title:
Theoretical description of power transfer in a magneto-acoustic
  resonator

abstract:
  We derive an analytical model for the power flows in a magnetoacoustic
resonator. The resonator consists of a piezoelectric-magnetostrictive bilayer
system that can function as an electromagnetic transducer. The derived model
captures the dynamic magnetic influence onto the elastodynamics via an
effective frequency dependent stiffness constant. This allows to calculate both
the transducer magnetic and elastic power loss as well as its effciency in
function of the frequency while also considering the resonance conditions. The
model is applied onto an example system consisting of piezoelectric ScAlN and
magnetostrictive CoFeB. In addition, the influence of the magnetic material
parameters onto the power and effciency are determined by comparing CoFeB with
Nickel and Terfenol-D magnetostrictive layers.


title:
Improved parallel-in-time integration via low-rank updates and
  interpolation

abstract:
  This work is concerned with linear matrix equations that arise from the
space-time discretization of time-dependent linear partial differential
equations (PDEs). Such matrix equations have been considered, for example, in
the context of parallel-in-time integration leading to a class of algorithms
called ParaDiag. We develop and analyze two novel approaches for the numerical
solution of such equations. Our first approach is based on the observation that
the modification of these equations performed by ParaDiag in order to solve
them in parallel has low rank. Building upon previous work on low-rank updates
of matrix equations, this allows us to make use of tensorized Krylov subspace
methods to account for the modification. Our second approach is based on
interpolating the solution of the matrix equation from the solutions of several
modifications. Both approaches avoid the use of iterative refinement needed by
ParaDiag and related space-time approaches in order to attain good accuracy. In
turn, our new approaches have the potential to outperform, sometimes
significantly, existing approaches. This potential is demonstrated for several
different types of PDEs.


title: OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System

abstract:
  Improving the retrieval relevance on noisy datasets is an emerging need for
the curation of a large-scale clean dataset in the medical domain. While
existing methods can be applied for class-wise retrieval (aka. inter-class),
they cannot distinguish the granularity of likeness within the same class (aka.
intra-class). The problem is exacerbated on medical external datasets, where
noisy samples of the same class are treated equally during training. Our goal
is to identify both intra/inter-class similarities for fine-grained retrieval.
To achieve this, we propose an Outlier-Sensitive Content-based rAdiologhy
Retrieval System (OSCARS), consisting of two steps. First, we train an outlier
detector on a clean internal dataset in an unsupervised manner. Then we use the
trained detector to generate the anomaly scores on the external dataset, whose
distribution will be used to bin intra-class variations. Second, we propose a
quadruplet (a, p, nintra, ninter) sampling strategy, where intra-class
negatives nintra are sampled from bins of the same class other than the bin
anchor a belongs to, while niner are randomly sampled from inter-classes. We
suggest a weighted metric learning objective to balance the intra and
inter-class feature learning. We experimented on two representative public
radiography datasets. Experiments show the effectiveness of our approach. The
training and evaluation code can be found in
https://github.com/XiaoyuanGuo/oscars.


title: Collision dynamics of discrete soliton pair in uniform waveguide arrays

abstract:
  In this article we numerically study the collision dynamics of a pair of
discrete solitons (DSs) in a semi-infinite nonlinear waveguide array (WA). We
investigate that, a co-moving pair of identical DSs heading towards each other
undergo elastic collision or a discrete breather formation depending on the
initial amplitude ($A_0$) and wavenumber ($k_0$) of the two colliding DSs.
Under elastic collision, the DS pair emerge unscathed and form a cross-over
state, while under breather formation, two DSs are fused to form a single
field. We map this phenomena by iterative simulation and present a phase plot
which describes regimes of cross-over state or breather formation in
($A_0$-$k_0$) parameter space. Interaction dynamics between nonidentical DSs
having same amplitudes but different wavenumbers significantly deviates from
that of identical DSs. In such case, varied degree of energy exchange occurs
which results three distinct output states accompanied by diffractive resonant
radiation (DifRR) generation due to the interaction between soliton and WA
lattice. Extending our investigation to a more generalized condition by taking
different amplitudes and wavenumber of DS pair, we find that an unique
secondary radiation is originated due to the collision of solitons. The
wavenumber of this collision-mediated radiation depends on the wavenumbers of
soliton pair. Our results shed light on the different aspects of collision
dynamics of identical and non-identical DS pair in nonlinear WA and useful in
understanding the complex mechanism.


title:
Approximation Algorithms and Hardness for $n$-Pairs Shortest Paths and
  All-Nodes Shortest Cycles

abstract:
  We study the approximability of two related problems: $n$-Pairs Shortest
Paths ($n$-PSP), where the goal is to find a shortest path between $O(n)$
prespecified pairs, and All Node Shortest Cycles (ANSC), where the goal is to
find the shortest cycle passing through each node. Approximate $n$-PSP has been
previously studied, mostly in the context of distance oracles. ANSC has also
been studied previously, but only in terms of exact algorithms, rather than
approximation.
  We provide a thorough study of the approximability of $n$-PSP and ANSC,
providing a wide array of algorithms and conditional lower bounds that trade
off between running time and approximation ratio. Our conditional hardness
results are based on well-established and believable fine-grained hypotheses.
  A highlight of our conditional lower bounds results is that under the
$(k,3)$-Hyperclique Hypothesis for any integer $k\ge 4$, there is no algorithm
for unweighted undirected $n$-PSP with approximation ratio better than $3-6/k$
that runs in $O(n^{k/(k-2)-\epsilon})$ time. This is the first known lower
bound with approximation ratio higher than 2 for any distance problem except
for the $ST$-Diameter problem, but unlike in $n$-PSP, the number of vertex
pairs one considers in $ST$-Diameter is much larger than the running time lower
bounds.
  A highlight of our algorithmic results is that one can solve both $n$-PSP and
ANSC in $\tilde O(m+ n^{3/2+\epsilon})$ time with approximation factor
$2+\epsilon$ (and additive error that is function of $\epsilon$), for any
constant $\epsilon>0$. For $n$-PSP, our conditional lower bounds imply that
this approximation ratio is nearly optimal for any subquadratic-time
combinatorial algorithm. We further extend these algorithms for $n$-PSP and
ANSC to obtain a time/accuracy trade-off that includes near-linear time
algorithms.


title: Control barrier function based attack-recovery with provable guarantees

abstract:
  This paper studies provable security guarantees for cyber-physical systems
(CPS) under actuator attacks. In particular, we consider CPS safety and propose
a new attack-detection mechanism based on a zeroing control barrier function
(ZCBF) condition. In addition we design an adaptive recovery mechanism based on
how close the system is from violating safety. We show that the
attack-detection mechanism is sound, i.e., there are no false negatives for
adversarial attacks. Finally, we use a Quadratic Programming (QP) approach for
online recovery (and nominal) control synthesis. We demonstrate the
effectiveness of the proposed method in a simulation case study involving a
quadrotor with an attack on its motors.


title:
Non-smoothability for a class of groups of piecewise linear
  homeomorphisms of the interval

abstract:
  For a certain class of groups of piecewise linear homeomorphisms of the
interval, we prove that they admit no sufficiently regular faithful action on
the line. Building on previous work of Brum, Matte Bon, Rivas, and the author
[arXiv:2104.14678], the new ingredient is an observation from a recent work of
Hyde and Tatch Moore [arXiv:2103.14911], which allows to reduce the problem to
the case of the circle, and then apply Herman-Yoccoz theory.


title: Invariant Ideal Axiom

abstract:
  We introduce and prove the consistency of a new set theoretic axiom we call
the \emph{Invariant Ideal Axiom}. The axiom enables us to provide
(consistently) a full topological classification of countable sequential
groups, as well as fully characterize the behavior of their finite products.
  We also construct examples that demonstrate the optimality of the conditions
in \IIA, and list a number of open questions.


title: Graph Neural Networks Designed for Different Graph Types: A Survey

abstract:
  Graphs are ubiquitous in nature and can therefore serve as models for many
practical but also theoretical problems. Based on this, the young research
field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the
field and the speed in which new models are developed, many good surveys have
been published in the last years. Nevertheless, an overview on which graph
types can be modeled by GNNs is missing. In this survey, we give a detailed
overview of already existing GNNs and, unlike previous surveys, categorize them
according to their ability to handle different graph types. We consider GNNs
operating on static as well as on dynamic graphs of different structural
constitutions, with or without node or edge attributes. Moreover in the dynamic
case, we separate the models in discrete-time and continuous-time dynamic
graphs based on their architecture. According to our findings, there are still
graph types, that are not covered by existing GNN models. Specifically, models
concerning heterogeneity in attributes are missing and the deletion of nodes
and edges is only covered rarely.


title: Generalised Gibbs Ensemble for spherically constrained harmonic models

abstract:
  We build and analytically calculate the Generalised Gibbs Ensemble partition
function of the integrable Soft Neumann Model. This is the model of a classical
particle which is constrained to move, on average over the initial conditions,
on an $N$ dimensional sphere, and feels the effect of anisotropic harmonic
potentials. We derive all relevant averaged static observables in the
(thermodynamic) $N\rightarrow\infty$ limit. We compare them to their long-term
dynamic averages finding excellent agreement in all phases of a non-trivial
phase diagram determined by the characteristics of the initial conditions and
the amount of energy injected or extracted in an instantaneous quench. We
discuss the implications of our results for the proper Neumann model in which
the spherical constraint is imposed strictly.


title:
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation
  GAN

abstract:
  Instance segmentation for unlabeled imaging modalities is a challenging but
essential task as collecting expert annotation can be expensive and
time-consuming. Existing works segment a new modality by either deploying a
pre-trained model optimized on diverse training data or conducting domain
translation and image segmentation as two independent steps. In this work, we
propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN)
that conducts image translation and instance segmentation jointly using a
unified framework. Besides the CycleGAN losses for image translation and
supervised losses for the annotated source domain, we introduce additional
self-supervised and segmentation-based adversarial objectives to improve the
model performance by leveraging unlabeled target domain images. We benchmark
our approach on the task of 3D neuronal nuclei segmentation with annotated
electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data.
Our CySGAN outperforms both pretrained generalist models and the baselines that
sequentially conduct image translation and segmentation. Our implementation and
the newly collected, densely annotated ExM nuclei dataset, named NucExM, are
available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html.


title: Audio-Visual Person-of-Interest DeepFake Detection

abstract:
  Face manipulation technology is advancing very rapidly, and new methods are
being proposed day by day. The aim of this work is to propose a deepfake
detector that can cope with the wide variety of manipulation methods and
scenarios encountered in the real world. Our key insight is that each person
has specific biometric characteristics that a synthetic generator cannot likely
reproduce. Accordingly, we extract high-level audio-visual biometric features
which characterize the identity of a person, and use them to create a
person-of-interest (POI) deepfake detector. We leverage a contrastive learning
paradigm to learn the moving-face and audio segments embeddings that are most
discriminative for each identity. As a result, when the video and/or audio of a
person is manipulated, its representation in the embedding space becomes
inconsistent with the real identity, allowing reliable detection. Training is
carried out exclusively on real talking-face videos, thus the detector does not
depend on any specific manipulation method and yields the highest
generalization ability. In addition, our method can detect both single-modality
(audio-only, video-only) and multi-modality (audio-video) attacks, and is
robust to low-quality or corrupted videos by building only on high-level
semantic features. Experiments on a wide variety of datasets confirm that our
method ensures a SOTA performance, with an average improvement in terms of AUC
of around 3%, 10%, and 7% for high-quality, low quality and attacked videos,
respectively.


title: Knowledge Infused Decoding

abstract:
  Pre-trained language models (LMs) have been shown to memorize a substantial
amount of knowledge from the pre-training corpora; however, they are still
limited in recalling factually correct knowledge given a certain context.
Hence, they tend to suffer from counterfactual or hallucinatory generation when
used in knowledge-intensive natural language generation (NLG) tasks. Recent
remedies to this problem focus on modifying either the pre-training or task
fine-tuning objectives to incorporate knowledge, which normally require
additional costly training or architecture modification of LMs for practical
applications. We present Knowledge Infused Decoding (KID) -- a novel decoding
algorithm for generative LMs, which dynamically infuses external knowledge into
each step of the LM decoding. Specifically, we maintain a local knowledge
memory based on the current context, interacting with a dynamically created
external knowledge trie, and continuously update the local memory as a
knowledge-aware constraint to guide decoding via reinforcement learning. On six
diverse knowledge-intensive NLG tasks, task-agnostic LMs (e.g., GPT-2 and BART)
armed with KID outperform many task-optimized state-of-the-art models, and show
particularly strong performance in few-shot scenarios over seven related
knowledge-infusion techniques. Human evaluation confirms KID's ability to
generate more relevant and factual language for the input context when compared
with multiple baselines. Finally, KID also alleviates exposure bias and
provides stable generation quality when generating longer sequences. Code for
KID is available at https://github.com/microsoft/KID.


title: New Directions in the Search for Dark Matter

abstract:
  The identification of the nature of dark matter is one of the most important
problems confronting particle physics. Current observational constraints permit
the mass of the dark matter to range from $10^{-22}$ eV - $10^{48}$ GeV. Given
the weak nature of these bounds and the ease with which dark matter models can
be constructed, it is clear that the problem can only be solved experimentally.
In these lectures, I discuss methods to experimentally probe a wide range of
dark matter candidates.


title:
Statistical physics of frictional grains: some simple applications of
  Edwards statistics

abstract:
  Granular matter like sand is composed of a large number of interacting
grains, and is thus expected to be amenable to a statistical physics treatment.
Yet, the frictional properties of grains make the statistical physics of
granular matter significantly different from the equilibrium statistical
physics of atomic or molecular systems. We illustrate here on simple models
some of the key concepts of the statistical physics introduced by Edwards and
coworkers more than thirty years ago to describe shaken granular piles. Quite
surprisingly, properties of such frictional systems observed at high effective
temperature (i.e., strong shaking) may share some analogies with some low
temperature properties of equilibrium systems. For instance, the effective
specific heat of non-interacting frictional grains under strong shaking in a
harmonic potential goes to zero in the high temperature limit. As a second
example, a chain of frictional grains linked by springs exhibits a critical
point at infinite effective temperature, at odds with the zero-temperature
critical point generically found in one-dimensional equilibrium systems in the
presence of local interactions.


title: Faster Pattern Matching under Edit Distance

abstract:
  We consider the approximate pattern matching problem under the edit distance.
Given a text $T$ of length $n$, a pattern $P$ of length $m$, and a threshold
$k$, the task is to find the starting positions of all substrings of $T$ that
can be transformed to $P$ with at most $k$ edits. More than 20 years ago, Cole
and Hariharan [SODA'98, J. Comput.'02] gave an $\mathcal{O}(n+k^4 \cdot n/
m)$-time algorithm for this classic problem, and this runtime has not been
improved since.
  Here, we present an algorithm that runs in time $\mathcal{O}(n+k^{3.5}
\sqrt{\log m \log k} \cdot n/m)$, thus breaking through this long-standing
barrier. In the case where $n^{1/4+\varepsilon} \leq k \leq
n^{2/5-\varepsilon}$ for some arbitrarily small positive constant
$\varepsilon$, our algorithm improves over the state-of-the-art by polynomial
factors: it is polynomially faster than both the algorithm of Cole and
Hariharan and the classic $\mathcal{O}(kn)$-time algorithm of Landau and
Vishkin [STOC'86, J. Algorithms'89].
  We observe that the bottleneck case of the alternative $\mathcal{O}(n+k^4
\cdot n/m)$-time algorithm of Charalampopoulos, Kociumaka, and Wellnitz
[FOCS'20] is when the text and the pattern are (almost) periodic. Our new
algorithm reduces this case to a new dynamic problem (Dynamic Puzzle Matching),
which we solve by building on tools developed by Tiskin [SODA'10,
Algorithmica'15] for the so-called seaweed monoid of permutation matrices. Our
algorithm relies only on a small set of primitive operations on strings and
thus also applies to the fully-compressed setting (where text and pattern are
given as straight-line programs) and to the dynamic setting (where we maintain
a collection of strings under creation, splitting, and concatenation),
improving over the state of the art.


title: Effective field theory of random quantum circuits

abstract:
  Quantum circuits have been widely used as a platform to simulate generic
quantum many-body systems. In particular, random quantum circuits provide a
means to probe universal features of many-body quantum chaos and ergodicity.
Some such features have already been experimentally demonstrated in the noisy
intermediate-scale quantum (NISQ) devices. On the theory side, properties of
random quantum circuits have been studied on a case-by-case basis and for
certain specific systems, a hallmark of quantum chaos - universal Wigner-Dyson
level statistics - has been derived. This work develops an effective field
theory for a large class of random quantum circuits. The theory has the form of
a replica sigma model and is similar to the low-energy approach to diffusion in
disordered systems. The method is used to explicitly derive universal random
matrix behavior of a large family of random circuits. In particular, we
rederive Wigner-Dyson spectral statistics of the brickwork circuit model by
Chan, De~Luca, and Chalker [Phys. Rev. X 8, 041019 (2018)] and show within the
same calculation that its various permutations and higher-dimensional
generalizations preserve the universal level statistics. Finally, we use the
replica sigma model framework to rederive the Weingarten calculus, which is a
method to evaluate integrals of polynomials of matrix elements with respect to
the Haar measure over compact groups and has many applications in the studies
of quantum circuits. The effective field theory, derived here, provides both a
method to quantitatively characterize quantum dynamics of random Floquet
systems (e.g., calculating operator and entanglement spreading) and also path
to understanding the general fundamental mechanism behind quantum chaos and
thermalization in these systems.

categories: quant-ph cond-mat.dis-nn cond-mat.stat-mech hep-th math-ph math.MP


title: Fluently specifying taint-flow queries with fluentTQL

abstract:
  Previous work has shown that taint analyses are only useful if correctly
customized to the context in which they are used. Existing domain-specific
languages (DSLs) allow such customization through the definition of
deny-listing data-flow rules that describe potentially vulnerable taint-flows.
These languages, however, are designed primarily for security experts who are
knowledgeable in taint analysis. Software developers consider these languages
to be complex. This paper presents fluentTQL, a query language particularly for
taint-flow. fluentTQL is internal Java DSL and uses a fluent-interface design.
fluentTQL queries can express various taint-style vulnerability types, e.g.
injections, cross-site scripting or path traversal. This paper describes
fluentTQL's abstract and concrete syntax and defines its runtime semantics. The
semantics are independent of any underlying analysis and allows evaluation of
fluentTQL queries by a variety of taint analyses. Instantiations of fluentTQL,
on top of two taint analysis solvers, Boomerang and FlowDroid, show and
validate fluentTQL expressiveness. Based on existing examples from the
literature, we implemented queries for 11 popular security vulnerability types
in Java. Using our SQL injection specification, the Boomerang-based taint
analysis found all 17 known taint-flows in the OWASP WebGoat application,
whereas with FlowDroid 13 taint-flows were found. Similarly, in a vulnerable
version of the Java PetClinic application, the Boomerang-based taint analysis
found all seven expected taint-flows. In seven real-world Android apps with 25
expected taint-flows, 18 were detected. In a user study with 26 software
developers, fluentTQL reached a high usability score. In comparison to CodeQL,
the state-of-the-art DSL by Semmle/GitHub, participants found fluentTQL more
usable and with it they were able to specify taint analysis queries in shorter
time.


title:
Advancing Data Justice Research and Practice: An Integrated Literature
  Review

abstract:
  The Advancing Data Justice Research and Practice (ADJRP) project aims to
widen the lens of current thinking around data justice and to provide
actionable resources that will help policymakers, practitioners, and impacted
communities gain a broader understanding of what equitable, freedom-promoting,
and rights-sustaining data collection, governance, and use should look like in
increasingly dynamic and global data innovation ecosystems. In this integrated
literature review we hope to lay the conceptual groundwork needed to support
this aspiration. The introduction motivates the broadening of data justice that
is undertaken by the literature review which follows. First, we address how
certain limitations of the current study of data justice drive the need for a
re-location of data justice research and practice. We map out the strengths and
shortcomings of the contemporary state of the art and then elaborate on the
challenges faced by our own effort to broaden the data justice perspective in
the decolonial context. The body of the literature review covers seven thematic
areas. For each theme, the ADJRP team has systematically collected and analysed
key texts in order to tell the critical empirical story of how existing social
structures and power dynamics present challenges to data justice and related
justice fields. In each case, this critical empirical story is also
supplemented by the transformational story of how activists, policymakers, and
academics are challenging longstanding structures of inequity to advance social
justice in data innovation ecosystems and adjacent areas of technological
practice.


title:
Incorporating information from LIGO data quality streams into the PyCBC
  search for gravitational waves

abstract:
  We present a new method which accounts for changes in the properties of
gravitational-wave detector noise over time in the PyCBC search for
gravitational waves from compact binary coalescences. We use information from
LIGO data quality streams that monitor the status of each detector and its
environment to model changes in the rate of noise in each detector. These data
quality streams allow candidates identified in the data during periods of
detector malfunctions to be more efficiently rejected as noise. This method
allows data from machine learning predictions of the detector state to be
included as part of the PyCBC search, increasing the the total number of
detectable gravitational-wave signals by up to 5%. When both machine learning
classifications and manually-generated flags are used to search data from
LIGO-Virgo's third observing run, the total number of detectable
gravitational-wave signals is increased by up to 20% compared to not using any
data quality streams. We also show how this method is flexible enough to
include information from large numbers of additional arbitrary data streams
that may be able to further increase the sensitivity of the search.


title:
Size effects in the Verwey transition of nanometer-thick micron-wide
  magnetite crystals

abstract:
  We have monitored the Verwey transition in micrometer-wide, nanometer-thick
magnetite islands on epitaxial Ru films on Al2O3(0001) using Raman
spectroscopy. The islands have been grown by high-temperature oxygen-assisted
molecular beam epitaxy. Below 100 K and for thicknesses above 20 nm the Raman
spectra correspond to those observed in bulk crystals and high quality thin
films for the sub-Verwey magnetite structure. At room temperature the width of
the cubic phase modes is similar to the best reported in bulk crystals,
indicating a similar level of electron-phonon interaction. The evolution of the
Raman spectra upon cooling suggests that for islands thicker than 20 nm,
structural changes appear first at temperatures starting at 150 K and the
Verwey transition itself takes place at around 115 K. However, islands thinner
than 20 nm show a very different Raman spectra indicating that while a
transition takes place, the charge order of the ultrathin islands differs
markedly from their thicker counterparts


title: Intertwiner Entanglement Excitation and Holonomy Operator

abstract:
  In the loop quantum gravity framework, spin network states carry entanglement
between quantum excitations of the geometry at different space points. This
intertwiner entanglement is gauge-invariant and comes from quantum
superposition of spins and intertwiners. Bipartite entanglement can be
interpreted as a witness of distance, while multipartite entanglement reflects
the curvature of the quantum geometry. The present work investigates how the
bipartite and multipartite intertwiner entanglement changes under the action of
the holonomy operator, which is the basic building block of loop quantum
gravity's dynamics. We reveal the relation between entanglement excitation and
the dispersion of the holonomy operator. This leads to a new interesting
connection between bulk geometry and boundary observables via the dynamics of
entanglement.


title:
Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map
  to Addressing the Infrastructure Gap

abstract:
  Enabling widespread electric vehicle (EV) adoption requires substantial
build-out of charging infrastructure in the coming decade. We formulate the
charging infrastructure needs as a scaling analysis problem and use it to
estimate the EV infrastructure needs of the US at a county-level resolution.
Surprisingly, we find that the current EV infrastructure deployment scales
super-linearly with population, deviating from the sub-linear scaling of
gasoline stations and other infrastructure. We discuss how this demonstrates
the infancy of EV station abundance compared to other mature transportation
infrastructures. By considering the power delivery of existing gasoline
stations, and appropriate EV efficiencies, we estimate the EV infrastructure
gap at the county level, providing a road map for future EV infrastructure
expansion. Our reliance on scaling analysis allows us to make a unique forecast
in this domain.


title: Quantisations of the Volterra hierarchy

abstract:
  In this paper we explore a recently emerged approach to the problem of
quantisation based on the notion of quantisation ideals. We explicitly prove
that the nonabelian Volterra equation and all its symmetries admit a
deformation quantisation. For all odd-degree symmetries of the Volterra
equation, we show that there is also a non-deformation quantisation. We also
discuss the quantisation problem for periodic Volterra hierarchy in details
including their Heisenberg's forms, Hamiltonian and central elements for the
obtained quantised equations, and thus demonstrate their super-integrability.


title:
From Little Things Big Things Grow: A Collection with Seed Studies for
  Medical Systematic Review Literature Search

abstract:
  Medical systematic review query formulation is a highly complex task done by
trained information specialists. Complexity comes from the reliance on lengthy
Boolean queries, which express a detailed research question. To aid query
formulation, information specialists use a set of exemplar documents, called
`seed studies', prior to query formulation. Seed studies help verify the
effectiveness of a query prior to the full assessment of retrieved studies.
Beyond this use of seeds, specific IR methods can exploit seed studies for
guiding both automatic query formulation and new retrieval models. One major
limitation of work to date is that these methods exploit `pseudo seed studies'
through retrospective use of included studies (i.e., relevance assessments).
However, we show pseudo seed studies are not representative of real seed
studies used by information specialists. Hence, we provide a test collection
with real world seed studies used to assist with the formulation of queries. To
support our collection, we provide an analysis, previously not possible, on how
seed studies impact retrieval and perform several experiments using seed-study
based methods to compare the effectiveness of using seed studies versus pseudo
seed studies. We make our test collection and the results of all of our
experiments and analysis available at
http://github.com/ielab/sysrev-seed-collection


title: Optimization of Quantum Read-Only Memory Circuits

abstract:
  Quantum computing is a rapidly expanding field with applications ranging from
optimization all the way to complex machine learning tasks. Quantum memories,
while lacking in practical quantum computers, have the potential to bring
quantum advantage. In quantum machine learning applications for example, a
quantum memory can simplify the data loading process and potentially accelerate
the learning task. Quantum memory can also store intermediate quantum state of
qubits that can be reused for computation. However, the depth, gate count and
compilation time of quantum memories such as, Quantum Read Only Memory (QROM)
scale exponentially with the number of address lines making them impractical in
state-of-the-art Noisy Intermediate-Scale Quantum (NISQ) computers beyond 4-bit
addresses. In this paper, we propose techniques such as, predecoding logic and
qubit reset to reduce the depth and gate count of QROM circuits to target wider
address ranges such as, 8-bits. The proposed approach reduces the number of
gates and depth count by at least 2X compared to the naive implementation at
only 36% qubit overhead. A reduction in circuit depth and gate count as high as
75X and compilation time by 85X at the cost of a maximum of 2.28X qubit
overhead is observed. Experimentally, the fidelity with the proposed
predecoding circuit compared to existing optimization approach is also higher
(as much as 73% compared to 40.8%) under reduced error rates.


title:
Closed Ranks: The Discursive Value of Military Support for Indian
  Politicians on Social Media

abstract:
  Influencers play a crucial role in shaping public narratives through
information creation and diffusion in the Global South. While public figures
from various walks of life and their impact on public discourse have been
studied, defence veterans as influencers of the political discourse have been
largely overlooked. Veterans matter in the public spehere as a normatively
important political lobby. They are also interesting because, unlike
active-duty military officers, they are not restricted from taking public sides
on politics, so their posts may provide a window into the views of those still
in the service. In this work, we systematically analyze the engagement on
Twitter of self-described defence-related accounts and politician accounts that
post on defence-related issues. We find that self-described defence-related
accounts disproportionately engage with the current ruling party in India. We
find that politicians promote their closeness to the defence services and
nationalist credentials through engagements with defence-related influencers.
We briefly consider the institutional implications of these patterns and
connections


title:
Nonlinear and Ultrafast All-Dielectric Metasurfaces at the Center for
  Integrated Nanotechnologies

abstract:
  Metasurfaces control optical wavefronts via arrays of nanoscale resonators
laid out across a surface. When combined with III-V semiconductors with strong
optical nonlinearities, a variety of nonlinear effects such as harmonic
generation and all optical modulation can be enabled and enhanced at the
nanoscale. This review presents our research on engineering and boosting
nonlinear effects in ultrafast and nonlinear semiconductor metasurfaces
fabricated at the Center for Integrated Nanotechnologies (CINT). We cover our
recent works on parametric generation of harmonic light via direct and cascaded
processes in GaAs-metasurfaces using Mie-like optical resonances or
symmetric-protected Bound State in the Continuum, and then describe the recent
advances on harmonic generation in all-dielectric metasurfaces coupled to
intersubband transitions in III-V semiconductor heterostructures. The review
concludes on the potential of metasurfaces to serve as the next platform for
on-chip quantum light generation.


title: Data Justice Stories: A Repository of Case Studies

abstract:
  The idea of "data justice" is of recent academic vintage. It has arisen over
the past decade in Anglo-European research institutions as an attempt to bring
together a critique of the power dynamics that underlie accelerating trends of
datafication with a normative commitment to the principles of social justice-a
commitment to the achievement of a society that is equitable, fair, and capable
of confronting the root causes of injustice.However, despite the seeming
novelty of such a data justice pedigree, this joining up of the critique of the
power imbalances that have shaped the digital and "big data" revolutions with a
commitment to social equity and constructive societal transformation has a
deeper historical, and more geographically diverse, provenance. As the stories
of the data justice initiatives, activism, and advocacy contained in this
volume well evidence, practices of data justice across the globe have, in fact,
largely preceded the elaboration and crystallisation of the idea of data
justice in contemporary academic discourse. In telling these data justice
stories, we hope to provide the reader with two interdependent tools of data
justice thinking: First, we aim to provide the reader with the critical
leverage needed to discern those distortions and malformations of data justice
that manifest in subtle and explicit forms of power, domination, and coercion.
Second, we aim to provide the reader with access to the historically effective
forms of normativity and ethical insight that have been marshalled by data
justice activists and advocates as tools of societal transformation-so that
these forms of normativity and insight can be drawn on, in turn, as
constructive resources to spur future transformative data justice practices.


title:
Hierarchical Self-supervised Representation Learning for Movie
  Understanding

abstract:
  Most self-supervised video representation learning approaches focus on action
recognition. In contrast, in this paper we focus on self-supervised video
learning for movie understanding and propose a novel hierarchical
self-supervised pretraining strategy that separately pretrains each level of
our hierarchical movie understanding model (based on [37]). Specifically, we
propose to pretrain the low-level video backbone using a contrastive learning
objective, while pretrain the higher-level video contextualizer using an event
mask prediction task, which enables the usage of different data sources for
pretraining different levels of the hierarchy. We first show that our
self-supervised pretraining strategies are effective and lead to improved
performance on all tasks and metrics on VidSitu benchmark [37] (e.g., improving
on semantic role prediction from 47% to 61% CIDEr scores). We further
demonstrate the effectiveness of our contextualized event features on LVU tasks
[54], both when used alone and when combined with instance features, showing
their complementarity.


title: An Entropic Model for Assessing Avian Flight Formations

abstract:
  Why do birds fly in well-defined formations? The observed formations of
birds-in-flight are studied with a thermal model. Avian formation-flying is
tested as a self-organization process. The maximum entropy production rate
(MEPR) postulate is examined to determine its applicability to the observed V
formation during the long-distance migration of large birds. A brief discussion
of the principles of the rate of entropy generation maximization or
minimization is first presented. The V formation is compared to other closely
related, but distinctly different formations that can also be adopted by a
flock. The thermodynamic analysis from the results of the model indicates that
the V formation (named the SV formation in the article) is found to be the
pattern that optimizes energy use for the flight duration. This is accomplished
with savings in the formation's thermal energy output (heat) by the trailing
birds. The MEPR postulate is found to hold for the V formation. The formation
also provides for the highest comfort temperatures, perhaps thereby allowing
for high altitude (low drag) flight levels to be accessed by the birds. The
results of the model point to thermal awareness as a possible key driver for
organization into a V formation. The thermal model appears to validate all the
major experimental observations of avian formation flying. Regardless, without
detailed thermal measurements to validate, and without a physical basis for the
constant used in equation (C4d), it is not yet definitive that this thermal
model is valid for avian flight formation. The analysis appears to indicate
that the organization into patterns of live objects may follow the MEPR
principle for self-organization in a manner previously noted for chemical and
metallurgical processes.


title:
Induced subgraphs and tree decompositions V. Small components of big
  vertices

abstract:
  Aboulker, Adler, Kim, Sintiari, and Trotignon conjectured that every graph
with bounded maximum degree and large treewidth must contain, as an induced
subgraph, a large subdivided wall, or the line graph of a large subdivided
wall. This conjecture was recently proved by Korhonen, but the problem of
identifying the obstacles to bounded treewidth in the general case (that is,
without the bounded maximum degree condition) remains wide open. Examples of
structures of large treewidth which avoid the "usual suspects" have been
constructed by Sintiari and Trotignon, and by Davies. In this note, we aim to
better isolate the features of these examples that lead to large treewidth. To
this end, we prove the following result. Let $G$ be a graph, and write
$\gamma(G)$ for the size of a largest connected component in the graph induced
by $G$ on the set of vertices of degree at least 3. If $\gamma(G)$ is small and
the treewidth of $G$ is large, then $G$ must contain a large subdivided wall or
the line graph of a large subdivided wall. This result is the best possible, in
the sense that the conclusion fails if we replace 3 by any larger number in the
definition of $\gamma(G)$, as evidenced by Davies' example.


title: Effective qubit dephasing induced by spectator-qubit relaxation

abstract:
  In many leading architectures for quantum computing, it remains to be
understood if we can equate single-qubit coherence times measured in isolation,
to that of coherence times measured in multi-qubit devices. On a multi-qubit
superconducting circuit platform we show an increase in the dephasing rate of a
control qubit due to the spontaneous relaxation of spectator qubits coupled to
the control qubit. We attribute this increased dephasing to random in time
Z-phase kicks on the control qubit due to the interplay between spectator
relaxation and the control-spectator ZZ- interaction. We measure the magnitude
of this extra dephasing using Ramsey decay experiments, show how it can be
corrected via dynamical decoupling pulse sequences, and demonstrate that
randomized benchmarking is insensitive to the effect. Our experimental results
are supported by a robust theoretical model that captures an arbitrary number
of spectator qubits, and gives a simple, intuitive picture for the mechanism
behind the enhanced dephasing.


title: AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis

abstract:
  In this paper, we address the problem of texture representation for 3D shapes
for the challenging and underexplored tasks of texture transfer and synthesis.
Previous works either apply spherical texture maps which may lead to large
distortions, or use continuous texture fields that yield smooth outputs lacking
details. We argue that the traditional way of representing textures with images
and linking them to a 3D mesh via UV mapping is more desirable, since
synthesizing 2D images is a well-studied problem. We propose AUV-Net which
learns to embed 3D surfaces into a 2D aligned UV space, by mapping the
corresponding semantic parts of different 3D shapes to the same location in the
UV space. As a result, textures are aligned across objects, and can thus be
easily synthesized by generative models of images. Texture alignment is learned
in an unsupervised manner by a simple yet effective texture alignment module,
taking inspiration from traditional works on linear subspace learning. The
learned UV mapping and aligned texture representations enable a variety of
applications including texture transfer, texture synthesis, and textured single
view 3D reconstruction. We conduct experiments on multiple datasets to
demonstrate the effectiveness of our method. Project page:
https://nv-tlabs.github.io/AUV-NET.


title: Torsors and stable equivariant birational geometry

abstract:
  We develop the formalism of universal torsors in equivariant birational
geometry and apply it to produce new examples of nonbirational but stably
birational actions of finite groups.


title: Homotopy and Arithmetic

abstract:
  We define the concept of a bi-operad. We develop the homotopy theory of
"Bital-Sets" and of infinite-bi-operads. We develop a geometry of generalized
schemes based on the spectra of distributive monochromatic bi-operads.


title: A close-in puffy Neptune with hidden friends: The enigma of TOI 620

abstract:
  We present the validation of a transiting low-density exoplanet orbiting the
M2.5 dwarf TOI 620 discovered by the NASA TESS mission. We utilize photometric
data from both TESS and ground-based follow-up observations to validate the
ephemerides of the 5.09-day transiting signal and vet false positive scenarios.
High-contrast imaging data are used to resolve the stellar host and exclude
stellar companions at separations $\gtrsim 0.2''$. We obtain follow-up
spectroscopy and corresponding precise radial velocities (RVs) with multiple
PRV spectrographs to confirm the planetary nature of the transiting exoplanet.
We calculate a 5$\sigma$ upper limit of $M_P < 7.1$ M$_\oplus$ and $\rho_P <
0.74$ g cm$^{-3}$, and we identify a non-transiting 17.7-day candidate. We also
find evidence for a substellar (1-20 M$_{\rm J}$) companion with a projected
separation $\lesssim 20$ au from a combined analysis of Gaia, AO imaging, and
RVs. With the discovery of this outer companion, we carry out a detailed
exploration of the possibilities that TOI 620 b might instead be a
circum-secondary planet or a pair of eclipsing binary stars orbiting the host
in a hierarchical triple system. We find, under scrutiny, that we can exclude
both of these scenarios from the multi-wavelength transit photometry, thus
validating TOI 620 b as a low-density exoplanet transiting the central star in
this system. The low density of TOI 620 b makes it one of the most amenable
exoplanets for atmospheric characterization, such as with JWST and Ariel,
validated or confirmed by the TESS mission to date.


title:
Feasibility Study to use Neutron Capture for an Ultra-low Energy
  Nuclear-recoil Calibration in Liquid Xenon

abstract:
  The feasibility of an ultra-low energy nuclear-recoil measurement in liquid
xenon using neutron capture is investigated for a small (sub-kilogram) liquid
xenon detector that is optimized for a high scintillation gain, and a pulsed
neutron source. The measurement uses the recoil energies imparted to xenon
nuclei during the de-excitation process following neutron capture, where
promptly emitted $\gamma$ cascades can provide the nuclei with up to $0.3$
keV$_\text{nr}$ of recoil energy due to conservation of momentum. A successful
calibration of scintillation photon and ionization electron yields below this
energy will contribute to a greater sensitivity for liquid xenon experiments in
searches for light WIMPs.


title:
Stability and Safety through Event-Triggered Intermittent Control with
  Application to Spacecraft Orbit Stabilization

abstract:
  In systems where the ability to actuate is a scarce resource, e.g.,
spacecrafts, it is desirable to only apply a given controller in an
intermittent manner--with periods where the controller is on and periods where
it is off. Motivated by the event-triggered control paradigm, where
state-dependent triggers are utilized in a sample-and-hold context, we
generalize this concept to include state triggers where the controller is off
thereby creating a framework for intermittent control. Our approach utilizes
certificates--either Lyapunov or barrier functions--to design intermittent
trigger laws that guarantee stability or safety; the controller is turned on
for the period for which is beneficial with regard to the certificate, and
turned off until a performance threshold is reached. The main result of this
paper is that the intermittent controller scheme guarantees (set) stability
when Lyapunov functions are utilized, and safety (forward set invariance) in
the setting of barrier functions. As a result, our trigger designs can leverage
the intermittent nature of the actuator, and at the same time, achieve the task
of stabilization or safety. We further demonstrate the application and benefits
of intermittent control in the context of the spacecraft orbit stabilization
problem.


title: UIGR: Unified Interactive Garment Retrieval

abstract:
  Interactive garment retrieval (IGR) aims to retrieve a target garment image
based on a reference garment image along with user feedback on what to change
on the reference garment. Two IGR tasks have been studied extensively:
text-guided garment retrieval (TGR) and visually compatible garment retrieval
(VCR). The user feedback for the former indicates what semantic attributes to
change with the garment category preserved, while the category is the only
thing to be changed explicitly for the latter, with an implicit requirement on
style preservation. Despite the similarity between these two tasks and the
practical need for an efficient system tackling both, they have never been
unified and modeled jointly. In this paper, we propose a Unified Interactive
Garment Retrieval (UIGR) framework to unify TGR and VCR. To this end, we first
contribute a large-scale benchmark suited for both problems. We further propose
a strong baseline architecture to integrate TGR and VCR in one model. Extensive
experiments suggest that unifying two tasks in one framework is not only more
efficient by requiring a single model only, it also leads to better
performance. Code and datasets are available at
https://github.com/BrandonHanx/CompFashion.


title:
An Instrumented Wheel-On-Limb System of Planetary Rovers for
  Wheel-Terrain Interactions: System Conception and Preliminary Design

abstract:
  Understanding the wheel-terrain interaction is of great importance to improve
the maneuverability and traversability of the rovers. A well-developed sensing
device carried by the rover would greatly facilitate the complex risk-reducing
operations on sandy terrains. In this paper, an instrumented wheel-on-limb
(WOL) system of planetary rovers for wheel-terrain interaction characterization
is presented. Assuming the function of a passive suspension of the wheel, the
WOL system allows itself to follow the terrain contour, and keep the wheel
remain lowered onto the ground during rover motion including climbing and
descending, as well as deploy and place the wheel on the ground before a drive
commanding. The system concept, functional requirements, and pre-design work,
as well as the system integration are presented.


title: P4BID: Information Flow Control in P4

abstract:
  Modern programmable network switches can implement custom applications using
efficient packet processing hardware, and the programming language P4 provides
high-level constructs to program such switches. The increase in speed and
programmability has inspired research in dataplane programming, where many
complex functionalities, e.g., key-value stores and load balancers, can be
implemented entirely in network switches. However, dataplane programs may
suffer from novel security errors that are not traditionally found in network
switches.
  To address this issue, we present a new information-flow control type system
for P4. We formalize our type system in a recently-proposed core version of P4,
and we prove a soundness theorem: well-typed programs satisfy non-interference.
We also implement our type system in a tool, P4bid, which extends the type
checker in the p4c compiler, the reference compiler for the latest version of
P4. We present several case studies showing that natural security, integrity,
and isolation properties in networks can be captured by non-interference, and
our type system can detect violations of these properties while certifying
correct programs.


title:
Do They Accept or Resist Cybersecurity Measures? Development and
  Validation of the 13-Item Security Attitude Inventory (SA-13)

abstract:
  We present SA-13, the 13-item Security Attitude inventory. We develop and
validate this assessment of cybersecurity attitudes by conducting an
exploratory factor analysis, confirmatory factor analysis, and other tests with
data from a U.S. Census-weighted Qualtrics panel (N=209). Beyond a core six
indicators of Engagement with Security Measures (SA-Engagement, three items)
and Attentiveness to Security Measures (SA-Attentiveness, three items), our
SA-13 inventory adds indicators of Resistance to Security Measures
(SA-Resistance, four items) and Concernedness with Improving Compliance
(SA-Concernedness, three items). SA-13 and the subscales exhibit desirable
psychometric qualities; and higher scores on SA-13 and on the SA-Engagement and
SA-Attentiveness subscales are associated with higher scores for security
behavior intention and for self-reported recent security behaviors. SA-13 and
the subscales are useful for researchers and security awareness teams who need
a lightweight survey measure of user security attitudes. The composite score of
the 13 indicators provides a compact measurement of cybersecurity decisional
balance.


title:
Adaptive Bayesian Selection of Basis Functions for Functional Data via
  Stochastic Penalization

abstract:
  Considering the context of Functional Data Analysis (FDA), we developed and
applied a new Bayesian approach for the selection of basis functions for a
finite representation of functional data. The procedure makes use of a latent
variable. The presented method allows for an adaptive basis selection since it
can determine the number of basis and which of them should be selected to
represent the functional data. Moreover, the proposed procedure measures the
uncertainty of the selection process. The methodology developed can also deal
with random effect curves since the observed curves may differ not only due to
experimental error but also due to random individual differences between
subjects, which can be observed in a real dataset application involving daily
numbers of COVID-19 cases in Brazil. Simulation studies show the main
properties of the proposed method, such as its accuracy in estimating the
coefficients and the strength of the procedure to find the true set of basis
functions. Despite having been developed in the context of functional data
analysis, the proposed model was also compared via simulation with well
established LASSO, and Bayesian LASSO, which are methods developed for
non-functional data.


title:
Radiative hadronization: Photon emission at hadronization from
  quark-gluon plasma

abstract:
  We investigate photon emission at the hadronization stage from a quark-gluon
plasma created in relativistic heavy-ion collisions. A recombination-model
picture suggests that a quark and an antiquark bound into a meson state in
hadronization, which would apparently violate the energy conservation if there
is nothing else involved. We consider here a hadronization process where the
recombination accompanies a photon emission. This is an analog of the "{\it
radiative recombination}" known in plasma physics, such as $e^- + p^+ \to {\rm
H}^0 +\gamma$, which occurs when an electromagnetic plasma goes back to a
neutral atomic gas. The "radiative hadronization" picture will bring about (i)
an enhancement of the photon yield, (ii) significant flow of photons similar to
that of hadrons, and (iii) the photon transverse momentum ($p_T$) distribution
with a thermal profile whose effective temperature is given by blue-shifted
temperature of quarks. Here as a simplest and phenomenological realization of
the radiative hadronization, we modify the recombination model to involve a
photon emission and evaluate the photon yield with this modifed model. Adding
this contribution to the direct photon yield along with thermal photon
contribution calculated with a hydrodynamic model and a parametrized
contirbution of prompt photons, we study the $p_T$ spectrum and elliptic flow
of the photons produced in heavy-ion collisions at RHIC and LHC energies.


title:
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based
  Sentiment Analysis

abstract:
  Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis
task that aims to align aspects and corresponding sentiments for
aspect-specific sentiment polarity inference. It is challenging because a
sentence may contain multiple aspects or complicated (e.g., conditional,
coordinating, or adversative) relations. Recently, exploiting dependency syntax
information with graph neural networks has been the most popular trend. Despite
its success, methods that heavily rely on the dependency tree pose challenges
in accurately modeling the alignment of the aspects and their words indicative
of sentiment, since the dependency tree may provide noisy signals of unrelated
associations (e.g., the "conj" relation between "great" and "dreadful" in
Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax
aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully
exploits the syntax information (e.g., phrase segmentation and hierarchical
structure) of the constituent tree of a sentence to model the sentiment-aware
context of every single aspect (called intra-context) and the sentiment
relations across aspects (called inter-context) for learning. Experiments on
four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the
state-of-the-art methods consistently.


title: A proper scoring rule for minimum information copulas

abstract:
  Multi-dimensional distributions whose marginal distributions are uniform are
called copulas. Among them, the one that satisfies given constraints on
expectation and is closest to the independent distribution in the sense of
Kullback-Leibler divergence is called the minimum information copula. The
density function of the minimum information copula contains a set of functions
called the normalizing functions, which are often difficult to compute.
Although a number of proper scoring rules for probability distributions having
normalizing constants such as exponential families are proposed, these scores
are not applicable to the minimum information copulas due to the normalizing
functions. In this paper, we propose the conditional Kullback-Leibler score,
which avoids computation of the normalizing functions. The main idea of its
construction is to use pairs of observations. We show that the proposed score
is strictly proper in the space of copula density functions and therefore the
estimator derived from it has asymptotic consistency. Furthermore, the score is
convex with respect to the parameters and can be easily optimized by the
gradient methods.


title: Fractional Sobolev regularity for fully nonlinear elliptic equations

abstract:
  We prove higher-order fractional Sobolev regularity for fully nonlinear,
uniformly elliptic equations in the presence of unbounded source terms. More
precisely, we show the existence of a universal number $0< \varepsilon <1$,
depending only on ellipticity constants and dimension, such that if $u$ is a
viscosity solution of $F(D^2u) = f(x) \in L^p$, then $u\in
W^{1+\varepsilon,p}$, with appropriate estimates. Our strategy suggests a sort
of fractional feature of fully nonlinear diffusion processes, as what we
actually show is that $F(D^2u) \in L^p \implies (-\Delta)^\theta u \in L^p$,
for a universal constant $\frac{1}{2} < \theta <1$. We believe our techniques
are flexible and can be adapted to various models and contexts.


title:
AutoCOR: Autonomous Condylar Offset Ratio Calculator on
  TKA-Postoperative Lateral Knee X-ray

abstract:
  The postoperative range of motion is one of the crucial factors indicating
the outcome of Total Knee Arthroplasty (TKA). Although the correlation between
range of knee flexion and posterior condylar offset (PCO) is controversial in
the literature, PCO maintains its importance on evaluation of TKA. Due to
limitations on PCO measurement, two novel parameters, posterior condylar offset
ratio (PCOR) and anterior condylar offset ratio (ACOR), were introduced.
Nowadays, the calculation of PCOR and ACOR on plain lateral radiographs is done
manually by orthopedic surgeons. In this regard, we developed a software,
AutoCOR, to calculate PCOR and ACOR autonomously, utilizing unsupervised
machine learning algorithm (k-means clustering) and digital image processing
techniques. The software AutoCOR is capable of detecting the anterior/posterior
edge points and anterior/posterior cortex of the femoral shaft on true
postoperative lateral conventional radiographs. To test the algorithm, 50
postoperative true lateral radiographs from Istanbul Kosuyolu Medipol Hospital
Database were used (32 patients). The mean PCOR was 0.984 (SD 0.235) in
software results and 0.972 (SD 0.164) in ground truth values. It shows strong
and significant correlation between software and ground truth values (Pearson
r=0.845 p<0.0001). The mean ACOR was 0.107 (SD 0.092) in software results and
0.107 (SD 0.070) in ground truth values. It shows moderate and significant
correlation between software and ground truth values (Spearman's rs=0.519
p=0.0001412). We suggest that AutoCOR is a useful tool that can be used in
clinical practice.


title:
Betelgeuse Constraints on Coupling between Axion-like Particles and
  Electrons

abstract:
  Axion-like particles (ALPs) can be produced by thermal processes in a stellar
interior, escape from the star and, if sufficiently light, be converted into
photons in the external Galactic magnetic field. Such a process could produce a
detectable hard X-ray excess in the direction of the star. In this scenario, a
promising class of targets is the red supergiants, massive stars which are
experiencing the late part of their evolution. We report on a search for
ALP-induced X-ray emission from Betelgeuse, produced via the combined processes
of Bremsstrahlung, Compton and Primakoff. Using a 50 ks observation of
Betelgeuse by the \emph{NuSTAR} satellite telescope, we set 95\% C.L. upper
limits on the ALP-electron ($g_{ae}$) and ALP-photon ($g_{a\gamma}$) couplings.
For masses ${m_{a}\leq(3.5-5.5)\times10^{-11}}$ eV, we find $g_{a\gamma} \times
g_{ae}< (0.4-2.8)\times10^{-24}$ GeV$^{-1}$ (depending on the stellar model and
assuming a value of the regular Galactic magnetic field in the direction
transverse to Betelgeuse of $B_T$=1.4 $\mu$G). This corresponds to
${g_{ae}<(0.4-2.8) \times10^{-12}}$ for ${g_{a\gamma}>1.0\times10^{-12}}$
GeV$^{-1}$. This analysis supercedes by over an order of magnitude the limit on
$g_{ae} \times g_{a\gamma}$ placed by the CAST solar axion experiment and is
among the strongest constraints on these couplings.


title:
Ogden Material Calibration via Magnetic Resonance Cartography, Parameter
  Sensitivity, and Variational System Identification

abstract:
  Contemporary techniques in the mechanical calibration of materials that
leverage full three-dimensional deformation fields and the weak form of the
equilibrium equations face challenges in the numerical solving procedure of the
inverse characterization problem. As material models and descriptions differ,
so too must the approaches for identifying their system mechanics. The
widely-used Ogden material model, comprised of an unknown number of terms of
the same mathematical form, presents challenges in interpretability, stability,
and parsimony. In turn, we intend to use our estimates to assess and improve
our experimental design. Using fully 3D displacement fields acquired in
silicone elastomers using our recently-developed magnetic resonance cartography
(MR-u) technique on the order of $>20,000$ points per sample, we leverage
PDE-constrained optimization as the basis of variational system identification
of our material parameters. We incorporate the statistical F-test to maintain
parsimony of representation. Using a new decomposition of the deformation field
locally into mixtures of biaxial and uniaxial tensile states, we evaluate
experiments based on an analytical sensitivity metric, and discuss the
implications for future experimental design.


title: A novel nonconvex, smooth-at-origin penalty for statistical learning

abstract:
  Nonconvex penalties are utilized for regularization in high-dimensional
statistical learning algorithms primarily because they yield unbiased or nearly
unbiased estimators for the parameters in the model. Nonconvex penalties
existing in the literature such as SCAD, MCP, Laplace and arctan have a
singularity at origin which makes them useful also for variable selection.
However, in several high-dimensional frameworks such as deep learning, variable
selection is less of a concern. In this paper, we present a nonconvex penalty
which is smooth at origin. The paper includes asymptotic results for ordinary
least squares estimators regularized with the new penalty function, showing
asymptotic bias that vanishes exponentially fast. We also conducted an
empirical study employing deep neural network architecture on three datasets
and convolutional neural network on four datasets. The empirical study showed
better performance for the new regularization approach in five out of the seven
datasets.


title: Efficient two-dimensional control of barrier crossing

abstract:
  Driven barrier crossings are pervasive in optical-trapping experiments and
steered molecular-dynamics simulations. Despite the high fidelity of control,
the freedom in the choice of driving protocol is rarely exploited to improve
efficiency. We design protocols that reduce dissipation for rapidly driven
barrier crossing under two-dimensional control of a harmonic trapping
potential, controlling both trap center and stiffness. For fast driving, the
minimum-dissipation protocol jumps halfway between the control-parameter
endpoints. For slow driving, the minimum-dissipation protocol generically slows
down and tightens the trap as it crosses the barrier, resulting in both
significant energy savings and increased flux compared to naive and
one-dimensional protocols (that only change trap center). Combining fast and
slow results, we design protocols that improve performance at all speeds.


title:
Deep transfer learning for system identification using long short-term
  memory neural networks

abstract:
  Recurrent neural networks (RNNs) have many advantages over more traditional
system identification techniques. They may be applied to linear and nonlinear
systems, and they require fewer modeling assumptions. However, these neural
network models may also need larger amounts of data to learn and generalize.
Furthermore, neural networks training is a time-consuming process. Hence,
building upon long-short term memory neural networks (LSTM), this paper
proposes using two types of deep transfer learning, namely parameter
fine-tuning and freezing, to reduce the data and computation requirements for
system identification. We apply these techniques to identify two dynamical
systems, namely a second-order linear system and a Wiener-Hammerstein nonlinear
system. Results show that compared with direct learning, our method accelerates
learning by 10% to 50%, which also saves data and computing resources.


title: Chromospheric Extension of the MURaM Code

abstract:
  Detailed numerical models of chromosphere and corona are required to
understand the heating of the solar atmosphere. An accurate treatment of the
solar chromosphere is complicated by the effects arising from Non Local
Thermodynamic Equilibrium (NLTE) radiative transfer. A small number of strong,
highly scattering lines dominate the cooling and heating in the chromosphere.
Additionally, the recombination times of ionised hydrogen are longer than the
dynamical timescales, requiring a non-equilibrium (NE) treatment of hydrogen
ionisation. The MURaM code is extended to include the physical process required
for accurate simulation of the solar chromosphere, as implemented in the
Bifrost code. This includes a time-dependent treatment of hydrogen ionisation,
a scattering multi-group radiation transfer scheme and approximations for NLTE
radiative cooling. The inclusion of NE and NLTE physics has a large impact on
the structure of the chromosphere; the NE treatment of hydrogen ionisation
leads to a higher ionisation fraction and enhanced populations in the first
excited state throughout cold inter-shock regions of the chromosphere.
Additionally this prevents hydrogen ioniation from buffering energy
fluctuations, leading to hotter shocks and cooler inter-shock regions. The
hydrogen populations in the ground and first excited state are enhanced by
$10^2-10^3$ in the upper chromosphere and up to $10^9$ near the transition
region. Including the necessary NLTE physics leads to significant differences
in chromospheric structure and dynamics. The thermodynamics and hydrogen
populations calculated using the extended version of the MURaM code are
consistent with previous non-equilibrium simulations. The electron number and
temperature calculated using the non-equilibrium treatment of the chromosphere
are required to accurately synthesise chromospheric spectral lines.


title: The slices of quaternionic Eilenberg-Mac Lane spectra

abstract:
  We compute the slices and slice spectral sequence of integral suspensions of
the equivariant Eilenberg-Mac Lane spectra $H\underline{\mathbb{Z}}$ for the
group of equivariance $Q_8$. Along the way, we compute the Mackey functors
$\underline{\pi}_{k\rho} H\underline{\mathbb{Z}}$.


title: Sigma Workbook: A Spreadsheet for Cloud Data Warehouses

abstract:
  Cloud data warehouses (CDWs) bring large-scale data and compute power closer
to users in enterprises. However, existing tools for analyzing data in CDWs are
either limited in ad-hoc transformations or difficult to use for business
users. Here we introduce Sigma Workbook, a new interactive system that enables
business users to easily perform a visual analysis of data in CDWs at scale.
For this, Sigma Workbook provides an accessible spreadsheet-like interface for
analysis through direct manipulation. Sigma Workbook dynamically constructs
matching SQL queries from user interactions, building on the versatility and
expressivity of SQL. Constructed queries are directly executed on CDWs,
leveraging the superior characteristics of the new generation CDWs, including
scalability. We demonstrate Sigma Workbook through 3 real-life use cases --
cohort analysis, sessionization, and data augmentation -- and underline
Workbook's ease of use, scalability, and expressivity.


title: Principal blocks for different primes, I

abstract:
  We propose new conjectures about the relationship between the principal
blocks of finite groups for different primes and establish evidence for these
conjectures.


title: Principal blocks for different primes, II

abstract:
  If G is a finite group, we have proposed new conjectures on the interaction
between different primes and their corresponding Brauer principal blocks. In
this paper, we give strong support to the validity of these conjectures.


title:
Symmetry Protected Topological Criticality: Decorated Defect
  Construction, Signatures and Stability

abstract:
  Symmetry protected topological (SPT) phases are one of the simplest, yet
nontrivial, gapped systems that go beyond the Landau paradigm. In this work, we
study the notion of SPT for critical systems, namely, symmetry protected
topological criticality (SPTC). We discuss a systematic way of constructing a
large class of SPTCs using decorated defect construction, study the physical
observables that characterize the nontrivial topological signatures of SPTCs,
and discuss the stability under symmetric perturbations. Our exploration of
SPTC is mainly based on several previous studies of gapless SPT: gapless
symmetry protected topological order [1], symmetry enriched quantum criticality
[2] and intrinsically gapless topological phases [3]. We partially reinterpret
these previous studies in terms of decorated defect construction, and discuss
their generalizations.


title:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium
  Problems

abstract:
  We consider the problem of computing an equilibrium in a class of nonlinear
generalized Nash equilibrium problems (NGNEPs) in which the strategy sets for
each player are defined by equality and inequality constraints that may depend
on the choices of rival players. While the asymptotic global convergence and
local convergence rate of solution procedures have been studied in this
setting, the analysis of iteration complexity is still in its infancy. Our
contribution is to provide two simple first-order algorithmic frameworks based
on the quadratic penalty method and the augmented Lagrangian method,
respectively, with an accelerated mirror-prox algorithm as the inner loop. We
provide nonasymptotic theoretical guarantees for these algorithms. More
specifically, we establish the global convergence rate of our algorithms for
solving (strongly) monotone NGNEPs and we provide iteration complexity bounds
expressed in terms of the number of gradient evaluations. Experimental results
demonstrate the efficiency of our algorithms.


title:
Bi-fidelity conditional-value-at-risk estimation by dimensionally
  decomposed generalized polynomial chaos expansion

abstract:
  Digital twin models allow us to continuously assess the possible risk of
damage and failure of a complex system. Yet high-fidelity digital twin models
can be computationally expensive, making quick-turnaround assessment
challenging. Towards this goal, this article proposes a novel bi-fidelity
method for estimating the conditional value-at-risk (CVaR) for nonlinear
systems subject to dependent and high-dimensional inputs. For models that can
be evaluated fast, a method that integrates the dimensionally decomposed
generalized polynomial chaos expansion (DD-GPCE) approximation with a standard
sampling-based CVaR estimation is proposed. For expensive-to-evaluate models, a
new bi-fidelity method is proposed that couples the DD-GPCE with a
Fourier-polynomial expansions of the mapping between the stochastic
low-fidelity and high-fidelity output data to ensure computational efficiency.
The method employs a measure-consistent orthonormal polynomial in the random
variable of the low-fidelity output to approximate the high-fidelity output.
Numerical results for a structural mechanics truss with 36-dimensional
(dependent random variable) inputs indicate that the DD-GPCE method provides
very accurate CVaR estimates that require much lower computational effort than
standard GPCE approximations. A second example considers the realistic problem
of estimating the risk of damage to a fiber-reinforced composite laminate. The
high-fidelity model is a finite element simulation that is prohibitively
expensive for risk analysis, such as CVaR computation. Here, the novel
bi-fidelity method can accurately estimate CVaR as it includes low-fidelity
models in the estimation procedure and uses only a few high-fidelity model
evaluations to significantly increase accuracy.


title:
Perception-aware receding horizon trajectory planning for multicopters
  with visual-inertial odometry

abstract:
  Visual inertial odometry (VIO) is widely used for the state estimation of
multicopters, but it may function poorly in environments with few visual
features or in overly aggressive flights. In this work, we propose a
perception-aware collision avoidance local planner for multicopters. Our
approach is able to fly the vehicle to a goal position at high speed, avoiding
obstacles in the environment while achieving good VIO state estimation
accuracy. The proposed planner samples a group of minimum jerk trajectories and
finds collision-free trajectories among them, which are then evaluated based on
their speed to the goal and perception quality. Both the features' motion blur
and their locations are considered for the perception quality. The best
trajectory from the evaluation is tracked by the vehicle and is updated in a
receding horizon manner when new images are received from the camera. All the
sampled trajectories have zero speed and acceleration at the end, and the
planner assumes no other visual features except those already found by the VIO.
As a result, the vehicle will follow the current trajectory to the end and stop
safely if no new trajectories are found, avoiding collision or flying into
areas without features. The proposed method can run in real time on a small
embedded computer on board. We validated the effectiveness of our proposed
approach through experiments in indoor and outdoor environments. Compared to a
perception-agnostic planner, the proposed planner kept more features in the
camera's view and made the flight less aggressive, making the VIO more
accurate. It also reduced VIO failures, which occurred for the
perception-agnostic planner but not for the proposed planner. The experiment
video can be found at https://youtu.be/LjZju4KEH9Q.


title:
Pogorelov type $C^2$ estimates for Sum Hessian equations and a rigidity
  theorem

abstract:
  We mainly study Pogorelov type $C^2$ estimates for solutions to the Dirichlet
problem of Sum Hessian equations. We establish respectively Pogorelov type
$C^2$ estimates for $k$-convex solutions and admissible solutions under some
conditions. Furthermore, we apply such estimates to obtain a rigidity theorem
for $k$-convex solutions of Sum Hessian equations in Euclidean space.


title: Simplicial Resolutions of Powers of Square-free Monomial Ideals

abstract:
  The Taylor resolution is almost never minimal for powers of monomial ideals,
even in the square-free case. In this paper we introduce a smaller resolution
for each power of any square-free monomial ideal, which depends only on the
number of generators of the ideal. More precisely, for every pair of fixed
integers $r$ and $q$, we construct a simplicial complex that supports a free
resolution of the $r$-th power of any square-free monomial ideal with $q$
generators. The resulting resolution is significantly smaller than the Taylor
resolution, and is minimal for special cases. Considering the relations on the
generators of a fixed ideal allows us to further reduce the resolutions for
powers of the ideal. Our results lead to upper bounds on Betti numbers of
powers of any square-free monomial ideal that greatly improve the binomial
bounds offered by the Taylor resolution.


title:
Classification of events from $\alpha$-induced reactions in the MUSIC
  detector via statistical and ML methods

abstract:
  The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to
measure nuclear reaction cross sections relevant for nuclear astrophysics,
fusion studies, and other applications. From the MUSIC data produced in one
experiment scientists carefully extract an order of $10^3$ events of interest
from about $10^{9}$ total events, where each event can be represented by an
18-dimensional vector. However, the standard data classification process is
based on expert-driven, manually intensive data analysis techniques that
require several months to identify patterns and classify the relevant events
from the collected data. To address this issue, we present a method for the
classification of events originating from specific $\alpha$-induced reactions
by combining statistical and machine learning methods that require
significantly less input from the domain scientist, relative to the standard
technique.
  We applied the new method to two experimental data sets and compared our
results with those obtained using traditional methods. With few exceptions, the
number of events classified by our method agrees within $\pm20\%$ with the
results obtained using traditional methods. With the present method, which is
the first of its kind for the MUSIC data, we have established the foundation
for the automated extraction of physical events of interest from experiments
using the MUSIC detector.


title:
High-bandwidth CMOS-voltage-level electro-optic modulation of 780 nm
  light in thin-film lithium niobate

abstract:
  Integrated photonics operating at visible-near-infrared (VNIR) wavelengths
offer scalable platforms for advancing optical systems for addressing atomic
clocks, sensors, and quantum computers. The complexity of free-space control
optics causes limited addressability of atoms and ions, and this remains an
impediment on scalability and cost. Networks of Mach-Zehnder interferometers
can overcome challenges in addressing atoms by providing high-bandwidth
electro-optic control of multiple output beams. Here, we demonstrate a VNIR
Mach-Zehnder interferometer on lithium niobate on sapphire with a CMOS
voltage-level compatible full-swing voltage of 4.2 V and an electro-optic
bandwidth of 2.7 GHz occupying only 0.35 mm$^2$. Our waveguides exhibit 1.6
dB/cm propagation loss and our microring resonators have intrinsic quality
factors of 4.4 $\times$ 10$^5$. This specialized platform for VNIR integrated
photonics can open new avenues for addressing large arrays of qubits with high
precision and negligible cross-talk.


title:
DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation
  and Rendering of Deformable Objects

abstract:
  Research in manipulation of deformable objects is typically conducted on a
limited range of scenarios, because handling each scenario on hardware takes
significant effort. Realistic simulators with support for various types of
deformations and interactions have the potential to speed up experimentation
with novel tasks and algorithms. However, for highly deformable objects it is
challenging to align the output of a simulator with the behavior of real
objects. Manual tuning is not intuitive, hence automated methods are needed. We
view this alignment problem as a joint perception-inference challenge and
demonstrate how to use recent neural network architectures to successfully
perform simulation parameter inference from real point clouds. We analyze the
performance of various architectures, comparing their data and training
requirements. Furthermore, we propose to leverage differentiable point cloud
sampling and differentiable simulation to significantly reduce the time to
achieve the alignment. We employ an efficient way to propagate gradients from
point clouds to simulated meshes and further through to the physical simulation
parameters, such as mass and stiffness. Experiments with highly deformable
objects show that our method can achieve comparable or better alignment with
real object behavior, while reducing the time needed to achieve this by more
than an order of magnitude. Videos and supplementary material are available at
https://tinyurl.com/diffcloud.


title:
Learning and Transferring Value Function for Robot Exploration in
  Subterranean Environments

abstract:
  In traditional robot exploration methods, the robot usually does not have
prior biases about the environment it is exploring. Thus the robot assigns
equal importance to the goals which leads to insufficient exploration
efficiency. Alternative, often a hand-tuned policy is used to tweak the value
of goals. In this paper, we present a method to learn how "good" some states
are, measured by the state value function, to provide a hint for the robot to
make exploration decisions. We propose to learn state value functions from
previous offline collected datasets and then transfer and improve the value
function during testing in a new environment. Moreover, the environments
usually have very few and even no extrinsic reward or feedback for the robot.
Therefore in this work, we also tackle the problem of sparse extrinsic rewards
from the environments. We design several intrinsic rewards to encourage the
robot to obtain more information during exploration. These reward functions
then become the building blocks of the state value functions. We test our
method on challenging subterranean and urban environments. To the best of our
knowledge, this work for the first time demonstrates value function prediction
with previous collected datasets to help exploration in challenging
subterranean environments.


title:
Adversarial Machine Learning Attacks Against Video Anomaly Detection
  Systems

abstract:
  Anomaly detection in videos is an important computer vision problem with
various applications including automated video surveillance. Although
adversarial attacks on image understanding models have been heavily
investigated, there is not much work on adversarial machine learning targeting
video understanding models and no previous work which focuses on video anomaly
detection. To this end, we investigate an adversarial machine learning attack
against video anomaly detection systems, that can be implemented via an
easy-to-perform cyber-attack. Since surveillance cameras are usually connected
to the server running the anomaly detection model through a wireless network,
they are prone to cyber-attacks targeting the wireless connection. We
demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform
and effective denial-of-service (DoS) attack, can be utilized to generate
adversarial data for video anomaly detection systems. Specifically, we apply
several effects caused by the Wi-Fi deauthentication attack on video quality
(e.g., slow down, freeze, fast forward, low resolution) to the popular
benchmark datasets for video anomaly detection. Our experiments with several
state-of-the-art anomaly detection models show that the attackers can
significantly undermine the reliability of video anomaly detection systems by
causing frequent false alarms and hiding physical anomalies from the
surveillance system.


title: On the wall-bounded model of fingering double diffusive convection

abstract:
  Fingering double diffusive convection with real seawater properties is
studied by direct numerical simulations for wall-bounded domain and compared
with the results for fully periodic domain. For fixed unstable salinity
difference between two horizontal plates, dominant flow structures change from
convection rolls to salt fingers as the global density ratio increases. Here
the global density ratio measures the relative strength of the stable
temperature difference to the salinity difference. Once in the salt-finger
state, the bulk density ratio calculated by the mean scalar gradients of the
middle part of domain increases above unity, i.e. the commonly believed regime
for salt fingers to grow. The finger width is consistent with the prediction of
linear stability analysis in unbounded domain. The ratio of finger height to
width depends solely on the bulk density ratio. When the salinity Rayleigh
number, i.e. the nondimensional salinity difference between the boundaries, is
high enough, salt-finger layers can first coexist with the middle convection
layers and then occupy the entire bulk as the global density ratio increases
gradually. The fluxes, when normalized by the bulk conductive fluxes, agree
quantitatively with those obtained in the fully periodic domain (e.g. Traxler
et al. J. Fluid Mech., vol. 677, pp. 530-553, 2011). These findings suggest
that the transport behaviours observed from wall-bounded model are essentially
the same as those from fully periodic domain, providing that the bulk density
ratio is used as the primary parameter.


title:
Unique electronic structure and the shape-change effect of stage-I
  graphite intercalation compounds with Thorium, Uranium and Plutonium

abstract:
  GICs doped with elements containing d and f orbitals have been studied
rarely. We control the distribution and density of intercalated actinide metals
(Th, U and Pu), and consider the effect of changing the distance of two
adjacent carbon layers on the electronic structure so as to infer the physical
properties of such materials under high pressure or high temperature - which is
of great significance in fundamental research. According to band schemas, those
GICs are all metallic. The projected density of states (PDOS) indicates that
the metal atoms first undergo hybridization of its s, p, d, and f orbitals, and
then bond with the carbon's pz orbitals of the nearest-neighbor octagons. The
electron orbital spin up of C and Th is symmetrical with spin down, so there is
no electron spin polarization. However, the s, p, d, and f orbitals of U and Pu
all exhibit electron spin polarization, which leads to the magnetic properties
of the material and make the p orbits of C appear spin polarization around
Fermi level. In addition, these three selected elements are the most commonly
used raw materials in nuclear fission, so such GICs are expected to become
novel nuclear energy storage materials.


title:
Exploring Cross-Domain Pretrained Model for Hyperspectral Image
  Classification

abstract:
  A pretrain-finetune strategy is widely used to reduce the overfitting that
can occur when data is insufficient for CNN training. First few layers of a CNN
pretrained on a large-scale RGB dataset are capable of acquiring general image
characteristics which are remarkably effective in tasks targeted for different
RGB datasets. However, when it comes down to hyperspectral domain where each
domain has its unique spectral properties, the pretrain-finetune strategy no
longer can be deployed in a conventional way while presenting three major
issues: 1) inconsistent spectral characteristics among the domains (e.g.,
frequency range), 2) inconsistent number of data channels among the domains,
and 3) absence of large-scale hyperspectral dataset.
  We seek to train a universal cross-domain model which can later be deployed
for various spectral domains. To achieve, we physically furnish multiple inlets
to the model while having a universal portion which is designed to handle the
inconsistent spectral characteristics among different domains. Note that only
the universal portion is used in the finetune process. This approach naturally
enables the learning of our model on multiple domains simultaneously which acts
as an effective workaround for the issue of the absence of large-scale dataset.
  We have carried out a study to extensively compare models that were trained
using cross-domain approach with ones trained from scratch. Our approach was
found to be superior both in accuracy and in training efficiency. In addition,
we have verified that our approach effectively reduces the overfitting issue,
enabling us to deepen the model up to 13 layers (from 9) without compromising
the accuracy.


title: DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors

abstract:
  DeepTensor is a computationally efficient framework for low-rank
decomposition of matrices and tensors using deep generative networks. We
decompose a tensor as the product of low-rank tensor factors (e.g., a matrix as
the outer product of two vectors), where each low-rank tensor is generated by a
deep network (DN) that is trained in a self-supervised manner to minimize the
mean-squared approximation error. Our key observation is that the implicit
regularization inherent in DNs enables them to capture nonlinear signal
structures (e.g., manifolds) that are out of the reach of classical linear
methods like the singular value decomposition (SVD) and principal component
analysis (PCA). Furthermore, in contrast to the SVD and PCA, whose performance
deteriorates when the tensor's entries deviate from additive white Gaussian
noise, we demonstrate that the performance of DeepTensor is robust to a wide
range of distributions. We validate that DeepTensor is a robust and
computationally efficient drop-in replacement for the SVD, PCA, nonnegative
matrix factorization (NMF), and similar decompositions by exploring a range of
real-world applications, including hyperspectral image denoising, 3D MRI
tomography, and image classification. In particular, DeepTensor offers a 6dB
signal-to-noise ratio improvement over standard denoising methods for signals
corrupted by Poisson noise and learns to decompose 3D tensors 60 times faster
than a single DN equipped with 3D convolutions.


title: A Modified Net Reclassification Improvement Statistic

abstract:
  The continuous net reclassification improvement (NRI) statistic is a popular
model change measure that was developed to assess the incremental value of new
factors in a risk prediction model. Two prominent statistical issues identified
in the literature call the utility of this measure into question: (1) it is not
a proper scoring function and (2) it has a high false positive rate when
testing whether new factors contribute to the risk model. For binary response
regression models, these subjects are interrogated and a modification of the
continuous NRI, guided by the likelihood-based score residual, is proposed to
address these issues. Within a nested model framework, the modified NRI may be
viewed as a distance measure between two risk models. An application of the
modified NRI is illustrated using prostate cancer data.


title: Visibility phenomena in hypercubes

abstract:
  We study the set of visible lattice points in multidimensional hypercubes.
The problems we investigate mix together geometric, probabilistic and number
theoretic tones. For example, we prove that almost all self-visible triangles
with vertices in the lattice of points with integer coordinates in $\mathcal
W=[0,N]^d$ are almost equilateral having all sides almost equal to
$\sqrt{d}N/\sqrt{6}$, and the sine of the typical angle between rays from the
visual spectra from the origin of $\mathcal W$ is, in the limit, equal to
$\sqrt{7}/4$, as $d$ and $N/d$ tend to infinity. We also show that there exists
an interesting number theoretic constant $\Lambda_{d,K}$, which is the limit
probability of the chance that a $K$-polytope with vertices in the lattice
$\mathcal W$ has all vertices visible from each other.


title: A strong Gram classification of non-negative unit forms of Dynkin type A

abstract:
  An integral quadratic form q is usually identified with a bilinear form b
such that its Gram matrix with respect to the canonical basis is upper
triangular. Two integral quadratic forms are called strongly (resp. weakly)
Gram congruent if their corresponding upper triangular bilinear forms (resp.
their symmetrizations) are equivalent. If q is unitary, such upper triangular
bilinear form is unimodular, and one considers the associated Coxeter
transformation and its characteristic polynomial, the so-called Coxeter
polynomial of q with this identification. Two strongly Gram congruent quadratic
unit forms are weakly Gram congruent and have the same Coxeter polynomial. Here
we show that the converse of this statement holds for the connected
non-negative case of Dynkin type A_r and arbitrary corank, and use this
characterization to complete a combinatorial classification of such quadratic
forms started in [Fundamenta Informaticae 184(1):49-82, 2021] and continued in
[arXiv:2010.09991].


title:
Heavy-Flavor-Conserving Hadronic Weak Decays of Charmed and Bottom
  Baryons

abstract:
  Three decades ago, heavy-flavor-conserving (HFC) weak decays of heavy baryons
such as $\Xi_Q\to\Lambda_Q\pi$ and $\Omega_Q\to\Xi_Q\pi$ for $Q=c,b$ had been
studied within the framework that incorporates both heavy-quark and chiral
symmetries. It was pointed out that if the heavy quark in the HFC process
behaves as a spectator, then the $P$-wave amplitude of ${\cal B}_{\bar 3}\to
{\cal B}_{\bar 3}+\pi$ with ${\cal B}_{\bar 3}$ being an antitriplet heavy
baryon will vanish in the heavy quark limit. Indeed, this is the case for
$\Xi_b\to\Lambda_b \pi$ decays. For $\Xi_c\to\Lambda_c\pi$ decays, they receive
additional nonspectator contributions arising from the $W$-exchange diagrams
through the $cs\to dc$ transition. Spectator and nonspectator $W$-exchange
contributions to the $S$-wave amplitude of $\Xi_c\to\Lambda_c\pi$ are
destructive, rendering the $S$-wave contribution even smaller. However, the
nonspectator effect on the $P$-wave amplitude was overlooked in all the
previous model calculations until a very recent investigation within the
framework of a constituent quark model in which the parity-conserving pole
terms were found to be dominant in $\Xi_c\to\Lambda_c\pi$ decays. Since the
pion produced in the HFC process is soft, we apply current algebra to study
both $S$- and $P$-wave amplitudes and employ the bag and diquark models to
estimate the matrix elements of four-quark operators. We confirm that
$\Xi_c\to\Lambda_c\pi$ decays are indeed dominated by the parity-conserving
transition induced from nonspectator $W$-exchange and that they receive largest
contributions from the intermediate $\Sigma_c$ pole terms. We also show that
the $S$-wave of $\Omega_b\to \Xi_b \pi$ decays vanishes in the heavy quark
limit, while $\Omega_c\to \Xi_c\pi$ receive additional $W$-exchange
contributions via $cs\to dc$ transition.


title:
Analytic Investigation for Spatio-temporal Patterns Propagation in
  Spiking Neural Networks

abstract:
  Based upon the moment closure approach, a Gaussian random field is
constructed to quantitatively and analytically characterize the dynamics of a
random point field. The approach provides us with a theoretical tool to
investigate synchronized spike propagation in a feedforward or recurrent
spiking neural network. We show that the balance between the excitation and
inhibition postsynaptic potentials is required for the occurrence of synfire
chains. In particular, with a balanced network, the critical packet size of
invasion and annihilation is observed. We also derive a sufficient analytic
condition for the synchronization propagation in an asynchronous environment,
which further allows us to disclose the possibility of spatial synaptic
structure to sustain a stable synfire chain. Our findings are in good agreement
with simulations and help us understand the propagation of spatio-temporal
patterns in a random point flied.