title:
Transition from localized to mean field behaviour of cascading failures
  in the fiber bundle model on complex networks

abstract:
  We study the failure process of fiber bundles on complex networks focusing on
the effect of the degree of disorder of fibers' strength on the transition from
localized to mean field behaviour. Starting from a regular square lattice we
apply the Watts-Strogatz rewiring technique to introduce long range random
connections in the load transmission network and analyze how the ultimate
strength of the bundle and the statistics of the size of failure cascades
change when the rewiring probability is gradually increased. Our calculations
revealed that the degree of strength disorder of nodes of the network has a
substantial effect on the localized to mean field transition. In particular, we
show that the transition sets on at a finite value of the rewiring probability,
which shifts to higher values as the degree of disorder is reduced. The
transition is limited to a well defined range of disorder, so that there exists
a threshold disorder of nodes' strength below which the randomization of the
network structure does not provide any improvement neither of the overall load
bearing capacity nor of the cascade tolerance of the system. At low strength
disorder the fully random network is the most stable one, while at high
disorder best cascade tolerance is obtained at a lower structural randomness.
Based on the interplay of the network structure and strength disorder we
construct an analytical argument which provides a reasonable description of the
numerical findings.


title: Investigating the low-flux states in six Intermediate Polars

abstract:
  We present optical photometry of six intermediate polars that exhibit
transitions to a low-flux state. For four of these systems, DW Cnc, V515 And,
V1223 Sgr and RX J2133.7+5107, we are able to perform timing analysis in and
out of the low states. We find that, for DW Cnc and V515 And, the dominant
periodicities in the light curves change as the flux decreases, indicating a
change in the sources' accretion properties as they transition to the low
state. For V1223 Sgr we find that the variability is almost completely quenched
at the lowest flux, but do not find evidence for a changing accretion geometry.
For RX J2133.7+5107, the temporal properties do not change in the low state,
but we do see a period of enhanced accretion that is coincident with increased
variability on the beat frequency, which we do not associate with a change in
the accretion mechanisms in the system.


title: Preference Learning in School Choice Problems

abstract:
  Many school districts in the U.S. have replaced the Boston mechanism with
deferred acceptance (DA) due to manipulability and unfairness. Nonetheless, the
Boston mechanism Pareto dominates the DA in ex-ante welfare, especially when
students have similar ordinal preferences and schools have coarse priorities.
We show that the DA's inefficiency relative to the Boston worsens if students
have to acquire information about preferences at a cost before submitting
rank-order lists to a given mechanism. Under the DA, greater homogeneity in
rank-order reports and less information acquisition reinforce each other. A
higher information cost intensifies this reinforcement and exacerbates the DA's
inefficiency.


title: Plasmoid-fed prominence formation (PF$^2$) during flux rope eruption

abstract:
  We report a new, plasmoid-fed scenario for the formation of an eruptive
prominence (PF$^2$), involving reconnection and condensation. We use
grid-adaptive resistive two-and-a-half-dimensional magnetohydrodynamic (MHD)
simulations in a chromosphere-to-corona setup to resolve this plasmoid-fed
scenario. We study a pre-existing flux rope (FR) in the low corona that
suddenly erupts due to catastrophe, which also drives a fast shock above the
erupting FR. A current sheet (CS) forms underneath the erupting FR, with
chromospheric matter squeezed into it. The plasmoid instability occurs and
multiple magnetic islands appear in the CS once the Lundquist number reaches
$\sim 3.5\times 10^{4}$. The remnant chromospheric matter in the CS is then
transferred to the FR by these newly formed magnetic islands. The dense and
cool mass transported by the islands accumulates in the bottom of the FR,
thereby forming a prominence during the eruption phase. More coronal plasma
continuously condenses into the prominence due to the thermal instability as
the FR rises. Due to the fine structure brought in by the PF$^2$ process, the
model naturally forms filament threads, aligned above the polarity inversion
line. Synthetic views at our resolution of $15 \mathrm{km}$ show many details
that may be verified in future high-resolution observations.


title: Posterior Predictive Propensity Scores and $p$-Values

abstract:
  \citet{Rosenbaum83ps} introduced the notion of the propensity score and
discussed its central role in causal inference with observational studies.
Their paper, however, caused a fundamental incoherence with an early paper by
\citet{Rubin78}, which showed that the propensity score does not play any role
in the Bayesian analysis of unconfounded observational studies if the priors on
the propensity score and outcome models are independent. Despite the serious
efforts made in the literature, it is generally difficult to reconcile these
contradicting results. We offer a simple approach to incorporating the
propensity score in Bayesian causal inference based on the posterior predictive
$p$-value. To motivate a simple procedure, we focus on the model with the
strong null hypothesis of no causal effects for any units whatsoever.
Computationally, the proposed posterior predictive $p$-value equals the classic
$p$-value based on the Fisher randomization test averaged over the posterior
predictive distribution of the propensity score. Moreover, using the
studentized doubly robust estimator as the test statistic, the proposed
$p$-value inherits the doubly robust property and is also asymptotically valid
for testing the weak null hypothesis of zero average causal effect. Perhaps
surprisingly, this Bayesianly motivated $p$-value can have better frequentist's
finite-sample performance than the frequentist's $p$-value based on the
asymptotic approximation especially when the propensity scores can take extreme
values.


title:
Accurate Ground-based Astrometry of Naked-eye Stars: The United States
  Naval Observatory Bright-Star Astrometric Database

abstract:
  We present the United States Naval Observatory (USNO) Bright-Star Astrometric
Database (UBAD), a current-epoch high-accuracy astrometric catalog. The catalog
consists of 364 bright northern hemisphere stars, including all but five such
stars with either $V < 3.5$ or with $I < 3.2$ and $V < 6$, as well as a large
fraction of slightly fainter stars; 36 of the brightest catalog stars are not
included in Gaia Early Data Release 3 (EDR3). Observations were conducted with
the USNO, Flagstaff Station, Kaj Strand 61-inch Astrometric Reflector. Target
stars were imaged through a small 12.5-magnitude neutral-density spot, while
the remainder of the stars in the field of view were unattenuated. This allowed
for unsaturated images of the bright target stars to be calibrated directly
against much fainter reference stars from Gaia EDR3. The median position errors
are 1.9 mas in both right ascension and declination at the catalog epoch of
2017.0, with 90% of catalog stars having errors less than 2.6 mas; systematic
errors are 1 -- 3 mas. Combining UBAD observations with Hipparcos-2 positions
yields proper motions with median errors of 0.045 and 0.049 mas year$^{-1}$ in
right ascension and declination, respectively, with 90% of stars having errors
less than 0.1 mas year$^{-1}$; systematic errors are about 0.1 mas year$^{-1}$.
Single-frame accuracy for positions of the target stars is typically 5 -- 6
mas. Gaia EDR3 astrometry for these bright stars, which are heavily saturated
in the Gaia observations, is validated over the magnitude range $2 \lesssim G
\lesssim 6$.


title:
Learning Transferrable Representations of Career Trajectories for
  Economic Prediction

abstract:
  Understanding career trajectories -- the sequences of jobs that individuals
hold over their working lives -- is important to economists for studying labor
markets. In the past, economists have estimated relevant quantities by fitting
predictive models to small surveys, but in recent years large datasets of
online resumes have also become available. These new datasets provide job
sequences of many more individuals, but they are too large and complex for
standard econometric modeling. To this end, we adapt ideas from modern language
modeling to the analysis of large-scale job sequence data. We develop CAREER, a
transformer-based model that learns a low-dimensional representation of an
individual's job history. This representation can be used to predict jobs
directly on a large dataset, or can be "transferred" to represent jobs in
smaller and better-curated datasets. We fit the model to a large dataset of
resumes, 24 million people who are involved in more than a thousand unique
occupations. It forms accurate predictions on held-out data, and it learns
useful career representations that can be fine-tuned to make accurate
predictions on common economics datasets.


title: The Quarks of Attention

abstract:
  Attention plays a fundamental role in both natural and artificial
intelligence systems. In deep learning, attention-based neural architectures,
such as transformer architectures, are widely used to tackle problems in
natural language processing and beyond. Here we investigate the fundamental
building blocks of attention and their computational properties. Within the
standard model of deep learning, we classify all possible fundamental building
blocks of attention in terms of their source, target, and computational
mechanism. We identify and study three most important mechanisms: additive
activation attention, multiplicative output attention (output gating), and
multiplicative synaptic attention (synaptic gating). The gating mechanisms
correspond to multiplicative extensions of the standard model and are used
across all current attention-based deep learning architectures. We study their
functional properties and estimate the capacity of several attentional building
blocks in the case of linear and polynomial threshold gates. Surprisingly,
additive activation attention plays a central role in the proofs of the lower
bounds. Attention mechanisms reduce the depth of certain basic circuits and
leverage the power of quadratic activations without incurring their full cost.


title: Fuzzy Pooling

abstract:
  Convolutional Neural Networks (CNNs) are artificial learning systems
typically based on two operations: convolution, which implements feature
extraction through filtering, and pooling, which implements dimensionality
reduction. The impact of pooling in the classification performance of the CNNs
has been highlighted in several previous works, and a variety of alternative
pooling operators have been proposed. However, only a few of them tackle with
the uncertainty that is naturally propagated from the input layer to the
feature maps of the hidden layers through convolutions. In this paper we
present a novel pooling operation based on (type-1) fuzzy sets to cope with the
local imprecision of the feature maps, and we investigate its performance in
the context of image classification. Fuzzy pooling is performed by
fuzzification, aggregation and defuzzification of feature map neighborhoods. It
is used for the construction of a fuzzy pooling layer that can be applied as a
drop-in replacement of the current, crisp, pooling layers of CNN architectures.
Several experiments using publicly available datasets show that the proposed
approach can enhance the classification performance of a CNN. A comparative
evaluation shows that it outperforms state-of-the-art pooling approaches.


title: Text-Based Action-Model Acquisition for Planning

abstract:
  Although there have been approaches that are capable of learning action
models from plan traces, there is no work on learning action models from
textual observations, which is pervasive and much easier to collect from
real-world applications compared to plan traces. In this paper we propose a
novel approach to learning action models from natural language texts by
integrating Constraint Satisfaction and Natural Language Processing techniques.
Specifically, we first build a novel language model to extract plan traces from
texts, and then build a set of constraints to generate action models based on
the extracted plan traces. After that, we iteratively improve the language
model and constraints until we achieve the convergent language model and action
models. We empirically exhibit that our approach is both effective and
efficient.


title: Composite excitonic states in doped semiconductors

abstract:
  We present a theoretical model of composite excitonic states in doped
semiconductors. Many-body interactions between a photoexcited electron-hole
pair and the electron gas are integrated into a computationally tractable
few-body problem, solved by the variational method. We focus on electron-doped
ML-MoSe$_2$ and ML-WSe$_2$ due to the contrasting character of their conduction
bands. In both cases, the core of the composite is a tightly-bound trion (two
electrons and valence-band hole), surrounded by a region depleted of electrons.
The composite in ML-WSe$_2$ further includes a satellite electron with
different quantum numbers. The theory is general and can be applied to
semiconductors with various energy-band properties, allowing one to calculate
their excitonic states and to quantify the interaction with the Fermi sea.


title: Hexcitons and oxcitons in monolayer WSe$_2$

abstract:
  In the archetypal monolayer semiconductor WSe$_2$, the distinct ordering of
spin-polarized valleys (low-energy pockets) in the conduction band allows for
studies of not only simple neutral excitons and charged excitons (i.e.,
trions), but also more complex many-body states that are predicted at higher
electron densities. We discuss magneto-optical measurements of electron-rich
WSe$_2$ monolayers, and interpret the spectral lines that emerge at high
electron doping as optical transitions of 6-body exciton states ("hexcitons")
and 8-body exciton states ("oxcitons"). These many-body states emerge when a
photoexcited electron-hole pair interacts simultaneously with multiple Fermi
seas, each having distinguishable spin and valley quantum numbers. In addition,
we identify the energies of primary and satellite optical transitions of
hexcitons in the photoluminescence spectrum.


title:
How to Fill the Optimum Set? Population Gradient Descent with Harmless
  Diversity

abstract:
  Although traditional optimization methods focus on finding a single optimal
solution, most objective functions in modern machine learning problems,
especially those in deep learning, often have multiple or infinite numbers of
optima. Therefore, it is useful to consider the problem of finding a set of
diverse points in the optimum set of an objective function. In this work, we
frame this problem as a bi-level optimization problem of maximizing a diversity
score inside the optimum set of the main loss function, and solve it with a
simple population gradient descent framework that iteratively updates the
points to maximize the diversity score in a fashion that does not hurt the
optimization of the main loss. We demonstrate that our method can efficiently
generate diverse solutions on a variety of applications, including
text-to-image generation, text-to-mesh generation, molecular conformation
generation and ensemble neural network training.


title: Generic nonadditivity of quantum capacity in simple channels

abstract:
  Determining capacities of quantum channels is a fundamental question in
quantum information theory. Despite having rigorous coding theorems quantifying
the flow of information across quantum channels, their capacities are poorly
understood due to super-additivity effects. Studying these phenomena is
important for deepening our understanding of quantum information, yet simple
and clean examples of super-additive channels are scarce. Here we study a
simple family of qutrit channels called the platypus channel, and show that it
exhibits super-additivity of coherent information when used jointly with a
variety of qubit channels. A higher-dimensional variant of the platypus channel
displays super-additivity of quantum capacity together with an erasure channel.
Subject to the "spin-alignment conjecture" introduced in a companion paper, our
results on super-additivity of quantum capacity extend to lower-dimensional
channels as well as larger parameter ranges. In particular, super-additivity
occurs between two weakly additive channels each with large capacity on their
own, in stark contrast to previous results. Remarkably, a single, novel
transmission strategy achieves super-additivity in all examples. Our results
show that super-additivity is much more prevalent than previously thought. It
can occur across a wide variety of channels, even when both participating
channels have large quantum capacity.


title:
Turning many-body problems to few-body ones in photoexcited
  semiconductors using the stochastic variational method in momentum space,
  SVM-k

abstract:
  We develop an efficient computational technique to calculate composite
excitonic states in photoexcited semiconductors through the stochastic
variational method (SVM). Many-body interactions between an electron gas and
the excitonic state are embodied in the problem through Fermi holes in the
conduction band, introduced when electrons are pulled out of the Fermi sea to
bind the photoexcited electron-hole pair. We consider the direct Coulomb
interaction between distinguishable particles in the complex, the
exchange-induced band-gap renormalization effect, and electron-hole exchange
interaction between an electron and its conduction-band hole. We provide
analytical expressions for potential matrix elements, using a technique that
allows us to circumvent the difficulty imposed by the occupation of low-energy
electron states in the conduction band. We discuss the computational steps one
should implement in order to perform the calculation, and how to extract
kinetic energies of individual particles in the complex, average inter-particle
distances, and density distributions.


title:
Tetrons, pexcitons, and hexcitons in monolayer transition-metal
  dichalcogenides

abstract:
  We present a comprehensive theoretical analysis of composite excitonic states
in doped transition-metal dichalcogenide monolayers. Making use of the pair
distribution function, we introduce a method to include the effect of screening
in the interaction between the excitonic state and Fermi-sea electrons.
Employing the screened potential, we study tetrons and pexcitons in which a
trion is bound to one and two holes in the conduction band, respectively. A
conduction-band hole denotes the lack of electrons with certain quantum numbers
at the vicinity of the trion. We then analyze the hexciton complex wherein
conduction-band holes facilitate binding of three electrons with one
valence-band hole. We introduce a simple model from which one can readily
calculate the binding energy of the third (satellite) electron in the hexciton
complex. Finally, we compare the simulated results with experiment and point
out the shortcomings and successes of the analysis.


title: The platypus of the quantum channel zoo

abstract:
  Understanding quantum channels and the strange behavior of their capacities
is a key objective of quantum information theory. Here we study a remarkably
simple, low-dimensional, single-parameter family of quantum channels with
exotic quantum information-theoretic features. As the simplest example from
this family, we focus on a qutrit-to-qutrit channel that is intuitively
obtained by hybridizing together a simple degradable channel and a completely
useless qubit channel. Such hybridizing makes this channel's capacities behave
in a variety of interesting ways. For instance, the private and classical
capacity of this channel coincide and can be explicitly calculated, even though
the channel does not belong to any class for which the underlying information
quantities are known to be additive. Moreover, the quantum capacity of the
channel can be computed explicitly, given a clear and compelling conjecture is
true. This "spin alignment conjecture", which may be of independent interest,
is proved in certain special cases and additional numerical evidence for its
validity is provided. Finally, we generalize the qutrit channel in two ways,
and the resulting channels and their capacities display similarly rich
behavior. In a companion paper, we further show that the qutrit channel
demonstrates superadditivity when transmitting quantum information jointly with
a variety of assisting channels, in a manner unknown before.


title:
Improving Performance Bounds for Weighted Round-Robin Schedulers under
  Constrained Cross-Traffic

abstract:
  Weighted round robin (WRR) is a simple, efficient packet scheduler providing
low latency and fairness by assigning flow weights that define the number of
possible packets to be sent consecutively. A variant of WRR that mitigates its
tendency to increase burstiness, called interleaved weighted round robin
(IWRR), has seen analytical treatment recently \cite{TLBB21}; a network
calculus approach was used to obtain the best-possible strict service curve.
From a different perspective, WRR can also be interpreted as an emulation of an
idealized fair scheduler known as generalized processor sharing (GPS). Inspired
by profound literature results on the performance analysis of GPS, we show that
both, WRR and IWRR, belong to a larger class of fair schedulers called
bandwidth-sharing policies. We use this insight to derive new strict service
curves for both schedulers that, under the additional assumption of constrained
cross-traffic flows, can significantly improve the state-of-the-art results and
lead to smaller delay bounds.


title: The relative class number one problem for function fields, I

abstract:
  We classify finite extensions of function fields (of curves over finite
fields) with the same class number, except when both curves have base field
$\mathbb{F}_2$ and genus $>1$. In this case, we show that the zeta functions of
the curves form one of 208 explicit pairs, and find 64 examples of cyclic
extensions; the list is provably complete except if the base curve has genus 6
or 7 or if the extension is noncyclic. These cases will be resolved in
subsequent papers.


title:
Morse Graphs: Topological Tools for Analyzing the Global Dynamics of
  Robot Controllers

abstract:
  Understanding the global dynamics of a robot controller, such as identifying
attractors and their regions of attraction (RoA), is important for safe
deployment and synthesizing more effective hybrid controllers. This paper
proposes a topological framework to analyze the global dynamics of robot
controllers, even data-driven ones, in an effective and explainable way. It
builds a combinatorial representation representing the underlying system's
state space and non-linear dynamics, which is summarized in a directed acyclic
graph, the Morse graph. The approach only probes the dynamics locally by
forward propagating short trajectories over a state-space discretization, which
needs to be a Lipschitz-continuous function. The framework is evaluated given
either numerical or data-driven controllers for classical robotic benchmarks.
It is compared against established analytical and recent machine learning
alternatives for estimating the RoAs of such controllers. It is shown to
outperform them in accuracy and efficiency. It also provides deeper insights as
it describes the global dynamics up to the discretization's resolution. This
allows to use the Morse graph to identify how to synthesize controllers to form
improved hybrid solutions or how to identify the physical limitations of a
robotic system.


title:
Limitations of Neural Collapse for Understanding Generalization in Deep
  Learning

abstract:
  The recent work of Papyan, Han, & Donoho (2020) presented an intriguing
"Neural Collapse" phenomenon, showing a structural property of interpolating
classifiers in the late stage of training. This opened a rich area of
exploration studying this phenomenon. Our motivation is to study the upper
limits of this research program: How far will understanding Neural Collapse
take us in understanding deep learning? First, we investigate its role in
generalization. We refine the Neural Collapse conjecture into two separate
conjectures: collapse on the train set (an optimization property) and collapse
on the test distribution (a generalization property). We find that while Neural
Collapse often occurs on the train set, it does not occur on the test set. We
thus conclude that Neural Collapse is primarily an optimization phenomenon,
with as-yet-unclear connections to generalization. Second, we investigate the
role of Neural Collapse in feature learning. We show simple, realistic
experiments where training longer leads to worse last-layer features, as
measured by transfer-performance on a downstream task. This suggests that
neural collapse is not always desirable for representation learning, as
previously claimed. Finally, we give preliminary evidence of a "cascading
collapse" phenomenon, wherein some form of Neural Collapse occurs not only for
the last layer, but in earlier layers as well. We hope our work encourages the
community to continue the rich line of Neural Collapse research, while also
considering its inherent limitations.


title:
Monte Carlo studies of the Blume-Capel model on nonregular two- and
  three-dimensional lattices: Phase diagrams, tricriticality, and critical
  exponents

abstract:
  We perform Monte Carlo simulations, combining both the Wang-Landau and the
Metropolis algorithms, to investigate the phase diagrams of the Blume-Capel
model on different types of nonregular lattices (Lieb lattice (LL), decorated
triangular lattice (DTL), and decorated simple cubic lattice (DSC)). The
nonregular character of the lattices induces a double transition (reentrant
behavior) in the region of the phase diagram at which the nature of the phase
transition changes from first-order to second-order. A physical mechanism
underlying this reentrance is proposed. The large-scale Monte Carlo simulations
are performed with the finite-size scaling analysis to compute the critical
exponents and the critical Binder cumulant for three different values of the
anisotropy $\Delta/J \in \big\{ 0, 1, 1.34 \textrm{ (for LL)}, 1.51 \textrm{
(for DTL and DSC)} \big\}$, showing thus no deviation from the standard Ising
universality class in two and three dimensions. We report also the location of
the tricritical point to considerable precision: ($\Delta_t/J=1.3457(1)$; $k_B
T_t/J=0.309(2)$), ($\Delta_t/J=1.5766(1)$; $k_B T_t/J=0.481(2)$), and
($\Delta_t/J=1.5933(1)$; $k_B T_t/J=0.569(4)$) for LL, DTL, and DSC,
respectively.


title: Laplacian operator on statistical manifold

abstract:
  In this paper, we define a Laplacian operator on a statistical manifold,
called the vector Laplacian. This vector Laplacian incorporates information
from the Amari-Chentsov tensor. We derive a formula for the vector Laplacian.
We also give two applications using the heat kernel associated with the vector
Laplacian.


title: TROPHY: Trust Region Optimization Using a Precision Hierarchy

abstract:
  We present an algorithm to perform trust-region-based optimization for
nonlinear unconstrained problems. The method selectively uses function and
gradient evaluations at different floating-point precisions to reduce the
overall energy consumption, storage, and communication costs; these
capabilities are increasingly important in the era of exascale computing. In
particular, we are motivated by a desire to improve computational efficiency
for massive climate models. We employ our method on two examples: the CUTEst
test set and a large-scale data assimilation problem to recover wind fields
from radar returns. Although this paper is primarily a proof of concept, we
show that if implemented on appropriate hardware, the use of mixed-precision
can significantly reduce the computational load compared with fixed-precision
solvers.


title: Generalizable Information Theoretic Causal Representation

abstract:
  It is evidence that representation learning can improve model's performance
over multiple downstream tasks in many real-world scenarios, such as image
classification and recommender systems. Existing learning approaches rely on
establishing the correlation (or its proxy) between features and the downstream
task (labels), which typically results in a representation containing cause,
effect and spurious correlated variables of the label. Its generalizability may
deteriorate because of the unstability of the non-causal parts. In this paper,
we propose to learn causal representation from observational data by
regularizing the learning procedure with mutual information measures according
to our hypothetical causal graph. The optimization involves a counterfactual
loss, based on which we deduce a theoretical guarantee that the
causality-inspired learning is with reduced sample complexity and better
generalization ability. Extensive experiments show that the models trained on
causal representations learned by our approach is robust under adversarial
attacks and distribution shift.


title: On solutions of codimension-one $A$-hypergeometric systems

abstract:
  By a codimension-one system we mean a system whose lattice of relations has
rank one. We consider codimension-one $A$-hypergeometric systems and explicitly
construct some of the logarithmic series solutions at the origin. When the
parameter vector $\beta$ is nonresonant we obtain a full set of logarithmic
series solutions at the origin by this procedure. We also determine when a
codimension-one system with nonresonant parameter can have maximal unipotent
monodromy at the origin.


title: An analogue of the Robin inequality of the second type for odd integers

abstract:
  In this paper we give a variant of the Robin inequality which states that
$\frac{\sigma(n)}{n} \leq \frac{e^\gamma}{2} \log\log n +
\frac{0.7398\cdots}{\log\log n}$ for any odd integer $n \geq 3$.


title: Graph Masked Autoencoder

abstract:
  Transformers have achieved state-of-the-art performance in learning graph
representations. However, there are still some challenges when applying
transformers to real-world scenarios due to the fact that deep transformers are
hard to be trained from scratch and the memory consumption is large. To address
the two challenges, we propose Graph Masked Autoencoders (GMAE), a
self-supervised model for learning graph representations, where vanilla graph
transformers are used as the encoder and the decoder. GMAE takes partially
masked graphs as input, and reconstructs the features of the masked nodes. We
adopt asymmetric encoder-decoder design, where the encoder is a deep graph
transformer and the decoder is a shallow graph transformer. The masking
mechanism and the asymmetric design make GMAE a memory-efficient model compared
with conventional transformers. We show that, compared with training from
scratch, the graph transformer pre-trained using GMAE can achieve much better
performance after fine-tuning. We also show that, when serving as a
conventional self-supervised graph representation model and using an SVM model
as the downstream graph classifier, GMAE achieves state-of-the-art performance
on 5 of the 7 benchmark datasets.


title:
Charge-Swapping Q-balls in a Logarithmic Potential and Affleck-Dine
  condensate fragmentation

abstract:
  We study charge-swapping Q-balls, a kind of composite Q-ball where positive
and negative charges co-exist and swap with time, in models with a logarithmic
potential that arises naturally in supersymmetric extensions of the Standard
Model. We show that charge-swapping Q-balls can be copiously generated in the
Affleck-Dine fragmentation process in the early universe. We find that the
charge-swapping Q-balls with the logarithmic potential are extremely stable. By
performing long time, parallelized lattice simulations with absorbing boundary
conditions, we find that the lifetimes of such objects with low multipoles are
at least $4.6 \times 10^5/m$ in 3+1D and $2.5 \times 10^7/m$ in 2+1D, where $m$
is the mass scale of the scalar field. We also chart the attractor basin of the
initial conditions to form these charge-swapping Q-balls.


title: Robust Reinforcement Learning via Genetic Curriculum

abstract:
  Achieving robust performance is crucial when applying deep reinforcement
learning (RL) in safety critical systems. Some of the state of the art
approaches try to address the problem with adversarial agents, but these agents
often require expert supervision to fine tune and prevent the adversary from
becoming too challenging to the trainee agent. While other approaches involve
automatically adjusting environment setups during training, they have been
limited to simple environments where low-dimensional encodings can be used.
Inspired by these approaches, we propose genetic curriculum, an algorithm that
automatically identifies scenarios in which the agent currently fails and
generates an associated curriculum to help the agent learn to solve the
scenarios and acquire more robust behaviors. As a non-parametric optimizer, our
approach uses a raw, non-fixed encoding of scenarios, reducing the need for
expert supervision and allowing our algorithm to adapt to the changing
performance of the agent. Our empirical studies show improvement in robustness
over the existing state of the art algorithms, providing training curricula
that result in agents being 2 - 8x times less likely to fail without
sacrificing cumulative reward. We include an ablation study and share insights
on why our algorithm outperforms prior approaches.


title:
Founder Effects and Dynamical Demographic Phases due to Decreasing
  Intrinsic Fluctuations in Growing Populations

abstract:
  The critical dependence of a population's long term behaviour on its initial
conditions -- the founder effect -- is an important concept in population
genetics and classical example of non-linear dynamics. It has been used both to
argue for the relevance of population fracture in speciation, and to explain
novel behaviours at the invasion front of range expansions. By modifying an
established model of population dynamics, we now demonstrate that founder
effects can be driven by growth in well-mixed populations. Such effects arise
due to intrinsic fluctuations that decrease with the inverse square root of the
population size, $N$. Whilst this guarantees that a population fixates on its
deterministic attractors as $t\to\infty$, the likelihood of fixation can still
depend on a population's founders, both by number and demographic mix. This
dependence can moreover be non-trivial, since decreasing $O(1/\sqrt{N})$
fluctuations couple dynamically to the geometry of the underlying attractors,
giving rise to distinct regimes of demographic behaviour that change abruptly
at critical population sizes. By changing the character and relative duration
of such dynamical demographic phases, fluctuation-mediated founder effects can
also depend on the rates of growth and mutation.


title: SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators

abstract:
  Computing-in-Memory architectures based on non-volatile emerging memories
have demonstrated great potential for deep neural network (DNN) acceleration
thanks to their high energy efficiency. However, these emerging devices can
suffer from significant variations during the mapping process i.e., programming
weights to the devices), and if left undealt with, can cause significant
accuracy degradation. The non-ideality of weight mapping can be compensated by
iterative programming with a write-verify scheme, i.e., reading the conductance
and rewriting if necessary. In all existing works, such a practice is applied
to every single weight of a DNN as it is being mapped, which requires extensive
programming time. In this work, we show that it is only necessary to select a
small portion of the weights for write-verify to maintain the DNN accuracy,
thus achieving significant speedup. We further introduce a second derivative
based technique SWIM, which only requires a single pass of forward and
backpropagation, to efficiently select the weights that need write-verify.
Experimental results on various DNN architectures for different datasets show
that SWIM can achieve up to 10x programming speedup compared with conventional
full-blown write-verify while attaining a comparable accuracy.


title:
Augment with Care: Contrastive Learning for the Boolean Satisfiability
  Problem

abstract:
  Supervised learning can improve the design of state-of-the-art solvers for
combinatorial problems, but labelling large numbers of combinatorial instances
is often impractical due to exponential worst-case complexity. Inspired by the
recent success of contrastive pre-training for images, we conduct a scientific
study of the effect of augmentation design on contrastive pre-training for the
Boolean satisfiability problem. While typical graph contrastive pre-training
uses label-agnostic augmentations, our key insight is that many combinatorial
problems have well-studied invariances, which allow for the design of
label-preserving augmentations. We find that label-preserving augmentations are
critical for the success of contrastive pre-training. We show that our
representations are able to achieve comparable test accuracy to
fully-supervised learning while using only 1% of the labels. We also
demonstrate that our representations are more transferable to larger problems
from unseen domains.


title:
Superrotation of Titan's stratosphere driven by the radiative heating of
  the haze layer

abstract:
  Titan's stratosphere has been observed in a superrotation state, where the
atmosphere rotates many times faster than the surface does. Another
characteristics of Titan's atmosphere is the presence of thick haze layer. In
this paper, we performed numerical experiments using a General Circulation
Model (GCM), to explore the effects of the haze layer on the stratospheric
superrotation. We employed a semi-gray radiation model of Titan's atmosphere
following McKay et al. (1999), which takes account of the sunlight absorption
by haze particles. The phase change of methane or the seasonal changes were not
taken into account. Our model with the radiation parameters tuned for Titan
yielded the global eastward wind around the equator with larger velocities at
higher altitudes except at around 70 km after $10^5$ Earth days. Although the
atmosphere is not in an equilibrium state, the zonal wind profiles is
approximately consistent with the observed one. Analysis on our experiments
suggests that the quasi-stationary stratospheric superrotation is maintained by
the balance between the meridional circulation decoupled from the surface, and
the eddies that transport angular momentum equatorward. This is different from,
but similar to, the so-called Gierasch mechanism, in which momentum is supplied
from the surface. This structure may explain the no-wind region at about $80$
km in altitude.


title:
Modelling stars with Gaussian Process Regression: Augmenting Stellar
  Model Grid

abstract:
  Grid-based modelling is widely used for estimating stellar parameters.
However, stellar model grid is sparse because of the computational cost. This
paper demonstrates an application of a machine-learning algorithm using the
Gaussian Process (GP) Regression that turns a sparse model grid onto a
continuous function. We train GP models to map five fundamental inputs (mass,
equivalent evolutionary phase, initial metallicity, initial helium fraction,
and the mixing-length parameter) to observable outputs (effective temperature,
surface gravity, radius, surface metallicity, and stellar age). We test the GP
predictions for the five outputs using off-grid stellar models and find no
obvious systematic offsets, indicating good accuracy in predictions.As a
further validation, we apply these GP models to characterise 1,000 fake stars.
Inferred masses and ages determined with GP models well recover true values
within one standard deviation. An important consequence of using GP-based
interpolation is that stellar ages are more precise than those estimated with
the original sparse grid because of the full sampling of fundamental inputs.


title: Shift-Memory Network for Temporal Scene Segmentation

abstract:
  Semantic segmentation has achieved great accuracy in understanding spatial
layout. For real-time tasks based on dynamic scenes, we extend semantic
segmentation in temporal domain to enhance the spatial accuracy with motion. We
utilize a shift-mode network over streaming input to ensure zero-latency
output. For the data overlap under shifting network, this paper identifies
repeated computation in fixed periods across network layers. To avoid this
redundancy, we derive a Shift-Memory Network (SMN) from encoding-decoding
baseline to reuse the network values without accuracy loss. Trained in
patch-mode, the SMN extracts the network parameters for SMN to perform
inference promptly in compact memory. We segment dynamic scenes from 1D
scanning input and 2D video. The experiments of SMN achieve equivalent accuracy
as shift-mode but in faster inference speeds and much smaller memory. This will
facilitate semantic segmentation in real-time application on edge devices.


title:
Real Time Motion Planning Using Constrained Iterative Linear Quadratic
  Regulator for On-Road Self-Driving

abstract:
  Collision avoidance is one of the most challenging tasks people need to
consider for developing the self-driving technology. In this paper we propose a
new spatiotemporal motion planning algorithm that efficiently solves a
constrained nonlinear optimal control problem using the iterative linear
quadratic regulator (iLQR), which takes into account the uncertain driving
behaviors of the traffic vehicles and minimizes the collision risks between the
self-driving vehicle (referred to as the "ego" vehicle) and the traffic
vehicles such that the ego vehicle is able to maintain sufficiently large
distances to all the surrounding vehicles for achieving the desired collision
avoidance maneuver in traffic. To this end, we introduce the concept of the
"collision polygon" for computing the minimum distances between the ego vehicle
and the traffic vehicles, and provide two different solutions for designing the
constraints of the motion planning problem by properly modeling the behaviors
of the traffic vehicles in order to evaluate the collision risk. Finally, the
iLQR motion planning algorithm is validated in multiple real-time tasks for
collision avoidance using both a simulator and a level-3 autonomous driving
test platform.


title:
Multi-Modal Fusion in Contact-Rich Precise Tasks via Hierarchical Policy
  Learning

abstract:
  Combined visual and force feedback play an essential role in contact-rich
robotic manipulation tasks. Current methods focus on developing the feedback
control around a single modality while underrating the synergy of the sensors.
Fusing different sensor modalities is necessary but remains challenging. A key
challenge is to achieve an effective multi-modal and generalized control scheme
to novel objects with precision. This paper proposes a practical multi-modal
sensor fusion mechanism using hierarchical policy learning. To begin with, we
use a self-supervised encoder that extracts multi-view visual features and a
hybrid motion/force controller that regulates force behaviors. Next, the
multi-modality fusion is simplified by hierarchical integration of the vision,
force, and proprioceptive data in the reinforcement learning (RL) algorithm.
Moreover, with hierarchical policy learning, the control scheme can exploit the
visual feedback limits and explore the contribution of individual modality in
precise tasks. Experiments indicate that robots with the control scheme could
assemble objects with 0.25mm clearance in simulation. The system could be
generalized to widely varied initial configurations and new shapes. Experiments
validate that the simulated system can be robustly transferred to reality
without fine-tuning.


title: Federated Stochastic Gradient Descent Begets Self-Induced Momentum

abstract:
  Federated learning (FL) is an emerging machine learning method that can be
applied in mobile edge systems, in which a server and a host of clients
collaboratively train a statistical model utilizing the data and computation
resources of the clients without directly exposing their privacy-sensitive
data. We show that running stochastic gradient descent (SGD) in such a setting
can be viewed as adding a momentum-like term to the global aggregation process.
Based on this finding, we further analyze the convergence rate of a federated
learning system by accounting for the effects of parameter staleness and
communication resources. These results advance the understanding of the
Federated SGD algorithm, and also forges a link between staleness analysis and
federated computing systems, which can be useful for systems designers.


title:
Moderate deviations for fully coupled multiscale weakly interacting
  particle systems

abstract:
  We consider a collection of fully coupled weakly interacting diffusion
processes moving in a two-scale environment. We study the moderate deviations
principle of the empirical distribution of the particles' positions in the
combined limit as the number of particles grow to infinity and the time-scale
separation parameter goes to zero simultaneously. We make use of weak
convergence methods, which provide a convenient representation for the moderate
deviations rate function in terms of an effective mean field control problem.
We rigorously obtain equivalent representations for the moderate deviations
rate function in an appropriate "negative Sobolev" form, which is reminiscent
of the large deviations rate function for the empirical measure of weakly
interacting diffusions obtained in the 1987 seminal paper by
Dawson-G\"{a}rtner. In the course of the proof we obtain related ergodic
theorems and we rigorously study the regularity of Poisson type of equations
associated to McKean-Vlasov problems, both of which are topics of independent
interest.


title:
Global-in-time existence of weak solutions for
  Vlasov-Manev-Fokker-Planck system

abstract:
  We consider the Vlasov-Manev-Fokker-Planck (VMFP) system in three dimensions,
which differs from the Vlasov-Poisson-Fokker-Planck in that it has the
gravitational potential of the form $-1/r - 1/r^2$ instead of the Newtonian
one. For the VMFP system, we establish the global-in-time existence of weak
solutions. The proof extends to several related kinetic systems.


title: UV counterpart of an X-ray ultra-fast outflow in IRAS 17020+4544

abstract:
  We report on the discovery of a UV absorption counterpart of a low-ionization
X-ray ultra-fast outflow (UFO) in the Narrow-Line Seyfert-1 galaxy IRAS
17020+4544. This UV signature of the UFO is seen as a narrow and blueshifted
Lyman-alpha absorption feature in the far-UV spectrum, taken with the Cosmic
Origins Spectrograph (COS) on the Hubble Space Telescope (HST). The Lyman-alpha
feature is found to be outflowing with a velocity of -23430 km/s (0.078 c). We
carry out high-resolution UV spectroscopy and photoionization modeling to study
the UFO that is seen in the HTS/COS spectrum. The results of our modeling show
that the UV UFO corresponds to a low-ionization, low-velocity component of the
X-ray UFO found previously with XMM-Newton's Reflection Grating Spectrometer
(RGS). The other higher-velocity and higher-ionization components of the X-ray
UFOs are not significantly detected in the HST/COS spectrum, consistent with
predictions of our photoionization calculations. The multiple ionization and
velocity components of the UFOs in IRAS 17020+4544 suggest a scenario where a
powerful primary UFO entrains and shocks the ambient medium, resulting in
formation of weaker secondary UFO components, such as the one found in the UV
band.


title:
The Unboxing Experience: Exploration and Design of Initial Interactions
  Between Children and Social Robots

abstract:
  Social robots are increasingly introduced into children's lives as
educational and social companions, yet little is known about how these products
might best be introduced to their environments. The emergence of the "unboxing"
phenomenon in media suggests that introduction is key to technology adoption
where initial impressions are made. To better understand this phenomenon toward
designing a positive unboxing experience in the context of social robots for
children, we conducted three field studies with families of children aged 8 to
13: (1) an exploratory free-play activity ($n=12$); (2) a co-design session
($n=11$) that informed the development of a prototype box and a curated
unboxing experience; and (3) a user study ($n=9$) that evaluated children's
experiences. Our findings suggest the unboxing experience of social robots can
be improved through the design of a creative aesthetic experience that engages
the child socially to guide initial interactions and foster a positive
child-robot relationship.


title:
AutoScore-Ordinal: An interpretable machine learning framework for
  generating scoring models for ordinal outcomes

abstract:
  Background: Risk prediction models are useful tools in clinical
decision-making which help with risk stratification and resource allocations
and may lead to a better health care for patients. AutoScore is a machine
learning-based automatic clinical score generator for binary outcomes. This
study aims to expand the AutoScore framework to provide a tool for
interpretable risk prediction for ordinal outcomes. Methods: The
AutoScore-Ordinal framework is generated using the same 6 modules of the
original AutoScore algorithm including variable ranking, variable
transformation, score derivation (from proportional odds models), model
selection, score fine-tuning, and model evaluation. To illustrate the
AutoScore-Ordinal performance, the method was conducted on electronic health
records data from the emergency department at Singapore General Hospital over
2008 to 2017. The model was trained on 70% of the data, validated on 10% and
tested on the remaining 20%. Results: This study included 445,989 inpatient
cases, where the distribution of the ordinal outcome was 80.7% alive without
30-day readmission, 12.5% alive with 30-day readmission, and 6.8% died
inpatient or by day 30 post discharge. Two point-based risk prediction models
were developed using two sets of 8 predictor variables identified by the
flexible variable selection procedure. The two models indicated reasonably good
performance measured by mean area under the receiver operating characteristic
curve (0.785 and 0.793) and generalized c-index (0.737 and 0.760), which were
comparable to alternative models. Conclusion: AutoScore-Ordinal provides an
automated and easy-to-use framework for development and validation of risk
prediction models for ordinal outcomes, which can systematically identify
potential predictors from high-dimensional data.


title: Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

abstract:
  Multivariate time series forecasting has long received significant attention
in real-world applications, such as energy consumption and traffic prediction.
While recent methods demonstrate good forecasting abilities, they suffer from
three fundamental limitations. (i) Discrete neural architectures: Interlacing
individually parameterized spatial and temporal blocks to encode rich
underlying patterns leads to discontinuous latent state trajectories and higher
forecasting numerical errors. (ii) High complexity: Discrete approaches
complicate models with dedicated designs and redundant parameters, leading to
higher computational and memory overheads. (iii) Reliance on graph priors:
Relying on predefined static graph structures limits their effectiveness and
practicability in real-world applications. In this paper, we address all the
above limitations by proposing a continuous model to forecast Multivariate Time
series with dynamic Graph neural Ordinary Differential Equations (MTGODE).
Specifically, we first abstract multivariate time series into dynamic graphs
with time-evolving node features and unknown graph structures. Then, we design
and solve a neural ODE to complement missing graph topologies and unify both
spatial and temporal message passing, allowing deeper graph propagation and
fine-grained temporal information aggregation to characterize stable and
precise latent spatial-temporal dynamics. Our experiments demonstrate the
superiorities of MTGODE from various perspectives on five time series benchmark
datasets.


title:
Million.js: A Fast, Compiler-Augmented Virtual DOM for Performant
  JavaScript UI Libraries

abstract:
  The need for developing and delivering interactive web applications has grown
rapidly. Thus, JavaScript User Interface (UI) libraries were introduced, at the
detriment of performance and bundle size. To solve this problem, Million.js was
created to allow for the creation of compiler-augmented JavaScript UI libraries
that are extensible, performant, and lightweight. This was accomplished by
designing a computationally efficient diffing algorithm that relies on a
compiler, and then measuring the performance with a series of relevant and
exhaustive benchmarks. Additionally, built-in mechanisms are implemented to
allow for imperative optimizations, allowing the compiler to directly skip
runtime diffing. When compared to other methods of virtual DOM rendering, these
findings showed that Million.js had superior performance, with 133% to 300%
more operations per second than other Virtual DOM libraries.


title: Non-linear Transport by Bethe Bound States

abstract:
  We consider non-linear ballistic spin transport in the XXZ spin chain and
derive an analytical result for the non-linear Drude weight $D^{(3)}$ at
infinite temperatures. In contrast to the linear Drude weight $D^{(1)}$, we
find that the result not only depends on anisotropy but also on the string
length of the quasiparticles transporting the spin current. Our result provides
further insights into transport by quasiparticles and raises questions about
Luttinger liquid universality.


title: On the local rank of fusion systems

abstract:
  In this paper we define the notion of local rank for fusion systems so as to
reformulate the Alperin's weight conjecture in the framework of block fusion
systems following the work by Kn\"orr and Robinson.


title: The Scale Invariant Vacuum Paradigm: main results and current progress

abstract:
  We present a summary of the main results within the Scale Invariant Vacuum
(SIV) paradigm as related to the Weyl Integrable Geometry. After a brief review
of the mathematical framework, we will highlight the main results related to
inflation within the SIV [9], the growth of the density fluctuations [8], and
the application of the SIV to scale-invariant dynamics of Galaxies, MOND, Dark
Matter, and the Dwarf Spheroidals [7]. The connection of the weak-field SIV
results to the un-proper time parametrization within the re-parametrization
paradigm is also discussed [14].


title: Entropic Associative Memory for Manuscript Symbols

abstract:
  Manuscript symbols can be stored, recognized and retrieved from an entropic
digital memory that is associative and distributed but yet declarative; memory
retrieval is a constructive operation, memory cues to objects not contained in
the memory are rejected directly without search, and memory operations can be
performed through parallel computations. Manuscript symbols, both letters and
numerals, are represented in Associative Memory Registers that have an
associated entropy. The memory recognition operation obeys an entropy trade-off
between precision and recall, and the entropy level impacts on the quality of
the objects recovered through the memory retrieval operation. The present
proposal is contrasted in several dimensions with neural networks models of
associative memory. We discuss the operational characteristics of the entropic
associative memory for retrieving objects with both complete and incomplete
information, such as severe occlusions. The experiments reported in this paper
add evidence on the potential of this framework for developing practical
applications and computational models of natural memory.


title: FPIC: A Novel Semantic Dataset for Optical PCB Assurance

abstract:
  The continued outsourcing of printed circuit board (PCB) fabrication to
overseas venues necessitates increased hardware assurance capabilities. Toward
this end, several automated optical inspection (AOI) techniques have been
proposed in the past exploring various aspects of PCB images acquired using
digital cameras. In this work, we review state-of-the-art AOI techniques and
observed the strong, rapid trend toward machine learning (ML) solutions. These
require significant amounts of labeled ground truth data, which is lacking in
the publicly available PCB data space. We propose the FICS PBC Image Collection
(FPIC) dataset to address this bottleneck in available large-volume, diverse,
semantic annotations. Additionally, this work covers the potential increase in
hardware security capabilities and observed methodological distinctions
highlighted during data collection.


title:
Continuity Postulates and Solvability Axioms in Economic Theory and in
  Mathematical Psychology: A Consolidation of the Theory of Individual Choice

abstract:
  This paper presents four theorems that connect continuity postulates in
mathematical economics to solvability axioms in mathematical psychology, and
ranks them under alternative supplementary assumptions. Theorem 1 connects
notions of continuity (full, separate, Wold, weak Wold, Archimedean, mixture)
with those of solvability (restricted, unrestricted) under the completeness and
transitivity of a binary relation. Theorem 2 uses the primitive notion of a
separately-continuous function to answer the question when an analogous
property on a relation is fully continuous. Theorem 3 provides a portmanteau
theorem on the equivalence between restricted solvability and various notions
of continuity under weak monotonicity. Finally, Theorem 4 presents a variant of
Theorem 3 that follows Theorem 1 in dispensing with the dimensionality
requirement and in providing partial equivalences between solvability and
continuity notions. These theorems are motivated for their potential use in
representation theorems.


title: Massive Galaxy Mergers Have Distinctive Global HI Profiles

abstract:
  The global 21 cm HI emission-line profile of a galaxy encodes valuable
information on the spatial distribution and kinematics of the neutral atomic
gas. Galaxy interactions significantly influence the HI disk and imprint
observable features on the integrated HI line profile. In this work, we study
the neutral atomic gas properties of galaxy mergers selected from the Great
Observatories All-sky LIRG Survey. The HI spectra come from new observations
with the Five-hundred-meter Aperture Spherical Telescope and from a collection
of archival data. We quantify the HI profile of the mergers with a newly
developed method that uses the curve-of-growth of the line profile. Using a
control sample of non-merger galaxies carefully selected to match the stellar
mass of the merger sample, we show that mergers have a larger proportion of
single-peaked HI profiles, as well as a greater tendency for the HI central
velocity to deviate from the systemic optical velocity of the galaxy. By
contrast, the HI profiles of mergers are not significantly more asymmetric than
those of non-mergers.


title: Retrieval-Augmented Reinforcement Learning

abstract:
  Most deep reinforcement learning (RL) algorithms distill experience into
parametric behavior policies or value functions via gradient updates. While
effective, this approach has several disadvantages: (1) it is computationally
expensive, (2) it can take many updates to integrate experiences into the
parametric model, (3) experiences that are not fully integrated do not
appropriately influence the agent's behavior, and (4) behavior is limited by
the capacity of the model. In this paper we explore an alternative paradigm in
which we train a network to map a dataset of past experiences to optimal
behavior. Specifically, we augment an RL agent with a retrieval process
(parameterized as a neural network) that has direct access to a dataset of
experiences. This dataset can come from the agent's past experiences, expert
demonstrations, or any other relevant source. The retrieval process is trained
to retrieve information from the dataset that may be useful in the current
context, to help the agent achieve its goal faster and more efficiently. We
integrate our method into two different RL agents: an offline DQN agent and an
online R2D2 agent. In offline multi-task problems, we show that the
retrieval-augmented DQN agent avoids task interference and learns faster than
the baseline DQN agent. On Atari, we show that retrieval-augmented R2D2 learns
significantly faster than the baseline R2D2 agent and achieves higher scores.
We run extensive ablations to measure the contributions of the components of
our proposed method.


title:
Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent
  Dynamics from Volumetric Video

abstract:
  We present Neural Marionette, an unsupervised approach that discovers the
skeletal structure from a dynamic sequence and learns to generate diverse
motions that are consistent with the observed motion dynamics. Given a video
stream of point cloud observation of an articulated body under arbitrary
motion, our approach discovers the unknown low-dimensional skeletal
relationship that can effectively represent the movement. Then the discovered
structure is utilized to encode the motion priors of dynamic sequences in a
latent structure, which can be decoded to the relative joint rotations to
represent the full skeletal motion. Our approach works without any prior
knowledge of the underlying motion or skeletal structure, and we demonstrate
that the discovered structure is even comparable to the hand-labeled ground
truth skeleton in representing a 4D sequence of motion. The skeletal structure
embeds the general semantics of possible motion space that can generate motions
for diverse scenarios. We verify that the learned motion prior is generalizable
to the multi-modal sequence generation, interpolation of two poses, and motion
retargeting to a different skeletal structure.


title: High-Dimensional High-Frequency Regression

abstract:
  In this paper, we develop a novel high-dimensional regression inference
procedure for high-frequency financial data. Unlike usual high-dimensional
regression for low-frequency data, we need to additionally handle the
time-varying coefficient problem. To accomplish this, we employ the Dantzig
selection scheme and apply a debiasing scheme, which provides well-performing
unbiased instantaneous coefficient estimators. With these schemes, we estimate
the integrated coefficient, and to further account for the sparsity of the beta
process, we apply thresholding schemes. We call this Thresholding dEbiased
Dantzig Integrated Beta (TEDI Beta). We establish asymptotic properties of the
proposed TEDI Beta estimator. In the empirical analysis, we apply the TEDI Beta
procedure to analyzing high-dimensional factor models using high-frequency
data.


title:
Time-Correlated Sparsification for Efficient Over-the-Air Model
  Aggregation in Wireless Federated Learning

abstract:
  Federated edge learning (FEEL) is a promising distributed machine learning
(ML) framework to drive edge intelligence applications. However, due to the
dynamic wireless environments and the resource limitations of edge devices,
communication becomes a major bottleneck. In this work, we propose
time-correlated sparsification with hybrid aggregation (TCS-H) for
communication-efficient FEEL, which exploits jointly the power of model
compression and over-the-air computation. By exploiting the temporal
correlations among model parameters, we construct a global sparsification mask,
which is identical across devices, and thus enables efficient model aggregation
over-the-air. Each device further constructs a local sparse vector to explore
its own important parameters, which are aggregated via digital communication
with orthogonal multiple access. We further design device scheduling and power
allocation algorithms for TCS-H. Experiment results show that, under limited
communication resources, TCS-H can achieve significantly higher accuracy
compared to the conventional top-K sparsification with orthogonal model
aggregation, with both i.i.d. and non-i.i.d. data distributions.


title: Some identities involving degenerate r-Stirling numbers

abstract:
  Recently, authors studied the unsigned degenerate r-Stirling number of the
first kind and the degenerate r-Stirling number of the second kind,
respectively of which are the degenerate versions of the unsigned r-Stirling
numbers of the first kind and those of the r-Stirling numbers of the second
kind. The aim of this paper is to derive some identities involving such special
numbers from the inverse relations for them.


title: Euler's scheme of Mckean-Vlasov SDEs with non-Lipschitz coefficients

abstract:
  In this paper, we show the strong well-posedness of Mckean-Vlasov SDEs with
non-Lipschitz coefficients. Moreover, propagation of chaos and the convergence
rate for Euler's scheme of Mckean-Vlasov SDEs are also obtained.


title:
Chord-Conditioned Melody Choralization with Controllable Harmonicity and
  Polyphonicity

abstract:
  Melody choralization, i.e. generating a four-part chorale based on a
user-given melody, has long been closely associated with J.S. Bach chorales.
Previous neural network-based systems rarely focus on chorale generation
conditioned on a chord progression, and none of them realised controllable
melody choralization. To enable neural networks to learn the general principles
of counterpoint from Bach's chorales, we first design a music representation
that encoded chord symbols for chord conditioning. We then propose DeepChoir, a
melody choralization system, which can generate a four-part chorale for a given
melody conditioned on a chord progression. Furthermore, with the improved
density sampling, a user can control the extent of harmonicity and
polyphonicity for the chorale generated by DeepChoir. Experimental results
reveal the effectiveness of our data representation and the controllability of
DeepChoir over harmonicity and polyphonicity. The code and generated samples
(chorales, folk songs and a symphony) of DeepChoir, and the dataset we use now
are available at https://github.com/sander-wood/deepchoir.


title:
Higher-order Topological Phases of Magnons in van der Waals Honeycomb
  Ferromagnets

abstract:
  We theoretically propose a second-order topological magnon insulator by
stacking the van der Waals honeycomb ferromagnets with antiferromagnetic
interlayer coupling. The system exhibits Z$_{2}$ topological phase, protected
by pseudo-time-reversal symmetry (PTRS). An easy-plane anisotropy term breaks
PTRS and destroys the topological phase. Nevertheless, it respects a magnetic
two-fold rotational symmetry which protects a second-order topological phase
with corner modes in bilayer and hinge modes along stacking direction.
Moreover, an introduced staggered interlayer coupling establishes a
Z$_{2}$$\times$Z topology, giving rise to gapped topological surface modes
carrying non-zero Chern numbers. Consequently, chiral hinge modes propagate
along the horizontal hinges in a cuboid geometry and are robust against
disorders. Our work bridges the higher-order topology and magnons in van der
Waals platforms, and could be used for constructing topological magnonic
devices.


title: The log canonical threshold and rational singularities

abstract:
  We show that if $f$ is a nonzero, noninvertible function on a smooth complex
variety $X$ and $J_f$ is the Jacobian ideal of $f$, then ${\rm lct}(f,J_f^2)>1$
if and only if the hypersurface defined by $f$ has rational singularities.
Moreover, if it does not have rational singularities, then ${\rm
lct}(f,J_f^2)={\rm lct}(f)$. We give two proofs, one relying on arc spaces and
one that goes through the inequality $\widetilde{\alpha}(f)\geq{\rm
lct}(f,J_f^2)$, where $\widetilde{\alpha}(f)$ is the minimal exponent of $f$.
In the case of a polynomial over $\overline{\mathbf{Q}}$, we also prove an
analogue of this latter inequality, with $\widetilde{\alpha}(f)$ replaced by
the motivic oscillation index ${\rm moi}(f)$. We also show a part of Igusa's
strong monodromy conjecture, for poles larger than $-{\rm lct}(f,J_f^2)$. We
end with a discussion of lct-maximal ideals: these are ideals $I$ with the
property that ${\rm lct}(I)<{\rm lct}(J)$ for every $J$ with $I\subsetneq J$.


title: Synthetic Control As Online Linear Regression

abstract:
  This paper notes a simple connection between synthetic control and online
learning. Specifically, we recognize synthetic control as an instance of
Follow-The-Leader (FTL). Standard results in online convex optimization then
imply that, even when outcomes are chosen by an adversary, synthetic control
predictions of counterfactual outcomes for the treated unit perform almost as
well as an oracle weighted average of control units' outcomes. Synthetic
control on differenced data performs almost as well as oracle weighted
difference-in-differences. We argue that this observation further supports the
use of synthetic control estimators in comparative case studies.


title: Weak-odd chromatic index of special digraph classes

abstract:
  Give a digraph $D=(V(D),A(D))$, let $\partial^+_D(v)=\{vw|w\in N^+_D(v)\}$
and $\partial^-_D(v)=\{uv|u\in N^-_D(v)\}$ be semi-cuts of $v$. A mapping
$\varphi:A(D)\rightarrow [k]$ is called a weak-odd $k$-edge coloring of $D$ if
it satisfies the condition: for each $v\in V(D)$, there is at least one color
with an odd number of occurrences on each non-empty semi-cut of $v$. We call
the minimum integer $k$ the weak-odd chromatic index of $D$. When limit to 2
colors, use $def(D)$ to denote the defect of $D$, the minimum number of
vertices in $D$ at which the above condition is not satisfied. In this paper,
we give a descriptive characterization about the weak-odd chromatic index and
the defect of semicomplete digraphs and extended tournaments, which generalize
results of tournaments to broader classes. And we initiated the study of
weak-odd edge covering on digraphs.


title:
Hidden local symmetry breaking in a kagome-lattice magnetic Weyl
  semimetal

abstract:
  Magnetic semimetals have attracted intense interest recently due to the
exotic quantum phenomena for potential application and exploring new
fundamental physics. Currently, theoretical studies and the interpretation of
the experimental results on them are based on utilizing the average crystal
structure. Unfortunately, the possibly distinct local symmetry has not been
carefully considered. Here, we report the discovery of local symmetry breaking
and its significant effect on the local ferromagnetism and band topology in a
kagome-lattice Weyl semimetal Co$_{3}$Sn$_{2}$S$_{2}$, by a combined use of
half polarized neutron diffraction, total scattering, and density function
theory. A local symmetry breaking from rhombohedral $R\mbox{-}3m$ to monoclinic
$Cm$ involving a local distortion of the cobalt kagome lattice with an in-plane
projection along the (100) direction surprisingly co-emerges with the onset of
ferromagnetic order, revealing a strong coupling between lattice instability
and the ferromagnetism. The local symmetry breaking may provide new insight to
the previously irreconcilable phase separation and spin glass like state in
Co$_{3}$Sn$_{2}$S$_{2}$. Furthermore, such local symmetry breaking induces the
existence of a local ferromagnetic instability with a tendency to drive a local
moment reorientation by around 19$^{\rm o}$ to the monoclinic $a_{m}$ axis, and
plays a detrimental role in the formation of the Weyl points by breaking mirror
symmetries. Our findings highlight the important role that the overlooked local
symmetry breaking plays in unveiling the novel interplay between structural
complexity, magnetism and topological properties in Co$_{3}$Sn$_{2}$S$_{2}$.


title: CHEX: Multiversion Replay with Ordered Checkpoints

abstract:
  In scientific computing and data science disciplines, it is often necessary
to share application workflows and repeat results. Current tools containerize
application workflows, and share the resulting container for repeating results.
These tools, due to containerization, do improve sharing of results. However,
they do not improve the efficiency of replay. In this paper, we present the
multiversion replay problem which arises when multiple versions of an
application are containerized, and each version must be replayed to repeat
results. To avoid executing each version separately, we develop CHEX, which
checkpoints program state and determines when it is permissible to reuse
program state across versions. It does so using system call-based execution
lineage. Our capability to identify common computations across versions enables
us to consider optimizing replay using an in-memory cache, based on a
checkpoint-restore-switch system. We show the multiversion replay problem is
NP-hard, and propose efficient heuristics for it. CHEX reduces overall replay
time by sharing common computations but avoids storing a large number of
checkpoints. We demonstrate that CHEX maintains lightweight package sharing,
and improves the total time of multiversion replay by 50% on average.


title: Emotion Recognition among Couples: A Survey

abstract:
  Couples' relationships affect the physical health and emotional well-being of
partners. Automatically recognizing each partner's emotions could give a better
understanding of their individual emotional well-being, enable interventions
and provide clinical benefits. In the paper, we summarize and synthesize works
that have focused on developing and evaluating systems to automatically
recognize the emotions of each partner based on couples' interaction or
conversation contexts. We identified 28 articles from IEEE, ACM, Web of
Science, and Google Scholar that were published between 2010 and 2021. We
detail the datasets, features, algorithms, evaluation, and results of each work
as well as present main themes. We also discuss current challenges, research
gaps and propose future research directions. In summary, most works have used
audio data collected from the lab with annotations done by external experts and
used supervised machine learning approaches for binary classification of
positive and negative affect. Performance results leave room for improvement
with significant research gaps such as no recognition using data from daily
life. This survey will enable new researchers to get an overview of this field
and eventually enable the development of emotion recognition systems to inform
interventions to improve the emotional well-being of couples.


title: Fixed point annihilation for a spin in a fluctuating field

abstract:
  A quantum spin impurity coupled to a critical free field (the Bose-Kondo
model) can be represented as a 0+1D field theory with long-range-in-time
interactions that decay as $|t-t'|^{-(2-\delta)}$. This theory is a simpler
analogue of nonlinear sigma models with topological Wess-Zumino-Witten terms in
higher dimensions. In this note we show that the RG flows for the impurity
problem exhibit an annihilation between two nontrivial RG fixed points at a
critical value $\delta_c$ of the interaction exponent. The calculation is
controlled at large spin $S$. This clarifies the phase diagram of the
Bose-Kondo model and shows that it serves as a toy model for phenomena
involving fixed-point annihilation and "quasiuniversality" in higher
dimensions.


title: AKB-48: A Real-World Articulated Object Knowledge Base

abstract:
  Human life is populated with articulated objects. A comprehensive
understanding of articulated objects, namely appearance, structure, physics
property, and semantics, will benefit many research communities. As current
articulated object understanding solutions are usually based on synthetic
object dataset with CAD models without physics properties, which prevent
satisfied generalization from simulation to real-world applications in visual
and robotics tasks. To bridge the gap, we present AKB-48: a large-scale
Articulated object Knowledge Base which consists of 2,037 real-world 3D
articulated object models of 48 categories. Each object is described by a
knowledge graph ArtiKG. To build the AKB-48, we present a fast articulation
knowledge modeling (FArM) pipeline, which can fulfill the ArtiKG for an
articulated object within 10-15 minutes, and largely reduce the cost for object
modeling in the real world. Using our dataset, we propose AKBNet, a novel
integral pipeline for Category-level Visual Articulation Manipulation (C-VAM)
task, in which we benchmark three sub-tasks, namely pose estimation, object
reconstruction and manipulation. Dataset, codes, and models will be publicly
available at https://liuliu66.github.io/articulationobjects/.


title: ADD 2022: the First Audio Deep Synthesis Detection Challenge

abstract:
  Audio deepfake detection is an emerging topic, which was included in the
ASVspoof 2021. However, the recent shared tasks have not covered many real-life
and challenging scenarios. The first Audio Deep synthesis Detection challenge
(ADD) was motivated to fill in the gap. The ADD 2022 includes three tracks:
low-quality fake audio detection (LF), partially fake audio detection (PF) and
audio fake game (FG). The LF track focuses on dealing with bona fide and fully
fake utterances with various real-world noises etc. The PF track aims to
distinguish the partially fake audio from the real. The FG track is a rivalry
game, which includes two tasks: an audio generation task and an audio fake
detection task. In this paper, we describe the datasets, evaluation metrics,
and protocols. We also report major findings that reflect the recent advances
in audio deepfake detection tasks.


title: A Survey of Explainable Reinforcement Learning

abstract:
  Explainable reinforcement learning (XRL) is an emerging subfield of
explainable machine learning that has attracted considerable attention in
recent years. The goal of XRL is to elucidate the decision-making process of
learning agents in sequential decision-making settings. In this survey, we
propose a novel taxonomy for organizing the XRL literature that prioritizes the
RL setting. We overview techniques according to this taxonomy. We point out
gaps in the literature, which we use to motivate and outline a roadmap for
future work.


title:
Redshift space distortions in Lagrangian space and the linear large
  scale velocity field of dark matter

abstract:
  Untangling the connection between redshift space coordinates, a velocity
measurement, and three dimensional real space coordinates, is a cosmological
problem that is often modeled through a linear understanding of the
velocity-position coupling. This linear information is better preserved in the
Lagrangian space picture of the matter density field. Through Lagrangian space
measurements, we can extract more information and make more accurate estimates
of the linear growth rate of the universe. In this paper, we address the linear
modelling of matter particle velocities through transfer functions, and in
doing so examine to what degree the decrease in correlation with initial
conditions may be contaminated by velocity-based nonlinearities. With a
thorough analysis of the monopole-quadrupole ratio, we find the best-fit value
for the Eulerian velocity dispersion, $\sigma_p = 378.3$ km/s. The covariance
of the cosmological linear growth rate $f$, is estimated in the Eulerian and
Lagrangian cases. Comparing Lagrangian and Eulerian, we find that the error in
$f$ improves by a factor of 3, without the need for nonlinear velocity
dispersion modelling.


title: PENCIL: Deep Learning with Noisy Labels

abstract:
  Deep learning has achieved excellent performance in various computer vision
tasks, but requires a lot of training examples with clean labels. It is easy to
collect a dataset with noisy labels, but such noise makes networks overfit
seriously and accuracies drop dramatically. To address this problem, we propose
an end-to-end framework called PENCIL, which can update both network parameters
and label estimations as label distributions. PENCIL is independent of the
backbone network structure and does not need an auxiliary clean dataset or
prior information about noise, thus it is more general and robust than existing
methods and is easy to apply. PENCIL can even be used repeatedly to obtain
better performance. PENCIL outperforms previous state-of-the-art methods by
large margins on both synthetic and real-world datasets with different noise
types and noise rates. And PENCIL is also effective in multi-label
classification tasks through adding a simple attention structure on backbone
networks. Experiments show that PENCIL is robust on clean datasets, too.


title:
Visual attention analysis of pathologists examining whole slide images
  of Prostate cancer

abstract:
  We study the attention of pathologists as they examine whole-slide images
(WSIs) of prostate cancer tissue using a digital microscope. To the best of our
knowledge, our study is the first to report in detail how pathologists navigate
WSIs of prostate cancer as they accumulate information for their diagnoses. We
collected slide navigation data (i.e., viewport location, magnification level,
and time) from 13 pathologists in 2 groups (5 genitourinary (GU) specialists
and 8 general pathologists) and generated visual attention heatmaps and
scanpaths. Each pathologist examined five WSIs from the TCGA PRAD dataset,
which were selected by a GU pathology specialist. We examined and analyzed the
distributions of visual attention for each group of pathologists after each WSI
was examined. To quantify the relationship between a pathologist's attention
and evidence for cancer in the WSI, we obtained tumor annotations from a
genitourinary specialist. We used these annotations to compute the overlap
between the distribution of visual attention and annotated tumor region to
identify strong correlations. Motivated by this analysis, we trained a deep
learning model to predict visual attention on unseen WSIs. We find that the
attention heatmaps predicted by our model correlate quite well with the ground
truth attention heatmap and tumor annotations on a test set of 17 WSIs by using
various spatial and temporal evaluation metrics.


title: Scattering Amplitudes: Celestial and Carrollian

abstract:
  Recent attempts at the construction of holography for asymptotically flat
spacetimes have taken two different routes. Celestial holography, involving a
two dimensional (2d) CFT dual to 4d Minkowski spacetime, has generated novel
results in asymptotic symmetry and scattering amplitudes. A different
formulation, using Carrollian CFTs, has been principally used to provide some
evidence for flat holography in lower dimensions. Understanding of flatspace
scattering has been lacking in the Carroll framework. In this work, using ideas
from Celestial holography, we show that 3d Carrollian CFTs living on the null
boundary of 4d flatspace can potentially compute bulk scattering amplitudes. 3d
Carrollian conformal correlators have two different branches, one depending on
the null time direction and one independent of it. We propose that it is the
time-dependent branch that is related to bulk scattering. We construct an
explicit field theoretic example of a free massless Carrollian scalar that
realises some desired properties.


title:
Galaxy Spectra neural Networks (GaSNets). I. Searching for strong lens
  candidates in eBOSS spectra using Deep Learning

abstract:
  With the advent of new spectroscopic surveys from ground and space, observing
up to hundreds of millions of galaxies, spectra classification will become
overwhelming for standard analysis techniques. To prepare for this challenge,
we introduce a family of deep learning tools to classify features in
one-dimensional spectra. As the first application of these Galaxy Spectra
neural Networks (GaSNets), we focus on tools specialized at identifying
emission lines from strongly lensed star-forming galaxies in the eBOSS spectra.
We first discuss the training and testing of these networks and define a
threshold probability, PL, of 95% for the high quality event detection. Then,
using a previous set of spectroscopically selected strong lenses from eBOSS,
confirmed with HST, we estimate a completeness of ~80% as the fraction of
lenses recovered above the adopted PL. We finally apply the GaSNets to ~1.3M
spectra to collect a first list of ~430 new high quality candidates identified
with deep learning applied to spectroscopy and visually graded as highly
probable real events. A preliminary check against ground-based observations
tentatively shows that this sample has a confirmation rate of 38%, in line with
previous samples selected with standard (no deep learning) classification tools
and follow-up by Hubble Space Telescope. This first test shows that machine
learning can be efficiently extended to feature recognition in the wavelength
space, which will be crucial for future surveys like 4MOST, DESI, Euclid, and
the Chinese Space Station Telescope (CSST).


title:
Electrical conductivity of random metallic nanowire networks: An
  analytical consideration along with computer simulation

abstract:
  We have proposed an analytical model for the electrical conductivity in
random, metallic, nanowire networks. We have mimicked such random nanowire
networks as random resistor networks (RRN) produced by the homogeneous,
isotropic, and random deposition of conductive zero-width sticks onto an
insulating substrate. We studied the electrical conductivity of these RRNs
using a mean-field approximation. An analytical dependency of the electrical
conductivity on the main physical parameters (the number density and electrical
resistances of these wires and of the junctions between them) has been derived.
Computer simulations have been performed to validate our theoretical
predictions. We computed the electrical conductivity of the RRNs against the
number density of the conductive fillers for the junction-resistance-dominated
case and for the case where the wire resistance and the junction resistance
were equal. The results of the computations were compared with this mean-field
approximation. Our computations demonstrated that our analytical expression
correctly predicts the electrical conductivity across a wide range of number
densities.


title:
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion
  Penalized Logistic Threshold Regression

abstract:
  In traditional logistic regression models, the link function is often assumed
to be linear and continuous in predictors. Here, we consider a threshold model
that all continuous features are discretized into ordinal levels, which further
determine the binary responses. Both the threshold points and regression
coefficients are unknown and to be estimated. For high dimensional data, we
propose a fusion penalized logistic threshold regression (FILTER) model, where
a fused lasso penalty is employed to control the total variation and shrink the
coefficients to zero as a method of variable selection. Under mild conditions
on the estimate of unknown threshold points, we establish the non-asymptotic
error bound for coefficient estimation and the model selection consistency.
With a careful characterization of the error propagation, we have also shown
that the tree-based method, such as CART, fulfill the threshold estimation
conditions. We find the FILTER model is well suited in the problem of early
detection and prediction for chronic disease like diabetes, using physical
examination data. The finite sample behavior of our proposed method are also
explored and compared with extensive Monte Carlo studies, which supports our
theoretical discoveries.


title: 2-2-holes simplified

abstract:
  Quadratic gravity illustrates how a replacement for black holes can emerge
from a UV completion of gravity. 2-2-holes are extremely compact horizonless
objects with an entropy $S_{22}$ due to trapped normal matter, and in this way
they are conceptually easy to understand. But the field equations are
cumbersome and the numerical analysis has so far been restricted to relatively
small size solutions. Here we show how the properties of arbitrarily large
2-2-holes can be found, including the time delay for gravitational wave echoes
and the result $T_\infty S_{22}=M/2$. The starting point is to formulate the
metric in terms of the tortoise coordinate, and to have one of the two metric
functions be a conformal factor. A large conformally-related volume becomes
associated with the interior of a 2-2-hole. We also discuss the weak gravity
conjecture.


title: Two continuous (4, 5) pairs of explicit 9-stage Runge-Kutta methods

abstract:
  An 11-dimensional family of embedded (4, 5) pairs of explicit 9-stage
Runge-Kutta methods with an interpolant of order 5 is derived. Two optimized
for efficiency pairs are presented.


title:
A Survey on Deep Reinforcement Learning-based Approaches for Adaptation
  and Generalization

abstract:
  Deep Reinforcement Learning (DRL) aims to create intelligent agents that can
learn to solve complex problems efficiently in a real-world environment.
Typically, two learning goals: adaptation and generalization are used for
baselining DRL algorithm's performance on different tasks and domains. This
paper presents a survey on the recent developments in DRL-based approaches for
adaptation and generalization. We begin by formulating these goals in the
context of task and domain. Then we review the recent works under those
approaches and discuss future research directions through which DRL algorithms'
adaptability and generalizability can be enhanced and potentially make them
applicable to a broad range of real-world problems.


title: Extended MSO Model Checking via Small Vertex Integrity

abstract:
  We study the model checking problem of an extended $\mathsf{MSO}$ with local
and global cardinality constraints, called
$\mathsf{MSO}^{\mathsf{GL}}_{\mathsf{Lin}}$, introduced recently by Knop,
Kouteck\'{y}, Masa\v{r}\'{i}k, and Toufar [Log. Methods Comput. Sci., 15(4),
2019]. We show that the problem is fixed-parameter tractable parameterized by
vertex integrity, where vertex integrity is a graph parameter standing between
vertex cover number and treedepth. Our result thus fill a gap between the
fixed-parameter tractability parameterized by vertex cover number and the
W[1]-hardness parameterized by treedepth.


title:
MeNTT: A Compact and Efficient Processing-in-Memory Number Theoretic
  Transform (NTT) Accelerator

abstract:
  Lattice-based cryptography (LBC) exploiting Learning with Errors (LWE)
problems is a promising candidate for post-quantum cryptography. Number
theoretic transform (NTT) is the latency- and energy- dominant process in the
computation of LWE problems. This paper presents a compact and efficient
in-MEmory NTT accelerator, named MeNTT, which explores optimized computation in
and near a 6T SRAM array. Specifically-designed peripherals enable fast and
efficient modular operations. Moreover, a novel mapping strategy reduces the
data flow between NTT stages into a unique pattern, which greatly simplifies
the routing among processing units (i.e., SRAM column in this work), reducing
energy and area overheads. The accelerator achieves significant latency and
energy reductions over prior arts.


title: RePair Grammars are the Smallest Grammars for Fibonacci Words

abstract:
  Grammar-based compression is a loss-less data compression scheme that
represents a given string $w$ by a context-free grammar that generates only
$w$. While computing the smallest grammar which generates a given string $w$ is
NP-hard in general, a number of polynomial-time grammar-based compressors which
work well in practice have been proposed. RePair, proposed by Larsson and
Moffat in 1999, is a grammar-based compressor which recursively replaces all
possible occurrences of a most frequently occurring bigrams in the string.
Since there can be multiple choices of the most frequent bigrams to replace,
different implementations of RePair can result in different grammars. In this
paper, we show that the smallest grammars generating the Fibonacci words $F_k$
can be completely characterized by RePair, where $F_k$ denotes the $k$-th
Fibonacci word. Namely, all grammars for $F_k$ generated by any implementation
of RePair are the smallest grammars for $F_k$, and no other grammars can be the
smallest for $F_k$. To the best of our knowledge, Fibonacci words are the first
non-trivial infinite family of strings for which RePair is optimal.


title: FM Band Channel Measurements and Modeling

abstract:
  As FM coverage is so ubiquitous around the world, several applications can be
considered to better exploit this useful band. Thus, it is of significant
interest to investigate and characterize channel properties of the FM Band for
the potential two-way digital wireless systems. In this paper, we present the
results of field measurements at 86 MHz conducted at Gebze, Kocaeli, Turkey.
Through the measurements, some of the FM channel characteristics are
identified. Measurements are performed for urban, hilly terrain, and rural
areas. Our results show that the FM channel expectedly has a large coverage
area but at the same time, it possesses large channel excess delays. While most
of COST-207 channel power delay profile models are also applicable for the FM
Band, for urban environments and hilly terrain environments, channel clusters
and excess delays are higher than those of COST-207 models. As 5G systems aim
to utilize lower frequency bands for supplementary links, FM Band can be
considered as one of the potential bands and the channel models proposed in
this study can then be exploited for performance analysis.


title:
V2X-Sim: A Virtual Collaborative Perception Dataset for Autonomous
  Driving

abstract:
  Vehicle-to-everything (V2X), which denotes the collaboration between a
vehicle and any entity in its surrounding, can fundamentally improve the
perception in self-driving systems. As the individual perception rapidly
advances, collaborative perception has made little progress due to the shortage
of public V2X datasets. In this work, we present the V2X-Sim dataset, the first
public large-scale collaborative perception dataset in autonomous driving.
V2X-Sim provides: 1) well-synchronized recordings from roadside infrastructure
and multiple vehicles at the intersection to enable collaborative perception,
2) multi-modality sensor streams to facilitate multi-modality perception, 3)
diverse well-annotated ground truth to support various downstream tasks
including detection, tracking, and segmentation. We seek to inspire research on
multi-agent multi-modality multi-task perception, and our virtual dataset is
promising to promote the development of collaborative perception before
realistic datasets become widely available.


title:
Design-Bench: Benchmarks for Data-Driven Offline Model-Based
  Optimization

abstract:
  Black-box model-based optimization (MBO) problems, where the goal is to find
a design input that maximizes an unknown objective function, are ubiquitous in
a wide range of domains, such as the design of proteins, DNA sequences,
aircraft, and robots. Solving model-based optimization problems typically
requires actively querying the unknown objective function on design proposals,
which means physically building the candidate molecule, aircraft, or robot,
testing it, and storing the result. This process can be expensive and time
consuming, and one might instead prefer to optimize for the best design using
only the data one already has. This setting -- called offline MBO -- poses
substantial and different algorithmic challenges than more commonly studied
online techniques. A number of recent works have demonstrated success with
offline MBO for high-dimensional optimization problems using high-capacity deep
neural networks. However, the lack of standardized benchmarks in this emerging
field is making progress difficult to track. To address this, we present
Design-Bench, a benchmark for offline MBO with a unified evaluation protocol
and reference implementations of recent methods. Our benchmark includes a suite
of diverse and realistic tasks derived from real-world optimization problems in
biology, materials science, and robotics that present distinct challenges for
offline MBO. Our benchmark and reference implementations are released at
github.com/rail-berkeley/design-bench and
github.com/rail-berkeley/design-baselines.


title: Modular representations of finite groups and Lie theory

abstract:
  This article discusses the modular representation theory of finite groups of
Lie type from the viewpoint of Broue's abelian defect group conjecture.
  We discuss both the defining characteristic case, the inspiration for
Alperin's weight conjecture, and the non-defining case, the inspiration for
Broue's conjecture. The modular representation theory of general finite groups
is conjectured to behave both like that of finite groups of Lie type in
defining characteristic, and in non-defining characteristic, to a large extent.
  The expected behaviour of modular representation theory of finite groups of
Lie type in defining characteristic is particularly difficult to grasp along
the lines of Broue's conjecture and we raise a new question related to the
change of central character.
  We introduce a degeneration method in the modular representation theory of
finite groups of Lie type in non-defining characteristic. Combined with the
rigidity property of perverse equivalences, this provides a setting for two
variable decomposition matrices, for large characteristic. This should help
make progress towards finding decomposition matrices, an outstanding problem
with few general results beyond the case of general linear groups. This last
part is based on joint work with David Craven and Olivier Dudas.