title:
Symmetry-Breaking and Self-Sorting in Block Copolymer-based
  Multicomponent Nanocomposites

abstract:
  Co-assembly of inorganic nanoparticles (NPs) and nanostructured polymer
matrix represents an intricate interplay of enthalpic or entropic forces.
Particle size largely affects the phase behavior of the nanocomposite.
Theoretical studies indicate that new morphologies would emerge when the
particles become comparable to the soft matrix's size, but this has rarely been
supported experimentally. By designing a multicomponent blend composed of NPs,
block copolymer-based supramolecules, and small molecules, a 3-D ordered
lattice beyond the native BCP's morphology was recently reported when the
particle is larger than the microdomain of BCP. The blend can accommodate
various formulation variables. In this contribution, when the particle size
equals the microdomain size, a symmetry-broken phase appears in a narrow range
of particle sizes and compositions, which we named the "train track" structure.
In this phase, the NPs aligned into a 3-D hexagonal lattice and packed
asymmetrically along the c axis, making the projection of the ac and the bc
plane resemble train tracks. Computation studies show that the broken symmetry
reduces the polymer chain deformation and stabilizes the metastable hexagonally
perforated lamellar morphology. Given the mobility of the multicomponent blend,
the system shows a self-sorting behavior: segregating into two macroscopic
phases with different nanostructures based on only a few nanometers NP size
differences. Smaller NPs form "train track" morphology, while larger NPs form
"simple hexagon" structure, where the NPs take a symmetric hexagonal
arrangement. Detailed structural evolution and simulation studies confirm the
systematic-wide cooperativity across different components, indicating the
strong self-regulation of the multicomponent system.


title:
Interactive Segmentation and Visualization for Tiny Objects in
  Multi-megapixel Images

abstract:
  We introduce an interactive image segmentation and visualization framework
for identifying, inspecting, and editing tiny objects (just a few pixels wide)
in large multi-megapixel high-dynamic-range (HDR) images. Detecting cosmic rays
(CRs) in astronomical observations is a cumbersome workflow that requires
multiple tools, so we developed an interactive toolkit that unifies model
inference, HDR image visualization, segmentation mask inspection and editing
into a single graphical user interface. The feature set, initially designed for
astronomical data, makes this work a useful research-supporting tool for
human-in-the-loop tiny-object segmentation in scientific areas like
biomedicine, materials science, remote sensing, etc., as well as computer
vision. Our interface features mouse-controlled, synchronized, dual-window
visualization of the image and the segmentation mask, a critical feature for
locating tiny objects in multi-megapixel images. The browser-based tool can be
readily hosted on the web to provide multi-user access and GPU acceleration for
any device. The toolkit can also be used as a high-precision annotation tool,
or adapted as the frontend for an interactive machine learning framework. Our
open-source dataset, CR detection model, and visualization toolkit are
available at https://github.com/cy-xu/cosmic-conn.


title: A Framework for Interactive Knowledge-Aided Machine Teaching

abstract:
  Machine Teaching (MT) is an interactive process where humans train a machine
learning model by playing the role of a teacher. The process of designing an MT
system involves decisions that can impact both efficiency of human teachers and
performance of machine learners. Previous research has proposed and evaluated
specific MT systems but there is limited discussion on a general framework for
designing them. We propose a framework for designing MT systems and also detail
a system for the text classification problem as a specific instance. Our
framework focuses on three components i.e. teaching interface, machine learner,
and knowledge base; and their relations describe how each component can benefit
the others. Our preliminary experiments show how MT systems can reduce both
human teaching time and machine learner error rate.


title:
Creative Problem Solving in Artificially Intelligent Agents: A Survey
  and Framework

abstract:
  Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence
(AI) that focuses on methods for solving off-nominal, or anomalous problems in
autonomous systems. Despite many advancements in planning and learning,
resolving novel problems or adapting existing knowledge to a new context,
especially in cases where the environment may change in unpredictable ways post
deployment, remains a limiting factor in the safe and useful integration of
intelligent systems. The emergence of increasingly autonomous systems dictates
the necessity for AI agents to deal with environmental uncertainty through
creativity. To stimulate further research in CPS, we present a definition and a
framework of CPS, which we adopt to categorize existing AI methods in this
field. Our framework consists of four main components of a CPS problem, namely,
1) problem formulation, 2) knowledge representation, 3) method of knowledge
manipulation, and 4) method of evaluation. We conclude our survey with open
research questions, and suggested directions for the future.


title: Boundary Adaptive Local Polynomial Conditional Density Estimators

abstract:
  We begin by introducing a class of conditional density estimators based on
local polynomial techniques. The estimators are automatically boundary adaptive
and easy to implement. We then study the (pointwise and) uniform statistical
properties of the estimators, offering nonasymptotic characterizations of both
probability concentration and distributional approximation. In particular, we
establish optimal uniform convergence rate in probability and valid Gaussian
distributional approximations for the t-statistic process indexed over the data
support. We also discuss implementation issues such as consistent estimation of
the covariance function of the Gaussian approximation, optimal integrated mean
squared error bandwidth selection, and valid robust bias-corrected inference.
We illustrate the applicability of our results by constructing valid confidence
bands and hypothesis tests for both parametric specification and shape
constraints, explicitly characterizing their nonasymptotic approximation
probability errors. A companion R software package implementing our main
results is provided.


title:
Decorate the Examples: A Simple Method of Prompt Design for Biomedical
  Relation Extraction

abstract:
  Relation extraction is a core problem for natural language processing in the
biomedical domain. Recent research on relation extraction showed that
prompt-based learning improves the performance on both fine-tuning on full
training set and few-shot training. However, less effort has been made on
domain-specific tasks where good prompt design can be even harder. In this
paper, we investigate prompting for biomedical relation extraction, with
experiments on the ChemProt dataset. We present a simple yet effective method
to systematically generate comprehensive prompts that reformulate the relation
extraction task as a cloze-test task under a simple prompt formulation. In
particular, we experiment with different ranking scores for prompt selection.
With BioMed-RoBERTa-base, our results show that prompting-based fine-tuning
obtains gains by 14.21 F1 over its regular fine-tuning baseline, and 1.14 F1
over SciFive-Large, the current state-of-the-art on ChemProt. Besides, we find
prompt-based learning requires fewer training examples to make reasonable
predictions. The results demonstrate the potential of our methods in such a
domain-specific relation extraction task.


title: On extremizing sequences for adjoint Fourier restriction to the sphere

abstract:
  In this article, we develop a linear profile decomposition for the $L^p \to
L^q$ adjoint Fourier restriction operator associated to the sphere, valid for
exponent pairs $p<q$ for which this operator is bounded. Such theorems are new
when $p \neq 2$. We apply these methods to prove new results regarding the
existence of extremizers and the behavior of extremizing sequences for the
spherical extension operator. Namely, assuming boundedness, extremizers exist
if $q>\max\{p,\tfrac{d+2}d p'\}$, or if $q=\tfrac{d+2}d p'$ and the operator
norm exceeds a certain constant times the operator norm of the parabolic
extension operator.


title: Human Preferences as Dueling Bandits

abstract:
  The dramatic improvements in core information retrieval tasks engendered by
neural rankers create a need for novel evaluation methods. If every ranker
returns highly relevant items in the top ranks, it becomes difficult to
recognize meaningful differences between them and to build reusable test
collections. Several recent papers explore pairwise preference judgments as an
alternative to traditional graded relevance assessments. Rather than viewing
items one at a time, assessors view items side-by-side and indicate the one
that provides the better response to a query, allowing fine-grained
distinctions. If we employ preference judgments to identify the probably best
items for each query, we can measure rankers by their ability to place these
items as high as possible. We frame the problem of finding best items as a
dueling bandits problem. While many papers explore dueling bandits for online
ranker evaluation via interleaving, they have not been considered as a
framework for offline evaluation via human preference judgments. We review the
literature for possible solutions. For human preference judgments, any usable
algorithm must tolerate ties, since two items may appear nearly equal to
assessors, and it must minimize the number of judgments required for any
specific pair, since each such comparison requires an independent assessor.
Since the theoretical guarantees provided by most algorithms depend on
assumptions that are not satisfied by human preference judgments, we simulate
selected algorithms on representative test cases to provide insight into their
practical utility. Based on these simulations, one algorithm stands out for its
potential. Our simulations suggest modifications to further improve its
performance. Using the modified algorithm, we collect over 10,000 preference
judgments for submissions to the TREC 2021 Deep Learning Track, confirming its
suitability.


title: The linear span of uniform matrix product states

abstract:
  The variety of uniform matrix product states arises both in algebraic
geometry as a natural generalization of the Veronese variety, and in quantum
many-body physics as a model for a translation-invariant system of sites placed
on a ring. Using methods from linear algebra, representation theory, and
invariant theory of matrices, we study the linear span of this variety.


title: Optimal Design of Best Response Dynamics in Resource Allocation Games

abstract:
  Designing distributed algorithms for multi-agent problems is vital for many
emerging application domains, and game-theoretic approaches are emerging as a
useful paradigm to design such algorithms. However, much of the emphasis of the
game-theoretic approach is on the study of equilibrium behavior, whereas
transient behavior is often less explored. Therefore, in this paper we study
the transient efficiency guarantees of best response processes in the context
of resource-allocation games, which are used to model a variety of engineering
applications. Specifically, the main focus of the paper is on designing utility
functions of agents to induce optimal short-term system-level behavior under a
best-response process. Interestingly, the resulting transient performance
guarantees are relatively close to the optimal asymptotic performance
guarantees. Furthermore, we characterize a trade-off that results when
optimizing for both asymptotic and transient efficiency through various utility
designs.


title:
Towards an Enhanced Understanding of Bias in Pre-trained Neural Language
  Models: A Survey with Special Emphasis on Affective Bias

abstract:
  The remarkable progress in Natural Language Processing (NLP) brought about by
deep learning, particularly with the recent advent of large pre-trained neural
language models, is brought into scrutiny as several studies began to discuss
and report potential biases in NLP applications. Bias in NLP is found to
originate from latent historical biases encoded by humans into textual data
which gets perpetuated or even amplified by NLP algorithm. We present a survey
to comprehend bias in large pre-trained language models, analyze the stages at
which they occur in these models, and various ways in which these biases could
be quantified and mitigated. Considering wide applicability of textual
affective computing based downstream tasks in real-world systems such as
business, healthcare, education, etc., we give a special emphasis on
investigating bias in the context of affect (emotion) i.e., Affective Bias, in
large pre-trained language models. We present a summary of various bias
evaluation corpora that help to aid future research and discuss challenges in
the research on bias in pre-trained language models. We believe that our
attempt to draw a comprehensive view of bias in pre-trained language models,
and especially the exploration of affective bias will be highly beneficial to
researchers interested in this evolving field.


title: Flops of any length, Gopakumar-Vafa invariants, and 5d Higgs Branches

abstract:
  The conifold is a basic example of a noncompact Calabi-Yau threefold that
admits a simple flop, and in M-theory, gives rise to a 5d hypermultiplet at low
energies, realized by an M2-brane wrapped on the vanishing sphere. We develop a
novel gauge-theoretic method to construct new classes of examples that
generalize the simple flop to so-called length l=1,...,6. The method allows us
to naturally read off the Gopakumar-Vafa invariants. Although they share
similar properties to the beloved conifold, these threefolds are expected to
admit M2-bound states of higher degree. We demonstrate this through our
computations of the GV invariants. Furthermore we fully characterize the
associated Higgs branches by computing their dimensions and flavor groups. With
our techniques we extract more refined data such as the charges of the hypers
under the flavor group.


title: A Note on the Gibbsian Representation of the Gradient of a Vector Field

abstract:
  In this short note, we provide a important considerations of a familiar
topic: the gradient of a vector field. The gradient of a vector field is a
common quantity represented in continuum mechanics. However, even for Cartesian
coordinate systems, there are two different representations for this quantity
in common use, which leads to ambiguity in some results. We review the history
leading to the \emph{Gibbsian representation} for the gradient of a vector
field, and provide some suggestions to help clarify the meaning of such terms
when represented in conventional Gibbsian vector or tensor notation. In an
appendix, we briefly expand on the connection with the Gibbsian representation
of the deformation and rotation tensors in the framework of geometric algebra
(GA).


title: Thermodynamic speed limits for mechanical work

abstract:
  Thermodynamic speed limits are a set of classical uncertainty relations that,
so far, place global bounds on the stochastic dissipation of energy as heat and
the irreversible production of entropy. Here, instead of constraints on these
thermodynamic costs, we derive upper and lower bounds on a thermodynamic
benefit -- the minimum time for an amount of mechanical work to be done on or
by a system. These bounds are expressed entirely in terms of measurable
quantities and can be extracted from experiments. In the short time limit, we
define an intrinsic timescale for work, recover the intrinsic timescales in
differential speed limits from integral speed limits, and turn the first law of
stochastic thermodynamics into a first law of speeds. As physical examples, we
consider the work done by a flashing Brownian ratchet and the work done on a
particle in a potential well subject to external driving.


title:
Local mathematics and scaling field: effects on local physics and on
  cosmology

abstract:
  The origin of this paper starts with the observation by Yang Mills that what
state represents a proton in isospin space at one location does not determine
what state represents a proton in isospin space at another location. This is
accounted for by the presence of a unitary gauge transformation operator,
$U(y,x)$, between vector spaces at different locations. This operator defines
the notion of same states for vector spaces at different locations. If $\psi$
is a state in a vector space at $x$ then $U(y,x)\psi$ is the same state in the
vector space at $y$. Vector spaces include scalar fields in their axiomatic
description. These appear as norms, closure under vector scalar multiplication,
etc. This leads to a conflict: local vector spaces and global scalar fields.
Here this conflict is removed by replacing global scalar fields with local
scalar fields. These are represented by $\bar{S}_{x}$ where $x$ is any location
in Euclidean space or space time. Here $S$ represents the different type of
numbers, (natural, integers, rational, real, and complex). The association of
scalar fields with vector spaces and the Yang Mills observation raises the
question, What corresponds to the Yang Mills observation for numbers? The
answer is that two different concepts, number and number meaning or value, are
conflated in the usual use of mathematics. These two concepts are distinct.


title: Passport: Improving Automated Formal Verification Using Identifiers

abstract:
  Tools that learn from proof corpora to suggest partial or complete proof
scripts have just begun to show their promise. The powerful logical systems
beneath proof assistants, together with the stylistic conventions followed by
communities of proof developers, bring richness to the data in these corpora.
But this richness remains little exploited, with most work thus far focusing on
architecture rather than on how to make the most of the proof data.
  We develop Passport, a fully-automated proof-synthesis tool that encodes one
additional aspect of that rich proof data: identifiers. In particular, Passport
enriches a proof-synthesis model for Coq with information about local
variables, global definitions, and inductive constructor names, encoding each
of these identifier categories in three different ways. We compare Passport to
three existing proof-synthesis tools which share similar architecture,
ASTactic, Tac, and Tok. In head-to-head comparisons, Passport automatically
proves 29% more theorems than the best-performing of these prior tools.
Combining the three prior tools enhanced with Passport's identifier information
automatically proves 38% more theorems than without that information. Finally,
together, these prior tools and Passport models enhanced with identifier
information prove 45% more theorems than the prior tools alone.
  In the course of building Passport, we encountered and overcame significant
challenges unique to building machine-learning-based proof synthesis tools. We
discuss these challenges and suggest potential solutions to support other
researchers building such tools.
  Overall, our findings suggest that modeling identifiers can play a
significant role in improving proof synthesis, leading to higher-quality
software.


title:
Resonant Semiconductor Metasurfaces for Generating Complex Quantum
  States

abstract:
  Quantum state engineering, the cornerstone of quantum photonic technologies,
mainly relies on spontaneous parametric down-conversion and four-wave mixing,
where one or two pump photons decay into a photon pair. Both these nonlinear
effects require momentum conservation (i.e., phase-matching) for the
participating photons, which strongly limits the versatility of the resulting
quantum states. Nonlinear metasurfaces, due to their subwavelength thickness,
relax this constraint and extend the boundaries of quantum state engineering.
Here, we generate entangled photons via spontaneous parametric down-conversion
in semiconductor metasurfaces with high-quality resonances. By enhancing the
quantum vacuum field, our metasurfaces boost the emission of photon pairs
within narrow resonance bands at multiple selected wavelengths. Due to the
relaxed momentum conservation, the same resonances support photon pair
generation from pump photons of practically any energy. This enables the
generation of complex frequency-multiplexed quantum states, in particular
cluster states. Our results demonstrate the multifunctional use of metasurfaces
for quantum state engineering.


title: Model-free Learning of Regions of Attraction via Recurrent Sets

abstract:
  We consider the problem of learning an inner approximation of the region of
attraction (ROA) of an asymptotically stable equilibrium point without an
explicit model of the dynamics. Rather than leveraging approximate models with
bounded uncertainty to find a (robust) invariant set contained in the ROA, we
propose to learn sets that satisfy a more relaxed notion of containment known
as recurrence. We define a set to be $\tau$-recurrent (resp. $k$-recurrent) if
every trajectory that starts within the set, returns to it after at most $\tau$
seconds (resp. $k$ steps). We show that under mild assumptions a
$\tau$-recurrent set containing a stable equilibrium must be a subset of its
ROA. We then leverage this property to develop algorithms that compute inner
approximations of the ROA using counter-examples of recurrence that are
obtained by sampling finite-length trajectories. Our algorithms process samples
sequentially, which allow them to continue being executed even after an initial
offline training stage. We further provide an upper bound on the number of
counter-examples used by the algorithm, and almost sure convergence guarantees.


title: Distributed Nonparametric Estimation under Communication Constraints

abstract:
  In the era of big data, it is necessary to split extremely large data sets
across multiple computing nodes and construct estimators using the distributed
data. When designing distributed estimators, it is desirable to minimize the
amount of communication across the network because transmission between
computers is slow in comparison to computations in a single computer. Our work
provides a general framework for understanding the behavior of distributed
estimation under communication constraints for nonparametric problems. We
provide results for a broad class of models, moving beyond the Gaussian
framework that dominates the literature. As concrete examples we derive minimax
lower and matching upper bounds in the distributed regression, density
estimation, classification, Poisson regression and volatility estimation models
under communication constraints. To assist with this, we provide sufficient
conditions that can be easily verified in all of our examples.


title:
Learning how to Interact with a Complex Interface using Hierarchical
  Reinforcement Learning

abstract:
  Hierarchical Reinforcement Learning (HRL) allows interactive agents to
decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks
can invoke the solutions of lower-level tasks as if they were primitive
actions. In this work, we study the utility of hierarchical decompositions for
learning an appropriate way to interact with a complex interface. Specifically,
we train HRL agents that can interface with applications in a simulated Android
device. We introduce a Hierarchical Distributed Deep Reinforcement Learning
architecture that learns (1) subtasks corresponding to simple finger gestures,
and (2) how to combine these gestures to solve several Android tasks. Our
approach relies on goal conditioning and can be used more generally to convert
any base RL agent into an HRL agent. We use the AndroidEnv environment to
evaluate our approach. For the experiments, the HRL agent uses a distributed
version of the popular DQN algorithm to train different components of the
hierarchy. While the native action space is completely intractable for simple
DQN agents, our architecture can be used to establish an effective way to
interact with different tasks, significantly improving the performance of the
same DQN agent over different levels of abstraction.


title: lpcde: Local Polynomial Conditional Density Estimation and Inference

abstract:
  This paper discusses the R package lpcde, which stands for local polynomial
conditional density estimation. It implements the kernel-based local polynomial
smoothing methods introduced in Cattaneo, Chandak, Jansson, Ma (2022) for
statistical estimation and inference of conditional distributions, densities,
and derivatives thereof. The package offers pointwise and integrated mean
square error optimal bandwidth selection and associated point estimators, as
well as uncertainty quantification based on robust bias correction both
pointwise (e.g., confidence intervals) and uniformly (e.g., confidence bands)
over evaluation points. The methods implemented are boundary adaptive whenever
the data is compactly supported. We contrast the functionalities of lpcde with
existing R packages, and showcase its main features using simulated data.


title: Differentially Private Learning with Margin Guarantees

abstract:
  We present a series of new differentially private (DP) algorithms with
dimension-independent margin guarantees. For the family of linear hypotheses,
we give a pure DP learning algorithm that benefits from relative deviation
margin guarantees, as well as an efficient DP learning algorithm with margin
guarantees. We also present a new efficient DP learning algorithm with margin
guarantees for kernel-based hypotheses with shift-invariant kernels, such as
Gaussian kernels, and point out how our results can be extended to other
kernels using oblivious sketching techniques. We further give a pure DP
learning algorithm for a family of feed-forward neural networks for which we
prove margin guarantees that are independent of the input dimension.
Additionally, we describe a general label DP learning algorithm, which benefits
from relative deviation margin bounds and is applicable to a broad family of
hypothesis sets, including that of neural networks. Finally, we show how our DP
learning algorithms can be augmented in a general way to include model
selection, to select the best confidence margin parameter.


title: Contrastive Test-Time Adaptation

abstract:
  Test-time adaptation is a special setting of unsupervised domain adaptation
where a trained model on the source domain has to adapt to the target domain
without accessing source data. We propose a novel way to leverage
self-supervised contrastive learning to facilitate target feature learning,
along with an online pseudo labeling scheme with refinement that significantly
denoises pseudo labels. The contrastive learning task is applied jointly with
pseudo labeling, contrasting positive and negative pairs constructed similarly
as MoCo but with source-initialized encoder, and excluding same-class negative
pairs indicated by pseudo labels. Meanwhile, we produce pseudo labels online
and refine them via soft voting among their nearest neighbors in the target
feature space, enabled by maintaining a memory queue. Our method, AdaContrast,
achieves state-of-the-art performance on major benchmarks while having several
desirable properties compared to existing works, including memory efficiency,
insensitivity to hyper-parameters, and better model calibration. Project page:
sites.google.com/view/adacontrast.


title: A Case for Transparent Reliability in DRAM Systems

abstract:
  Today's systems have diverse needs that are difficult to address using
one-size-fits-all commodity DRAM. Unfortunately, although system designers can
theoretically adapt commodity DRAM chips to meet their particular design goals
(e.g., by reducing access timings to improve performance, implementing
system-level RowHammer mitigations), we observe that designers today lack
sufficient insight into commodity DRAM chips' reliability characteristics to
implement these techniques in practice. In this work, we make a case for DRAM
manufacturers to provide increased transparency into key aspects of DRAM
reliability (e.g., basic chip design properties, testing strategies). Doing so
enables system designers to make informed decisions to better adapt commodity
DRAM to meet modern systems' needs while preserving its cost advantages.
  To support our argument, we study four ways that system designers can adapt
commodity DRAM chips to system-specific design goals: (1) improving DRAM
reliability; (2) reducing DRAM refresh overheads; (3) reducing DRAM access
latency; and (4) mitigating RowHammer attacks. We observe that adopting
solutions for any of the four goals requires system designers to make
assumptions about a DRAM chip's reliability characteristics. These assumptions
discourage system designers from using such solutions in practice due to the
difficulty of both making and relying upon the assumption.
  We identify DRAM standards as the root of the problem: current standards
rigidly enforce a fixed operating point with no specifications for how a system
designer might explore alternative operating points. To overcome this problem,
we introduce a two-step approach that reevaluates DRAM standards with a focus
on transparency of DRAM reliability so that system designers are encouraged to
make the most of commodity DRAM technology for both current and future DRAM
chips.


title:
Text-mined dataset of gold nanoparticle synthesis procedures,
  morphologies, and size entities

abstract:
  Gold nanoparticles are highly desired for a range of technological
applications due to their tunable properties, which are dictated by the size
and shape of the constituent particles. Many heuristic methods for controlling
the morphological characteristics of gold nanoparticles are well known.
However, the underlying mechanisms controlling their size and shape remain
poorly understood, partly due to the immense range of possible combinations of
synthesis parameters. Data-driven methods can offer insight to help guide
understanding of these underlying mechanisms, so long as sufficient synthesis
data are available. To facilitate data mining in this direction, we have
constructed and made publicly available a dataset of codified gold nanoparticle
synthesis protocols and outcomes extracted directly from the nanoparticle
materials science literature using natural language processing and text-mining
techniques. This dataset contains 5,154 data records, each representing a
single gold nanoparticle synthesis article, filtered from a database of
4,973,165 publications. Each record contains codified synthesis protocols and
extracted morphological information from a total of 7,608 experimental and
12,519 characterization paragraphs.


title: The 6th AI City Challenge

abstract:
  The 6th edition of the AI City Challenge specifically focuses on problems in
two domains where there is tremendous unlocked potential at the intersection of
computer vision and artificial intelligence: Intelligent Traffic Systems (ITS),
and brick and mortar retail businesses. The four challenge tracks of the 2022
AI City Challenge received participation requests from 254 teams across 27
countries. Track 1 addressed city-scale multi-target multi-camera (MTMC)
vehicle tracking. Track 2 addressed natural-language-based vehicle track
retrieval. Track 3 was a brand new track for naturalistic driving analysis,
where the data were captured by several cameras mounted inside the vehicle
focusing on driver safety, and the task was to classify driver actions. Track 4
was another new track aiming to achieve retail store automated checkout using
only a single view camera. We released two leader boards for submissions based
on different methods, including a public leader board for the contest, where no
use of external data is allowed, and a general leader board for all submitted
results. The top performance of participating teams established strong
baselines and even outperformed the state-of-the-art in the proposed challenge
tracks.


title: Diffeological submanifolds and their friends

abstract:
  A smooth manifold hosts different types of submanifolds, including embedded,
weakly-embedded, and immersed submanifolds. The notion of an immersed
submanifold requires additional structure (namely, the choice of a topology);
when this additional structure is unique, we call the subset a uniquely
immersed submanifold. Diffeology provides yet another intrinsic notion of
submanifold: a diffeological submanifold.
  We show that from a categorical perspective diffeology rises above the
others: viewing manifolds as a concrete category over the category of sets, the
initial morphisms are exactly the (diffeological) inductions, which are the
diffeomorphisms with diffeological submanifolds. Moreover, if we view manifolds
as a concrete category over the category of topological spaces, we recover
Joris and Preissmann's notion of pseudo-immersions.
  We show that these notions are all different. In particular, a theorem of
Joris from 1982 yields a diffeological submanifold whose inclusion is not an
immersion, answering a question that was posed by Iglesias-Zemmour. We also
characterize local inductions as those pseudo-immersions that are locally
injective.
  In appendices, we review a proof of Joris' theorem, pointing at a flaw in one
of the several other proofs that occur in the literature, and we illustrate how
submanifolds inherit paracompactness from their ambient manifold.


title:
Facilitating automated conversion of scientific knowledge into
  scientific simulation models with the Machine Assisted Generation,
  Calibration, and Comparison (MAGCC) Framework

abstract:
  The Machine Assisted Generation, Comparison, and Calibration (MAGCC)
framework provides machine assistance and automation of recurrent crucial steps
and processes in the development, implementation, testing, and use of
scientific simulation models. MAGCC bridges systems for knowledge extraction
via natural language processing or extracted from existing mathematical models
and provides a comprehensive workflow encompassing the composition of
scientific models and artificial intelligence (AI) assisted code generation.
MAGCC accomplishes this through: 1) the development of a comprehensively
expressive formal knowledge representation knowledgebase, the Structured
Scientific Knowledge Representation (SSKR) that encompasses all the types of
information needed to make any simulation model, 2) the use of an artificially
intelligent logic reasoning system, the Computational Modeling Assistant (CMA),
that takes information from the SSKR and generates, in a traceable fashion,
model specifications across a range of simulation modeling methods, and 3) the
use of the CMA to generate executable code for a simulation model from those
model specifications. The MAGCC framework can be customized any scientific
domain, and future work will integrate newly developed code-generating AI
systems.


title:
Autonomous Vehicle Parking in Dynamic Environments: An Integrated System
  with Prediction and Motion Planning

abstract:
  This paper presents an integrated motion planning system for autonomous
vehicle (AV) parking in the presence of other moving vehicles. The proposed
system includes 1) a hybrid environment predictor that predicts the motions of
the surrounding vehicles and 2) a strategic motion planner that reacts to the
predictions. The hybrid environment predictor performs short-term predictions
via an extended Kalman filter and an adaptive observer. It also combines
short-term predictions with a driver behavior cost-map to make long-term
predictions. The strategic motion planner comprises 1) a model predictive
control-based safety controller for trajectory tracking; 2) a search-based
retreating planner for finding an evasion path in an emergency; 3) an
optimization-based repairing planner for planning a new path when the original
path is invalidated. Simulation validation demonstrates the effectiveness of
the proposed method in terms of initial planning, motion prediction, safe
tracking, retreating in an emergency, and trajectory repairing.


title:
Monocular Depth Estimation Using Cues Inspired by Biological Vision
  Systems

abstract:
  Monocular depth estimation (MDE) aims to transform an RGB image of a scene
into a pixelwise depth map from the same camera view. It is fundamentally
ill-posed due to missing information: any single image can have been taken from
many possible 3D scenes. Part of the MDE task is, therefore, to learn which
visual cues in the image can be used for depth estimation, and how. With
training data limited by cost of annotation or network capacity limited by
computational power, this is challenging. In this work we demonstrate that
explicitly injecting visual cue information into the model is beneficial for
depth estimation. Following research into biological vision systems, we focus
on semantic information and prior knowledge of object sizes and their
relations, to emulate the biological cues of relative size, familiar size, and
absolute size. We use state-of-the-art semantic and instance segmentation
models to provide external information, and exploit language embeddings to
encode relational information between classes. We also provide a prior on the
average real-world size of objects. This external information overcomes the
limitation in data availability, and ensures that the limited capacity of a
given network is focused on known-helpful cues, therefore improving
performance. We experimentally validate our hypothesis and evaluate the
proposed model on the widely used NYUD2 indoor depth estimation benchmark. The
results show improvements in depth prediction when the semantic information,
size prior and instance size are explicitly provided along with the RGB images,
and our method can be easily adapted to any depth estimation system.


title:
Lattice and Non-lattice Piercing of Axis-Parallel Rectangles: Exact
  Algorithms and a Separation Result

abstract:
  For a given family of shapes ${\mathcal F}$ in the plane, we study what is
the lowest possible density of a point set $P$ that pierces ("intersects",
"hits") all translates of each shape in ${\mathcal F}$. For instance, if
${\mathcal F}$ consists of two axis-parallel rectangles the best known piercing
set, i.e., one with the lowest density, is a lattice: for certain families the
known lattices are provably optimal whereas for other, those lattices are just
the best piercing sets currently known.
  Given a finite family ${\mathcal F}$ of axis-parallel rectangles, we present
two algorithms for finding an optimal ${\mathcal F}$-piercing lattice. Both
algorithms run in time polynomial in the number of rectangles and the maximum
aspect ratio of the rectangles in the family. No prior algorithms were known
for this problem.
  Then we prove that for every $n \geq 3$, there exist a family of $n$
axis-parallel rectangles for which the best piercing density achieved by a
lattice is separated by a positive (constant) gap from the optimal piercing
density for the respective family. Finally, we sharpen our separation result by
running the first algorithm on a suitable instance, and show that the best
lattice can be sometimes worse by $20\%$ than the optimal piercing set.


title:
Measurement of beauty-strange meson production in Pb$-$Pb collisions at
  $\sqrt{s_{\rm NN}} = 5.02$ TeV via non-prompt $\mathrm{D_s}^{+}$ mesons

abstract:
  The production yields of non-prompt $\mathrm{D_s}^{+}$ mesons, namely
$\mathrm{D_s}^{+}$ mesons from beauty-hadron decays, were measured for the
first time as a function of the transverse momentum ($p_{\rm T}$) at
midrapidity ($|y|<0.5$) in central and semi-central Pb$-$Pb collisions at a
centre-of-mass energy per nucleon pair $\sqrt{s_{\rm NN}}=5.02$ TeV with the
ALICE experiment at the LHC. The $\mathrm{D_s}^{+}$ mesons and their charge
conjugates were reconstructed from the hadronic decay channel $\mathrm{D_s}^{+}
\to \phi\pi^+$, with $\phi \to {\rm K}^-{\rm K}^+$, in the $4 < p_{\rm T}<36$
GeV/$c$ and $2 < p_{\rm T}<24$ GeV/$c$ intervals for the 0$-$10% and 30$-$50%
centrality classes, respectively. The measured yields of non-prompt
$\mathrm{D_s}^{+}$ mesons are compared to those of prompt $\mathrm{D_s}^{+}$
and non-prompt $\mathrm{D^0}$ mesons by calculating the ratios of the
production yields in Pb$-$Pb collisions and the nuclear modification factor
$R_\mathrm{AA}$. The ratio between the $R_\mathrm{AA}$ of non-prompt
$\mathrm{D_s}^{+}$ and prompt $\mathrm{D_s}^{+}$ mesons, and that between the
$R_\mathrm{AA}$ of non-prompt $\mathrm{D_s}^{+}$ and non-prompt $\mathrm{D^0}$
mesons in central Pb$-$Pb collisions are found to be on average higher than
unity in the $4< p_{\rm T}<12$ GeV/$c$ interval with a statistical significance
of about $1.6\,\sigma$ and $1.7\,\sigma$, respectively. The measured
$R_\mathrm{AA}$ ratios are compared with the predictions of theoretical models
of heavy-quark transport in a hydrodynamically expanding QGP that incorporate
hadronisation via quark recombination.


title:
Enabling Effective Error Mitigation in Memory Chips That Use On-Die
  Error-Correcting Codes

abstract:
  Improvements in main memory storage density are primarily driven by process
technology scaling, which negatively impacts reliability by exacerbating
various circuit-level error mechanisms. To compensate for growing error rates,
both memory manufacturers and consumers use error-mitigation mechanisms that
improve manufacturing yield and allow system designers to meet reliability
targets. Developing effective error mitigations requires understanding the
errors' characteristics (e.g., worst-case behavior, statistical properties).
Unfortunately, we observe that proprietary on-die Error-Correcting Codes (ECC)
used in modern memory chips introduce new challenges to efficient error
mitigation by obfuscating CPU-visible error characteristics in an
unpredictable, ECC-dependent manner.
  This dissertation builds a detailed understanding of how on-die ECC
obfuscates the statistical properties of main memory error mechanisms using a
combination of real-chip experiments and statistical analyses. We
experimentally study memory errors, examine how on-die ECC obfuscates their
statistical characteristics, and develop new testing techniques to overcome the
obfuscation. Our results show that the obfuscated error characteristics can be
recovered using new memory testing techniques that exploit the interaction
between on-die ECC and the statistical characteristics of memory error
mechanisms to expose physical cell behavior. We conclude by discussing the
critical need for transparency in DRAM reliability characteristics in order to
enable DRAM consumers to better understand and adapt commodity DRAM chips to
their system-specific needs. We hope and believe that the analysis, techniques,
and results we present in this dissertation will enable the community to better
understand and tackle current and future reliability challenges as well as
adapt commodity memory to new advantageous applications.


title: On alleged discovery of satellites of Jupiter by Simon Mayr (Marius)

abstract:
  In 1614, the German astronomer Simon Mayr published his claim to have
discovered the satellites of Jupiter. Writing in the treatise Mundus Jovialis,
Mayr made his assertion in a convoluted but unequivocal manner, earning the
displeasure of Galileo Galilei, who published his harsh protest in 1623 in Il
Saggiatore. Though objections of Galileo were fallacious in some respects, and
though numerous scholars took to the field to prove claim of Mayr, none ever
really succeeded, and the historical evidence remains to detriment of Mayr. On
the basis of such historical evidence, including comparisons between Mundus
Jovialis and earlier works of Mayr, independent discovery of the satellites by
Mayr can be ruled out. Indeed, it is very likely that he never observed them
before 30 December 1610, nearly a year after Galileo. The absence of a corpus
of observations by Mayr and the inaccuracy of his tables are also puzzling.


title:
Underlying-event properties in pp and p$-$Pb collisions at $\sqrt{s_{\rm
  NN}} = 5.02$ TeV

abstract:
  We report about the properties of the underlying event measured with ALICE at
the LHC in pp and p$-$Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV. The event
activity, quantified by charged-particle number and summed-$p_{\rm T}$
densities, is measured as a function of the leading-particle transverse
momentum ($p_{\rm T}^{\rm trig}$). These quantities are studied in three
azimuthal-angle regions relative to the leading particle in the event: toward,
away, and transverse. Results are presented for three different $p_{\rm T}$
thresholds (0.15, 0.5, and 1 GeV/$c$) at mid-pseudorapidity ($|\eta|<0.8$). The
event activity in the transverse region, which is the most sensitive to the
underlying event, exhibits similar behaviour in both pp and p$-$Pb collisions,
namely, a steep increase with $p_{\rm T}^{\rm trig}$ for low $p_{\rm T}^{\rm
trig}$, followed by a saturation at $p_{\rm T}^{\rm trig} \approx 5$ GeV/$c$.
The results from pp collisions are compared with existing measurements at other
centre-of-mass energies. The quantities in the toward and away regions are also
analyzed after the subtraction of the contribution measured in the transverse
region. The remaining jet-like particle densities are consistent in pp and
p$-$Pb collisions for $p_{\rm T}^{\rm trig}>10$ GeV/$c$, whereas for lower
$p_{\rm T}^{\rm trig}$ values the event activity is slightly higher in p$-$Pb
than in pp collisions. The measurements are compared with predictions from the
PYTHIA 8 and EPOS LHC Monte Carlo event generators.


title: SoftEdge: Regularizing Graph Classification with Random Soft Edges

abstract:
  Graph data augmentation plays a vital role in regularizing Graph Neural
Networks (GNNs), which leverage information exchange along edges in graphs, in
the form of message passing, for learning. Due to their effectiveness, simple
edge and node manipulations (e.g., addition and deletion) have been widely used
in graph augmentation. In this paper, we identify a limitation in such a common
augmentation technique. That is, simple edge and node manipulations can create
graphs with an identical structure or indistinguishable structures to message
passing GNNs but of conflict labels, leading to the sample collision issue and
thus the degradation of model performance. To address this problem, we propose
SoftEdge, which assigns random weights to a portion of the edges of a given
graph to construct dynamic neighborhoods over the graph. We prove that SoftEdge
creates collision-free augmented graphs. We also show that this simple method
obtains superior accuracy to popular node and edge manipulation approaches and
notable resilience to the accuracy degradation with the GNN depth.


title: Chaos in Matrix Gauge Theories with Massive Deformations

abstract:
  Starting from an $SU(N)$ matrix quantum mechanics model with massive
deformation terms and by introducing an ansatz configuration involving fuzzy
four- and two-spheres with collective time dependence, we obtain a family of
effective Hamiltonians, $H_n \,, (N = \frac{1}{6}(n+1)(n+2)(n+3))$ and examine
their emerging chaotic dynamics. Through numerical work, we model the variation
of the largest Lyapunov exponents as a function of the energy and find that
they vary either as $\propto (E-(E_n)_F)^{1/4} $ or $\propto E^{1/4}$, where
$(E_n)_F$ stand for the energies of the unstable fixed points of the phase
space. We use our results to put upper bounds on the temperature above which
the Lyapunov exponents comply with the Maldacena-Shenker-Stanford (MSS) bound,
$2 \pi T $, and below which it will eventually be violated.


title:
Cosmological Analysis of Three-Dimensional BOSS Galaxy Clustering and
  Planck CMB Lensing Cross Correlations via Lagrangian Perturbation Theory

abstract:
  We present a formalism for jointly fitting pre- and post-reconstruction
redshift-space clustering (RSD) and baryon acoustic oscillations (BAO) plus
gravitational lensing (of the CMB) that works directly with the observed
2-point statistics. The formalism is based upon (effective) Lagrangian
perturbation theory and a Lagrangian bias expansion, which models RSD, BAO and
galaxy-lensing cross correlations within a consistent dynamical framework. As
an example we present an analysis of clustering measured by the Baryon
Oscillation Spectroscopic Survey in combination with CMB lensing measured by
Planck. The post-reconstruction BAO strongly constrains the distance-redshift
relation, the full-shape redshift-space clustering constrains the matter
density and growth rate, and CMB lensing constrains the clustering amplitude.
Using only the redshift space data we obtain $\Omega_\mathrm{m} = 0.303\pm
0.008$, $H_0 = 69.21\pm 0.78$ and $\sigma_8 = 0.743\pm 0.043$. The addition of
lensing information, even when restricted to the Northern Galactic Cap,
improves constraints to $\Omega_m = 0.300 \pm 0.008$, $H_0 = 69.21 \pm 0.77$
and $\sigma_8 = 0.707 \pm 0.035$, in tension with CMB and cosmic shear
constraints. The combination of $\Omega_m$ and $H_0$ are consistent with
Planck, though their constraints derive mostly from redshift-space clustering.
The low $\sigma_8$ value are driven by cross correlations with CMB lensing in
the low redshift bin ($z\simeq 0.38$) and at large angular scales, which show a
$20\%$ deficit compared to expectations from galaxy clustering alone. We
conduct several systematics tests on the data and find none that could fully
explain these tensions.


title:
An Analysis of Conversational Volatility During Telecollaboration
  Sessions for Second Language Learning

abstract:
  Tandem telecollaboration is a pedagogy used in second language learning where
mixed groups of students meet online in videoconferencing sessions to practice
their conversational skills in their target language. We have built and
deployed a system called L2 Learning to support post-session review and
self-reflection on students participation in such meetings. We automatically
compute a metric called Conversational Volatility which quantifies the amount
of interaction among participants, indicating how dynamic or flat the
conversations were. Our analysis on more than 100 hours of video recordings
involving 28 of our students indicates that conversations do not get more
dynamic as meetings progress, that there is a wide variety of levels of
interaction across students and student groups, and the speaking in French
appears to have more animated conversations than speaking in English, though
the reasons for that are not clear.


title:
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop
  Simulations

abstract:
  Nitrogen (N) management is critical to sustain soil fertility and crop
production while minimizing the negative environmental impact, but is
challenging to optimize. This paper proposes an intelligent N management system
using deep reinforcement learning (RL) and crop simulations with Decision
Support System for Agrotechnology Transfer (DSSAT). We first formulate the N
management problem as an RL problem. We then train management policies with
deep Q-network and soft actor-critic algorithms, and the Gym-DSSAT interface
that allows for daily interactions between the simulated crop environment and
RL agents. According to the experiments on the maize crop in both Iowa and
Florida in the US, our RL-trained policies outperform previous empirical
methods by achieving higher or similar yield while using less fertilizers


title:
Effect of Wigner rotation on estimating unitary-shift parameter of
  relativistic spin-1/2 particle

abstract:
  We obtain the accuracy limit for estimating the expectation value of the
position of a relativistic particle for an observer moving along one direction
at a constant velocity. We use a specific model of a relativistic spin-1/2
particle described by a gaussian wave function with a spin down in the rest
frame. To derive the state vector of the particle for the moving observer, we
use the Wigner rotation that entangles the spin and the momentum of the
particle. Based on this wave function for the moving frame, we obtain the
symmetric logarithmic derivative (SLD) Cramer-Rao bound that sets the
estimation accuracy limit for an arbitrary moving observer. It is shown that
estimation accuracy decreases monotonically in the velocity of the observer
when the moving observer does not measure the spin degree of freedom. This
implies that the estimation accuracy limit worsens with increasing the
observer's velocity, but it is finite even in the relativistic limit. We derive
the amount of this information loss by the exact calculation of the SLD Fisher
information matrix in an arbitrary moving frame.


title:
Analytic Solutions of Brans-Dicke Cosmology: Early Inflation and Late
  Time Accelerated Expansion

abstract:
  We investigate the most general exact solutions of Brans-Dicke cosmology by
choosing the scale factor "a" as the new independent variable. It is shown that
a set of three field equations can be reduced to a constraint equation and a
first order linear differential equation. Thus this new set of equations is
solvable when one supplies one of the following pairs of functions: ({\Phi}(a),
\r{ho}(a)), ({\Phi}(a), V(a)) or ({\Phi}(a), H(a)). A universe with a single
component energy-matter density is studied. It is seen that when there is no
constant energy density, the Hubble function still contains a constant term
which causes exponential expansion. This constant is expressed in terms of the
initial values of the universe. An early universe and the present universe with
dark energy are studied. In addition late time accelerated expansion is also
explained with cosmic domain walls. If we take Brans-Dicke parameter w>4*10^4
formulas of the Hubble function reduce to solutions of {\Lambda}CDM cosmology.
Therefore comparison of our results with recent observations of type Ia
supernovae indicates that eighty-nine percent of present universe may consist
of domain walls while rest is matter.


title:
The Role of the Hadron-Quark Phase Transition in Core-Collapse
  Supernovae

abstract:
  The hadron-quark phase transition in quantum chromodyanmics has been
suggested as an alternative explosion mechanism for core-collapse supernovae.
We study the impact of three different hadron-quark equations of state (EoS)
with first-order (DD2F, STOF-B145) and second-order (CMF) phase transitions on
supernova dynamics by performing 97 simulations for solar- and zero-metallicity
progenitors in the range of $14\texttt{-}100\,\text{M}_\odot$. We find
explosions only for two low-compactness models ($14 \text{M}_\odot$ and
$16\,\text{M}_\odot$) with the DD2F EoS, both with low explosion energies of
$\mathord{\sim}10^{50}\,\mathrm{erg}$. These weak explosions are characterised
by a neutrino signal with several mini-bursts in the explosion phase due to
complex reverse shock dynamics, in addition to the typical second neutrino
burst for phase-transition driven explosions. The nucleosynthesis shows
significant overproduction of nuclei such as $^{90}\mathrm{Zr}$ for the
$14\,\text{M}_\odot$ zero-metallicity model and $^{94}\mathrm{Zr}$ for the
$16\,\text{M}_\odot$ solar-metallicity model, but the overproduction factors
are not large enough to place constraints on the occurrence of such explosions.
Several other low-compactness models using the DD2F EoS and two
high-compactness models using the STOS EoS end up as failed explosions and emit
a second neutrino burst. For the CMF EoS, the phase transition never leads to a
second bounce and explosion. For all three EoS, inverted convection occurs deep
in the core of the proto-compact star due to anomalous behaviour of
thermodynamic derivatives in the mixed phase, which heats the core to entropies
up to $4k_\text{B}/\text{baryon}$ and may have a distinctive gravitational wave
signature, also for a second-order phase transition.


title: STD: A Seasonal-Trend-Dispersion Decomposition of Time Series

abstract:
  The decomposition of a time series is an essential task that helps to
understand its very nature. It facilitates the analysis and forecasting of
complex time series expressing various hidden components such as the trend,
seasonal components, cyclic components and irregular fluctuations. Therefore,
it is crucial in many fields for forecasting and decision processes. In recent
years, many methods of time series decomposition have been developed, which
extract and reveal different time series properties. Unfortunately, they
neglect a very important property, i.e. time series variance. To deal with
heteroscedasticity in time series, the method proposed in this work -- a
seasonal-trend-dispersion decomposition (STD) -- extracts the trend, seasonal
component and component related to the dispersion of the time series. We define
STD decomposition in two ways: with and without an irregular component. We show
how STD can be used for time series analysis and forecasting.


title:
Energy-Efficient Classification at the Wireless Edge with Reliability
  Guarantees

abstract:
  Learning at the edge is a challenging task from several perspectives, since
data must be collected by end devices (e.g. sensors), possibly pre-processed
(e.g. data compression), and finally processed remotely to output the result of
training and/or inference phases. This involves heterogeneous resources, such
as radio, computing and learning related parameters. In this context, we
propose an algorithm that dynamically selects data encoding scheme, local
computing resources, uplink radio parameters, and remote computing resources,
to perform a classification task with the minimum average end devices' energy
consumption, under E2E delay and inference reliability constraints. Our method
does not assume any prior knowledge of the statistics of time varying context
parameters, while it only requires the solution of low complexity per-slot
deterministic optimization problems, based on instantaneous observations of
these parameters and that of properly defined state variables. Numerical
results on convolutional neural network based image classification illustrate
the effectiveness of our method in striking the best trade-off between energy,
delay and inference reliability.


title:
Interpolation of Missing Swaption Volatility Data using Gibbs Sampling
  on Variational Autoencoders

abstract:
  Albeit of crucial interest for both financial practitioners and researchers,
market-implied volatility data of European swaptions often exhibit large
portions of missing quotes due to illiquidity of the various underlying
swaption instruments. In this case, standard stochastic interpolation tools
like the common SABR model often cannot be calibrated to observed implied
volatility smiles, due to data being only available for the at-the-money quote
of the respective underlying swaption. Here, we propose to infer the geometry
of the full unknown implied volatility cube by learning stochastic latent
representations of implied volatility cubes via variational autoencoders,
enabling inference about the missing volatility data conditional on the
observed data by an approximate Gibbs sampling approach. Imputed estimates of
missing quotes can afterwards be used to fit a standard stochastic volatility
model. Since training data for the employed variational autoencoder model is
usually sparsely available, we test the robustness of the approach for a model
trained on synthetic data on real market quotes and we show that SABR
interpolated volatilites calibrated to reconstructed volatility cubes with
artificially imputed missing values differ by not much more than two basis
points compared to SABR fits calibrated to the complete cube. Moreover, we show
how the imputation can be used to successfully set up delta-neutral portfolios
for hedging purposes.


title:
Investigation of the effect of the grain sizes on the dynamic strength
  of the fine-grained alumina ceramics obtained by Spark Plasma Sintering

abstract:
  The results of dynamic strength tests of the alumina ceramics with various
grain sizes are presented. The ceramics were obtained by Spark Plasma Sintering
(SPS) of industrial submicron and fine Al2O3 powders. The heating up was
performed with the rate of 10 oC/min; the grain sizes in the ceramics was
controlled by varying the SPS temperature and the heating rate as well as by
varying the initial sizes of the Al2O3 particles in the powders. The ceramics
had a high density (over 98%) and a uniform fine-grained microstructure (the
mean grain sizes varied from 0.8 to 13.4 mkm). The dynamic compressing tests
were carried out by modified Kolsky method with using split Hopkinson pressure
bar. The tests were performed at room temperature using a 20-mm PG-20 gas gun
with the strain rate of ~10^3 s-1. The dependence of the dynamic ultimate
strength of alumina on the grain size was found for the first time to have a
non-monotonous character (with a maximum). The maximum value of the dynamic
ultimate compression strength (SY = 1060 MPa) was provided at the mean grain
size of ~2.9-3 mkm. The reduction of SY for alumina in the range of submicron
grain sizes was shown to originate from the reduction of the relative density
of the ceramics sintered at lower SPS temperatures.


title: Parallel Vertex Cover Algorithms on GPUs

abstract:
  Finding small vertex covers in a graph has applications in numerous domains.
Two common formulations of the problem include: Minimum Vertex Cover, which
finds the smallest vertex cover in a graph, and Parameterized Vertex Cover,
which finds a vertex cover whose size is less than or equal to some parameter
$k$. Algorithms for both formulations traverse a search tree, which grows
exponentially with the size of the graph or the value of $k$.
  Parallelizing the traversal of the vertex cover search tree on GPUs is
challenging for multiple reasons. First, the search tree is a narrow binary
tree which makes it difficult to extract enough sub-trees to process in
parallel to fully utilize the GPU's resources. Second, the search tree is
highly imbalanced which makes load balancing across a massive number of
parallel GPU workers challenging. Third, keeping around all the intermediate
state needed to traverse many sub-trees in parallel puts high pressure on the
GPU's memory resources and may act as a limiting factor to parallelism.
  To address these challenges, we propose an approach to traverse the vertex
cover search tree in parallel using GPUs while handling dynamic load balancing.
Each thread block traverses a different sub-tree using a local stack, however,
we also use a global worklist to balance load. Blocks contribute branches of
their sub-trees to the global worklist on an as-needed basis, while blocks that
finish their sub-trees get new ones from the global worklist. We use degree
arrays to represent intermediate graphs so that the representation is compact
in memory to avoid limiting parallelism, but self-contained which is necessary
for load balancing. Our evaluation shows that compared to prior work, our
hybrid approach of using local stacks and a global worklist substantially
improves performance and reduces load imbalance, especially on difficult
instances of the problem.


title: Sparse Graphical Modelling via the Sorted L$_1$-Norm

abstract:
  Sparse graphical modelling has attained widespread attention across various
academic fields. We propose two new graphical model approaches, Gslope and
Tslope, which provide sparse estimates of the precision matrix by penalizing
its sorted L1-norm, and relying on Gaussian and T-student data, respectively.
We provide the selections of the tuning parameters which provably control the
probability of including false edges between the disjoint graph components and
empirically control the False Discovery Rate for the block diagonal covariance
matrices. In extensive simulation and real world analysis, the new methods are
compared to other state-of-the-art sparse graphical modelling approaches. The
results establish Gslope and Tslope as two new effective tools for sparse
network estimation, when dealing with both Gaussian, t-student and mixture
data.


title:
Free-energy landscapes and insertion pathways for peptides in membrane
  environment

abstract:
  Free-energy landscapes for short peptides -- specifically for variants of the
pH Low Insertion Peptide (pHLIP) -- in the heterogeneous environment of a lipid
bilayer or cell membrane are constructed, taking into account a set of dominant
interactions and the conformational preferences of the peptide backbone. Our
methodology interprets broken internal H-bonds along the backbone of a
polypeptide as statistically interacting quasiparticles, activated from the
helix reference state. The favored conformation depends on the local
environment (ranging from polar to non-polar), specifically on the availability
of external H-bonds (with $\mathrm{H_2O}$ molecules or lipid headgroups) to
replace internal H-bonds. The dominant side-chain contribution is accounted for
by residue-specific transfer free energies between polar and non-polar
environments. The free-energy landscape is sensitive to the level of pH in the
aqueous environment surrounding the membrane. For high pH, we identify pathways
of descending free energy that suggest a coexistence of membrane-adsorbed
peptides with peptides in solution. A drop in pH raises the degree of
protonation of negatively charged residues and thus increases the
hydrophobicity of peptide segments near the C terminus. For low pH, we identify
insertion pathways between the membrane-adsorbed state and a stable
trans-membrane state with the C terminus having crossed the membrane.


title:
Ideal Conductor Model: An analytical finite-size correction for
  non-equilibrium molecular dynamics simulations of ion transport through
  nanoporous membranes

abstract:
  Modulating ion transport through nanoporous membranes is critical to many
important chemical and biological separation processes. The corresponding
transport timescales, however, are often too long to capture accurately using
conventional molecular dynamics (\textsc{Md}). Recently, path sampling
techniques such as forward-flux sampling (\textsc{Ffs}) have emerged as
attractive alternatives for efficiently and accurately estimating arbitrarily
long ionic passage times. Here, we use non-equilibrium \textsc{Md} and
\textsc{Ffs} to explore how the kinetics and mechanism of pressure-driven
chloride transport through a nanoporous graphitic membrane are affected by its
lateral dimensions. We not only find ionic passage times and free energy
barriers to decrease dramatically upon increasing the membrane surface area,
but also observe an abrupt and discontinuous change in the locus of the
transition state. These strong finite size effects arise due to the cumulative
effect of the periodic images of the leading ion entering the pore on the
distribution of the induced excess charge at the membrane surface in the feed.
By assuming that the feed is an ideal conductor, we analytically derive a
finite size correction term that can be computed from the information obtained
from a single simulation and successfully use it to obtain corrected free
energy profiles with no dependence on system size. We then estimate ionic
passage times in the thermodynamic limit by assuming an Eyring-type dependence
of rates on barriers with a size-independent prefactor. This approach
constitutes a universal framework for removing finite size artifacts in
molecular simulations of ion transport through nanoporous membranes and
biological channel proteins.


title:
Performance Analysis of Automotive SAR With Radar Based Motion
  Estimation

abstract:
  Automotive synthetic aperture radar (SAR) can achieve a significant angular
resolution enhancement for detecting static objects, which is essential for
automated driving. Obtaining high resolution SAR images requires precise ego
vehicle velocity estimation. A small velocity estimation error can result in a
focused SAR image with objects at offset angles. In this paper, we consider an
automotive SAR system that produces SAR images of static objects based on ego
vehicle velocity estimation from the radar return signal without the overhead
in complexity and cost of using an auxiliary global navigation satellite system
(GNSS) and inertial measurement unit (IMU). We derive a novel analytical
approximation for the automotive SAR angle estimation error variance when the
velocity is estimated by the radar. The developed analytical analysis closely
predicts the true SAR angle estimation variance, and also provides insights on
the effects of the radar parameters and the environment condition on the
automotive SAR angle estimation error. We evaluate via the analytical analysis
and simulation tests the radar settings and environment condition in which the
automotive SAR attains a significant performance gain over the angular
resolution of the short aperture physical antenna array. We show that, perhaps
surprisingly, when the velocity is estimated by the radar the performance
advantage of automotive SAR is realized only in limited conditions. Hence since
its implementation comes with an increase in computation and system complexity
as well as an increase in the detection delay it should be used carefully and
selectively.


title:
Improving Distribution System Resilience by Undergrounding Lines and
  Deploying Mobile Generators

abstract:
  To improve the resilience of electric distribution systems, this paper
proposes a stochastic multi-period mixed-integer linear programming model that
determines where to underground distribution lines and how to coordinate mobile
generators in order to serve critical loads during extreme events. The proposed
model represents the service restoration process using the linearized DistFlow
approximation of the AC power flow equations as well as binary variables for
the undergrounding statuses of the lines, the configurations of switches, and
the locations of mobile generators during each time period. The model also
enforces a radial configuration of the distribution network and considers the
transportation times needed to reposition the mobile generators. Using an
extended version of the IEEE 123-bus test system, numerical simulations show
that combining the ability to underground distribution lines with the
deployment of mobile generators can significantly improve the resilience of the
power supply to critical loads.


title: ICDBigBird: A Contextual Embedding Model for ICD Code Classification

abstract:
  The International Classification of Diseases (ICD) system is the
international standard for classifying diseases and procedures during a
healthcare encounter and is widely used for healthcare reporting and management
purposes. Assigning correct codes for clinical procedures is important for
clinical, operational, and financial decision-making in healthcare. Contextual
word embedding models have achieved state-of-the-art results in multiple NLP
tasks. However, these models have yet to achieve state-of-the-art results in
the ICD classification task since one of their main disadvantages is that they
can only process documents that contain a small number of tokens which is
rarely the case with real patient notes. In this paper, we introduce ICDBigBird
a BigBird-based model which can integrate a Graph Convolutional Network (GCN),
that takes advantage of the relations between ICD codes in order to create
'enriched' representations of their embeddings, with a BigBird contextual model
that can process larger documents. Our experiments on a real-world clinical
dataset demonstrate the effectiveness of our BigBird-based model on the ICD
classification task as it outperforms the previous state-of-the-art models.


title: Band-type resonance: non-discrete energetically-optimal resonant states

abstract:
  Structural resonance involves the absorption of inertial loads by a tuned
structural elasticity: a process playing a key role in a wide range of
biological and technological systems, including many biological and
bio-inspired locomotion systems. Conventional linear and nonlinear resonant
states typically exist at specific discrete frequencies, and specific symmetric
waveforms. This discreteness can be an obstacle to resonant control modulation:
deviating from these states, by breaking waveform symmetry or modulating drive
frequency, generally leads to losses in system efficiency. Here, we demonstrate
a new strategy for achieving these modulations at no loss of energetic
efficiency. Leveraging fundamental advances in nonlinear dynamics, we
characterise a new form of structural resonance: band-type resonance,
describing a continuous band of energetically-optimal resonant states existing
around conventional discrete resonant states. These states are a counterexample
to the common supposition that deviation from a linear (or nonlinear) resonant
frequency necessarily involves a loss of efficiency. We demonstrate how
band-type resonant states can be generated via a spectral shaping approach:
with small modifications to the system kinematic and load waveforms, we
construct sets of frequency-modulated and symmetry-broken resonant states that
show equal energetic optimality to their conventional discrete analogues. The
existence of these non-discrete resonant states in a huge range of oscillators
- linear and nonlinear, in many different physical contexts - is a new
dynamical-systems phenomenon. It has implications not only for biological and
bio-inspired locomotion systems but for a constellation of forced oscillator
systems across physics, engineering, and biology.


title: The physics of boundary conditions in reaction-diffusion problems

abstract:
  The use of fully or partially absorbing boundary conditions for
diffusion-based problems has become paradigmatic in physical chemistry and
biochemistry to describe reactions occurring in solutions or in living media.
However, as chemical states may indeed disappear, particles cannot, unless such
degradation happens physically and should thus be accounted for explicitly.
Here, we introduce a simple, yet general idea that allows one to derive the
appropriate boundary conditions self-consistently from the chemical reaction
scheme and the geometry of the physical reaction boundaries. As an
illustration, we consider two paradigmatic examples, where the known results
are recovered by taking specific physical limits. More generally, we
demonstrate that our mathematical analysis delivers physical insight that
cannot be accessed through standard treatments.


title: Decomposition Without Regret

abstract:
  Programming languages are embracing both functional and object-oriented
paradigms. A key difference between the two paradigms is the way of achieving
data abstraction. That is, how to organize data with associated operations.
There are important tradeoffs between functional and object-oriented
decomposition in terms of extensibility and expressiveness. Unfortunately,
programmers are usually forced to select a particular decomposition style in
the early stage of programming. Once the wrong design decision has been made,
the price for switching to the other decomposition style could be rather high
since pervasive manual refactoring is often needed. To address this issue, this
paper presents a bidirectional transformation system between functional and
object-oriented decomposition. We formalize the core of the system in the FOOD
calculus, which captures the essence of functional and object-oriented
decomposition. We prove that the transformation preserves the type and
semantics of the original program. We further implement FOOD in Scala as a
translation tool called Cook and conduct several case studies to demonstrate
the applicability and effectiveness of Cook.


title:
Students' experiences of learning Sciences during the Covid-19 pandemic
  and their suggestions for the next day: A Greek University Department case
  study

abstract:
  Around the globe Covid-19 pandemic has influenced not only the education, but
also our everyday life, among other aspects. In Greece, distance learning
started to get in use widely in tertiary education, since the first national
lockdown was announced and reshaped education in many ways. In the University
of Thessaly, in the Department of Mathematics, undergraduate students opt a lot
of different courses to attend and due to the Covid-19 crisis all of them are
taught via web platforms. Some of the most significant theoretical subjects are
"Calculus" (with applications in Science and Mechanics), "Physics" (Classical
Mechanics) and "Philosophy of Science". In addition, some other applied
subjects are "Programming Languages" and "Digital Technologies in Mathematics
Education" and the impact the above have, generally in education and society
itself. In this paper, we describe the different ways students have reacted
regarding learning Sciences in a distance learning environment. We split our
case study in two parts. The first one is about the way students experience
e-learning and the second one is about their suggestions for the next day.
Integrating e-questionnaires and interviews and taking into account parameters
like economic factors and the permanent residence issue, we asked the students
about their preferences among face-to-face learning, distance learning and a
blended model. Remarks about the academic life and the possible ways of taking
the extra step, after the Covid-19 crisis ceases to exist, are made.


title:
Staying the course: Locating equilibria of dynamical systems on
  Riemannian manifolds defined by point-clouds

abstract:
  We introduce a method to successively locate equilibria (steady states) of
dynamical systems on Riemannian manifolds. The manifolds need not be
characterized by an atlas or by the zeros of a smooth map. Instead, they can be
defined by point-clouds and sampled as needed through an iterative process. If
the manifold is an Euclidean space, our method follows isoclines, curves along
which the direction of the vector field $X$ is constant. For a generic vector
field $X$, isoclines are smooth curves and every equilibrium is a limit point
of isoclines. We generalize the definition of isoclines to Riemannian manifolds
through the use of parallel transport: generalized isoclines are curves along
which the directions of $X$ are parallel transports of each other. As in the
Euclidean case, generalized isoclines of generic vector fields $X$ are smooth
curves that connect equilibria of $X$.
  Our work is motivated by computational statistical mechanics, specifically
high dimensional (stochastic) differential equations that model the dynamics of
molecular systems. Often, these dynamics concentrate near low-dimensional
manifolds and have transitions (sadddle points with a single unstable
direction) between metastable equilibria We employ iteratively sampled data and
isoclines to locate these saddle points. Coupling a black-box sampling scheme
(e.g., Markov chain Monte Carlo) with manifold learning techniques (diffusion
maps in the case presented here), we show that our method reliably locates
equilibria of $X$.


title: A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting

abstract:
  Hierarchical forecasting is a key problem in many practical multivariate
forecasting applications - the goal is to obtain coherent predictions for a
large number of correlated time series that are arranged in a pre-specified
tree hierarchy. In this paper, we present a probabilistic top-down approach to
hierarchical forecasting that uses a novel attention-based RNN model to learn
the distribution of the proportions according to which each parent prediction
is split among its children nodes at any point in time. These probabilistic
proportions are then coupled with an independent univariate probabilistic
forecasting model (such as Prophet or STS) for the root time series. The
resulting forecasts are computed in a top-down fashion and are naturally
coherent, and also support probabilistic predictions over all time series in
the hierarchy. We provide theoretical justification for the superiority of our
top-down approach compared to traditional bottom-up hierarchical modeling.
Finally, we experiment on three public datasets and demonstrate significantly
improved probabilistic forecasts, compared to state-of-the-art probabilistic
hierarchical models.


title: Testing Velocity Field Lensing on Realistic Galaxy Models

abstract:
  Weak gravitational lensing shear could be measured far more precisely if
information about unlensed attributes of source galaxies were available. Disk
galaxy velocity fields supply such information, at least in principle, with
idealized models predicting orders of magnitude more Fisher information when
velocity field observations are used to complement images. To test the level at
which realistic features of disk galaxies (warps, bars, spiral arms, and other
substructure) inject noise or bias into such shear measurements, we fit an
idealized disk model, including shear, to unsheared galaxies in the Illustris
TNG simulation. The inferred shear thus indicates the extent to which unmodeled
galaxy features inject noise and bias. We find that $\gamma_+$, the component
of shear parallel to the galaxy's first principal axis, is highly biased and
noisy because disks display a range of intrinsic axis ratios from 0.8-1. The
other shear component, $\gamma_\times$, shows little bias and is well-described
by a double Gaussian distribution with central core scatter
$\sigma_{\text{core}} \approx$ 0.03, with low-amplitude, broad wings. This is
the first measurement of the natural noise floor in the proposed velocity field
lensing technique. We conclude that the technique will achieve impressive
precision gains for measurements of $\gamma_\times$, but little gain for
measurements of $\gamma_+$.


title:
CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile
  Motion Sensors

abstract:
  In cities worldwide, cars cause health and traffic problems which could be
partly mitigated through an increased modal share of bicycles. Many people,
however, avoid cycling due to a lack of perceived safety. For city planners,
addressing this is hard as they lack insights into where cyclists feel safe and
where they do not. To gain such insights, we have in previous work proposed the
crowdsourcing platform SimRa, which allows cyclists to record their rides and
report near miss incidents via a smartphone app. In this paper, we present
CycleSense, a combination of signal processing and Machine Learning techniques,
which partially automates the detection of near miss incidents. Using the SimRa
data set, we evaluate CycleSense by comparing it to a baseline method used by
SimRa and show that it significantly improves incident detection.


title: A Study of Flares in the Ultra-Cool Regime from SPECULOOS-South

abstract:
  We present a study of photometric flares on 154 low-mass ($\leq 0.2
\textrm{M}_{\odot}$) objects observed by the SPECULOOS-South Observatory from
1st June 2018 to 23rd March 2020. In this sample we identify 85 flaring
objects, ranging in spectral type from M4 to L0. We detect 234 flares in this
sample, with energies between $10^{29.2}$ and $10^{32.7}$ erg, using both
automated and manual methods. With this work, we present the largest
photometric sample of flares on late-M and ultra-cool dwarfs to date. By
extending previous M dwarf flare studies into the ultra-cool regime, we find
M5-M7 stars are more likely to flare than both earlier, and later, M dwarfs. By
performing artificial flare injection-recovery tests we demonstrate that we can
detect a significant proportion of flares down to an amplitude of 1 per cent,
and we are most sensitive to flares on the coolest stars. Our results reveal an
absence of high-energy flares on the reddest dwarfs. To probe the relations
between rotation and activity for fully convective stars, we extract rotation
periods for fast rotators and lower-bound period estimates of slow rotators.
These rotation periods span from 2.2 hours to 65 days, and we find that the
proportion of flaring stars increases for the very fastest rotators. Finally,
we discuss the impact of our flare sample on planets orbiting ultra-cool stars.
As stars become cooler, they flare less frequently; therefore, it is unlikely
that planets around the very reddest dwarfs would enter the `abiogenesis' zone
or drive visible-light photosynthesis through flares alone.


title:
Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor
  Classification

abstract:
  Machine learning models have become an essential tool in current indoor
positioning solutions, given their high capabilities to extract meaningful
information from the environment. Convolutional neural networks (CNNs) are one
of the most used neural networks (NNs) due to that they are capable of learning
complex patterns from the input data. Another model used in indoor positioning
solutions is the Extreme Learning Machine (ELM), which provides an acceptable
generalization performance as well as a fast speed of learning. In this paper,
we offer a lightweight combination of CNN and ELM, which provides a quick and
accurate classification of building and floor, suitable for power and
resource-constrained devices. As a result, the proposed model is 58\% faster
than the benchmark, with a slight improvement in the classification accuracy
(by less than 1\%


title:
Learning Sequential Latent Variable Models from Multimodal Time Series
  Data

abstract:
  Sequential modelling of high-dimensional data is an important problem that
appears in many domains including model-based reinforcement learning and
dynamics identification for control. Latent variable models applied to
sequential data (i.e., latent dynamics models) have been shown to be a
particularly effective probabilistic approach to solve this problem, especially
when dealing with images. However, in many application areas (e.g., robotics),
information from multiple sensing modalities is available -- existing latent
dynamics methods have not yet been extended to effectively make use of such
multimodal sequential data. Multimodal sensor streams can be correlated in a
useful manner and often contain complementary information across modalities. In
this work, we present a self-supervised generative modelling framework to
jointly learn a probabilistic latent state representation of multimodal data
and the respective dynamics. Using synthetic and real-world datasets from a
multimodal robotic planar pushing task, we demonstrate that our approach leads
to significant improvements in prediction and representation quality.
Furthermore, we compare to the common learning baseline of concatenating each
modality in the latent space and show that our principled probabilistic
formulation performs better. Finally, despite being fully self-supervised, we
demonstrate that our method is nearly as effective as an existing supervised
approach that relies on ground truth labels.


title: PG3: Policy-Guided Planning for Generalized Policy Generation

abstract:
  A longstanding objective in classical planning is to synthesize policies that
generalize across multiple problems from the same domain. In this work, we
study generalized policy search-based methods with a focus on the score
function used to guide the search over policies. We demonstrate limitations of
two score functions and propose a new approach that overcomes these
limitations. The main idea behind our approach, Policy-Guided Planning for
Generalized Policy Generation (PG3), is that a candidate policy should be used
to guide planning on training problems as a mechanism for evaluating that
candidate. Theoretical results in a simplified setting give conditions under
which PG3 is optimal or admissible. We then study a specific instantiation of
policy search where planning problems are PDDL-based and policies are lifted
decision lists. Empirical results in six domains confirm that PG3 learns
generalized policies more efficiently and effectively than several baselines.
Code: https://github.com/ryangpeixu/pg3


title: Data Driven Modeling of Turbocharger Turbine using Koopman Operator

abstract:
  A turbocharger plays an essential part in reducing emissions and increasing
the fuel efficiency of road vehicles. The pulsating flow of exhaust gases,
along with high heat exchange from the turbocharger casing, makes developing
control-oriented models difficult. Several researchers have used maps provided
by manufacturers to solve this problem. These maps often fail to incorporate
any heat transfer effects and are unsuitable for wide operating regions. Also,
with the availability of more and better sensor data, there is a need for a
method that can exploit this to obtain a better predictive model. Koopman
approaches rely on the observation that one can lift the nonlinear dynamics of
the turbine into an infinite-dimensional function space over which dynamics are
linear. The objective of this paper is to develop a model to predict the
transient and steady-state behavior of the turbine using the Koopman operator
which can be helpful for control design and analysis. Our approach is as
follows. We use experimental data from a Cummins heavy-duty diesel engine to
develop a turbine model using Extended Dynamic Mode Decomposition, which
approximates the action of the Koopman operator on a finite-dimensional
subspace of the space of observables. The results demonstrate superior
performance compared to a tuned nonlinear autoregressive network with an
exogenous input model widely used in the literature. The performance of these
two models is analyzed based on their ability to predict turbine transient and
steady-state behavior.


title: German Parliamentary Corpus (GerParCor)

abstract:
  Parliamentary debates represent a large and partly unexploited treasure trove
of publicly accessible texts. In the German-speaking area, there is a certain
deficit of uniformly accessible and annotated corpora covering all
German-speaking parliaments at the national and federal level. To address this
gap, we introduce the German Parliament Corpus (GerParCor). GerParCor is a
genre-specific corpus of (predominantly historical) German-language
parliamentary protocols from three centuries and four countries, including
state and federal level data. In addition, GerParCor contains conversions of
scanned protocols and, in particular, of protocols in Fraktur converted via an
OCR process based on Tesseract. All protocols were preprocessed by means of the
NLP pipeline of spaCy3 and automatically annotated with metadata regarding
their session date. GerParCor is made available in the XMI format of the UIMA
project. In this way, GerParCor can be used as a large corpus of historical
texts in the field of political communication for various tasks in NLP.


title: Social Distancing Network Creation

abstract:
  During a pandemic people have to find a trade-off between meeting others and
staying safely at home. While meeting others is pleasant, it also increases the
risk of infection. We consider this dilemma by introducing a game-theoretic
network creation model in which selfish agents can form bilateral connections.
They benefit from network neighbors, but at the same time, they want to
maximize their distance to all other agents. This models the inherent conflict
that social distancing rules impose on the behavior of selfish agents in a
social network. Besides addressing this familiar issue, our model can be seen
as the inverse to the well-studied Network Creation Game by Fabrikant et al.
[PODC 2003] where agents aim at being as central as possible in the created
network. Thus, our work is in-line with studies that compare minimization
problems with their maximization versions.
  We look at two variants of network creation governed by social distancing. In
the first variant, there are no restrictions on the connections being formed.
We characterize optimal and equilibrium networks, and we derive asymptotically
tight bounds on the Price of Anarchy and Price of Stability. The second variant
is the model's generalization that allows restrictions on the connections that
can be formed. As our main result, we prove that Swap-Maximal Routing-Cost
Spanning Trees, an efficiently computable weaker variant of Maximum
Routing-Cost Spanning Trees, actually resemble equilibria for a significant
range of the parameter space. Moreover, we give almost tight bounds on the
Price of Anarchy and Price of Stability. These results imply that, compared the
well-studied inverse models, under social distancing the agents' selfish
behavior has a significantly stronger impact on the quality of the equilibria,
i.e., allowing socially much worse stable states.


title:
Neural Networks with Local Converging Inputs (NNLCI) for Solving
  Conservation Laws, Part II: 2D Problems

abstract:
  In our prior work [arXiv:2109.09316], neural network methods with inputs
based on domain of dependence and a converging sequence were introduced for
solving one dimensional conservation laws, in particular the Euler systems. To
predict a high-fidelity solution at a given space-time location, two solutions
of a conservation law from a converging sequence, computed from low-cost
numerical schemes, and in a local domain of dependence of the space-time
location, serve as the input of a neural network. In the present work, we
extend the methods to two dimensional Euler systems and introduce variations.
Numerical results demonstrate that the methods not only work very well in one
dimension [arXiv:2109.09316], but also perform well in two dimensions. Despite
smeared local input data, the neural network methods are able to predict
shocks, contacts, and smooth regions of the solution accurately. The neural
network methods are efficient and relatively easy to train because they are
local solvers.


title:
Spectrum of inner-product kernel matrices in the polynomial regime and
  multiple descent phenomenon in kernel ridge regression

abstract:
  We study the spectrum of inner-product kernel matrices, i.e., $n \times n$
matrices with entries $h (\langle \textbf{x}_i ,\textbf{x}_j \rangle/d)$ where
the $( \textbf{x}_i)_{i \leq n}$ are i.i.d.~random covariates in
$\mathbb{R}^d$. In the linear high-dimensional regime $n \asymp d$, it was
shown that these matrices are well approximated by their linearization, which
simplifies into the sum of a rescaled Wishart matrix and identity matrix. In
this paper, we generalize this decomposition to the polynomial high-dimensional
regime $n \asymp d^\ell,\ell \in \mathbb{N}$, for data uniformly distributed on
the sphere and hypercube. In this regime, the kernel matrix is well
approximated by its degree-$\ell$ polynomial approximation and can be
decomposed into a low-rank spike matrix, identity and a `Gegenbauer matrix'
with entries $Q_\ell (\langle \textbf{x}_i , \textbf{x}_j \rangle)$, where
$Q_\ell$ is the degree-$\ell$ Gegenbauer polynomial. We show that the spectrum
of the Gegenbauer matrix converges in distribution to a Marchenko-Pastur law.
  This problem is motivated by the study of the prediction error of kernel
ridge regression (KRR) in the polynomial regime $n \asymp d^\kappa, \kappa >0$.
Previous work showed that for $\kappa \not\in \mathbb{N}$, KRR fits exactly a
degree-$\lfloor \kappa \rfloor$ polynomial approximation to the target
function. In this paper, we use our characterization of the kernel matrix to
complete this picture and compute the precise asymptotics of the test error in
the limit $n/d^\kappa \to \psi$ with $\kappa \in \mathbb{N}$. In this case, the
test error can present a double descent behavior, depending on the effective
regularization and signal-to-noise ratio at level $\kappa$. Because this double
descent can occur each time $\kappa$ crosses an integer, this explains the
multiple descent phenomenon in the KRR risk curve observed in several previous
works.


title: Marginal Structural Illness-Death Models for Semi-Competing Risks Data

abstract:
  The three-state illness death model has been established as a general
approach for regression analysis of semi-competing risks data. In this paper,
we apply it to a class of marginal structural models for observational data. We
consider two specific such models, the usual Markov illness-death structural
model and the general Markov illness-death structural model which incorporates
a frailty term. For interpretation purposes, risk contrasts under the
structural models are defined. Inference under the usual Markov model can be
carried out using estimating equations with inverse probability weighting,
while inference under the general Markov model requires a weighted EM
algorithm. We study the inference procedures under both models using extensive
simulations and apply them to the analysis of mid-life alcohol exposure on
late-life cognitive impairment as well as mortality using the Honolulu Asia
Aging Study data set. The R codes developed in this work have been implemented
in the R package semicmprskcoxmsm that is publicly available on CRAN.


title: The K\"ahler different of a 0-dimensional scheme

abstract:
  Given a 0-dimensional scheme $\mathbb{X}$ in the projective $n$-space
$\mathbb{P}^n_K$ over a field $K$, we are interested in studying the K\"ahler
different of $\mathbb{X}$ and its applications. Using the K\"ahler different,
we characterize the generic position and Cayley-Bacharach properties of
$\mathbb{X}$ in several certain cases. When $\mathbb{X}$ is in generic
position, we prove a generalized version of the Ap\'{e}ry-Gorenstein-Samuel
theorem about arithmetically Gorenstein schemes. We also characterize
0-dimensional complete intersections in terms of the K\"ahler different and the
Cayley-Bacharach property.