title: The Distribution of Rational Points on Conics

abstract:
  We examine the counting function for rational points on conics, and show how
the point where the asymptotic behaviour begins depends on the size of the
smallest zero.


title: Improving robustness of jet tagging algorithms with adversarial training

abstract:
  Deep learning is a standard tool in the field of high-energy physics,
facilitating considerable sensitivity enhancements for numerous analysis
strategies. In particular, in identification of physics objects, such as jet
flavor tagging, complex neural network architectures play a major role.
However, these methods are reliant on accurate simulations. Mismodeling can
lead to non-negligible differences in performance in data that need to be
measured and calibrated against. We investigate the classifier response to
input data with injected mismodelings and probe the vulnerability of flavor
tagging algorithms via application of adversarial attacks. Subsequently, we
present an adversarial training strategy that mitigates the impact of such
simulated attacks and improves the classifier robustness. We examine the
relationship between performance and vulnerability and show that this method
constitutes a promising approach to reduce the vulnerability to poor modeling.


title: Light Management in Perovskite Photovoltaic Solar Cells: a perspective

abstract:
  Light Management (LM) is essential for metal-halide perovskite solar cells in
their race for record performance. In this review, criteria on materials,
processes and photonic engineering are established such as to enhance mainly
the short circuit current density, towards high energy yields. These criteria
are used to analyse a large panel of solutions envisaged in the literature for
single junction cells. Moreover, a perspective based on rigorous
electromagnetic simulations performed on various comparable structures is
proposed in order to clarify the conclusions, and to pave the way to further
performance enhancement in the case of all-perovskite, two-terminal tandem
cells.


title: TQSim: A Case for Reuse-Focused Tree-Based Quantum Circuit Simulation

abstract:
  Quantum computers can speed up computationally hard problems. However, to
realize their full potential, we must mitigate qubit errors (from noise) by
developing noise-aware algorithms, compilers, and architectures. Thus,
simulating quantum programs on classical computers with different noise models
is a de-facto tool that is used by researchers and practitioners.
Unfortunately, noisy quantum simulators iteratively execute the same circuit
across multiple trials (shots), thereby incurring high-performance overheads.
To address this, we propose a noisy simulation technique called Tree-Based
Quantum Circuit Simulation (TQSim). TQSim exploits the reusability of the
intermediate results during the noisy simulation and reduces computation. TQSim
dynamically partitions a circuit into several subcircuits. It then reuses the
intermediate results from these subcircuits during computation. As compared to
a noisy Qulacs-based baseline simulator, TQSim achieves an average speedup of
2.51x across 48 different benchmark circuits. Additionally, across benchmarks,
TQSim produces results with a normalized fidelity that is within the 0.016
range of the baseline normalized fidelity.


title: Manipulating Twitter Through Deletions

abstract:
  Research into influence campaigns on Twitter has mostly relied on identifying
malicious activities from tweets obtained via public APIs. These APIs provide
access to public tweets that have not been deleted. However, bad actors can
delete content strategically to manipulate the system. Unfortunately, estimates
based on publicly available Twitter data underestimate the true deletion
volume. Here, we provide the first exhaustive, large-scale analysis of
anomalous deletion patterns involving more than a billion deletions by over 11
million accounts. We find that a small fraction of accounts delete a large
number of tweets daily. We also uncover two abusive behaviors that exploit
deletions. First, limits on tweet volume are circumvented, allowing certain
accounts to flood the network with over 26 thousand daily tweets. Second,
coordinated networks of accounts engage in repetitive likes and unlikes of
content that is eventually deleted, which can manipulate ranking algorithms.
These kinds of abuse can be exploited to amplify content and inflate
popularity, while evading detection. Our study provides platforms and
researchers with new methods for identifying social media abuse.


title:
Geophysical inversions to delineate rocks with CO$_2$ sequestration
  potential through carbon mineralization

abstract:
  In addition to reducing anthropogenic emissions of CO$_2$, it is increasingly
clear we also need to remove CO$_2$ from the atmosphere in order to avoid some
of the worst case scenarios for climate change. Geologic sequestration of
CO$_2$ is among the most attractive approaches because of the large global
capacity and long-time scales for storage. One mechanism of geologic storage is
through carbon mineralization. Some mafic and ultramafic rocks contain minerals
that will react with CO$_2$ in a carbonation reaction and convert it to
carbonated minerals. This is effectively a permanent CO$_2$ storage mechanism.
The geologic question we are faced with is then to locate, delineate and
estimate the volume of potentially reactive rocks. Using a synthetic model that
emulates a prospective site for carbon mineralization in British Columbia, we
simulate and invert gravity and magnetic data to delineate reactive rocks. We
begin by inverting each data set independently and introduce a proxy experiment
to contend with the challenging problem of choosing an appropriate
physical-property threshold to estimate volumes from the recovered model. We
use this proxy experiment to estimate thresholds for standard, $\ell_2$
inversion of the gravity and magnetics, as well as for inversions which use
sparse and compact norms. A Petrophysically and Geologically Guided Inversion
(PGI) framework is used to construct quasi-geologic models from which volumes
can be estimated directly. We apply the PGI framework to the magnetics and
gravity data independently. The framework is also used to jointly invert these
data and produce a model that is consistent with both data sets. Cumulative
volume estimates with depth are informative and can help decide whether in situ
or ex situ sequestration might be appropriate. Using each of the inverted
models, we estimate cumulative volume of reactive rock as a function of depth.


title: Khovanov homology of strongly invertible knots and their quotients

abstract:
  We construct a spectral sequence relating the Khovanov homology of a strongly
invertible knot to the annular Khovanov homologies of the two natural quotient
knots. Using this spectral sequence, we re-prove that Khovanov homology
distinguishes certain slice disks. We also give an analogous spectral sequence
for the Heegaard Floer homology of the branched double cover.


title: Using Multiple Instance Learning for Explainable Solar Flare Prediction

abstract:
  In this work we leverage a weakly-labeled dataset of spectral data from NASAs
IRIS satellite for the prediction of solar flares using the Multiple Instance
Learning (MIL) paradigm. While standard supervised learning models expect a
label for every instance, MIL relaxes this and only considers bags of instances
to be labeled. This is ideally suited for flare prediction with IRIS data that
consists of time series of bags of UV spectra measured along the instrument
slit. In particular, we consider the readout window around the Mg II h&k lines
that encodes information on the dynamics of the solar chromosphere. Our MIL
models are not only able to predict whether flares occur within the next
$\sim$25 minutes with accuracies of around 90%, but are also able to explain
which spectral profiles were particularly important for their bag-level
prediction. This information can be used to highlight regions of interest in
ongoing IRIS observations in real-time and to identify candidates for typical
flare precursor spectral profiles. We use k-means clustering to extract groups
of spectral profiles that appear relevant for flare prediction. The recovered
groups show high intensity, triplet red wing emission and single-peaked h and k
lines, as found by previous works. They seem to be related to small-scale
explosive events that have been reported to occur tens of minutes before a
flare.


title: Spectral Measurement Sparsification for Pose-Graph SLAM

abstract:
  Simultaneous localization and mapping (SLAM) is a critical capability in
autonomous navigation, but in order to scale SLAM to the setting of "lifelong"
SLAM, particularly under memory or computation constraints, a robot must be
able to determine what information should be retained and what can safely be
forgotten. In graph-based SLAM, the number of edges (measurements) in a pose
graph determines both the memory requirements of storing a robot's observations
and the computational expense of algorithms deployed for performing state
estimation using those observations; both of which can grow unbounded during
long-term navigation. To address this, we propose a spectral approach for pose
graph sparsification which maximizes the algebraic connectivity of the
sparsified measurement graphs, a key quantity which has been shown to control
the estimation error of pose graph SLAM solutions. Our algorithm, MAC (for
"maximizing algebraic connectivity"), which is based on convex relaxation, is
simple and computationally inexpensive, and admits formal post hoc performance
guarantees on the quality of the solutions it provides. In experiments on
benchmark pose-graph SLAM datasets, we show that our approach quickly produces
high-quality sparsification results which retain the connectivity of the graph
and, in turn, the quality of corresponding SLAM solutions, as compared to a
baseline approach which does not consider graph connectivity.


title: The Spectrum of an Almost Maximally Open Quantized Cat Map

abstract:
  We consider eigenvalues of a quantized cat map (i.e. hyperbolic symplectic
integer matrix), cut off in phase space to include a fixed point as its only
periodic orbit on the torus. We prove a simple formula for the eigenvalues on
both the quantized real line and the quantized torus in the semiclassical limit
as $h \to 0$. We then consider the case with no fixed points, and prove a
superpolynomial decay bound on the eigenvalues.


title: Exact Matching: Algorithms and Related Problems

abstract:
  In 1982, Papadimitriou and Yannakakis introduced the Exact Matching (EM)
problem where given an edge colored graph, with colors red and blue, and an
integer $k$, the goal is to decided whether or not the graph contains a perfect
matching with exactly $k$ red edges. Although they conjectured it to be
$\textbf{NP}$-complete, soon after it was shown to be solvable in randomized
polynomial time in the seminal work of Mulmuley et al. placing it in the
complexity class $\textbf{RP}$. Since then, all attempts at finding a
deterministic algorithm for EM have failed, thus leaving it as one of the few
natural combinatorial problems in $\textbf{RP}$ but not known to be contained
in $\textbf{P}$, and making it an interesting instance for testing the
hypothesis $\textbf{RP}=\textbf{P}$. Progress has been lacking even on very
restrictive classes of graphs despite the problem being quite well known as
evidenced by the number of works citing it. In this paper we aim to gain more
insight into the problem by considering two directions of study. In the first
direction, we study EM on bipartite graphs with a relaxation of the color
constraint and provide an algorithm where the output is required to be a
perfect matching with a number of red edges differing from $k$ by at most
$k/2$. We also introduce an optimisation problem we call Top-k Perfect Matching
(TkPM) that shares many similarities with EM. By virtue of being an
optimization problem, it is more natural to approximate so we provide
approximation algorithms for it. In the second direction, we look at the
parameterized algorithms. Here we introduce new tools and FPT algorithms for
the study of EM and TkPM.


title: 4-Dimensional Trackers

abstract:
  4-dimensional (4D) trackers with ultra fast timing (10-30 ps) and very fine
spatial resolution (O(few $\mu$m)) represent a new avenue in the development of
silicon trackers, enabling new physics capabilities beyond the reach of the
existing tracking detectors. This paper reviews the impact of integrating 4D
tracking capabilities on several physics benchmarks both in potential upgrades
of the HL-LHC experiments and in several detectors at future colliders, and
summarizes the currently available sensor technologies as well as electronics,
along with their limitations and directions for R$\&$D.


title: AUTOLEX: An Automatic Framework for Linguistic Exploration

abstract:
  Each language has its own complex systems of word, phrase, and sentence
construction, the guiding principles of which are often summarized in grammar
descriptions for the consumption of linguists or language learners. However,
manual creation of such descriptions is a fraught process, as creating
descriptions which describe the language in "its own terms" without bias or
error requires both a deep understanding of the language at hand and
linguistics as a whole. We propose an automatic framework AutoLEX that aims to
ease linguists' discovery and extraction of concise descriptions of linguistic
phenomena. Specifically, we apply this framework to extract descriptions for
three phenomena: morphological agreement, case marking, and word order, across
several languages. We evaluate the descriptions with the help of language
experts and propose a method for automated evaluation when human evaluation is
infeasible.


title: Balanced Allocations in Batches: Simplified and Generalized

abstract:
  We consider the allocation of $m$ balls (jobs) into $n$ bins (servers). In
the Two-Choice process, for each of $m$ sequentially arriving balls, two
randomly chosen bins are sampled and the ball is placed in the least loaded
bin. It is well-known that the maximum load is $m/n+\log_2 \log n + O(1)$
w.h.p.
  Berenbrink, Czumaj, Englert, Friedetzky and Nagel (2012) introduced a
parallel version of this process, where $m$ balls arrive in consecutive batches
of size $b=n$ each. Balls within the same batch are allocated in parallel,
using the load information of the bins at the beginning of the batch. They
proved that the gap of this process is $O(\log n)$ with high probability.
  In this work, we present a new analysis of this setting, which is based on
exponential potential functions. This allows us to both simplify and generalize
the analysis of [BCE12] in different ways:
  $\quad 1.$ Our analysis covers a broad class of processes. This includes not
only Two-Choice, but also processes with fewer bin samples like $(1+\beta)$,
processes which can only receive one bit of information from each bin sample
and graphical allocation, where bins correspond to vertices in a graph.
  $\quad 2.$ Balls may be of different weights, as long as their weights are
independent samples from a distribution satisfying a technical condition on its
moment generating function.
  $\quad 3.$ For arbitrary batch sizes $b \geq n$, we prove a gap of $O(b/n
\cdot \log n)$. For any $b \in [n , n^3]$, we improve this to $O(b/n + \log n)$
and show that it is tight for a family of processes. This implies the
unexpected result that for e.g. $(1+\beta)$ with constant $\beta \in (0, 1]$,
the gap is $\Theta(\log n)$ for all $b \in [n,n \log n]$.
  We also conduct experiments which support our theoretical results, and even
hint at a superiority of less powerful processes like $(1+\beta)$ for large
batch sizes.


title:
Sylph: A Hypernetwork Framework for Incremental Few-shot Object
  Detection

abstract:
  We study the challenging incremental few-shot object detection (iFSD)
setting. Recently, hypernetwork-based approaches have been studied in the
context of continuous and finetune-free iFSD with limited success. We take a
closer look at important design choices of such methods, leading to several key
improvements and resulting in a more accurate and flexible framework, which we
call Sylph. In particular, we demonstrate the effectiveness of decoupling
object classification from localization by leveraging a base detector that is
pretrained for class-agnostic localization on a large-scale dataset. Contrary
to what previous results have suggested, we show that with a carefully designed
class-conditional hypernetwork, finetune-free iFSD can be highly effective,
especially when a large number of base categories with abundant data are
available for meta-training, almost approaching alternatives that undergo
test-time-training. This result is even more significant considering its many
practical advantages: (1) incrementally learning new classes in sequence
without additional training, (2) detecting both novel and seen classes in a
single pass, and (3) no forgetting of previously seen classes. We benchmark our
model on both COCO and LVIS, reporting as high as 17% AP on the long-tail rare
classes on LVIS, indicating the promise of hypernetwork-based iFSD.


title:
Nucleon spin structure functions, considering target mass correction and
  higher twist effects at the NNLO accuracy and their transverse momentum
  dependence

abstract:
  Using recent and updated world data on polarized structure functions $g_1$
and $g_2$ we perform QCD analysis at the next-next-to-leading-order (NNLO)
accuracy. We include also target mass correction and higher twist effect to get
more precise results in our fitting procedure. To confirm the validity of our
fitting results several sum rules are examined and we do a comparison for them
with results from other models. In our analysis we employ Jacobi polynomials
approach to obtain analytical solutions of the DGLAP evolution equations for
parton distribution functions (PDFs). Using the extracted PDFs from our data
analysis as input we also compute the $x$- and $\mathbf{p}_{T}$-dependence of
some transverse momentum dependence (TMD) PDFs in polarized case, based on
covariant parton model. These functions are naively even time-reversal (T-even)
at twist-2 approximation. The results for TMDs are indicating proper and
acceptable behaviour with respect to what are presented in other literatures.


title: Variaiton and $\lambda$-jump inequalities on $H^p$ spaces

abstract:
  Let $\phi\in \mathscr{S}$ with $\int\phi (x)\, dx=1$, and define
$$\phi_t(x)=\frac{1}{t^n}\phi (\frac{x}{t}),$$ and denote the function family
$\{\phi_t\ast f(x)\}_{t>0}$ by $\Phi\ast f(x)$. Suppose that there exists a
constant $C_1$ such that $$\sum_{t>0} |\hat{\phi}_t(x)|^2<C_1$$ for all $x\in
\mathbb{R}^n$. Then
  (i) There exists constant $C_2>0$ such that $$\|\mathscr{V}_2(\Phi\ast
f)\|_{L^p}\leq C_2\|f\|_{H^p},\;\;\frac{n}{n+1}<p\leq 1$$ for all $f\in
H^p(\mathbb{R}^n)$, $\frac{n}{n+1}<p\leq 1$.
  (ii) The $\lambda$-jump operator $N_{\lambda}(\Phi\ast f)$ satisfies
$$\|\lambda [N_{\lambda}(\Phi\ast f)]^{1/2}\|_{L^p}\leq
C_3\|f\|_{H^p},\;\;\frac{n}{n+1}<p\leq 1,$$ uniformly in $\lambda >0$ for some
constant $C_3>0$.


title:
Biolink Model: A Universal Schema for Knowledge Graphs in Clinical,
  Biomedical, and Translational Science

abstract:
  Within clinical, biomedical, and translational science, an increasing number
of projects are adopting graphs for knowledge representation. Graph-based data
models elucidate the interconnectedness between core biomedical concepts,
enable data structures to be easily updated, and support intuitive queries,
visualizations, and inference algorithms. However, knowledge discovery across
these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity
and complexity; the proliferation of ad hoc data formats; poor compliance with
guidelines on findability, accessibility, interoperability, and reusability;
and, in particular, the lack of a universally-accepted, open-access model for
standardization across biomedical KGs has left the task of reconciling data
sources to downstream consumers. Biolink Model is an open source data model
that can be used to formalize the relationships between data structures in
translational science. It incorporates object-oriented classification and
graph-oriented features. The core of the model is a set of hierarchical,
interconnected classes (or categories) and relationships between them (or
predicates), representing biomedical entities such as gene, disease, chemical,
anatomical structure, and phenotype. The model provides class and edge
attributes and associations that guide how entities should relate to one
another. Here, we highlight the need for a standardized data model for KGs,
describe Biolink Model, and compare it with other models. We demonstrate the
utility of Biolink Model in various initiatives, including the Biomedical Data
Translator Consortium and the Monarch Initiative, and show how it has supported
easier integration and interoperability of biomedical KGs, bringing together
knowledge from multiple sources and helping to realize the goals of
translational science.


title:
A Risk-Driven Probabilistic Approach to Quantify Resilience in Power
  Distribution Systems

abstract:
  It is of growing concern to ensure resilience in power distribution systems
to extreme weather events. However, there are no clear methodologies or metrics
available for resilience assessment that allows system planners to assess the
impact of appropriate planning measures and new operational procedures for
resilience enhancement. In this paper, we propose a resilience metric using
parameters that define system attributes and performance. To represent extreme
events (tail probability), the conditional value-at-risk of each of the
parameters are combined using Choquet Integral to evaluate the overall
resilience. The effectiveness of the proposed resilience metric is studied
within the simulation-based framework under extreme weather scenarios with the
help of a modified IEEE 123-bus system. With the proposed framework, system
operators will have additional flexibility to prioritize one investment over
the others to enhance the resilience of the grid.


title:
On efficient algorithms for computing near-best polynomial
  approximations to high-dimensional, Hilbert-valued functions from limited
  samples

abstract:
  Sparse polynomial approximation has become indispensable for approximating
smooth, high- or infinite-dimensional functions from limited samples. This is a
key task in computational science and engineering, e.g., surrogate modelling in
UQ where the function is the solution map of a parametric or stochastic PDE.
Yet, sparse polynomial approximation lacks a complete theory. On the one hand,
there is a well-developed theory of best $s$-term polynomial approximation,
which asserts exponential or algebraic rates of convergence for holomorphic
functions. On the other hand, there are increasingly mature methods such as
(weighted) $\ell^1$-minimization for computing such approximations. While the
sample complexity of these methods has been analyzed in detail, the matter of
whether or not these methods achieve such rates is not well understood.
Furthermore, these methods are not algorithms per se, since they involve exact
minimizers of nonlinear optimization problems.
  This paper closes these gaps. Specifically, we pose and answer the following
question: are there robust, efficient algorithms for computing approximations
to finite- or infinite-dimensional, holomorphic and Hilbert-valued functions
from limited samples that achieve best $s$-term rates? We answer this in the
affirmative by introducing algorithms and theoretical guarantees that assert
exponential or algebraic rates of convergence, along with robustness to
sampling, algorithmic, and physical discretization errors. We tackle both
scalar- and Hilbert-valued functions, this being particularly relevant to
parametric and stochastic PDEs. Our work involves several significant
developments of existing techniques, including a novel restarted primal-dual
iteration for solving weighted $\ell^1$-minimization problems in Hilbert
spaces. Our theory is supplemented by numerical experiments demonstrating the
practical efficacy of these algorithms.


title: Concept Embedding Analysis: A Review

abstract:
  Deep neural networks (DNNs) have found their way into many applications with
potential impact on the safety, security, and fairness of
human-machine-systems. Such require basic understanding and sufficient trust by
the users. This motivated the research field of explainable artificial
intelligence (XAI), i.e. finding methods for opening the "black-boxes" DNNs
represent. For the computer vision domain in specific, practical assessment of
DNNs requires a globally valid association of human interpretable concepts with
internals of the model. The research field of concept (embedding) analysis (CA)
tackles this problem: CA aims to find global, assessable associations of
humanly interpretable semantic concepts (e.g., eye, bearded) with internal
representations of a DNN. This work establishes a general definition of CA and
a taxonomy for CA methods, uniting several ideas from literature. That allows
to easily position and compare CA approaches. Guided by the defined notions,
the current state-of-the-art research regarding CA methods and interesting
applications are reviewed. More than thirty relevant methods are discussed,
compared, and categorized. Finally, for practitioners, a survey of fifteen
datasets is provided that have been used for supervised concept analysis. Open
challenges and research directions are pointed out at the end.


title: On finite time blow-up for a 3D Davey-Stewartson system

abstract:
  We consider the elliptic-elliptic Davey-Stewartson system in the
three-dimensional Euclidean space, and we give sufficient conditions for the
existence of finite time blow-up solutions in non-isotropic spaces. The proof
is based on some general results on distributions defined via homogeneous
symbols, in conjunction with a convexity argument.


title:
Theoretical Connection between Locally Linear Embedding, Factor
  Analysis, and Probabilistic PCA

abstract:
  Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality
reduction and manifold learning method. It has two main steps which are linear
reconstruction and linear embedding of points in the input space and embedding
space, respectively. In this work, we look at the linear reconstruction step
from a stochastic perspective where it is assumed that every data point is
conditioned on its linear reconstruction weights as latent factors. The
stochastic linear reconstruction of LLE is solved using expectation
maximization. We show that there is a theoretical connection between three
fundamental dimensionality reduction methods, i.e., LLE, factor analysis, and
probabilistic Principal Component Analysis (PCA). The stochastic linear
reconstruction of LLE is formulated similar to the factor analysis and
probabilistic PCA. It is also explained why factor analysis and probabilistic
PCA are linear and LLE is a nonlinear method. This work combines and makes a
bridge between two broad approaches of dimensionality reduction, i.e., the
spectral and probabilistic algorithms.


title: Logical and Physical Reversibility of Conservative Skyrmion Logic

abstract:
  Magnetic skyrmions are nanoscale whirls of magnetism that can be propagated
with electrical currents. The repulsion between skyrmions inspires their use
for reversible computing based on the elastic billiard ball collisions proposed
for conservative logic in 1982. Here we evaluate the logical and physical
reversibility of this skyrmion logic paradigm, as well as the limitations that
must be addressed before dissipation-free computation can be realized.


title: SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks

abstract:
  While (message-passing) graph neural networks have clear limitations in
approximating permutation-equivariant functions over graphs or general
relational data, more expressive, higher-order graph neural networks do not
scale to large graphs. They either operate on $k$-order tensors or consider all
$k$-node subgraphs, implying an exponential dependence on $k$ in memory
requirements, and do not adapt to the sparsity of the graph. By introducing new
heuristics for the graph isomorphism problem, we devise a class of universal,
permutation-equivariant graph networks, which, unlike previous architectures,
offer a fine-grained control between expressivity and scalability and adapt to
the sparsity of the graph. These architectures lead to vastly reduced
computation times compared to standard higher-order graph networks in the
supervised node- and graph-level classification and regression regime while
significantly improving over standard graph neural network and graph kernel
architectures in terms of predictive performance.


title: Wald's entropy in Coincident General Relativity

abstract:
  The equivalence principle and its universality enables the geometrical
formulation of gravity. In the standard formulation of General Relativity \'a
la Einstein, the gravitational interaction is geometrized in terms of the
spacetime curvature. However, if we embrace the geometrical character of
gravity, two alternative, though equivalent, formulations of General Relativity
emerge in flat spacetimes, in which gravity is fully ascribed either to torsion
or to non-metricity. The latter allows a much simpler formulation of General
Relativity oblivious to the affine spacetime structure, the Coincident General
Relativity. The entropy of a black hole can be computed using the Euclidean
path integral approach, which strongly relies on the addition of boundary or
regulating terms in the standard formulation of General Relativity. A more
fundamental derivation can be performed using Wald's formula, in which the
entropy is directly related to Noether charges and is applicable to general
theories with diffeomorphism invariance. In this work we extend Wald's Noether
charge method for calculating black hole entropy to spacetimes endowed with
non-metricity. Using this method, we show that Coincident General Relativity
with an improved action principle gives the same entropy as the well-known
entropy in standard General Relativity. Furthermore the first law of black hole
thermodynamics holds and an explicit expression for the energy appearing in the
first law is obtained.


title:
Sensitivity of non-radiative cloud-wind interactions to the
  hydrodynamics solver

abstract:
  Cloud-wind interactions are common in the interstellar and circumgalactic
media. Many studies have used simulations of such interactions to investigate
the effect of particular physical processes, but the impact of the choice of
hydrodynamics solver has largely been overlooked. Here we study the cloud-wind
interaction, also known as the "blob test", using seven different hydrodynamics
solvers: Three flavours of SPH, a moving mesh, adaptive mesh refinement and two
meshless schemes. The evolution of masses in dense gas and
intermediate-temperature gas, as well as the covering fraction of
intermediate-temperature gas, are systematically compared for initial density
contrasts of 10 and 100, and four numerical resolutions. To isolate the
differences due to the hydrodynamics solvers, we use non-radiative simulations
without physical conduction. We find large differences between these methods.
SPH methods show slower dispersal of the cloud, particularly for the higher
density contrast, but faster convergence, especially for the lower density
contrast. Predictions for the intermediate-temperature gas differ particularly
strongly, also between non-SPH codes, and converge most slowly. We conclude
that the hydrodynamical interaction between a dense cloud and a supersonic wind
remains an unsolved problem. Studies aiming to understand the physics or
observational signatures of cloud-wind interactions should test the robustness
of their results by comparing different hydrodynamics solvers.


title: Intermunicipal Travel Networks of Mexico (2020-2021)

abstract:
  We present a collection of networks that describe the travel patterns between
municipalities in Mexico between 2020 and 2021. Using anonymized mobile device
geo-location data we constructed directed, weighted networks representing the
(normalized) volume of travels between municipalities. We analysed changes in
global (graph total weight sum), local (centrality measures), and mesoscale
(community structure) network features. We observe that changes in these
features are associated with factors such as Covid-19 restrictions and
population size. In general, events in early 2020 (when initial Covid-19
restrictions were implemented) induced more intense changes in network
features, whereas later events had a less notable impact in network features.
We believe these networks will be useful for researchers and decision makers in
the areas of transportation, infrastructure planning, epidemic control and
network science at large.


title:
Extreme Divisors on $\bar{M}_{0,7}$ and Differences over Characteristic
  2

abstract:
  We find 101,052 new extreme divisors on $\bar{M}_{0,7}$ (in 31 $S_7$-orbits)
and millions of extreme nef curves over characteristic 0. Over characteristic
2, we identify two more $S_7$-orbits of extreme divisors, and prove
$\bar{\text{Eff}}^k (\bar{M}_{0,n})$ is strictly larger over characteristic 2
than it is over characteristic 0, for all $1\leq k \leq n-6$. For each such $k$
we provide explicit cycles which are extreme in $\text{Eff}^k(\bar{M}_{0,n})$
over characteristic 2 but external to $\bar{\text{Eff}}^k(\bar{M}_{0,n})$ over
characteristic 0. We apply our method of finding new extreme divisors to
compute $\text{Eff}(\bar{M}_{0,\mathcal{A}})$ for $\mathcal{A}=(\frac{1}{3},
\frac{1}{3}, \frac{1}{3}, \frac{1}{3}, \frac{1}{3}, \frac{1}{3}, 1)$, proving
it is polyhedral over any field, and conjecture a description of
$\text{Eff}(\text{Bl}_e \bar{LM}_7)$.


title: Near-Ambient Pressure Velocity Map Imaging

abstract:
  We present a new velocity map imaging instrument for studying molecular beam
surface scattering in a near-ambient pressure (NAP-VMI) environment. The
instrument offers the possibility to study chemical reaction dynamics and
kinetics where higher pressures are either desired or unavoidable. NAP-VMI
conditions are created by two sets of ion optics that guide ions through an
aperture and map their velocities. The aperture separates the high pressure
ionization region and maintains the necessary vacuum in the detector region.
The performance of the NAP-VMI is demonstrated with results from N$_2$O
photodissociation and N$_2$ scattering from Pd(110) surface, which are compared
under vacuum and at near-ambient pressure ($ 1\times10^{-3} $ mbar). NAP-VMI
has the potential to be a applied to, and useful for, a broader range of
experiments including photoelectron spectroscopy and scattering with liquid
microjets.


title:
Spatial Processing Front-End For Distant ASR Exploiting Self-Attention
  Channel Combinator

abstract:
  We present a novel multi-channel front-end based on channel shortening with
theWeighted Prediction Error (WPE) method followed by a fixed MVDR beamformer
used in combination with a recently proposed self-attention-based channel
combination (SACC) scheme, for tackling the distant ASR problem. We show that
the proposed system used as part of a ContextNet based end-to-end (E2E) ASR
system outperforms leading ASR systems as demonstrated by a 21.6% reduction in
relative WER on a multi-channel LibriSpeech playback dataset. We also show how
dereverberation prior to beamforming is beneficial and compare the WPE method
with a modified neural channel shortening approach. An analysis of the
non-intrusive estimate of the signal C50 confirms that the 8 channel WPE method
provides significant dereverberation of the signals (13.6 dB improvement). We
also show how the weights of the SACC system allow the extraction of accurate
spatial information which can be beneficial for other speech processing
applications like diarization.


title: Canary Extraction in Natural Language Understanding Models

abstract:
  Natural Language Understanding (NLU) models can be trained on sensitive
information such as phone numbers, zip-codes etc. Recent literature has focused
on Model Inversion Attacks (ModIvA) that can extract training data from model
parameters. In this work, we present a version of such an attack by extracting
canaries inserted in NLU training data. In the attack, an adversary with
open-box access to the model reconstructs the canaries contained in the model's
training set. We evaluate our approach by performing text completion on
canaries and demonstrate that by using the prefix (non-sensitive) tokens of the
canary, we can generate the full canary. As an example, our attack is able to
reconstruct a four digit code in the training dataset of the NLU model with a
probability of 0.5 in its best configuration. As countermeasures, we identify
several defense mechanisms that, when combined, effectively eliminate the risk
of ModIvA in our experiments.


title:
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software
  Co-Design of Neural Accelerators

abstract:
  In view of the performance limitations of fully-decoupled designs for neural
architectures and accelerators, hardware-software co-design has been emerging
to fully reap the benefits of flexible design spaces and optimize neural
network performance. Nonetheless, such co-design also enlarges the total search
space to practically infinity and presents substantial challenges. While the
prior studies have been focusing on improving the search efficiency (e.g., via
reinforcement learning), they commonly rely on co-searches over the entire
architecture-accelerator design space. In this paper, we propose a
\emph{semi}-decoupled approach to reduce the size of the total design space by
orders of magnitude, yet without losing optimality. We first perform neural
architecture search to obtain a small set of optimal architectures for one
accelerator candidate. Importantly, this is also the set of (close-to-)optimal
architectures for other accelerator designs based on the property that neural
architectures' ranking orders in terms of inference latency and energy
consumption on different accelerator designs are highly similar. Then, instead
of considering all the possible architectures, we optimize the accelerator
design only in combination with this small set of architectures, thus
significantly reducing the total search cost. We validate our approach by
conducting experiments on various architecture spaces for accelerator designs
with different dataflows. Our results highlight that we can obtain the optimal
design by only navigating over the reduced search space. The source code of
this work is at \url{https://github.com/Ren-Research/CoDesign}.


title: AutoML for Deep Recommender Systems: A Survey

abstract:
  Recommender systems play a significant role in information filtering and have
been utilised in different scenarios, such as e-commerce and social media. With
the prosperity of deep learning, deep recommender systems show superior
performance by capturing non-linear information and item-user relationships.
However, the design of deep recommender systems heavily relies on human
experiences and expert knowledge. To tackle this problem, Automated Machine
Learning (AutoML) is introduced to automatically search for the proper
candidates for different parts of deep recommender systems. This survey
performs a comprehensive review of the literature in this field. Firstly, we
propose an abstract concept for AutoML for deep recommender systems
(AutoRecSys) that describes its building blocks and distinguishes it from
conventional AutoML techniques and recommender systems. Secondly, we present a
taxonomy as a classification framework containing embedding dimension search,
feature interaction search, model design search and other components search.
Furthermore, we put a particular emphasis on the search space and search
strategy, as they are the common thread to connect all methods within each
category and enable practitioners to analyse and compare various approaches.
Finally, we propose four future promising research directions that will lead
this line of research.


title: Snowmass 2021 whitepaper: Proton structure at the precision frontier

abstract:
  An overwhelming number of theoretical predictions for hadron colliders
require parton distribution functions (PDFs), which are an important ingredient
of theory infrastructure for the next generation of high-energy experiments.
This whitepaper summarizes the status and future prospects for determination of
high-precision PDFs applicable in a wide range of energies and experiments, in
particular in precision tests of the Standard Model and in new physics searches
at the high-luminosity Large Hadron Collider and Electron-Ion Collider. We
discuss the envisioned advancements in experimental measurements, QCD theory,
global analysis methodology, and computing that are necessary to bring
unpolarized PDFs in the nucleon to the N2LO and N3LO accuracy in the QCD
coupling strength. Special attention is given to the new tasks that emerge in
the era of the precision PDF analysis, such as those focusing on the robust
control of systematic factors both in experimental measurements and theoretical
computations. Various synergies between experimental and theoretical studies of
the hadron structure are explored, including opportunities for studying PDFs
for nuclear and meson targets, PDFs with electroweak contributions or
dependence on the transverse momentum, for incisive comparisons between
phenomenological models for the PDFs and computations on discrete lattice, and
for cross-fertilization with machine learning/AI approaches. [Submitted to the
US Community Study on the Future of Particle Physics (Snowmass 2021).]


title:
Achieving the ultimate end-to-end rates of lossy quantum communication
  networks

abstract:
  The field of quantum communications promises the faithful distribution of
quantum information, quantum entanglement, and absolutely secret keys. The
highest rates of these tasks are fundamentally limited by the transmission
distance between quantum repeaters. Previous protocols are unable to saturate
the ultimate rates, which motivates our paper. We use entanglement purification
or equivalently, quantum error detection, to remove the effects of the bosonic
pure-loss channel, and prove the highest rates of our protocol saturate the
ultimate limits. By simplifying our protocol, we show that states can be
purified completely in a single attempt at rates which are close to the
ultimate limits. We propose a linear-optics implementation which is robust to
inefficient operations and measurements, showcasing its near-term potential for
real-world practical applications.


title:
Hydrodynamic Interactions Between Charged and Uncharged Brownian
  Colloids at a Fluid-Fluid Interface

abstract:
  Hypothesis: The collective dynamics and self-assembly of colloids floating at
a fluid/fluid interface is a balance between deterministic lateral interaction
forces, viscous resistance to colloid motion along the surface and thermal
(Brownian) fluctuations. As the colloid size decreases, thermal forces become
important and can affect the self assembly into ordered patterns and crystal
structures that are the starting point for various materials applications.
Numerics: Langevin dynamic simulations involving two particles straddling a
liquid/liquid interface with a high viscosity contrast are presented to
describe the lateral interfacial assembly of particles in Brownian and
non-Brownian dominated regimes. These simulations incorporate capillary
attraction, electrostatic repulsion, thermal fluctuations and HI between
particles (including the effect of the particle immersion depth). Simulation
results are presented for neutrally wetted particles which form a contact angle
of 90 degrees at the interface. Findings: Clustering, fractal growth and
particle ordering are observed at critically large values of the Pe numbers,
while smaller Pe numbers exhibit higher probabilities of yielding states in
which particles remain uncorrelated in space and more widely separated.


title: CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues

abstract:
  This paper addresses the problem of dialogue reasoning with contextualized
commonsense inference. We curate CICERO, a dataset of dyadic conversations with
five types of utterance-level reasoning-based inferences: cause, subsequent
event, prerequisite, motivation, and emotional reaction. The dataset contains
53,105 of such inferences from 5,672 dialogues. We use this dataset to solve
relevant generative and discriminative tasks: generation of cause and
subsequent event; generation of prerequisite, motivation, and listener's
emotional reaction; and selection of plausible alternatives. Our results
ascertain the value of such dialogue-centric commonsense knowledge datasets. It
is our hope that CICERO will open new research avenues into commonsense-based
dialogue reasoning.


title:
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog
  Evaluation

abstract:
  Accurate automatic evaluation metrics for open-domain dialogs are in high
demand. Existing model-based metrics for system response evaluation are trained
on human annotated data, which is cumbersome to collect. In this work, we
propose to use information that can be automatically extracted from the next
user utterance, such as its sentiment or whether the user explicitly ends the
conversation, as a proxy to measure the quality of the previous system
response. This allows us to train on a massive set of dialogs with weak
supervision, without requiring manual system turn quality annotations.
Experiments show that our model is comparable to models trained on human
annotated data. Furthermore, our model generalizes across both spoken and
written open-domain dialog corpora collected from real and paid users.


title:
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for
  Contextualized Language Representations

abstract:
  Multiple metrics have been introduced to measure fairness in various natural
language processing tasks. These metrics can be roughly categorized into two
categories: 1) \emph{extrinsic metrics} for evaluating fairness in downstream
applications and 2) \emph{intrinsic metrics} for estimating fairness in
upstream contextualized language representation models. In this paper, we
conduct an extensive correlation study between intrinsic and extrinsic metrics
across bias notions using 19 contextualized language models. We find that
intrinsic and extrinsic metrics do not necessarily correlate in their original
setting, even when correcting for metric misalignments, noise in evaluation
datasets, and confounding factors such as experiment configuration for
extrinsic metrics. %al


title: A Meta Survey of Quality Evaluation Criteria in Explanation Methods

abstract:
  Explanation methods and their evaluation have become a significant issue in
explainable artificial intelligence (XAI) due to the recent surge of opaque AI
models in decision support systems (DSS). Since the most accurate AI models are
opaque with low transparency and comprehensibility, explanations are essential
for bias detection and control of uncertainty. There are a plethora of criteria
to choose from when evaluating explanation method quality. However, since
existing criteria focus on evaluating single explanation methods, it is not
obvious how to compare the quality of different methods. This lack of consensus
creates a critical shortage of rigour in the field, although little is written
about comparative evaluations of explanation methods. In this paper, we have
conducted a semi-systematic meta-survey over fifteen literature surveys
covering the evaluation of explainability to identify existing criteria usable
for comparative evaluations of explanation methods. The main contribution in
the paper is the suggestion to use appropriate trust as a criterion to measure
the outcome of the subjective evaluation criteria and consequently make
comparative evaluations possible. We also present a model of explanation
quality aspects. In the model, criteria with similar definitions are grouped
and related to three identified aspects of quality; model, explanation, and
user. We also notice four commonly accepted criteria (groups) in the
literature, covering all aspects of explanation quality: Performance,
appropriate trust, explanation satisfaction, and fidelity. We suggest the model
be used as a chart for comparative evaluations to create more generalisable
research in explanation quality.


title: An elementary proof of a recursive formula for $\zeta(2n)$

abstract:
  In 1735 Euler \cite{Euler} demonstrated that for each positive integer $n$,
the series ${\zeta(2n)}=\sum_{k=1}^\infty k^{-2n}$ converges to a rational
multiple of $\pi^{2n}$. Many demonstrations of this fact are now known, and
Euler's discovery is traditionally proven using non-elementary techniques, such
as Fourier series and the calculus of residues (see \cite{Chapman}). We present
an elementary proof, similar to Cauchy's \cite{Cauchy} proof of the identity
$\zeta(2)=\pi^2/6$, only extended recursively for all values $\zeta(2n)$. Our
main formula
$\zeta(2k)=\dfrac{(-1)^{k-1}}{16^k-4^k}\left[k\frac{4^k}{(2k)!}+\sum_{\ell=0}^{k-1}(-1)^\ell
(16^\ell-4^\ell)\frac{4^{k-\ell}}{(2k-2\ell)!}\frac{\zeta(2\ell)}{\pi^{2\ell}}
\right]{\pi^{2k}}$, $(k=1,2,3,\dots)$ is not new in that it may be derived from
previous known formulae (see \cite{Saals}). Remarkably, Apostol \cite{Apostol}
discovered a proof very similar to ours, yet arrived at a different formula
relating $\zeta(2n)$ to the Bernoulli numbers, \`a la Euler.


title:
Geophysical Electromagnetics: A retrospective, DISC 2017, and a look
  forward

abstract:
  Geophysical electromagnetics (EM) plays an important role in mineral
exploration and is increasingly being used to help solve other problems of
relevance to society. In this article we reflect, from our perspective at
University of British Columbia (UBC), on the development of EM geophysics over
the years and on our attempts to enhance its understanding, and its visibility
and usefulness to the community. The availability of open-source resources, and
a shift within the EM community towards collaborative practices for sharing and
creating software and educational resources, has been a driver of progress
towards these goals. In this article we provide some background about this
trajectory and how the SEG Distinguished Instructor Short Course (DISC) was a
catalyst in our development of software and resources and in our broader goal
of creating more collaborative connections within the EM community.


title:
A Cross-Domain Approach for Continuous Impression Recognition from
  Dyadic Audio-Visual-Physio Signals

abstract:
  The impression we make on others depends not only on what we say, but also,
to a large extent, on how we say it. As a sub-branch of affective computing and
social signal processing, impression recognition has proven critical in both
human-human conversations and spoken dialogue systems. However, most research
has studied impressions only from the signals expressed by the emitter,
ignoring the response from the receiver. In this paper, we perform impression
recognition using a proposed cross-domain architecture on the dyadic IMPRESSION
dataset. This improved architecture makes use of cross-domain attention and
regularization. The cross-domain attention consists of intra- and
inter-attention mechanisms, which capture intra- and inter-domain relatedness,
respectively. The cross-domain regularization includes knowledge distillation
and similarity enhancement losses, which strengthen the feature connections
between the emitter and receiver. The experimental evaluation verified the
effectiveness of our approach. Our approach achieved a concordance correlation
coefficient of 0.770 in competence dimension and 0.748 in warmth dimension.


title:
Quasinormal modes and stability of boosted Reissner-Nordstr\"om AdS
  black holes

abstract:
  We investigate the numerical stability of accelerated AdS black holes against
linear scalar perturbations. In particular, we study the evolution of a probe
non-minimally coupled scalar field on Schwarzschild and Reissner-Nordstr\"om
AdS black holes with small accelerations by computing the quasinormal modes of
the perturbation spectrum. We decompose the scalar field Klein-Gordon equation
and study the eigenvalue problem for its angular and radial-temporal parts
using different numerical methods. The angular part is written in terms of the
Heun solution and expanded through the Frobenius method which turns out to give
eigenvalues qualitatively similar to the ones obtained through the spherical
harmonics representation. The radial-temporal evolution renders a stable field
profile which is decomposed in terms of damped and purely imaginary
oscillations of the quasinormal modes. We calculate the respective frequencies
for different spacetime parameters showing the existence of a fine-structure in
the modes, for both real and imaginary parts, which is not present in the
non-accelerated AdS black holes. Our results indicate that the Schwarzschild
and Reissner-Nordstr\"om AdS black holes with small accelerations are stable
against linear scalar perturbations.


title: GraphBLAS on the Edge: High Performance Streaming of Network Traffic

abstract:
  Long range detection is a cornerstone of defense in many operating domains
(land, sea, undersea, air, space, ..,). In the cyber domain, long range
detection requires the analysis of significant network traffic from a variety
of observatories and outposts. Construction of anonymized hypersparse traffic
matrices on edge network devices can be a key enabler by providing significant
data compression in a rapidly analyzable format that protects privacy.
GraphBLAS is ideally suited for both constructing and analyzing anonymized
hypersparse traffic matrices. The performance of GraphBLAS on an Accolade
Technologies edge network device is demonstrated on a near worse case traffic
scenario using a continuous stream of CAIDA Telescope darknet packets. The
performance for varying numbers of traffic buffers, threads, and processor
cores is explored. Anonymized hypersparse traffic matrices can be constructed
at a rate of over 50,000,000 packets per second; exceeding a typical 400
Gigabit network link. This performance demonstrates that anonymized hypersparse
traffic matrices are readily computable on edge network devices with minimal
compute resources and can be a viable data product for such devices.


title:
Offline Reinforcement Learning Under Value and Density-Ratio
  Realizability: the Power of Gaps

abstract:
  We consider a challenging theoretical problem in offline reinforcement
learning (RL): obtaining sample-efficiency guarantees with a dataset lacking
sufficient coverage, under only realizability-type assumptions for the function
approximators. While the existing theory has addressed learning under
realizability and under non-exploratory data separately, no work has been able
to address both simultaneously (except for a concurrent work which we compare
in detail). Under an additional gap assumption, we provide guarantees to a
simple pessimistic algorithm based on a version space formed by marginalized
importance sampling, and the guarantee only requires the data to cover the
optimal policy and the function classes to realize the optimal value and
density-ratio functions. While similar gap assumptions have been used in other
areas of RL theory, our work is the first to identify the utility and the novel
mechanism of gap assumptions in offline RL with weak function approximation.


abstract:
  Considerable effort has been made recently in the development of heuristic
quantum algorithms for solving combinatorial optimization problems. Meanwhile,
these problems have been studied extensively in classical computing for
decades. In this paper, we explore a natural approach to leveraging existing
classical techniques to enhance quantum optimization. Specifically, we run a
classical algorithm to find an approximate solution and then use a quantum
circuit to search its "neighborhood" for higher-quality solutions. We propose
the Classically-Boosted Quantum Optimization Algorithm (CBQOA) that is based on
this idea and can solve a wide range of combinatorial optimization problems,
including all unconstrained problems and many important constrained problems
such as Max Bisection, Maximum Independent Set, Minimum Vertex Cover, Portfolio
Optimization, Traveling Salesperson and so on. A crucial component of this
algorithm is an efficiently-implementable continuous-time quantum walk (CTQW)
on a properly-constructed graph that connects the feasible solutions. CBQOA
utilizes this CTQW and the output of an efficient classical procedure to create
a suitable superposition of the feasible solutions which is then processed in
certain way. This algorithm has the merits that it solves constrained problems
without modifying their cost functions, confines the evolution of the quantum
state to the feasible subspace, and does not rely on efficient indexing of the
feasible solutions. We demonstrate the applications of CBQOA to Max 3SAT and
Max Bisection, and provide empirical evidence that it outperforms previous
approaches on these problems.


title:
Tests of General Relativity with Gravitational-Wave Observations using a
  Flexible--Theory-Independent Method

abstract:
  We perform tests of General Relativity (GR) with gravitational waves (GWs)
from the inspiral stage of compact binaries using a theory-independent
framework, which adds generic phase corrections to each multipole of a GR
waveform model in frequency domain. This method has been demonstrated on
LIGO-Virgo observations to provide stringent constraints on post-Newtonian
predictions of the inspiral and to assess systematic biases that may arise in
such parameterized tests. Here, we detail the anatomy of our framework for
aligned-spin waveform models. We explore the effects of higher modes in the
underlying signal on tests of GR through analyses of two unequal-mass,
simulated binary signals similar to GW190412 and GW190814. We show that the
inclusion of higher modes improves both the precision and the accuracy of the
measurement of the deviation parameters. Our testing framework also allows us
to vary the underlying baseline GR waveform model and the frequency at which
the non-GR inspiral corrections are tapered off. We find that to optimize the
GR test of high-mass binaries, comprehensive studies would need to be done to
determine the best choice of the tapering frequency as a function of the
binary's properties. We also carry out an analysis on the binary neutron-star
event GW170817 to set bounds on the coupling constant $\alpha_0$ of
Jordan-Fierz-Brans-Dicke gravity. We take two plausible approaches; in the
first \emph{theory-agnostic} approach we find a bound $\alpha_0 \lesssim
2\times 10^{-1}$ from measuring the dipole-radiation for different neutron-star
equations of state, while in the second \emph{theory-specific} approach we
obtain $\alpha_0 \lesssim 4\times 10^{-1}$, both at $68\%$ credible level.
These differences arise mainly due to different statistical hypotheses used for
the analysis.


title:
Neural Network Layers for Prediction of Positive Definite Elastic
  Stiffness Tensors

abstract:
  Machine learning models can be used to predict physical quantities like
homogenized elasticity stiffness tensors, which must always be symmetric
positive definite (SPD) based on conservation arguments. Two datasets of
homogenized elasticity tensors of lattice materials are presented as examples,
where it is desired to obtain models that map unit cell geometric and material
parameters to their homogenized stiffness. Fitting a model to SPD data does not
guarantee the model's predictions will remain SPD. Existing Cholsesky
factorization and Eigendecomposition schemes are abstracted in this work as
transformation layers which enforce the SPD condition. These layers can be
included in many popular machine learning models to enforce SPD behavior. This
work investigates the effects that different positivity functions have on the
layers and how their inclusion affects model accuracy. Commonly used models are
considered, including polynomials, radial basis functions, and neural networks.
Ultimately it is shown that a single SPD layer improves the model's average
prediction accuracy.


title: A Gauss-Newton based Quantum Algorithm for Combinatorial Optimization

abstract:
  In this work, we present a Gauss-Newton based quantum algorithm (GNQA) for
combinatorial optimization problems that rapidly converges to one of the
optimal solutions without being trapped in local minima or plateaus. Quantum
optimization algorithms have been explored for decades, but more recent
investigations have been on variational quantum algorithms, which often suffer
from the aforementioned problems. Our approach mitigates those by employing a
tensor product state that accurately represents the optimal solution, and an
appropriate function for the Hamiltonian, containing all the combinations of
binary variables. Numerical experiments presented here demonstrate the
effectiveness of our approach, and they show that GNQA outperforms other
optimization methods in both convergence properties and accuracy for all
problems considered here. Finally, we briefly discuss the potential impact of
the approach to other problems, including those in quantum chemistry and higher
order binary optimization.


title: Hamiltonian description of internal ocean waves with Coriolis force

abstract:
  The interfacial internal waves are formed at the pycnocline or thermocline in
the ocean and are influenced by the Coriolis force due to the Earth's rotation.
A derivation of the model equations for the internal wave propagation taking
into account the Coriolis effect is proposed. It is based on the Hamiltonian
formulation of the internal wave dynamics in the irrotational case,
appropriately extended to a {\it nearly} Hamiltonian formulation which
incorporates the Coriolis forces.
  Two propagation regimes are examined, the long-wave and the intermediate
long-wave propagation with a small amplitude approximation for certain
geophysical scales of the physical variables. The obtained models are of the
type of the well-known Ostrovsky equation and describe the wave propagation
over the two spatial horizontal dimensions of the ocean surface.


title:
Using Virtual Reality to Design and Evaluate a Lunar Lander: The EL3
  Case Study

abstract:
  The European Large Logistics Lander (EL3) is being designed to carry out
cargo delivery missions in support of future lunar ground crews. The capacity
of virtual reality (VR) to visualize and interactively simulate the unique
lunar environment makes it a potentially powerful design tool during the early
development stages of such solutions. Based on input from the EL3 development
team, we have produced a VR-based operational scenario featuring a hypothetical
configuration of the lander. Relying on HCI research methods, we have
subsequently evaluated this scenario with relevant experts (n=10). Qualitative
findings from this initial pilot study have demonstrated the usefulness of VR
as a design tool in this context, but likewise surfaced a number of limitations
in the form of potentially impaired validity and generalizability. We conclude
by outlining our future research plan and reflect on the potential use of
physical stimuli to improve the validity of VR-based simulations in forthcoming
design activities.


title:
Fourier transform inversion: Bounded variation, polynomial growth,
  Henstock--Stieltjes integration

abstract:
  In this paper we prove pointwise and distributional Fourier transform
inversion theorems for functions on the real line that are locally of bounded
variation, while in a neighbourhood of infinity are Lebesgue integrable or have
polynomial growth. We also allow the Fourier transform to exist in the
principal value sense. A function is called regulated if it has a left limit
and a right limit at each point. The main inversion theorem is obtained by
solving the differential equation $df(t)-i\omega f(t)=g(t)$ for a regulated
function $f$, where $\omega$ is a complex number with positive imaginary part.
This is done using the Henstock--Stieltjes integral. This is an integral
defined with Riemann sums and a gauge. Some variants of the integration by
parts formula are also proved for this integral. When the function is of
polynomial growth its Fourier transform exists in a distributional sense,
although the inversion formula only involves integration of functions and
returns pointwise values.


title: How fragile is your network? More than you think

abstract:
  Graphs are pervasive in our everyday lives, with relevance to biology, the
internet, and infrastructure, as well as numerous other applications. It is
thus necessary to have an understanding as to how quickly a graph
disintegrates, whether by random failure or by targeted attack. While much of
the interest in this subject has been focused on targeted removal of nodes,
there has been some recent interest in targeted edge removal. Here, we focus on
how robust a graph is against edge removal. We define a measure of network
fragility that relates the fraction of edges removed to the largest connected
component. We construct a class of graphs that is robust to edge removal.
Furthermore, it is demonstrated that graphs generally disintegrate faster than
would be anticipated by greedy targeted attack. Finally it is shown that our
fragility measure as demonstrated real and natural networks.


title: SolidGen: An Autoregressive Model for Direct B-rep Synthesis

abstract:
  The Boundary representation (B-rep) format is the de-facto shape
representation in computer-aided design (CAD) to model watertight solid
objects. Recent approaches to generating CAD models have focused on learning
sketch-and-extrude modeling sequences that are executed by a solid modeling
kernel in postprocess to recover a B-rep. In this paper we present a new
approach that enables learning from and synthesizing B-reps without the need
for supervision through CAD modeling sequence data. Our method SolidGen, is an
autoregressive neural network that models the B-rep directly by predicting the
vertices, edges and faces using Transformer-based and pointer neural networks.
Key to achieving this is our Indexed Boundary Representation that references
B-rep vertices, edges and faces in a well-defined hierarchy to capture the
geometric and topological relations suitable for use with machine learning.
SolidGen can be easily conditioned on contexts e.g., class labels thanks to its
probabilistic modeling of the B-rep distribution. We demonstrate qualitatively,
quantitatively and through perceptual evaluation by human subjects that
SolidGen can produce high quality, realistic looking CAD models.


title: Exploration of Wire Array Metamaterials for the Plasma Axion Haloscope

abstract:
  A plasma haloscope has recently been proposed as a feasible approach to
extend the search for dark matter axions above 10 GHz ($\sim$ 40 $\mu$eV),
whereby the microwave cavity in a conventional axion haloscope is supplanted by
a wire array metamaterial. As the plasma frequency of a metamaterial is
determined by its unit cell, and is thus a bulk property, a metamaterial
resonator of any frequency can be made arbitrarily large, in contrast to a
microwave cavity which incurs a steep penalty in volume with increasing
frequency. We have investigated the basic properties of wire array
metamaterials through $S_{21}$ measurements in the 10 GHz range. Excellent
agreement with theoretical models is found, by which we project achievable
quality factors to be of order $10^{4}$ in an actual axion search. Furthermore,
schemes for tuning the array over a usable dynamic range ($30\%$ in frequency)
appear practical from an engineering perspective.


title: Using genome-wide expression compendia to study microorganisms

abstract:
  A gene expression compendium is a heterogeneous collection of gene expression
experiments assembled from data collected for diverse purposes. The widely
varied experimental conditions and genetic backgrounds across samples creates a
tremendous opportunity for gaining a systems level understanding of the
transcriptional responses that influence phenotypes. Variety in experimental
design is particularly important for studying microbes, where the
transcriptional responses integrate many signals and demonstrate plasticity
across strains including response to what nutrients are available and what
microbes are present. Advances in high-throughput measurement technology have
made it feasible to construct compendia for many microbes. In this review we
discuss how these compendia are constructed and analyzed to reveal
transcriptional patterns.


title:
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic
  Dataset for Narrative Comprehension

abstract:
  Question answering (QA) is a fundamental means to facilitate assessment and
training of narrative comprehension skills for both machines and young
children, yet there is scarcity of high-quality QA datasets carefully designed
to serve this purpose. In particular, existing datasets rarely distinguish
fine-grained reading skills, such as the understanding of varying narrative
elements. Drawing on the reading education research, we introduce FairytaleQA,
a dataset focusing on narrative comprehension of kindergarten to eighth-grade
students. Generated by educational experts based on an evidence-based
theoretical framework, FairytaleQA consists of 10,580 explicit and implicit
questions derived from 278 children-friendly stories, covering seven types of
narrative elements or relations. Our dataset is valuable in two folds: First,
we ran existing QA models on our dataset and confirmed that this annotation
helps assess models' fine-grained learning skills. Second, the dataset supports
question generation (QG) task in the education domain. Through benchmarking
with QG models, we show that the QG model trained on FairytaleQA is capable of
asking high-quality and more diverse questions.


title:
AI-augmented histopathologic review using image analysis to optimize DNA
  yield and tumor purity from FFPE slides

abstract:
  To achieve minimum DNA input and tumor purity requirements for
next-generation sequencing (NGS), pathologists visually estimate
macrodissection and slide count decisions. Misestimation may cause tissue waste
and increased laboratory costs. We developed an AI-augmented smart pathology
review system (SmartPath) to empower pathologists with quantitative metrics for
determining tissue extraction parameters. Using digitized H&E-stained FFPE
slides as inputs, SmartPath segments tumors, extracts cell-based features, and
suggests macrodissection areas. To predict DNA yield per slide, the extracted
features are correlated with known DNA yields. Then, a pathologist-defined
target yield divided by the predicted DNA yield/slide gives the number of
slides to scrape. Following model development, an internal validation trial was
conducted within the Tempus Labs molecular sequencing laboratory. We evaluated
our system on 501 clinical colorectal cancer slides, where half received
SmartPath-augmented review and half traditional pathologist review. The
SmartPath cohort had 25% more DNA yields within a desired target range of
100-2000ng. The SmartPath system recommended fewer slides to scrape for large
tissue sections, saving tissue in these cases. Conversely, SmartPath
recommended more slides to scrape for samples with scant tissue sections,
helping prevent costly re-extraction due to insufficient extraction yield. A
statistical analysis was performed to measure the impact of covariates on the
results, offering insights on how to improve future applications of SmartPath.
Overall, the study demonstrated that AI-augmented histopathologic review using
SmartPath could decrease tissue waste, sequencing time, and laboratory costs by
optimizing DNA yields and tumor purity.


title:
Ultrafast Ultrasound Imaging for 3D Shear Wave Absolute
  Vibro-Elastography

abstract:
  Shear wave absolute vibro-elastography (S-WAVE) is an imaging technique that
generates steady-state shear waves inside the tissue using multi-frequency
excitation from an external vibration source. In this work, plane wave imaging
is introduced to reduce total acquisition time while retaining the benefit of
3D formulation. Plane wave imaging with a frame rate of 3000 frames/s is
followed by 3D absolute elasticity estimation. We design two imaging sequences
of ultrafast S-WAVE for two sets of excitation frequencies using a Verasonics
system and a motorized swept ultrasound transducer to synchronize ultrasound
acquisition with the external mechanical excitation. The overall data
collection time is improved by 83-88% compared to the original 3D S-WAVE
because of the per-channel acquisition offered by the Verasonics system. Tests
are performed on liver fibrosis tissue-mimicking phantoms and on ex vivo bovine
liver. The curl operator was previously used in magnetic resonance elastography
(MRE) to cancel out the effect of the compressional waves. In this work, we
apply the curl operator to the full 3D displacement field followed by 3D
elasticity reconstruction. The results of phantom experiment show more accurate
elasticity estimation as well as 18% less standard deviation (STD) compared to
reconstruction using the curl of a 2D displacement field and 45% less STD than
without the curl. We also compare our experimental results with a previous
method based on acoustic radiation force impulse (ARFI) and achieve closer
results to phantom manufacturer provided values with ultrafast S-WAVE.
Furthermore, the dependency of the bovine liver elasticity on the frequency of
excitation was also shown with our system.


title:
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized
  Floating Aggregation Point

abstract:
  We propose cooperative edge-assisted dynamic federated learning (CE-FL).
CE-FL introduces a distributed machine learning (ML) architecture, where data
collection is carried out at the end devices, while the model training is
conducted cooperatively at the end devices and the edge servers, enabled via
data offloading from the end devices to the edge servers through base stations.
CE-FL also introduces floating aggregation point, where the local models
generated at the devices and the servers are aggregated at an edge server,
which varies from one model training round to another to cope with the network
evolution in terms of data distribution and users' mobility. CE-FL considers
the heterogeneity of network elements in terms of communication/computation
models and the proximity to one another. CE-FL further presumes a dynamic
environment with online variation of data at the network devices which causes a
drift at the ML model performance. We model the processes taken during CE-FL,
and conduct analytical convergence analysis of its ML model training. We then
formulate network-aware CE-FL which aims to adaptively optimize all the network
elements via tuning their contribution to the learning process, which turns out
to be a non-convex mixed integer problem. Motivated by the large scale of the
system, we propose a distributed optimization solver to break down the
computation of the solution across the network elements. We finally demonstrate
the effectiveness of our framework with the data collected from a real-world
testbed.


title:
Research on Flexibility Margin of Electric-Hydrogen Coupling Energy
  Block Based on Model Predictive Control

abstract:
  Hydrogen energy plays an important role in the transformation of low-carbon
energy, and electric hydrogen coupling will become a typical energy scenario.
Aiming at the operation flexibility of low-carbon electricity hydrogen coupling
system with high proportion of wind power and photovoltaic, this paper studies
the flexibility margin of electricity hydrogen coupling energy block based on
model predictive control (MPC). By analyzing the power exchange characteristics
of heterogeneous energy, the homogenization models of various heterogeneous
energy sources are established. According to the analysis of power system
flexibility margin, three dimensions of flexibility margin evaluation indexes
are defined from the dimension of system operation, and an electricity hydrogen
coupling energy block scheduling model is established. The model predictive
control algorithm is used to optimize the power balance operation of the
electro hydrogen coupling energy block, and the flexibility margin of the
energy block is quantitatively analyzed and calculated. Through the example
analysis, it is verified that the calculation method proposed in this paper can
not only realize the on-line power balance optimization of electric hydrogen
coupling energy block, but also effectively quantify the operation flexibility
margin of electric hydrogen coupling energy block.


title:
Ion Alfv\'en velocity fluctuations and implications for the diffusion of
  streaming cosmic rays

abstract:
  The interstellar medium (ISM) of star-forming galaxies is magnetized and
turbulent. Cosmic rays (CRs) propagate through it, and those with energies from
$\sim\,\rm{GeV} - \rm{TeV}$ are likely subject to the streaming instability,
whereby the wave damping processes balances excitation of resonant ionic
Alfv\'en waves by the CRs, reaching an equilibrium in which the propagation
speed of the CRs is very close to the local ion Alfv\'en velocity. The
transport of streaming CRs is therefore sensitive to ionic Alfv\'en velocity
fluctuations. In this paper we systematically study these fluctuations using a
large ensemble of compressible MHD turbulence simulations. We show that for
sub-Alfv\'enic turbulence, as obtains for a strongly magnetized ISM, the ionic
Alfv\'en velocity probability density function (PDF) is determined solely by
the density fluctuations from shocked gas forming parallel to the magnetic
field, and we develop analytical models for the ionic Alfv\'en velocity PDF up
to second moments. For super-Alfv\'enic turbulence, magnetic and density
fluctuations are correlated in complex ways, and these correlations as well as
contributions from the magnetic fluctuations sets the ionic Alfv\'en velocity
PDF. We discuss the implications of these findings for underlying "macroscopic"
diffusion mechanisms in CRs undergoing the streaming instability, including
modeling the macroscopic diffusion coefficient for the parallel transport in a
sub-Alfv\'enic plasma. We also describe how, for highly-magnetized turbulent
gas, the gas density PDF, and hence column density PDF, can be used to access
information about ionic Alfv\'en velocity structure from observations of the
magnetized ISM.


title:
A Densely Connected Criss-Cross Attention Network for Document-level
  Relation Extraction

abstract:
  Document-level relation extraction (RE) aims to identify relations between
two entities in a given document. Compared with its sentence-level counterpart,
document-level RE requires complex reasoning. Previous research normally
completed reasoning through information propagation on the mention-level or
entity-level document-graph, but rarely considered reasoning at the
entity-pair-level.In this paper, we propose a novel model, called Densely
Connected Criss-Cross Attention Network (Dense-CCNet), for document-level RE,
which can complete logical reasoning at the entity-pair-level. Specifically,
the Dense-CCNet performs entity-pair-level logical reasoning through the
Criss-Cross Attention (CCA), which can collect contextual information in
horizontal and vertical directions on the entity-pair matrix to enhance the
corresponding entity-pair representation. In addition, we densely connect
multiple layers of the CCA to simultaneously capture the features of single-hop
and multi-hop logical reasoning.We evaluate our Dense-CCNet model on three
public document-level RE datasets, DocRED, CDR, and GDA. Experimental results
demonstrate that our model achieves state-of-the-art performance on these three
datasets.


title:
GEN-VLKT: Simplify Association and Enhance Interaction Understanding for
  HOI Detection

abstract:
  The task of Human-Object Interaction~(HOI) detection could be divided into
two core problems, i.e., human-object association and interaction
understanding. In this paper, we reveal and address the disadvantages of the
conventional query-driven HOI detectors from the two aspects. For the
association, previous two-branch methods suffer from complex and costly
post-matching, while single-branch methods ignore the features distinction in
different tasks. We propose Guided-Embedding Network~(GEN) to attain a
two-branch pipeline without post-matching. In GEN, we design an instance
decoder to detect humans and objects with two independent query sets and a
position Guided Embedding~(p-GE) to mark the human and object in the same
position as a pair. Besides, we design an interaction decoder to classify
interactions, where the interaction queries are made of instance Guided
Embeddings (i-GE) generated from the outputs of each instance decoder layer.
For the interaction understanding, previous methods suffer from long-tailed
distribution and zero-shot discovery. This paper proposes a Visual-Linguistic
Knowledge Transfer (VLKT) training strategy to enhance interaction
understanding by transferring knowledge from a visual-linguistic pre-trained
model CLIP. In specific, we extract text embeddings for all labels with CLIP to
initialize the classifier and adopt a mimic loss to minimize the visual feature
distance between GEN and CLIP. As a result, GEN-VLKT outperforms the state of
the art by large margins on multiple datasets, e.g., +5.05 mAP on HICO-Det. The
source codes are available at https://github.com/YueLiao/gen-vlkt.


title:
Mode decomposition-based time-varying phase synchronization for fMRI
  Data

abstract:
  Recently there has been significant interest in measuring time-varying
functional connectivity (TVC) between different brain regions using
resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to
assess the relationship between signals from different brain regions is to
measure their phase synchronization (PS) across time. However, this requires
the \textit{a priori} choice of type and cut-off frequencies for the bandpass
filter needed to perform the analysis. Here we explore alternative approaches
based on the use of various mode decomposition (MD) techniques that circumvent
this issue. These techniques allow for the data driven decomposition of signals
jointly into narrow-band components at different frequencies, thus fulfilling
the requirements needed to measure PS. We explore several variants of MD,
including empirical mode decomposition (EMD), bivariate EMD (BEMD),
noise-assisted multivariate EMD (na-MEMD), and introduce the use of
multivariate variational mode decomposition (MVMD) in the context of estimating
time-varying PS. We contrast the approaches using a series of simulations and
application to rs-fMRI data. Our results show that MVMD outperforms other
evaluated MD approaches, and further suggests that this approach can be used as
a tool to reliably investigate time-varying PS in rs-fMRI data.


title:
From Intervention to Domain Transportation: A Novel Perspective to
  Optimize Recommendation

abstract:
  The interventional nature of recommendation has attracted increasing
attention in recent years. It particularly motivates researchers to formulate
learning and evaluating recommendation as causal inference and data
missing-not-at-random problems. However, few take seriously the consequence of
violating the critical assumption of overlapping, which we prove can
significantly threaten the validity and interpretation of the outcome. We find
a critical piece missing in the current understanding of information retrieval
(IR) systems: as interventions, recommendation not only affects the already
observed data, but it also interferes with the target domain (distribution) of
interest. We then rephrase optimizing recommendation as finding an intervention
that best transports the patterns it learns from the observed domain to its
intervention domain. Towards this end, we use domain transportation to
characterize the learning-intervention mechanism of recommendation. We design a
principled transportation-constraint risk minimization objective and convert it
to a two-player minimax game. We prove the consistency, generalization, and
excessive risk bounds for the proposed objective, and elaborate how they
compare to the current results. Finally, we carry out extensive real-data and
semi-synthetic experiments to demonstrate the advantage of our approach, and
launch online testing with a real-world IR system.


title:
Design and performance evaluation in Kiefer-Weiss problems when sampling
  from discrete exponential families

abstract:
  In this paper, we deal with problems of testing hypotheses in the framework
of sequential statistical analysis. The main concern is the optimal design and
performance evaluation of sampling plans in the Kiefer-Weiss problems.
  For the observations which follow a discrete exponential family, we provide
algorithms for optimal design in the modified Kiefer-Weiss problem, and obtain
formulas for evaluating their performance, calculating operating characteristic
function, average sample number, and some related characteristics. These
formulas cover, as a particular case, the sequential probability ratio tests
(SPRT) and their truncated versions, as well as optimal finite-horizon
sequential tests.
  On the basis of the developed algorithms we propose a method of construction
of optimal tests and their performance evaluation for the original Kiefer-Weiss
problem.
  All the algorithms are implemented as functions in the R programming language
and can be downloaded from {\tt https://github.com/tosinabase/Kiefer-Weiss},
where the functions for the binomial, Poisson, and negative binomial
distributions are readily available.
  Finally, we make numerical comparisons of the Kiefer-Weiss solution with the
SPRT and the fixed-sample-size test having the same levels of the error
probabilities.


title:
Global existence and stabilization in a diffusive predator-prey model
  with population flux by attractive transition

abstract:
  The diffusive Lotka-Volterra predator-prey model \begin{eqnarray*} \left\{
\begin{array}{rcll} u_t &=& \nabla\cdot \left[ d_1\nabla u + \chi v^2 \nabla
\Big(\dfrac{u}{v}\Big)\right] +u(m_1-u+av), \qquad & x\in\Omega, \ t>0, \\ v_t
&=& d_2\Delta v+v(m_2-bu-v), \qquad & x\in\Omega, \ t>0, \end{array} \right.
\end{eqnarray*} is considered in a bounded domain $\Omega\subset\mathbb{R}^n$,
$n \in\{2,3\}$, under Neumann boundary condition, where $d_1, d_2, m_1, \chi,
a, b$ are positive constants and $m_2$ is a real constant. The purpose of this
paper is to establish global existence and boundedness of classical solutions
in the case $n=2$ and global existence of weak solutions in the case $n=3$ as
well as show long-time stabilization. More precisely, we prove that the
solutions $(u(\cdot,t), v(\cdot,t))$ converge to the constant steady state
$(u_*, v_*)$ as $t \to \infty$, where $u_*, v_*$ solves
$u_*(m_1-u_*+av_*)=v_*(m_2-bu_*-v_*)=0$ with $u_* > 0$ (covering both
coexistence as well as prey-extinction cases).


title:
Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking
  Controllers for the Uncertain Quadrotor Systems

abstract:
  Quadrotors are one of the popular unmanned aerial vehicles (UAVs) due to
their versatility and simple design. However, the tuning of gains for quadrotor
flight controllers can be laborious, and accurately stable control of
trajectories can be difficult to maintain under exogenous disturbances and
uncertain system parameters. This paper introduces a novel robust and adaptive
control synthesis methodology for a quadrotor robot's attitude and altitude
stabilization. The developed method is based on the fuzzy reinforcement
learning and Strictly Negative Imaginary (SNI) property. The first stage of our
control approach is to transform a nonlinear quadrotor system into an
equivalent Negative-Imaginary (NI) linear model by means of the feedback
linearization (FL) technique. The second phase is to design a control scheme
that adapts online the Strictly Negative Imaginary (SNI) controller gains via
fuzzy Q-learning, inspired by biological learning. The proposed controller does
not require any prior training. The performance of the designed controller is
compared with that of a fixed-gain SNI controller, a fuzzy-SNI controller, and
a conventional PID controller in a series of numerical simulations.
Furthermore, the stability of the proposed controller and the adaptive laws are
proofed using the NI theorem.


title:
A Relation of the Allen-Cahn equations and the Euler equations and
  applications of the Equipartition

abstract:
  We will prove that solutions of the Allen-Cahn equations that satisfy the
equipartition can be transformed into solutions of the Euler equations with
constant pressure. As a consequence, we obtain De Giorgi type results, that is,
the level sets of entire solutions are hyperplanes. In addition, we obtain some
examples of smooth entire solutions of the Euler equations in particular cases.
For specific type of initial conditions, some of these solutions can be
extended to the Navier-Stokes equations. Also, we will determine the structure
of solutions of the Allen-Cahn system in two dimensions that satisfy the
equipartition. In either cases, we do not assume that the solutions are
bounded. Finally, we apply the Leray projection on the Allen-Cahn system and
provide some explicit entire solutions.


title:
On the Meaning of Berry Force For Unrestricted Systems Treated With
  Mean-Field Electronic Structure

abstract:
  We show that the Berry force as computed by an approximate, mean-field
electronic structure can be meaningful if properly interpreted. In particular,
for a model Hamiltonian representing a molecular system with an even number of
electrons interacting via a two-body (Hubbard) interaction and a spin-orbit
coupling, we show that a meaningful nonzero Berry force emerges whenever there
is spin unrestriction--even though the Hamiltonian is real-valued and formally
the on-diagonal single-surface Berry force must be zero. Moreover, if properly
applied, this mean-field Berry force yields roughly the correct asymptotic
motion for scattering through an avoided crossing. That being said, within the
context of a ground-state calculation, several nuances do arise as far
interpreting the Berry force correctly, and as a practical matter, the Berry
force diverges near the Coulson-Fisher point (which can lead to numerical
instabilities). We do not address magnetic fields here.


title:
Tutorial: Modern Theoretical Tools for Understanding and Designing
  Next-generation Information Retrieval System

abstract:
  In the relatively short history of machine learning, the subtle balance
between engineering and theoretical progress has been proved critical at
various stages. The most recent wave of AI has brought to the IR community
powerful techniques, particularly for pattern recognition. While many benefits
from the burst of ideas as numerous tasks become algorithmically feasible, the
balance is tilting toward the application side. The existing theoretical tools
in IR can no longer explain, guide, and justify the newly-established
methodologies.
  The consequences can be suffering: in stark contrast to how the IR industry
has envisioned modern AI making life easier, many are experiencing increased
confusion and costs in data manipulation, model selection, monitoring,
censoring, and decision making. This reality is not surprising: without handy
theoretical tools, we often lack principled knowledge of the pattern
recognition model's expressivity, optimization property, generalization
guarantee, and our decision-making process has to rely on over-simplified
assumptions and human judgments from time to time.
  Time is now to bring the community a systematic tutorial on how we
successfully adapt those tools and make significant progress in understanding,
designing, and eventually productionize impactful IR systems. We emphasize
systematicity because IR is a comprehensive discipline that touches upon
particular aspects of learning, causal inference analysis, interactive (online)
decision-making, etc. It thus requires systematic calibrations to render the
actual usefulness of the imported theoretical tools to serve IR problems, as
they usually exhibit unique structures and definitions. Therefore, we plan this
tutorial to systematically demonstrate our learning and successful experience
of using advanced theoretical tools for understanding and designing IR systems.


title:
Transformer-empowered Multi-scale Contextual Matching and Aggregation
  for Multi-contrast MRI Super-resolution

abstract:
  Magnetic resonance imaging (MRI) can present multi-contrast images of the
same anatomical structures, enabling multi-contrast super-resolution (SR)
techniques. Compared with SR reconstruction using a single-contrast,
multi-contrast SR reconstruction is promising to yield SR images with higher
quality by leveraging diverse yet complementary information embedded in
different imaging modalities. However, existing methods still have two
shortcomings: (1) they neglect that the multi-contrast features at different
scales contain different anatomical details and hence lack effective mechanisms
to match and fuse these features for better reconstruction; and (2) they are
still deficient in capturing long-range dependencies, which are essential for
the regions with complicated anatomical structures. We propose a novel network
to comprehensively address these problems by developing a set of innovative
Transformer-empowered multi-scale contextual matching and aggregation
techniques; we call it McMRSR. Firstly, we tame transformers to model
long-range dependencies in both reference and target images. Then, a new
multi-scale contextual matching method is proposed to capture corresponding
contexts from reference features at different scales. Furthermore, we introduce
a multi-scale aggregation mechanism to gradually and interactively aggregate
multi-scale matched features for reconstructing the target SR MR image.
Extensive experiments demonstrate that our network outperforms state-of-the-art
approaches and has great potential to be applied in clinical practice. Codes
are available at https://github.com/XAIMI-Lab/McMRSR.


title:
Fusing Global and Local Features for Generalized AI-Synthesized Image
  Detection

abstract:
  With the development of the Generative Adversarial Networks (GANs) and
DeepFakes, AI-synthesized images are now of such high quality that humans can
hardly distinguish them from real images. It is imperative for media forensics
to develop detectors to expose them accurately. Existing detection methods have
shown high performance in generated images detection, but they tend to
generalize poorly in the real-world scenarios, where the synthetic images are
usually generated with unseen models using unknown source data. In this work,
we emphasize the importance of combining information from the whole image and
informative patches in improving the generalization ability of AI-synthesized
image detection. Specifically, we design a two-branch model to combine global
spatial information from the whole image and local informative features from
multiple patches selected by a novel patch selection module. Multi-head
attention mechanism is further utilized to fuse the global and local features.
We collect a highly diverse dataset synthesized by 19 models with various
objects and resolutions to evaluate our model. Experimental results demonstrate
the high accuracy and good generalization ability of our method in detecting
generated images.


title: Augmenting Knowledge Graphs for Better Link Prediction

abstract:
  Embedding methods have demonstrated robust performance on the task of link
prediction in knowledge graphs, by mostly encoding entity relationships. Recent
methods propose to enhance the loss function with a literal-aware term. In this
paper, we propose KGA: a knowledge graph augmentation method that incorporates
literals in an embedding model without modifying its loss function. KGA
discretizes quantity and year values into bins, and chains these bins both
horizontally, modeling neighboring values, and vertically, modeling multiple
levels of granularity. KGA is scalable and can be used as a pre-processing step
for any existing knowledge graph embedding model. Experiments on legacy
benchmarks and a new large benchmark, DWD, show that augmenting the knowledge
graph with quantities and years is beneficial for predicting both entities and
numbers, as KGA outperforms the vanilla models and other relevant baselines.
Our ablation studies confirm that both quantities and years contribute to KGA's
performance, and that its performance depends on the discretization and binning
settings. We make the code, models, and the DWD benchmark publicly available to
facilitate reproducibility and future research.


title: Stability of Linear Set-Membership Filters

abstract:
  Stability is fundamental to filter design. It reflects the sensitivity of the
estimate to initial conditions. In this paper, we focus on the stability of
linear set-membership filters (SMFs), which has not yet been well understood.
Considering linear time-invariant sampled-data systems, we explore the
stability issue of the existing/classical SMFing framework and establish a new
framework with guaranteed stability. First, we propose a new concept -- the
Observation-Information-Tower (OIT), which describes how the measurements
affect the estimate in a set-intersection manner. The OIT enables a rigorous
stability analysis of the classical linear SMFing framework, where an explicit
stability criterion is provided. It turns out that the classical framework
requires the knowledge of the true initial range of the system state to
guarantee stability, which is hence sensitive to initial conditions. To handle
this problem, we establish an OIT-inspired filtering framework such that the
stability is unaffected by the initial conditions. Under this
stability-guaranteed framework, we develop a stable and fast constrained
zonotopic SMF.