavity, the non-inertial
contribution to the geometric phase can be isolated from or strengthened
relative to the inertial contribution. We show that the accumulative nature of
the geometric phase may facilitate the experimental observation of the
resulting, otherwise feeble, non-inertial contribution to the modified field
correlations inside the cavity. Specifically, we show that the atom acquires an
experimentally detectable geometric phase at accelerations of the order of
$\sim 10^{7}$ m/s$^2$ which is experimentally feasible.


title: Stellar halos from The Dragonfly Edge-on Galaxies Survey

abstract:
  We present the primary results from the Dragonfly Edge-on Galaxies Survey
(DEGS), an exploration of the stellar halos of twelve nearby ($d < 25$ Mpc)
edge-on disc galaxies with the Dragonfly Telephoto Array. The edge-on
orientation of these galaxies allows their stellar halos to be explored with
minimal obscuration by or confusion with the much brighter disc light. Galaxies
in the sample span a range of stellar masses from $10^{9.68} - 10^{10.88}
M_\odot$. We confirm that the wide range of stellar halo mass fractions
previously seen for Milky Way-mass galaxies is also found among less massive
spiral galaxies. The scatter in stellar halo mass fraction is large but we do
find a significant positive correlation between stellar halo mass fraction and
total stellar mass when the former is measured beyond five half-mass radii.
Reasonably good agreement is found with predictions from cosmological
hydrodynamical simulations, although observed stellar halo fractions appear to
be somewhat lower than expected from these simulations.


title:
Almost simple linear graphs, homology cobordism and connected Heegaard
  Floer homology

abstract:
  Continuing our previous work, we effectively compute connected Heegaard Floer
homologies of two families of Brieskorn spheres realized as the boundaries of
almost simple linear graphs. Using Floer theoretic invariants of Dai, Hom,
Stoffregen, and Truong, we show that these Brieskorn spheres also generate
infinite rank summands in the homology cobordism group. Our computations also
have applications to the concordance of classical knots and $0$-concordance of
$2$-knots.


title:
Deep Relation Learning for Regression and Its Application to Brain Age
  Estimation

abstract:
  Most deep learning models for temporal regression directly output the
estimation based on single input images, ignoring the relationships between
different images. In this paper, we propose deep relation learning for
regression, aiming to learn different relations between a pair of input images.
Four non-linear relations are considered: "cumulative relation", "relative
relation", "maximal relation" and "minimal relation". These four relations are
learned simultaneously from one deep neural network which has two parts:
feature extraction and relation regression. We use an efficient convolutional
neural network to extract deep features from the pair of input images and apply
a Transformer for relation learning. The proposed method is evaluated on a
merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold
cross-validation for the task of brain age estimation. The experimental results
have shown that the proposed method achieved a mean absolute error (MAE) of
2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms
with statistical significance (p$<$0.05) in paired T-test (two-side).


title:
Coupled electronic and magnetic excitations in the cuprates and their
  role in the superconducting transition

abstract:
  The formation of Cooper pairs, a bound state of two electrons of opposite
spin and momenta by exchange of a phonon [1], is a defining feature of
conventional superconductivity. In the cuprate high temperature
superconductors, even though it has been established that the superconducting
state also consists of Cooper pairs, the pairing mechanism remains intensely
debated. Here we investigate superconducting pairing in the
Bi2Sr2CaCu2O8+\delta (Bi2212) cuprate by employing spectral functions obtained
directly from angle-resolved photoemission (ARPES) experiments as input to the
Bethe-Salpeter gap equation. Assuming that Cooper pairing is driven solely by
spin fluctuations, we construct the single-loop spin-fluctuation-mediated
pairing interaction, and use it to compute the eigenfunctions and eigenvalues
of the Bethe-Salpeter equation in the particle-particle channel for multiple
Bi2212 samples. The key point of our results is that, as the temperature is
reduced, the leading eigenvalue increases upon approaching Tc, reaching a value
of approximately 1 at the Tc corresponding to each doping value, indicating a
superconducting transition with d-wave eigenfunctions. This suggests that spin
fluctuations can approximately account for Tc and, consequently, mediate
pairing in the cuprate high temperature superconductors.


title: Load balancing in a network of queueing-inventory systems

abstract:
  We study a supply chain consisting of production-inventory systems at several
locations which are coupled by a common supplier. Demand of customers arrives
at each production system according to a Poisson process and is lost if the
local inventory is depleted ("lost sales"). To satisfy a customer's demand a
server at the production system needs raw material from the associated local
inventory. The supplier manufactures raw material to replenish the local
inventories, which are controlled by a continuous review base stock policy. The
routing of items depends on the on-hand inventory at the locations with the aim
to obtain "load balancing". We show that the stationary distribution has a
product form of the marginal distributions of the production subsystem and the
inventory-replenishment subsystem. For the marginal distribution of the
production subsystem we derive an explicit solution and for the marginal
distribution of the inventory-replenishment subsystem we derive an explicit
solution resp. a recursive algorithm for some special cases.


title: A Study of Causal Confusion in Preference-Based Reward Learning

abstract:
  Learning robot policies via preference-based reward learning is an
increasingly popular method for customizing robot behavior. However, in recent
years, there has been a growing body of anecdotal evidence that learning reward
functions from preferences is prone to spurious correlations and reward gaming
or hacking behaviors. While there is much anecdotal, empirical, and theoretical
analysis of causal confusion and reward gaming behaviors both in reinforcement
learning and imitation learning approaches that directly map from states to
actions, we provide the first systematic study of causal confusion in the
context of learning reward functions from preferences. To facilitate this
study, we identify a set of three preference learning benchmark domains where
we observe causal confusion when learning from offline datasets of pairwise
trajectory preferences: a simple reacher domain, an assistive feeding domain,
and an itch-scratching domain. To gain insight into this observed causal
confusion, we present a sensitivity analysis that explores the effect of
different factors--including the type of training data, reward model capacity,
and feature dimensionality--on the robustness of rewards learned from
preferences. We find evidence that learning rewards from pairwise trajectory
preferences is highly sensitive and non-robust to spurious features and
increasing model capacity, but not as sensitive to the type of training data.
Videos, code, and supplemental results are available at
https://sites.google.com/view/causal-reward-confusion.


title: An inequality for completely monotone functions

abstract:
  An inequality, which combines the concept of completely monotone functions
with the theory of divided differences, is proposed. It is a straightforward
generalization of a result, recently introduced by two of the present authors.


title:
Agent-based Constraint Solving for Resource Allocation in Manycore
  Systems

abstract:
  For efficiency reasons, manycore systems are increasingly heterogeneous,
which makes the mapping of complex workloads a key problem with a high
optimization potential. Constraints express the application requirements like
which core type to choose, how many cores to choose, exclusively or
non-exclusively, using a certain core, etc. In this work, we propose a
decentralized solution for solving application resource constraints by means of
an agent-based approach in order to obtain scalability. We translate the
constraints into a Distributed Constraint Optimization Problem (DCOP) and
propose a local search algorithm RESMGM to solve them. For the first time, we
demonstrate the viability and efficiency of the DCOP approach for heterogeneous
manycore systems. Our RESMGM algorithm supports a far wider range of
constraints than state-of-the-art, leading to superior results, but still has
comparable overheads w.r.t. computation and communication.


title:
EHRKit: A Python Natural Language Processing Toolkit for Electronic
  Health Record Texts

abstract:
  The Electronic Health Record (EHR) is an essential part of the modern medical
system and impacts healthcare delivery, operations, and research. Unstructured
text is attracting much attention despite structured information in the EHRs
and has become an exciting research field. The success of the recent neural
Natural Language Processing (NLP) method has led to a new direction for
processing unstructured clinical notes. In this work, we create a python
library for clinical texts, EHRKit. This library contains two main parts:
MIMIC-III-specific functions and tasks specific functions. The first part
introduces a list of interfaces for accessing MIMIC-III NOTEEVENTS data,
including basic search, information retrieval, and information extraction. The
second part integrates many third-party libraries for up to 12 off-shelf NLP
tasks such as named entity recognition, summarization, machine translation,
etc.


title:
Classical and relavistic simple wave problems solved with Smoothed
  Particle Hydrodynamics

abstract:
  To simulate the expansion of the matter created in relativistic nuclear
collisions, codes in 3+1 dimensions are used and we are developing a new one.
To benchmark such codes, the Sod's shock tube is often used. A closely related
problem is the one-dimensional expansion of a gas into vacuum. In this paper,
we study this problem classically and relativistically with the Smoothed
Particle Method and test various techniques to improve the precision and speed
of the solution.


title:
Axial curvatures for corank 1 singular $n$-manifolds in $\mathbb
  R^{n+k}$

abstract:
  For singular $n$-manifolds in $\mathbb R^{n+k}$ with a corank 1 singular
point at $p\in M^n_{\mbox{sing}}$ we define up to $l(n-1)$ different axial
curvatures at $p$, where $l=\min\{n,k+1\}$. These curvatures are obtained using
the curvature locus (the image by the second fundamental form of the unitary
tangent vectors) and are therefore second order invariants. In fact, in the
case $n=2$ they generalise all second order curvatures which have been defined
for frontal type surfaces. We relate these curvatures with the principal
curvatures in certain normal directions of an associated regular
$(n-1)$-manifold contained in $M^n_{\mbox{sing}}$. We obtain many interesting
geometrical interpretations in the cases $n=2,3$. For instance, for frontal
type 3-manifolds with 2-dimensional singular set, the Gaussian curvature of the
singular set can be expressed in terms of the axial curvatures. Similarly for
the curvature of the singular set when it is 1-dimensional. Finally, we show
that all the umbilic curvatures which have been defined for singular manifolds
up to now can be seen as the absolute value of one of our axial curvatures.


title: Towards Metrical Reconstruction of Human Faces

abstract:
  Face reconstruction and tracking is a building block of numerous applications
in AR/VR, human-machine interaction, as well as medical applications. Most of
these applications rely on a metrically correct prediction of the shape,
especially, when the reconstructed subject is put into a metrical context
(i.e., when there is a reference object of known size). A metrical
reconstruction is also needed for any application that measures distances and
dimensions of the subject (e.g., to virtually fit a glasses frame).
State-of-the-art methods for face reconstruction from a single image are
trained on large 2D image datasets in a self-supervised fashion. However, due
to the nature of a perspective projection they are not able to reconstruct the
actual face dimensions, and even predicting the average human face outperforms
some of these methods in a metrical sense. To learn the actual shape of a face,
we argue for a supervised training scheme. Since there exists no large-scale 3D
dataset for this task, we annotated and unified small- and medium-scale
databases. The resulting unified dataset is still a medium-scale dataset with
more than 2k identities and training purely on it would lead to overfitting. To
this end, we take advantage of a face recognition network pretrained on a
large-scale 2D image dataset, which provides distinct features for different
faces and is robust to expression, illumination, and camera changes. Using
these features, we train our face shape estimator in a supervised fashion,
inheriting the robustness and generalization of the face recognition network.
Our method, which we call MICA (MetrIC fAce), outperforms the state-of-the-art
reconstruction methods by a large margin, both on current non-metric benchmarks
as well as on our metric benchmarks (15% and 24% lower average error on NoW,
respectively).


title:
Modularity benefits reinforcement learning agents with competing
  homeostatic drives

abstract:
  The problem of balancing conflicting needs is fundamental to intelligence.
Standard reinforcement learning algorithms maximize a scalar reward, which
requires combining different objective-specific rewards into a single number.
Alternatively, different objectives could also be combined at the level of
action value, such that specialist modules responsible for different objectives
submit different action suggestions to a decision process, each based on
rewards that are independent of one another. In this work, we explore the
potential benefits of this alternative strategy. We investigate a biologically
relevant multi-objective problem, the continual homeostasis of a set of
variables, and compare a monolithic deep Q-network to a modular network with a
dedicated Q-learner for each variable. We find that the modular agent: a)
requires minimal exogenously determined exploration; b) has improved sample
efficiency; and c) is more robust to out-of-domain perturbation.


title:
A Bandwagon Bias Based Model for Opinion Dynamics: Intertwining between
  Homophily and Influence Mechanisms

abstract:
  Recently a model for the interplay between homophily-based appraisal dynamics
and influence-based opinion dynamics has been proposed. The model explores for
the first time how the opinions of a group of agents on a certain number of
issues/topics is influenced by the agents' mutual appraisal and, conversely,
the agents' mutual appraisal is updated based on the agents' opinions on the
various issues, according to a homophily model. In this paper we show that a
simplified (and, in some situations, more feasible) version of the model, that
accounts only for the signs of the agents' appraisals rather than for their
numerical values, provides an equally accurate and effective model of the
opinion dynamics in small networks. The equilibria reached by this model
correspond, almost surely, to situations in which the agents' network is
complete and structurally balanced. On the other hand, we ensure that such
equlibria can always be reached in a finite number of steps, and, differently
from the original model, we rule out other types of equilibria that correspond
to disconnected social networks.


title:
Infinite Hidden Markov Models for Multiple Multivariate Time Series with
  Missing Data

abstract:
  Exposure to air pollution is associated with increased morbidity and
mortality. Recent technological advancements permit the collection of
time-resolved personal exposure data. Such data are often incomplete with
missing observations and exposures below the limit of detection, which limit
their use in health effects studies. In this paper we develop an infinite
hidden Markov model for multiple asynchronous multivariate time series with
missing data. Our model is designed to include covariates that can inform
transitions among hidden states. We implement beam sampling, a combination of
slice sampling and dynamic programming, to sample the hidden states, and a
Bayesian multiple imputation algorithm to impute missing data. In simulation
studies, our model excels in estimating hidden states and state-specific means
and imputing observations that are missing at random or below the limit of
detection. We validate our imputation approach on data from the Fort Collins
Commuter Study. We show that the estimated hidden states improve imputations
for data that are missing at random compared to existing approaches. In a case
study of the Fort Collins Commuter Study, we describe the inferential gains
obtained from our model including improved imputation of missing data and the
ability to identify shared patterns in activity and exposure among repeated
sampling days for individuals and among distinct individuals.


title:
Information and Communication Technology in Migration: A Framework for
  Applications, Customization, and Research

abstract:
  This paper addresses the role of Information and Communication Technology
(ICT) in migration governance, support, and experience with particular
attention to emerging technologies such as artificial intelligence, social
media, and virtual reality. We propose a framework for technology use based on
user groups and process types. We provide examples of using emerging
technologies for migration-related tasks within the context of this framework.
We then identify how such technologies can be applied to migration-related
tasks, developed for customized use, and improved through research to add new
features that can help different migration stakeholders. We suggest a series of
possible directions for future research and development to take advantage of
specific affordances of those emerging technologies more effectively.


title:
Geometry of uniqueness varieties for a three-point Pick problem in
  $\mathbb{D}^3$

abstract:
  Motivated by the recent progress of research on extending holomorphic
functions defined on subvarieties of classical domains and its connections to
the 3-point Pick interpolation, we study a special class of two-dimensional
algebraic subvarieties $M_\alpha$ of the unit tridisc, defined as the sets
$$\lbrace (z_1,z_2,z_3)\in
\mathbb{D}^3:\alpha_1z_1+\alpha_2z_2+\alpha_3z_3=\overline{\alpha}_1z_2z_3+\overline{\alpha}_2z_1z_3+\overline{\alpha}_3z_1z_2\rbrace.$$
In this paper we show that given non-degenerated extremal maximal $3$-point
Pick problem there exists an $\alpha$ such that $M_\alpha$ appears as its
uniqueness variety. We also describe several geometric properties of $M_\alpha$
and show the biholomorphic equivalence between any two surfaces $M_\alpha$ and
$M_\beta$, where the triples $\alpha$ and $\beta$ satisfy the so called
triangle inequality.


title:
Coupling derivation of optimal-order central moment bounds in
  exponential last-passage percolation

abstract:
  We introduce new probabilistic arguments to derive optimal-order central
moment bounds in planar directed last-passage percolation. Our technique is
based on couplings with the increment-stationary variants of the model, and is
presented in the context of i.i.d. exponential weights for both zero and
near-stationary boundary conditions. A main technical novelty in our approach
is a new proof of the left-tail fluctuation upper bound with exponent 3/2 for
the last-passage times.


title:
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets
  and Contrastive Molecular Explanations

abstract:
  Explainable ML for molecular toxicity prediction is a promising approach for
efficient drug development and chemical safety. A predictive ML model of
toxicity can reduce experimental cost and time while mitigating ethical
concerns by significantly reducing animal and clinical testing. Herein, we use
a deep learning framework for simultaneously modeling in vitro, in vivo, and
clinical toxicity data. Two different molecular input representations are used:
Morgan fingerprints and pre-training SMILES embeddings. A multi-task deep
learning model accurately predicts toxicity for all endpoints, including
clinical, as indicated by AUROC and balanced accuracy. In particular, SMILES
embeddings as input to the multi-task model improved clinical toxicity
predictions compared to existing models in MoleculeNet benchmark. Additionally,
our multi-task approach is comprehensive in the sense that it is comparable to
state-of-the-art approaches for specific endpoints in in vitro, in vivo and
clinical platforms. Through both the multi-task model and transfer learning, we
were able to indicate the minimal need of in vivo data for clinical toxicity
predictions. To provide confidence and explain the model's predictions, we
adapt a post-hoc contrastive explanation method that returns pertinent positive
and pertinent negative features, which correspond well to known mutagenic and
reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic
amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent
feature analysis captures more of the in vitro (53%) and in vivo (56%), rather
than of the clinical (8%), endpoints, and indeed uncovers a preference in known
toxicophore data towards in vitro and in vivo experimental data. To our
knowledge, this is the first contrastive explanation, using both present and
absent substructures, for predictions of clinical and in vivo molecular
toxicity.


title: Polarised random k-SAT

abstract:
  In this paper we study a variation of the random $k$-SAT problem, called
polarized random $k$-SAT. In this model there is a polarization parameter $p$,
and in half of the clauses each variable occurs negated with probability $p$
and pure otherwise, while in the other half the probabilities are interchanged.
For $p=1/2$ we get the classical random $k$-SAT model, and at the other extreme
we have the fully polarized model where $p=0$, or $1$. Here there are only two
types of clauses: clauses where all $k$ variables occur pure, and clauses where
all $k$ variables occur negated. That is, for $p=0$ we get an instance of
random monotone $k$-SAT. We show that the threshold of satisfiability does not
decrease as $p$ moves away from $\frac{1}{2}$ and thus that the satisfiability
threshold for polarized random $k$-SAT is an upper bound on the threshold for
random $k$-SAT. In fact, we conjecture that asymptotically the two thresholds
coincide.


title:
Predicting score distribution to improve non-intrusive speech quality
  estimation

abstract:
  Deep noise suppressors (DNS) have become an attractive solution to remove
background noise, reverberation, and distortions from speech and are widely
used in telephony/voice applications. They are also occasionally prone to
introducing artifacts and lowering the perceptual quality of the speech.
Subjective listening tests that use multiple human judges to derive a mean
opinion score (MOS) are a popular way to measure these models' performance.
Deep neural network based non-intrusive MOS estimation models have recently
emerged as a popular cost-efficient alternative to these tests. These models
are trained with only the MOS labels, often discarding the secondary statistics
of the opinion scores. In this paper, we investigate several ways to integrate
the distribution of opinion scores (e.g. variance, histogram information) to
improve the MOS estimation performance. Our model is trained on a corpus of
419K denoised samples by 320 different DNS models and model variations and
evaluated on 18K test samples from DNSMOS. We show that with very minor
modification of a single task MOS estimation pipeline, these freely available
labels can provide up to a 0.016 RMSE and 1% SRCC improvement.


title: Model knot solutions for the twisted Bogomolny equations

abstract:
  In this paper we prove existence for model knot solutions to the
dimensionally reduced twisted Kapustin-Witten equations on $\mathbb{R}^3_+$ for
any twisting parameter $t\in(0,\infty)$. We start with the explicit solutions
for $t = 1$ derived in \cite{WFivebranes} and perform a continuity argument in
$t$. This corroborates a prediction of Gaiotto and Witten \cite[][p. 961]{GW}.


title:
Formal Language Recognition by Hard Attention Transformers: Perspectives
  from Circuit Complexity

abstract:
  This paper analyzes three formal models of Transformer encoders that differ
in the form of their self-attention mechanism: unique hard attention (UHAT);
generalized unique hard attention (GUHAT), which generalizes UHAT; and
averaging hard attention (AHAT). We show that UHAT and GUHAT Transformers,
viewed as string acceptors, can only recognize formal languages in the
complexity class AC$^0$, the class of languages recognizable by families of
Boolean circuits of constant depth and polynomial size. This upper bound
subsumes Hahn's (2020) results that GUHAT cannot recognize the DYCK languages
or the PARITY language, since those languages are outside AC$^0$ (Furst et al.,
1984). In contrast, the non-AC$^0$ languages MAJORITY and DYCK-1 are
recognizable by AHAT networks, implying that AHAT can recognize languages that
UHAT and GUHAT cannot.


title: The heat kernel on the diagonal for a compact metric graph

abstract:
  We analyze the heat kernel associated to the Laplacian on a compact metric
graph, with standard Kirchoff-Neumann vertex conditions. An explicit formula
for the heat kernel as a sum over loops, developed by Kostrykin, Potthoff, and
Schrader, allows for a straightforward analysis of small-time asymptotics. We
show that the restriction of the heat kernel to the diagonal satisfies a
modified version of the heat equation. This observation leads to an "edge" heat
trace formula, expressing the a sum over eigenfunction amplitudes on a single
edge as a sum over closed loops containing that edge. The proof of this formula
relies on a modified heat equation satisfied by the diagonal restriction of the
heat kernel. Further study of this equation leads to explicit formulas for
completely symmetric graphs.


title:
Cartesian convexity as the key notion in the variational existence
  theory for nonlocal supremal functionals

abstract:
  Motivated by the direct method in the calculus of variations in $L^{\infty}$,
our main result identifies the notion of convexity characterizing the
weakly$^*$ lower semicontinuity of nonlocal supremal functionals: Cartesian
level convexity. This new concept coincides with separate level convexity in
the one-dimensional setting and is strictly weaker for higher dimensions. We
discuss relaxation in the vectorial case, showing that the relaxed functional
will not generally maintain the supremal form. Apart from illustrating this
fact with examples of multi-well type, we present precise criteria for
structure-preservation. When the structure is preserved, a representation
formula is given in terms of the Cartesian level convex envelope of the
(diagonalized) original supremand. This work does not only complete the picture
of the analysis initiated in [Kreisbeck \& Zappale, Calc.~Var.~PDE, 2020], but
also establishes a connection with double integrals. We relate the two classes
of functionals via an $L^p$-approximation in the sense of $\Gamma$-convergence
for diverging integrability exponents. The proofs exploit recent results on
nonlocal inclusions and their asymptotic behavior, and use tools from Young
measure theory and convex analysis.


title: Crystalline prisms: Reflections on the present and past

abstract:
  Let $Y/S$ be a morphism of crystalline prisms, i.e., a $ p$-torsion free
$p$-adic formal schemes endowed with a Frobenius lift, and let $\overline
Y/\overline S$ denote its reduction modulo $p$. We show that the category of
crystals on the prismatic site of $\overline Y/S$ is equivalent to the category
of $\mathcal{O}_Y$-modules with integrable and quasi-nilpotent $p$-connection
and that the cohomology of such a crystal is computed by the associated $p$-de
Rham complex. If $X$ is a closed subscheme of $\overline Y$, smooth over
$\overline S$, then the prismatic envelope $\Delta_X(Y)$ of $X$ in $Y$ admits
such a $p$-connection, and the category of prismatic crystals on $X/S$ is
equivalent to the category of $\mathcal{ O}_ {\Delta_X(Y)}$-modules with
compatible $p$-connection, and cohomology is again computed by $p$-de Rham
complexes. Our main tools are a detailed study of prismatic envelopes and a
formal smoothness property for $Y/S$ when working with prisms in the
$p$-completely flat topology. We also explain how earlier work by several
authors relating Higgs fields, $p$-connections, and connections can be placed
in the prismatic context.


title:
PDE-constrained optimization for electroencephalographic source
  reconstruction

abstract:
  This paper introduces a novel numerical method for the inverse problem of
electroencephalography(EEG). We pose the inverse EEG problem as an optimal
control (OC) problem for Poisson's equation. The optimality conditions lead to
a variational system of differential equations. It is discretized directly in
finite-element spaces leading to a system of linear equations with a sparse
Karush-Kuhn-Tucker matrix. The method uses finite-element discretization and
thus can handle MRI-based meshes of almost arbitrary complexity. It extends the
well-known mixed quasi-reversibility method (mQRM) in that pointwise noisy data
explicitly appear in the formulation making unnecessary tedious interpolation
of the noisy data from the electrodes to the scalp surface. The resulting
algebraic problem differs considerably from that obtained in the mixed
quasi-reversibility, but only slightly larger. An interesting feature of the
algorithm is that it does not require the formation of the lead-field matrix.
Our tests, both with spherical and MRI-based meshes, demonstrates that the
method accurately reconstructs cortical activity.


title:
Lags of the KiloHertz Quasi-Periodic Oscillations in the transient
  source XTE J1701$-$462

abstract:
  We analysed 14 observations with kilohertz quasi-periodic oscillations (kHz
QPOs) of the neutron star X-ray binary XTE J1701$-$462, the first source to
show a clear transition between atoll and Z-like behaviour during a single
outburst. We calculated the average cross-spectrum of both atoll and Z-phase
observations of XTE J1701$-$462 between a reference/hard band (6.1 - 25.7 keV)
and a subject/soft band (2.1 - 5.7 keV) to obtain, using a novel technique, the
average time lags of the lower and upper kHz QPOs. During the atoll phase, we
found that at the frequency of the lower kHz QPO the soft photons lag behind
the hard ones by $18 \pm 8$ $\mu$s, whereas during the Z phase the lags are
$33\pm35$ $\mu$s, consistent with zero. This difference in the lags of both
phases suggests that in XTE J1701$-$462, as observed in other sources, the lags
decrease with increasing luminosity. We found that for both the atoll and Z
phase observations the fractional rms amplitude increases with energy up to
$\sim$10 keV and remains more or less constant at higher energies. Since these
changes in the variability of XTE J1701$-$462 occur within the same outburst,
properties like the mass of the neutron star or the inclination of the system
cannot be responsible for the differences in the timing properties of the kHz
QPOs in the atoll and Z phase. Here we suggest that these differences are
driven by a Comptonizing component or corona, possibly oscillating in a coupled
mode with the innermost regions of the accretion disc.


title:
A Natural Language Processing Approach for Instruction Set Architecture
  Identification

abstract:
  Binary analysis of software is a critical step in cyber forensics
applications such as program vulnerability assessment and malware detection.
This involves interpreting instructions executed by software and often
necessitates converting the software's binary file data to assembly language.
The conversion process requires information about the binary file's target
instruction set architecture (ISA). However, ISA information might not be
included in binary files due to compilation errors, partial downloads, or
adversarial corruption of file metadata. Machine learning (ML) is a promising
methodology that can be used to identify the target ISA using binary data in
the object code section of binary files. In this paper we propose a binary code
feature extraction model to improve the accuracy and scalability of ML-based
ISA identification methods. Our feature extraction model can be used in the
absence of domain knowledge about the ISAs. Specifically, we adapt models from
natural language processing (NLP) to i) identify successive byte patterns
commonly observed in binary codes, ii) estimate the significance of each byte
pattern to a binary file, and iii) estimate the relevance of each byte pattern
in distinguishing between ISAs. We introduce character-level features of
encoded binaries to identify fine-grained bit patterns inherent to each ISA. We
use a dataset with binaries from 12 different ISAs to evaluate our approach.
Empirical evaluations show that using our byte-level features in ML-based ISA
identification results in an 8% higher accuracy than the state-of-the-art
features based on byte-histograms and byte pattern signatures. We observe that
character-level features allow reducing the size of the feature set by up to
16x while maintaining accuracy above 97%.


title:
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models
  with Weight Sharing

abstract:
  Model ensemble is a popular approach to produce a low-variance and
well-generalized model. However, it induces large memory and inference costs,
which are often not affordable for real-world deployment. Existing work has
resorted to sharing weights among models. However, when increasing the
proportion of the shared weights, the resulting models tend to be similar, and
the benefits of using model ensemble diminish. To retain ensemble benefits
while maintaining a low memory cost, we propose a consistency-regularized
ensemble learning approach based on perturbed models, named CAMERO.
Specifically, we share the weights of bottom layers across all models and apply
different perturbations to the hidden representations for different models,
which can effectively promote the model diversity. Meanwhile, we apply a
prediction consistency regularizer across the perturbed models to control the
variance due to the model diversity. Our experiments using large language
models demonstrate that CAMERO significantly improves the generalization
performance of the ensemble model. Specifically, CAMERO outperforms the
standard ensemble of 8 BERT-base models on the GLUE benchmark by 0.7 with a
significantly smaller model size (114.2M vs. 880.6M).


title: Adaptive Memory Management for Video Object Segmentation

abstract:
  Matching-based networks have achieved state-of-the-art performance for video
object segmentation (VOS) tasks by storing every-k frames in an external memory
bank for future inference. Storing the intermediate frames' predictions
provides the network with richer cues for segmenting an object in the current
frame. However, the size of the memory bank gradually increases with the length
of the video, which slows down inference speed and makes it impractical to
handle arbitrary length videos.
  This paper proposes an adaptive memory bank strategy for matching-based
networks for semi-supervised video object segmentation (VOS) that can handle
videos of arbitrary length by discarding obsolete features. Features are
indexed based on their importance in the segmentation of the objects in
previous frames. Based on the index, we discard unimportant features to
accommodate new features. We present our experiments on DAVIS 2016, DAVIS 2017,
and Youtube-VOS that demonstrate that our method outperforms state-of-the-art
that employ first-and-latest strategy with fixed-sized memory banks and
achieves comparable performance to the every-k strategy with increasing-sized
memory banks. Furthermore, experiments show that our method increases inference
speed by up to 80% over the every-k and 35% over first-and-latest strategies.


title:
Performance Assessment of different Machine Learning Algorithm for
  Life-Time Prediction of Solder Joints based on Synthetic Data

abstract:
  This paper proposes a computationally efficient methodology to predict the
damage progression in solder contacts of electronic components using
temperature-time curves. For this purpose, two machine learning algorithms, a
Multilayer Perceptron and a Long Short-Term Memory network, are trained and
compared with respect to their prediction accuracy and the required amount of
training data. The training is performed using synthetic, normally distributed
data that is realistic for automotive applications. A finite element model of a
simple bipolar chip resistor in surface mount technology configuration is used
to numerically compute the synthetic data. As a result, both machine learning
algorithms show a relevant accuracy for the prediction of accumulated creep
strains. With a training data length of 350 hours (12.5% of the available
training data), both models show a constantly good fitting performance of $R^2$
of 0.72 for the Multilayer Perceptron and $R^2$ of 0.87 for the Long Short-Term
Memory network. The prediction errors of the accumulated creep strains are less
than 10% with an amount of 350 hours training data and decreases to less than 5
% when using further data. Therefore, both approaches are promising for the
lifetime prediction directly on the electronic device.


title: Spectroscopic analysis of VVV CL001 cluster with MUSE

abstract:
  Like most spiral galaxies, the Milky Way contains a population of blue,
metal-poor globular clusters and another of red, metal-rich ones. Most of the
latter belong to the bulge, and therefore they are poorly studied compared to
the blue (halo) ones because they suffer higher extinction and larger
contamination from field stars. These intrinsic difficulties, together with a
lack of low-mass bulge globular clusters, are reasons to believe that their
census is not complete yet. Indeed, a few new clusters have been confirmed in
the last few years. One of them is VVV CL001, the subject of the present study.
We present a new spectroscopic analysis of the recently confirmed globular
cluster VVV CL001, made by means of MUSE@VLT integral field data. Individual
spectra were extracted for stars in the VVV CL001 field. Radial velocities were
derived by cross-correlation with synthetic templates. Coupled with PMs from
the VVV survey, these data allow us to select 55 potential cluster members, for
which we derive metallicities using the public code The Cannon. The mean radial
velocity of the cluster is Vhelio = -324.9 +- 0.8 km/s,as estimated from 55
cluster members. This high velocity, together with a low metallicity [Fe/H] =
-2.04 +- 0.02 dex suggests that VVV CL001 could be a very old cluster. The
estimated distance is d = 8.23 +- 0.46 kpc, placing the cluster in the Galactic
bulge. Furthermore, both its current position and the orbital parameters
suggest that VVV CL001 is most probably a bulge globular cluster.


title:
A fast direct solver for two dimensional quasi-periodic multilayered
  media scattering problems, Part II

abstract:
  This manuscript is the second in a series presenting fast direct solution
techniques for solving two-dimensional wave scattering problems from
quasi-periodic multilayered structures. The fast direct solvers presented in
the series are for the linear system that results from the discretization of a
robust integral formulation. The fast direct solver presented in this
manuscript has a computational cost that scales linearly with respect to the
number of discretization points on the interfaces and the number of layers. The
latter is an improvement over the previous solver and makes the new solver more
efficient especially for problems involving multiple incident angles and
changes to the layered media. Numerical results illustrate the improved
performance of the new solver over the previous one.


title: Colourings of path systems

abstract:
  A $P_m$ path in a graph is a path on $m$ vertices. A $P_m$ system of order
$n>1$ is a partition of the edges of the complete graph $K_n$ into $P_m$ paths.
A $P_m$ system is said to be $k$-colourable if the vertex set of $K_n$ can be
partitioned into $k$ sets called colour classes such that no path in the system
is monochromatic. The system is $k$-chromatic if it is $k$-colourable but is
not $(k-1)$-colourable. If every $k$-colouring of a $P_m$ system can be
obtained from some $k$-colouring $\phi$ by a permutation of the colours, we say
that the system is uniquely $k$-colourable. In this paper, we first observe
that there exists a $k$-chromatic $P_m$ system for any $k\geq 2$ and $m\geq 4$
where $m$ is even. Next, we prove that there exists an equitably 2-chromatic
$P_4$ system of order $n$ for each admissible order $n$. We then show that for
all $k\geq 3$, there exists a $k$-chromatic $P_4$ system of order $n$ for all
sufficiently large admissible $n$. Finally, we show that there exists a
uniquely 2-chromatic $P_4$ system of order $n$ for each admissible $n \geq
109$.


title:
A self-consistent analytical theory for rotator networks under
  stochastic forcing: effects of intrinsic noise and common input

abstract:
  Despite the incredible complexity of our brains' neural networks, theoretical
descriptions of neural dynamics have led to profound insights into possible
network states and dynamics. It remains challenging to develop theories that
apply to spiking networks and thus allow one to characterize the dynamic
properties of biologically more realistic networks. Here, we build on recent
work by van Meegen & Lindner who have shown that "rotator networks," while
considerably simpler than real spiking networks and therefore more amenable to
mathematical analysis, still allow to capture dynamical properties of networks
of spiking neurons. This framework can be easily extended to the case where
individual units receive uncorrelated stochastic input which can be interpreted
as intrinsic noise. However, the assumptions of the theory do not apply anymore
when the input received by the single rotators is strongly correlated among
units. As we show, in this case the network fluctuations become significantly
non-Gaussian, which calls for a reworking of the theory. Using a cumulant
expansion, we develop a self-consistent analytical theory that accounts for the
observed non-Gaussian statistics. Our theory provides a starting point for
further studies of more general network setups and information transmission
properties of these networks.


title:
Recurrent event analysis in the presence of real-time high frequency
  data via random subsampling

abstract:
  Digital monitoring studies collect real-time high frequency data via mobile
sensors in the subjects' natural environment. This data can be used to model
the impact of changes in physiology on recurrent event outcomes such as
smoking, drug use, alcohol use, or self-identified moments of suicide ideation.
Likelihood calculations for the recurrent event analysis, however, become
computationally prohibitive in this setting. Motivated by this, a random
subsampling framework is proposed for computationally efficient, approximate
likelihood-based estimation. A subsampling-unbiased estimator for the
derivative of the cumulative hazard enters into an approximation of
log-likelihood. The estimator has two sources of variation: the first due to
the recurrent event model and the second due to subsampling. The latter can be
reduced by increasing the sampling rate; however, this leads to increased
computational costs. The approximate score equations are equivalent to logistic
regression score equations, allowing for standard, "off-the-shelf" software to
be used in fitting these models. Simulations demonstrate the method and
efficiency-computation trade-off. We end by illustrating our approach using
data from a digital monitoring study of suicidal ideation.


title:
Assessing the data-analysis impact of LISA orbit approximations using a
  GPU-accelerated response model

abstract:
  The analysis of gravitational wave (GW) datasets is based on the comparison
of measured time series with theoretical templates of the detector's response
to a variety of source parameters. For LISA, the main scientific observables
will be the so-called time-delay interferometry (TDI) combinations, which
suppress the otherwise overwhelming laser noise. Computing the TDI response to
GW involves projecting the GW polarizations onto the LISA constellation arms,
and then combining projections delayed by a multiple of the light propagation
time along the arms. Both computations are difficult to perform efficiently for
generic LISA orbits and GW signals. Various approximations are currently used
in practice, e.g., assuming constant and equal armlengths, which yields
analytical TDI expressions. In this article, we present 'fastlisaresponse', a
new efficient GPU-accelerated code that implements the generic TDI response to
GWs in the time domain. We use it to characterize the parameter-estimation bias
incurred by analyzing loud Galactic-binary signals using the equal-armlength
approximation. We conclude that equal-armlength parameter-estimation codes
should be upgraded to the generic response if they are to achieve optimal
accuracy for high (but reasonable) SNR sources within the actual LISA data.


title: Seaweed algebras

abstract:
  The index of a Lie algebra is an important algebraic invariant, but it is
notoriously difficult to compute. However, for the suggestively-named seaweed
algebras, the computation of the index can be reduced to a combinatorial
formula based on the connected components of a "meander": a planar graph
associated with the algebra. Our index analysis on seaweed algebras requires
only basic linear and abstract algebra. Indeed, the main goal of this
survey-type article is to introduce a broader audience to seaweed algebras with
minimal appeal to specialized language and notation from Lie theory. This said,
we present several results that do not appear elsewhere and do appeal to more
advanced language in the Introduction to provide added context.


title:
A Novel Approach for Optimum-Path Forest Classification Using Fuzzy
  Logic

abstract:
  In the past decades, fuzzy logic has played an essential role in many
research areas. Alongside, graph-based pattern recognition has shown to be of
great importance due to its flexibility in partitioning the feature space using
the background from graph theory. Some years ago, a new framework for both
supervised, semi-supervised, and unsupervised learning named Optimum-Path
Forest (OPF) was proposed with competitive results in several applications,
besides comprising a low computational burden. In this paper, we propose the
Fuzzy Optimum-Path Forest, an improved version of the standard OPF classifier
that learns the samples' membership in an unsupervised fashion, which are
further incorporated during supervised training. Such information is used to
identify the most relevant training samples, thus improving the classification
step. Experiments conducted over twelve public datasets highlight the
robustness of the proposed approach, which behaves similarly to standard OPF in
worst-case scenarios.


title:
Characterizations of fractional Sobolev--Poincar\'e and (localized)
  Hardy inequalities

abstract:
  In this paper, we prove capacitary versions of the fractional
Sobolev--Poincar\'e inequalities. We characterize localized variant of the
boundary fractional Sobolev--Poincar\'e inequalities through uniform fatness
condition of the domain in $\mathbb{R}^n$. Existence type results on the
fractional Hardy inequality are established in the supercritical case $sp>n$
for $s\in(0,1)$, $p>1$. Characterization of the fractional Hardy inequality
through weak supersolution of the associate problem is also addressed.


title:
Nonparametric Identification of Differentiated Products Demand Using
  Micro Data

abstract:
  We examine identification of differentiated products demand when one has
"micro data" linking individual consumers' characteristics and choices. Our
model nests standard specifications featuring rich observed and unobserved
consumer heterogeneity as well as product/market-level unobservables that
introduce the problem of econometric endogeneity. Previous work establishes
identification of such models using market-level data and instruments for all
prices and quantities. Micro data provides a panel structure that facilitates
richer demand specifications and reduces requirements on both the number and
types of instrumental variables. We address identification of demand in the
standard case in which non-price product characteristics are assumed exogenous,
but also cover identification of demand elasticities and other key features
when product characteristics are endogenous. We discuss implications of these
results for applied work.


title:
Clifford Circuits can be Properly PAC Learned if and only if
  $\textsf{RP}=\textsf{NP}$

abstract:
  Given a dataset of input states, measurements, and probabilities, is it
possible to efficiently predict the measurement probabilities associated with a
quantum circuit? Recent work of Caro and Datta (2020) studied the problem of
PAC learning quantum circuits in an information theoretic sense, leaving open
questions of computational efficiency. In particular, one candidate class of
circuits for which an efficient learner might have been possible was that of
Clifford circuits, since the corresponding set of states generated by such
circuits, called stabilizer states, are known to be efficiently PAC learnable
(Rocchetto 2018). Here we provide a negative result, showing that proper
learning of CNOT circuits is hard for classical learners unless $\textsf{RP} =
\textsf{NP}$. As the classical analogue and subset of Clifford circuits, this
naturally leads to a hardness result for Clifford circuits as well.
Additionally, we show that if $\textsf{RP} = \textsf{NP}$ then there would
exist efficient proper learning algorithms for CNOT and Clifford circuits. By
similar arguments, we also find that an efficient proper quantum learner for
such circuits exists if and only if $\textsf{NP} \subseteq \textsf{RQP}$.


title: Observation of bosonic stimulation in light scattering

abstract:
  For bosons, the transition rate into an already occupied quantum state is
enhanced by its occupation number: the effect of bosonic stimulation. Bosonic
enhancement of light scattering has been predicted more than 30 years ago but
has not been observed before. Here we theoretically investigate and
experimentally demonstrate this effect. We show that the bosonic enhancement
factor for a harmonically trapped gas is bounded by a universal constant
$\zeta(2)/\zeta(3)$ above the phase transition to a Bose-Einstein condensate
(BEC), and depends linearly on the BEC fraction just below the phase
transition. Bosonically enhanced light scattering is observed and characterized
above and below the phase transition, and the effect of interactions is
discussed. For a multi-level system, bosonic enhancement is reduced because
bosonic stimulation occurs only for Rayleigh scattering, but not for Raman
scattering.


title:
Temperature-Dependent Diffuse Reflectance Measurements of Ceramic
  Powders in the Near- and Mid-Infrared Spectra

abstract:
  This study focuses on experimentally measuring temperature-dependent diffuse
reflectance in the near- and mid-infrared spectra for ceramic particles with
applications as heat-transfer and thermal-storage media in concentrated solar
power (CSP) plants. Specifically, a commercially available sintered bauxite
ceramic powder, ACCUCAST ID80, and its primary chemical constituents, alumina
(Al2O3) and silica (SiO2), are measured using a Fourier transform infrared
spectrometer (FTIR) coupled with a specialized diffuse reflectance accessory
and a heated stage. Room-temperature diffuse reflectance measurements show
increased absorption in tests with greater mass fractions of the ceramic
samples. There is a strong correlation in the measured reflectance spectra of
ACCUCAST with alumina and silica in the spectral range 2000-500 cm-1. For the
first time, temperature-dependent diffuse reflectance measurements are reported
for ACCUCAST, including a novel technique for accessing reflectance values
above the limiting temperature of the background material KBr. All three
materials exhibit a calculated emittance of ~0.9 at room temperature. However,
this value drops to 0.68 at 1000 C for ACCUCAST and ~0.43 for alumina and
silica. Thermal cycling in air from 25 C to 1000 C resulted in a visible color
change from dark grey to light orange for ACCUCAST and a subsequent 5X greater
increase in reflectance at 4000 cm-1 as compared to ACCUCAST thermally cycled
at 1000 C in vacuum. Alumina and silica spectra proved to be largely unaffected
by thermal cycling under atmospheric and evacuated conditions.


title: Dipolar quantum spin Hall insulator phase in extended Haldane model

abstract:
  The higher-order corner modes for quantum anomalous Hall insulators in $C_3$
symmetry broken honeycomb lattice have been engineered recently. Here we
consider an extended Haldane model in presence of inversion symmetry breaking
sub-lattice mass, time-reversal symmetry breaking Zeeman field and spin-orbit
coupling interaction where we find that only the quantum spin Hall insulator
can host the second-order dipolar phase while the remaining two first-order
topological phases do not morph into the latter. Remarkably, four-fold
degeneracy of zero-energy dipolar states can be reduced to two-fold under the
application (withdrawn) of sub-lattice mass (Zeeman field) term when the
spin-orbit coupling is already present. On the other hand, the sub-lattice mass
and Zeeman field terms compete with each other to pin down the two mid-gap
states at zero-energy in the absence or presence of spin-orbit coupling.
Interestingly, the bulk-polarization can topologically characterize the dipolar
phase irrespective of the energy of the mid-gap states as long as inversion
symmetry is preserved. The effective gap criterion can qualitatively mimic the
extent of SOT phase originated by the interplay between finite Zeeman exchange
field, sub-lattice mass, and SOC interaction.


title:
Optimal resource allocation for flexible-grid entanglement distribution
  networks

abstract:
  We use a genetic algorithm (GA) as a design aid for determining the optimal
provisioning of entangled photon spectrum in flex-grid quantum networks with
arbitrary numbers of channels and users. After introducing a general model for
entanglement distribution based on frequency-polarization hyperentangled
biphotons, we derive upper bounds on fidelity and entangled bit rate for
networks comprising one-to-one user connections. Simple conditions based on
user detector quality and link efficiencies are found that determine whether
entanglement is possible. We successfully apply a GA to find optimal resource
allocations in four different representative network scenarios and validate
features of our model experimentally in a quantum local area network in
deployed fiber. Our results show promise for the rapid design of large-scale
entanglement distribution networks.


title: Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar

abstract:
  We introduce NSEdit (neural-symbolic edit), a novel Transformer-based code
repair method. Given only the source code that contains bugs, NSEdit predicts
an editing sequence that can fix the bugs. The edit grammar is formulated as a
regular language, and the Transformer uses it as a neural-symbolic scripting
interface to generate editing programs. We modify the Transformer and add a
pointer network to select the edit locations. An ensemble of rerankers are
trained to re-rank the editing sequences generated by beam search. We fine-tune
the rerankers on the validation set to reduce over-fitting. NSEdit is evaluated
on various code repair datasets and achieved a new state-of-the-art accuracy
($24.04\%$) on the Tufano small dataset of the CodeXGLUE benchmark. NSEdit
performs robustly when programs vary from packages to packages and when buggy
programs are concrete. We conduct detailed analysis on our methods and
demonstrate the effectiveness of each component.


title:
METRO: Efficient Denoising Pretraining of Large Scale Autoencoding
  Language Models with Model Generated Signals

abstract:
  We present an efficient method of pretraining large-scale autoencoding
language models using training signals generated by an auxiliary model.
Originated in ELECTRA, this training strategy has demonstrated
sample-efficiency to pretrain models at the scale of hundreds of millions of
parameters. In this work, we conduct a comprehensive empirical study, and
propose a recipe, namely "Model generated dEnoising TRaining Objective"
(METRO), which incorporates some of the best modeling techniques developed
recently to speed up, stabilize, and enhance pretrained language models without
compromising model effectiveness. The resultant models, METRO-LM, consisting of
up to 5.4 billion parameters, achieve new state-of-the-art on the GLUE,
SuperGLUE, and SQuAD benchmarks. More importantly, METRO-LM are efficient in
that they often outperform previous large models with significantly smaller
model sizes and lower pretraining cost.


title: Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning

abstract:
  In this paper, we propose Wasserstein Isometric Mapping (Wassmap), a
parameter-free nonlinear dimensionality reduction technique that provides
solutions to some drawbacks in existing global nonlinear dimensionality
reduction algorithms in imaging applications. Wassmap represents images via
probability measures in Wasserstein space, then uses pairwise quadratic
Wasserstein distances between the associated measures to produce a
low-dimensional, approximately isometric embedding. We show that the algorithm
is able to exactly recover parameters of some image manifolds including those
generated by translations or dilations of a fixed generating measure.
Additionally, we show that a discrete version of the algorithm retrieves
parameters from manifolds generated from discrete measures by providing a
theoretical bridge to transfer recovery results from functional data to
discrete data. Testing of the proposed algorithms on various image data
manifolds show that Wassmap yields good embeddings compared with other global
techniques.


title:
The Many-Faceted Light Curves of Young Disk-bearing Stars in Taurus as
  Seen by K2

abstract:
  We present a comprehensive study of the variability properties of young
disk-bearing stars in the Taurus star-forming region, paralleling our previous
investigation in rho Oph and Upper Sco. A sample of 99 confirmed Taurus
association members is placed in the diagnostic Q-M plane of flux asymmetry (M)
and quasi-periodicity (Q), which guides our assignment of variability classes.
We find a similar proportion of flux-symmetric variables in Taurus, but more
bursters and fewer dippers relative to Upper Sco. The regions also differ in
that the amplitudes for periodic and quasi-periodic sources are larger in
Taurus relative to the more evolved Upper Sco star/disk systems. The
relationship between photometric variability patterns at optical wavelengths,
which arise in the inner disk and at the stellar surface, are assessed relative
to available disk inclination measurements.


title:
Copiloting Autonomous Multi-Robot Missions: A Game-inspired Supervisory
  Control Interface

abstract:
  Real-world deployment of new technology and capabilities can be daunting. The
recent DARPA Subterranean (SubT) Challenge, for instance, aimed at the
advancement of robotic platforms and autonomy capabilities in three one-year
development pushes. While multi-agent systems are traditionally deployed in
controlled and structured environments that allow for controlled testing (e.g.,
warehouses), the SubT challenge targeted various types of unknown underground
environments that imposed the risk of robot loss in the case of failure. In
this work, we introduce a video game-inspired interface, an autonomous mission
assistant, and test and deploy these using a heterogeneous multi-agent system
in challenging environments. This work leads to improved human-supervisory
control for a multi-agent system reducing overhead from application switching,
task planning, execution, and verification while increasing available
exploration time with this human-autonomy teaming platform.


title: Simplicial quantum contextuality

abstract:
  We introduce a new framework for contextuality based on simplicial sets,
combinatorial models of topological spaces that play a prominent role in modern
homotopy theory. Our approach extends measurement scenarios to consist of
spaces (rather than sets) of measurements and outcomes, and thereby generalizes
nonsignaling distributions to simplicial distributions, which are distributions
on spaces modeled by simplicial sets. Using this formalism we present a
topologically inspired new proof of Fine's theorem for characterizing
noncontextuality in Bell scenarios. Strong contextuality is generalized
suitably for simplicial distributions, allowing us to define cohomological
witnesses that extend the earlier topological constructions restricted to
algebraic relations among quantum observables to the level of probability
distributions. Foundational theorems of quantum theory such as the Gleason's
theorem and Kochen-Specker theorem can be expressed naturally within this new
language.


title:
Pointed Gromov-Hausdorff Topological Stability for non-compact metric
  spaces

abstract:
  We combine the pointed Gromov-Hausdorff metric [Ron10] with the locally $C^0$
distance to obtain the pointed $C^0$-Gromov-Hausdorff distance between maps of
possibly different non-compact pointed metric spaces. The latter is then
combined with Walters's locally topological stability [LNY18] and
$GH$-stability from [AMR17] to obtain the notion of topologically $GH$-stable
pointed homeomorphism. We give one example to show the difference between the
distance when take different base point in a pointed metric space.


title:
$\mathsf{SOCA}$ and $\mathsf{OGA}$ for $\mathsf{HL}$ spaces with strong
  properties

abstract:
  We study open colorings in certain classes of hereditary Lindel\"{o}f
($\mathsf{HL}$) spaces and submetrizable spaces. In particular, we show that
the definible version for the Open Graph Axiom ($\mathsf{OGA}$) holds for the
class of $\mathsf{HL}$ strong Choquet submetrizable spaces extending a
well-known result of Feng. Furthermore, we show the consistency of the Open
Graph Axiom for regular spaces that have countable spread and it's square also
has it, reaching closer to a well known conjecture of Todor\v{c}evi\'{c}: "It
is consistent that all regular spaces with countable spread satisfy
$\mathsf{OGA}$".


title:
Observability Analysis of Visual-Inertial Odometry with Online
  Calibration of Velocity-Control Based Kinematic Motion Models

abstract:
  In this paper, we analyze the observability of the visual-inertial odometry
(VIO) using stereo cameras with a velocity-control based kinematic motion
model. Previous work shows that in general case the global position and yaw are
unobservable in VIO system, additionally the roll and pitch become also
unobservable if there is no rotation. We prove that by integrating a planar
motion constraint roll and pitch become observable. We also show that the
parameters of the motion model are observable.


title:
Joint Coreset Construction and Quantization for Distributed Machine
  Learning

abstract:
  Coresets are small, weighted summaries of larger datasets, aiming at
providing provable error bounds for machine learning (ML) tasks while
significantly reducing the communication and computation costs. To achieve a
better trade-off between ML error bounds and costs, we propose the first
framework to incorporate quantization techniques into the process of coreset
construction. Specifically, we theoretically analyze the ML error bounds caused
by a combination of coreset construction and quantization. Based on that, we
formulate an optimization problem to minimize the ML error under a fixed budget
of communication cost. To improve the scalability for large datasets, we
identify two proxies of the original objective function, for which efficient
algorithms are developed. For the case of data on multiple nodes, we further
design a novel algorithm to allocate the communication budget to the nodes
while minimizing the overall ML error. Through extensive experiments on
multiple real-world datasets, we demonstrate the effectiveness and efficiency
of our proposed algorithms for a variety of ML tasks. In particular, our
algorithms have achieved more than 90% data reduction with less than 10%
degradation in ML performance in most cases.


title: Sketching Algorithms and Lower Bounds for Ridge Regression

abstract:
  We give a sketching-based iterative algorithm that computes $1+\varepsilon$
approximate solutions for the ridge regression problem $\min_x \|{Ax-b}\|_2^2
+\lambda\|{x}\|_2^2$ where $A \in \mathbb{R}^{n \times d}$ with $d \ge n$. Our
algorithm, for a constant number of iterations (requiring a constant number of
passes over the input), improves upon earlier work of Chowdhury et al., by
requiring that the sketching matrix only has a weaker Approximate Matrix
Multiplication (AMM) guarantee that depends on $\epsilon$, along with a
constant subspace embedding guarantee. The earlier work instead requires that
the sketching matrix have a subspace embedding guarantee that depends on
$\epsilon$. For example, to produce a $1+\varepsilon$ approximate solution in
$1$ iteration, which requires $2$ passes over the input, our algorithm requires
the OSNAP embedding to have $m= O(n\sigma^2/\lambda\varepsilon)$ rows with a
sparsity parameter $s = O(\log(n))$, whereas the earlier algorithm of Chowdhury
et al., with the same number of rows of OSNAP requires a sparsity $s =
O(\sqrt{\sigma^2/\lambda\varepsilon} \cdot \log(n))$, where $\sigma =
\|{A}\|_2$ is the spectral norm of the matrix $A$. We also show that this
algorithm can be used to give faster algorithms for kernel ridge regression.
Finally, we show that the sketch size required for our algorithm is essentially
optimal for a natural framework of algorithms for ridge regression by proving
lower bounds on oblivious sketching matrices for AMM. The sketch size lower
bounds for AMM may be of independent interest.


title:
Exploring the Impacts of Power Grid Signals on Data Center Operations
  using a Receding-Horizon Scheduling Model

abstract:
  Data centers (DaCes) can help decarbonize the power grid by helping absorb
renewable power (e.g., wind and solar) due to their ability to shift power
loads across space and time. However, to harness such load-shifting
flexibility, it is necessary to understand how grid signals (carbon signals and
price/load allocations) affect DaCe operations. An obstacle that arises here is
the lack of computationally-tractable DaCe scheduling models that can capture
objectives, constraints, and information flows that arise at the interface of
DaCes and the grid. To address this gap, we present a receding-horizon
scheduling model (a mixed-integer programming model) that captures the resource
management layer between the DaCe scheduler and the grid while accounting for
logical constraints, different types of objectives, and forecasts of incoming
job profiles and of available computing capacity. We use our model to conduct
extensive case studies based on public data from Microsoft Azure and MISO. Our
studies show that DaCes can provide significant temporal load-shifting
flexibility in response to carbon emission signals and peak demand charges from
electricity markets without sacrificing much scheduling performance. Models and
case studies are shared as easy-to-use Julia code.


title:
Performance of a convolutional autoencoder designed to remove electronic
  noise from p-type point contact germanium detector signals

abstract:
  We present a convolutional autoencoder to denoise pulses from a p-type point
contact high-purity germanium detector similar to those used in several rare
event searches. While we focus on training procedures that rely on detailed
detector physics simulations, we also present implementations requiring only
noisy detector pulses to train the model. We validate our autoencoder on both
simulated data and calibration data from an $^{241}$Am source, the latter of
which is used to show that the denoised pulses are statistically compatible
with data pulses. We also show that the shaping time used to calculate energy
with a trapezoidal filter can be significantly reduced while maintaining a
comparable energy resolution. Under certain circumstances, our denoising method
can improve the overall energy resolution. The methods we developed to remove
electronic noise are straightforward to extend to other detector technologies.
Furthermore, the latent representation from the encoder is also of use in
quantifying shape-based characteristics of the signals. Our work has great
potential to be used in particle physics experiments and beyond.


title:
Photodynamical analysis of the nearly resonant planetary system
  WASP-148: Accurate transit-timing variations and mutual orbital inclination

abstract:
  WASP-148 is a recently announced extra-solar system harbouring at least two
giant planets. The inner planet transits its host star. The planets travel on
eccentric orbits and are near the 4:1 mean-motion resonance, which implies
significant mutual gravitational interactions. In particular, this causes
transit-timing variations of a few minutes, which were detected based on
ground-based photometry. This made WASP-148 one of the few cases where such a
phenomenon was detected without space-based photometry. Here, we present a
self-consistent model of WASP-148 that takes into account the gravitational
interactions between all known bodies in the system. Our analysis
simultaneously fits the available radial velocities and transit light curves.
In particular, we used the photometry secured by the TESS space telescope and
made public after the WASP-148 discovery announcement. The TESS data confirm
the transit-timing variations, but only in combination with previously measured
transit times. The system parameters we derived agree with those previously
reported and have a significantly improved precision, including the mass of the
non-transiting planet. We found a significant mutual inclination between the
orbital planes of the two planets: I=41.0 +6.2 -7.6 deg based on the modelling
of the observations, although we found I=20.8 +/- 4.6 deg when we imposed a
constraint on the model enforcing long-term dynamical stability. When a third
planet was added to the model - based on a candidate signal in the radial
velocity - the mutual inclination between planets b and c changed significantly
allowing solutions closer to coplanar. We conclude that more data are needed to
establish the true architecture of the system. If the significant mutual
inclination is confirmed, WASP-148 would become one of the only few candidate
non-coplanar planetary systems. We discuss possible origins for this
misalignment.


title:
A Bayesian Machine Learning Approach for Estimating Heterogeneous
  Survivor Causal Effects: Applications to a Critical Care Trial

abstract:
  Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork)
ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian
machine learning approach to estimate the average causal effect and
heterogeneous causal effects among the always-survivors stratum when clinical
outcomes are subject to truncation. We adopted Bayesian additive regression
trees (BART) to flexibly specify separate models for the potential outcomes and
latent strata membership. In the analysis of the ARMA trial, we found that the
low tidal volume treatment had an overall benefit for participants sustaining
acute lung injuries on the outcome of time to returning home, but substantial
heterogeneity in treatment effects among the always-survivors, driven most
strongly by sex and the alveolar-arterial oxygen gradient at baseline (a
physiologic measure of lung function and source of hypoxemia). These findings
illustrate how the proposed methodology could guide the prognostic enrichment
of future trials in the field. We also demonstrated through a simulation study
that our proposed Bayesian machine learning approach outperforms other
parametric methods in reducing the estimation bias in both the average causal
effect and heterogeneous causal effects for always-survivors.


title: On Periodic Decompositions and Nonexpansive Lines

abstract:
  In his Ph.D. thesis, Michal Szabados conjectured that for a not fully
periodic configuration with a minimal periodic decomposition the nonexpansive
lines are exactly the lines that contain a period for some periodic
configuration in such decomposition. In this paper, we study Szabados's
conjecture. First, we show that we may consider a minimal periodic
decomposition where each periodic configuration is defined on a finite
alphabet. Then we prove that Szabados's conjecture holds for a wide class of
configurations, which includes all not fully periodic low convex pattern
complexity configurations.