title:
The Power of Media Agencies in Ad Auctions: Improving Utility through
  Coordinated Bidding

abstract:
  The increasing competition in digital advertising induced a proliferation of
media agencies playing the role of intermediaries between advertisers and
platforms selling ad slots. When a group of competing advertisers is managed by
a common agency, many forms of collusion, such as bid rigging, can be
implemented by coordinating bidding strategies, dramatically increasing
advertisers' value. We study the problem of finding bids and monetary transfers
maximizing the utility of a group of colluders, under GSP and VCG mechanisms.
First, we introduce an abstract bid optimization problem -- called weighted
utility problem (WUP) -- , which is useful in proving our results. We show that
the utilities of bidding strategies are related to the length of paths in a
directed acyclic weighted graph, whose structure and weights depend on the
mechanism under study. This allows us to solve WUP in polynomial time by
finding a shortest path of the graph. Next, we switch to our original problem,
focusing on two settings that differ for the incentives they allow for.
Incentive constraints ensure that colluders do not leave the agency, and they
can be enforced by implementing monetary transfers between the agency and the
advertisers. In particular, we study the arbitrary transfers setting, where any
kind of monetary transfer to and from the advertisers is allowed, and the more
realistic limited liability setting, in which no advertiser can be paid by the
agency. In the former, we cast the problem as a WUP instance and solve it by
our graph-based algorithm, while, in the latter, we formulate it as a linear
program with exponentially-many variables efficiently solvable by applying the
ellipsoid algorithm to its dual. This requires to solve a suitable separation
problem in polynomial time, which can be done by reducing it to a WUP instance.


title:
Soliton trapping and orthogonal Raman scattering in a birefringent
  microstructured fiber

abstract:
  We report on trapped pulse generation in birefringent microstructured optical
fiber. Linearly polarized fs pulses are injected into the microstructured fiber
in anomalous dispersion regime. We observed experimentally that soliton pulse
polarized along the fast fiber axis partially transfers its energy to the
orthogonal polarization. The generated pulse is amplified through the
orthogonal Raman gain. The two polarization components are located at
group-velocity matched wavelengths. The experimental works are supported with
numerical simulations. The obtained results are important for the light sources
using self-frequency shifted solitons in applications demanding high
polarization purity.


title:
Algorithms for TRISO Fuel Identification Based on X-ray CT Validated on
  Tungsten-Carbide Compacts

abstract:
  Tristructural-isotropic (TRISO) fuel is one of the most mature fuel types for
candidate advanced reactor types under development. TRISO-fuel pebbles flow
continuously through the reactor core and can be reinserted into the reactor
several times until a target burnup is reached. The capability of identifying
individual fuel pebbles would allow us to calculate the fuel residence time in
the core and validate pebble flow computational models, prevent excessive
burnup accumulation or premature fuel discharge, and maintain accountability of
special nuclear materials during fuel circulation. In this work, we have
developed a 3D image reconstruction and segmentation algorithm to accurately
segment TRISO particles and extract the unique 3D distribution. We have
developed a rotation-invariant and noise-robust identification algorithm that
allows us to identify the pebble and retrieve the pebble ID in the presence of
rotations and noises. We also report the results of 200kV X-ray CT image
reconstruction of a mock-up fuel sample consisting of tungsten-carbide (WC)
kernels in a lucite matrix. The 3D distribution of TRISO particles along with
other signatures such as $^{235}$U enrichment and burnup level extracted
through neutron multiplicity counting, would enable accurate fuel
identification in a reasonable amount of time.


title:
CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models
  from Data and Priors

abstract:
  Structural causal models (SCMs) provide a principled approach to identifying
causation from observational and experimental data in disciplines ranging from
economics to medicine. SCMs, however, require domain knowledge, which is
typically represented as graphical models. A key challenge in this context is
the absence of a methodological framework for encoding priors (background
knowledge) into causal models in a systematic manner. We propose an abstraction
called causal knowledge hierarchy (CKH) for encoding priors into causal models.
Our approach is based on the foundation of "levels of evidence" in medicine,
with a focus on confidence in causal information. Using CKH, we present a
methodological framework for encoding causal priors from various data sources
and combining them to derive an SCM. We evaluate our approach on a simulated
dataset and demonstrate overall performance compared to the ground truth causal
model with sensitivity analysis.


title:
The Implications of N = 2 Supergravity Cosmology On the Topology of the
  Calabi-Yau Manifold

abstract:
  When N= D=11 supergravity is compactified on CY threefold to N=2 D=5
supergravity the action of the last is given in terms of the geometery of the
CY manifold space, namely, in terms of the hypermultiplets. There are
$z^i(i=1,...,h^{2,1})$ complex structure moduli in the moduli space of the CY
manifold which is a special K\"ahler manifold with a metric $G_{i\bar{j}}$. We
solve the field equations of the complex structure moduli with the solution of
the Einstein field equations to the moduli velocity norm $G_{i\bar{j}} z^i
z^{\bar{j}}$ in case of a 3- brane filled with radiation, dust and energy
embedded in the bulk of D=5 supergravity. We get the time dependence of the
moduli and the metric. Then we can further deduce the geometry of the moduli
space by getting the K\"ahler potential which directly relates to the volume of
the CY manifold.


title:
A geometric perspective: experimental evaluation of the quantum
  Cramer-Rao bound

abstract:
  The power of quantum sensing rests on its ultimate precision limit,
quantified by the quantum Cram\'er-Rao bound (QCRB), which can surpass
classical bounds. In multi-parameter estimation, the QCRB is not always
saturated as the quantum nature of associated observables may lead to their
incompatibility. Here we explore the precision limits of multi-parameter
estimation through the lens of quantum geometry, enabling us to experimentally
evaluate the QCRB via quantum geometry measurements. Focusing on two- and
three-parameter estimation, we elucidate how fundamental quantum uncertainty
principles prevent the saturation of the bound. By linking a metric of
"quantumness" to the system geometric properties, we investigate and
experimentally extract the attainable QCRB for three-parameter estimations.


title: Inferring Implicit Relations with Language Models

abstract:
  A prominent challenge for modern language understanding systems is the
ability to answer implicit reasoning questions, where the required reasoning
steps for answering the question are not mentioned in the text explicitly. In
this work, we investigate why current models struggle with implicit reasoning
question answering (QA) tasks, by decoupling inference of reasoning steps from
their execution. We define a new task of implicit relation inference and
construct a benchmark, IMPLICITRELATIONS, where given a question, a model
should output a list of concept-relation pairs, where the relations describe
the implicit reasoning steps required for answering the question. Using
IMPLICITRELATIONS, we evaluate models from the GPT-3 family and find that,
while these models struggle on the implicit reasoning QA task, they often
succeed at inferring implicit relations. This suggests that the bottleneck for
answering implicit reasoning questions is in the ability of language models to
retrieve and reason over information rather than to plan an accurate reasoning
strategy


title: Formulating Robustness Against Unforeseen Attacks

abstract:
  Existing defenses against adversarial examples such as adversarial training
typically assume that the adversary will conform to a specific or known threat
model, such as $\ell_p$ perturbations within a fixed budget. In this paper, we
focus on the scenario where there is a mismatch in the threat model assumed by
the defense during training, and the actual capabilities of the adversary at
test time. We ask the question: if the learner trains against a specific
"source" threat model, when can we expect robustness to generalize to a
stronger unknown "target" threat model during test-time? Our key contribution
is to formally define the problem of learning and generalization with an
unforeseen adversary, which helps us reason about the increase in adversarial
risk from the conventional perspective of a known adversary. Applying our
framework, we derive a generalization bound which relates the generalization
gap between source and target threat models to variation of the feature
extractor, which measures the expected maximum difference between extracted
features across a given threat model. Based on our generalization bound, we
propose adversarial training with variation regularization (AT-VR) which
reduces variation of the feature extractor across the source threat model
during training. We empirically demonstrate that AT-VR can lead to improved
generalization to unforeseen attacks during test-time compared to standard
adversarial training on Gaussian and image datasets.


title:
Galaxy cluster SZ detection with unbiased noise estimation: an iterative
  approach

abstract:
  Multi-frequency matched filters (MMFs) are routinely used to detect galaxy
clusters from CMB data through the thermal Sunyaev-Zeldovich (tSZ) effect,
leading to cluster catalogues that can be used for cosmological inference. In
order to be applied, MMFs require knowledge of the cross-frequency power
spectra of the noise in the maps. This is typically estimated from the data and
taken to be equal to the power spectra of the data, assuming the contribution
from the tSZ signal of the detections to be negligible. Using both analytical
arguments and \textit{Planck}-like mock observations, we show that doing so
causes the MMF noise to be overestimated, inducing a loss of signal-to-noise.
Furthermore, the MMF cluster observable (the amplitude $\hat{y}_0$ or the
signal-to-noise $q$) does not behave as expected, which can potentially bias
cosmological inference. In particular, the observable becomes biased with
respect to its theoretical prediction and displays a variance that also differs
from its predicted value. We propose an iterative MMF (iMMF) approach designed
to mitigate these effects. In this approach, after a first standard MMF step,
the noise power spectra are reestimated by masking the detections from the
data, delivering an updated iterative cluster catalogue. Applying our iMMF to
our \textit{Planck}-like mock observations, we find that the aforementioned
effects are completely suppressed. This leads to a signal-to-noise gain
relative to the standard MMF, with more significant detections and a higher
number of them, and to a cluster observable with the expected theoretical
properties, thus eliminating any potential biases in the cosmological
constraints.


title: DMRadio-m$^3$: A Search for the QCD Axion Below $1\,\mu$eV

abstract:
  The QCD axion is one of the most compelling candidates to explain the dark
matter abundance of the universe. With its extremely small mass ($\ll 1
\mathrm{eV}/c^2$), axion dark matter interacts as a classical field rather than
a particle. Its coupling to photons leads to a modification of Maxwell's
equations that can be measured with extremely sensitive readout circuits.
DMRadio-m$^3$ is a next-generation search for axion dark matter below $1
\mu\mathrm{eV}$ using a 4T static magnetic field, a coaxial inductive pickup, a
tunable LC resonator, and a DC-SQUID readout. It is designed to search for QCD
axion dark matter over the range $20 \mathrm{neV}\lesssim m_a c^2\lesssim
800\mathrm{neV}$ ($5\mathrm{MHz}<\nu<200\mathrm{MHz}$) with at least KSVZ
sensitivity over the full range, and DFSZ sensitivity above $m_a c^2\approx 120
\mathrm{neV}$ (30 MHz) with a 5 year scan time. In this letter, we present a
baseline design and sensitivity for DMRadio-m$^3$.


title: Pragmatic Clinical Trials in the Rubric of Structural Causal Models

abstract:
  Explanatory studies, such as randomized controlled trials, are targeted to
extract the true causal effect of interventions on outcomes and are by design
adjusted for covariates through randomization. On the contrary, observational
studies are a representation of events that occurred without intervention. Both
can be illustrated using the Structural Causal Model (SCM), and do-calculus can
be employed to estimate the causal effects. Pragmatic clinical trials (PCT)
fall between these two ends of the trial design spectra and are thus hard to
define. Due to its pragmatic nature, no standardized representation of PCT
through SCM has been yet established. In this paper, we approach this problem
by proposing a generalized representation of PCT under the rubric of structural
causal models (SCM). We discuss different analysis techniques commonly employed
in PCT using the proposed graphical model, such as intention-to-treat,
as-treated, and per-protocol analysis. To show the application of our proposed
approach, we leverage an experimental dataset from a pragmatic clinical trial.
Our proposition of SCM through PCT creates a pathway to leveraging do-calculus
and related mathematical operations on clinical datasets.


title: Mapping cumulus clouds to scale invariant rough surfaces

abstract:
  Motivated by a recent observation on the self-organized criticality of
cumulus clouds (Phys. Rev E 103, 052106, 2021) we study their connection to
self-similar rough surfaces, in which $f\equiv \log I$ plays the role of the
main field, where $I$ is the intensity of the received visible light. By
simulating the light scattering based on a coarse-grained phenomenological
model in a two-dimensional cloud, we argue the possible connection of $I$ to
the actual cloud thickness. Although in the vertical incident light $f$ is
proportional to the cloud thickness, in the general case it is complected. We
study the statistical properties of observational data for $f$ with a focus on
the conventional exponents of this scale-invariant rough surface. By
calculating the roughness exponents, and comparing them with other exponents
like the fractal dimension of loops, the distribution function of the radius of
gyration and loop lengths, and the exponent of the green function, we prove
that this surface is unconventional in the sense that it is the non-Gaussian
self-affine random surface which violates the Kondev hyper-scaling relations.


title: AGIC: Approximate Gradient Inversion Attack on Federated Learning

abstract:
  Federated learning is a private-by-design distributed learning paradigm where
clients train local models on their own data before a central server aggregates
their local updates to compute a global model. Depending on the aggregation
method used, the local updates are either the gradients or the weights of local
learning models. Recent reconstruction attacks apply a gradient inversion
optimization on the gradient update of a single minibatch to reconstruct the
private data used by clients during training. As the state-of-the-art
reconstruction attacks solely focus on single update, realistic adversarial
scenarios are overlooked, such as observation across multiple updates and
updates trained from multiple mini-batches. A few studies consider a more
challenging adversarial scenario where only model updates based on multiple
mini-batches are observable, and resort to computationally expensive simulation
to untangle the underlying samples for each local step. In this paper, we
propose AGIC, a novel Approximate Gradient Inversion Attack that efficiently
and effectively reconstructs images from both model or gradient updates, and
across multiple epochs. In a nutshell, AGIC (i) approximates gradient updates
of used training samples from model updates to avoid costly simulation
procedures, (ii) leverages gradient/model updates collected from multiple
epochs, and (iii) assigns increasing weights to layers with respect to the
neural network structure for reconstruction quality. We extensively evaluate
AGIC on three datasets, CIFAR-10, CIFAR-100 and ImageNet. Our results show that
AGIC increases the peak signal-to-noise ratio (PSNR) by up to 50% compared to
two representative state-of-the-art gradient inversion attacks. Furthermore,
AGIC is faster than the state-of-the-art simulation based attack, e.g., it is
5x faster when attacking FedAvg with 8 local steps in between model updates.


title:
Multicarrier-Division Duplex for Solving the Channel Aging Problem in
  Massive MIMO Systems

abstract:
  The separation of training and data transmission as well as the frequent
uplink/downlink (UL/DL) switching make time-division duplex (TDD)-based massive
multiple-input multiple-output (mMIMO) systems less competent in fast
time-varying scenarios due to the resulted severe channel aging. To this end, a
multicarrier-division duplex (MDD) mMIMO scheme associated with two types of
well-designed frame structures are introduced for combating channel aging when
communicating over fast time-varying channels. To compare with TDD, the
corresponding frame structures related to 3GPP standards and their variant
forms are presented. The MDD-specific general Wiener predictor and
decision-directed Wiener predictor are introduced to predict the channel state
information, respectively, in the time domain based on UL pilots and in the
frequency domain based on the detected UL data, considering the impact of
residual self-interference (SI). Moreover, by applying the zero-forcing
precoding and maximum ratio combining, the closed-form approximations for the
lower bounded rate achieved by TDD and MDD systems over time-varying channels
are derived. Our main conclusion from this study is that the MDD, endowed with
the capability of full-duplex but less demand on SI cancellation than in-band
full-duplex (IBFD), outperforms both the conventional TDD and IBFD in combating
channel aging.


title: The Physical Mathematics of Segal Topoi and Strings

abstract:
  We introduce a notion of dynamics in the setting of Segal topos, by
considering the Segal category of stacks $\mathcal{X} =
\text{dAff}_{\mathcal{C}}^{\, \sim, \tau}$ on a Segal category
$\text{dAff}_{\mathcal{C}}=$ L(Comm($\mathcal{C})^{op})$ as our system, and by
regarding objects of $\mathbb{R}\underline{\text{Hom}}(\mathcal{X},
\mathcal{X})$ as its states. We develop the notion of quantum state in this
setting and construct local and global flows of such states. In this formalism,
strings are given by equivalences between elements of commutative monoids of
$\mathcal{C}$, a base symmetric monoidal model category. The connection with
standard string theory is made, and with M-theory in particular.


title:
Nonlinear Addition of Qubit States Using Entangled Quaternionic Powers
  of Single-Qubit Gates

abstract:
  This paper presents a novel way to use the algebra of unit quaternions to
express arbitrary roots or fractional powers of single-qubit gates, and to use
such fractional powers as generators for algebras that combine these fractional
input signals, behaving as a kind of nonlinear addition. The method works by
connecting several well-known equivalences. The group of all single-qubit gates
is $U(2)$, the unitary transformations of $C^2$. Using an appropriate phase
multiplier, every element of $U(2)$ can be mapped to a corresponding element of
$SU(2)$ with unit determinant, whose quantum mechanical behavior is identical.
The group $SU(2)$ is isomorphic to the group of unit quaternions. Powers and
roots of unit quaternions can be constructed by extending de Moivre's theorem
for roots of complex numbers to the quaternions by selecting a preferred square
root of -1. Using this chain of equivalences, for any single-qubit gate $A$ and
real exponent $k$, a gate $B$ can be predictably constructed so that $B^k = A$.
Different fractions generated in this way can be combined by connecting the
individually rotated qubits to a common 'sum' qubit using 2-qubit CNOT gates.
Examples of such algebras are explored including those generated by roots of
the quaternion $k$ (which corresponds to and $X$-rotation of the Bloch sphere),
the quaternion $\frac{\sqrt{2}}{2}(i + k)$ (which corresponds to the Hadamard
gate), and a mixture of these. One of the goals of this research is to develop
quantum versions of classical components such as the classifier ensembles and
activation functions used in machine learning and artificial intelligence. An
example application for text classification is presented, which uses fractional
rotation gates to represent classifier weights, and classifies new input by
using CNOT gates to collect the appropriate classifier weights in a
topic-scoring qubit.


title: FPIRM: Floating-point Processing in Racetrack Memories

abstract:
  Convolutional neural networks (CNN) have become a ubiquitous algorithm with
growing applications in mobile and edge settings. We describe a
compute-in-memory (CIM) technique called FPIRM using Racetrack Memory (RM) to
accelerate CNNs for edge systems. Using transverse read, a technique that can
determine the number of '1's multiple adjacent domains, FPIRM can efficiently
implement multi-operand bulk-bitwise and addition computations, and two-operand
multiplication. We discuss how FPIRM can implement both variable precision
integer and floating point arithmetic. This allows both CNN inference and
on-device training without expensive data movement to the cloud. Based on these
functions we demonstrate implementation of several CNNs with back propagation
using RM CIM and compare these to state-of-the-art implementations of CIM
inference and training in Field-Programmable Gate Arrays. During training FPIRM
improves by 2$\times$ the efficiency, by reducing the energy consumption by at
least 27% and increasing the throughput by at least 18% against FPGA.


title:
An $\ell_0\ell_2$-norm regularized regression model for construction of
  robust cluster expansion in multicomponent systems

abstract:
  We introduce the $\ell_0\ell_2$-norm regularization and hierarchy constraints
into linear regression for the construction of cluster expansion to describe
configurational disorder in materials. The approach is implemented through
mixed integer quadratic programming (MIQP). The $\ell_2$-norm regularization is
used to compress intrinsic data noise, while $\ell_0$-norm is used to penalize
the number of non-zero elements in the solution. The hierarchy relation between
clusters imposes relevant physics and is naturally included by the MIQP
paradigm. As such, sparseness and cluster hierarchy can be well optimized to
obtain a robust, converged, and effective cluster interactions with improved
physical meaning. We demonstrate the effectiveness of $\ell_0\ell_2$-norm
regularization in two high-component disordered rocksalt cathode material
systems, where we compare the cross-validation and convergence speed,
reproduction of phase diagrams, voltage profiles, and Li-occupancy energies
with those of the conventional $\ell_1$-norm regularized cluster expansion
model.


title: Semi-equational theories

abstract:
  We introduce and study semi-equational and weakly semi-equational theories,
generalizing equationality in stable theories (in the sense of Srour) to the
NIP context. In particular, we establish a connection to distality via
one-sided strong honest definitions; demonstrate that certain trees are
semi-equational, while algebraically closed valued fields are not weakly
semi-equational; and obtain a general criterion for weak semi-equationality of
an expansion of a distal structure by a new predicate.


abstract:
  Transformer and its variants have shown state-of-the-art results in many
vision tasks recently, ranging from image classification to dense prediction.
Despite of their success, limited work has been reported on improving the model
efficiency for deployment in latency-critical applications, such as autonomous
driving and robotic navigation. In this paper, we aim at improving upon the
existing transformers in vision, and propose a method for self-supervised
monocular Depth Estimation with Simplified Transformer (DEST), which is
efficient and particularly suitable for deployment on GPU-based platforms.
Through strategic design choices, our model leads to significant reduction in
model size, complexity, as well as inference latency, while achieving superior
accuracy as compared to state-of-the-art. We also show that our design
generalize well to other dense prediction task without bells and whistles.


title: Probabilistic Models for Manufacturing Lead Times

abstract:
  In this study, we utilize Gaussian processes, probabilistic neural network,
natural gradient boosting, and quantile regression augmented gradient boosting
to model lead times of laser manufacturing processes. We introduce
probabilistic modelling in the domain and compare the models in terms of
different abilities. While providing a comparison between the models in
real-life data, our work has many use cases and substantial business value. Our
results indicate that all of the models beat the company estimation benchmark
that uses domain experience and have good calibration with the empirical
frequencies.


title: Towards Understanding the Skill Gap in Cybersecurity

abstract:
  Given the ongoing "arms race" in cybersecurity, the shortage of skilled
professionals in this field is one of the strongest in computer science. The
currently unmet staffing demand in cybersecurity is estimated at over 3 million
jobs worldwide. Furthermore, the qualifications of the existing workforce are
largely believed to be insufficient. We attempt to gain deeper insights into
the nature of the current skill gap in cybersecurity. To this end, we correlate
data from job ads and academic curricula using two kinds of skill
characterizations: manual definitions from established skill frameworks as well
as "skill topics" automatically derived by text mining tools. Our analysis
shows a strong agreement between these two analysis techniques and reveals a
substantial undersupply in several crucial skill categories, e.g., software and
application security, security management, requirements engineering,
compliance, and certification. Based on the results of our analysis, we provide
recommendations for future curricula development in cybersecurity so as to
decrease the identified skill gaps.


title:
Multi-field cold and warm inflation and the de Sitter swampland
  conjectures

abstract:
  We discuss under which conditions multi-field cold and warm inflationary
models with canonical kinetic energy terms are compatible with the swampland
conjectures about the emergence of de Sitter solutions in string theory. We
find that under quite general conditions the slow-roll conditions for
multi-field cold inflation are at odds with the swampland conjectures for an
arbitrary number of scalar fields driving inflation. However, slow-roll
conditions can be reconciled with the swampland conjectures in the strong
dissipative regime of warm inflation.


title: Interor and h operators on the category of locales

abstract:
  We present the concept of interior operator $I$ on the category
$\mathbf{Loc}$ of locales and then we construct a topological category
\linebreak $\big(\mathbf{I\text{-}Loc},\ U\big)$, where
$U:\mathbf{I\text{-}Loc}\rightarrow \mathbf{Loc}$ is a forgetful functor; and
we also introduce the notion of $\mathfrak h$ operator on the category
$\mathbf{Loc}$ and discuss some of their properties for constructing the
topological category $\big(\mathbf{\mathfrak h\text{-}Loc},\ U\big)$ associated
to the forgetful functor $U:\mathbf{\mathfrak h\text{-}Loc}\rightarrow
\mathbf{Loc}$.


title: Instilling Type Knowledge in Language Models via Multi-Task QA

abstract:
  Understanding human language often necessitates understanding entities and
their place in a taxonomy of knowledge -- their types. Previous methods to
learn entity types rely on training classifiers on datasets with coarse, noisy,
and incomplete labels. We introduce a method to instill fine-grained type
knowledge in language models with text-to-text pre-training on type-centric
questions leveraging knowledge base documents and knowledge graphs. We create
the WikiWiki dataset: entities and passages from 10M Wikipedia articles linked
to the Wikidata knowledge graph with 41K types. Models trained on WikiWiki
achieve state-of-the-art performance in zero-shot dialog state tracking
benchmarks, accurately infer entity types in Wikipedia articles, and can
discover new types deemed useful by human judges.


title:
Interplay between Young Stars and Molecular Clouds in the Ophiuchus
  Star-forming Complex

abstract:
  We present spatial and kinematic correlation between the young stellar
population and the cloud clumps in the Ophiuchus star-forming region. The
stellar sample consists of known young objects at various evolutionary stages,
taken from the literature, some of which are diagnosed with Gaia EDR3 parallax
and proper-motion measurements. The molecular gas is traced by the 850 $\mu$m
Submillimetre Common-User Bolometer Array-2 image, reaching $\sim$2.3 mJy
beam$^{-1}$, the deepest so far for the region, stacked from the James Clerk
Maxwell Telescope/Transient program aiming to detect submillimeter outburst
events. Our analysis indicates that the more evolved sources, namely the class
II and III young stars, are located further away from clouds than class I and
flat-spectrum sources that have ample circumstellar matter and are closely
associated with natal clouds. Particularly the class II and III population is
found to exhibit a structured spatial distribution indicative of passage of
shock fronts from the nearby Sco-Cen OB association thereby compressing clouds
to trigger star formation, with the latest starbirth episode occurring now in
the densest cloud filaments. The young stars at all evolutionary stages share
similar kinematics. This suggests that the stellar patterns trace the relics of
parental cloud filaments that now have been dispersed.


title:
Quantum versus classical nature of a low-temperature magnetic phase
  transition in TbAl$_3$(BO$_3$)$_4$

abstract:
  Specific heat, $C_B$, of a TbAl$_3$(BO$_3$)$_4$ crystal was studied for 50 mK
$<T<$ 300 K, with emphasis on $T<1$ K, where a phase transition was found at
$T_c =0.68$ K. Nuclear, non-phonon ($C_m$), and lattice contributions to $C_B$
were separated. Lowering of $T_c$ with ncrease of magnetic field parallel to
the easy magnetization axis, $B_{||}$, was found. It was established that $C_m$
and a Gr\"uneisen ratio depend on $B_{||}$ and $T$ in a way characteristic of
systems, in which a classical transition is driven by quantum fluctuations, QF,
to a quantum critical point at $T=0$, by tuning a control parameter ($B_{||}$).
The $B_{||} - T$ phase diagram was constructed and the dynamical critical
exponent $0.82 \le z \le 0.96$ was assessed. Nature of the transition was not
established explicitly. Magnetization studies point at the ferromagnetic
ordering of Tb$^{3+}$ magnetic moments, however, lowering of $T_c$ with
increase in $B_{||}$ is opposite to the classical behavior. Hence, a dominant
role of QF was supposed.


title: Topological Data Analysis for Multivariate Time Series Data

abstract:
  Over the last two decades, topological data analysis (TDA) has emerged as a
very powerful data analytic approach which can deal with various data
modalities of varying complexities. One of the most commonly used tools in TDA
is persistent homology (PH) which can extract topological properties from data
at various scales. Our aim in this article is to introduce TDA concepts to a
statistical audience and provide an approach to analyze multivariate time
series data. The application focus will be on multivariate brain signals and
brain connectivity networks. Finally, the paper concludes with an overview of
some open problems and potential application of TDA to modeling directionality
in a brain network as well as the casting of TDA in the context of mixed
effects models to capture variations in the topological properties of data
collected from multiple subjects


title:
Jahn-Teller Distortion and Dissociation of CCl$_4^+$ by Transient X-ray
  Spectroscopy Simultaneously at the Carbon K- and Chlorine L-Edge

abstract:
  X-ray Transient Absorption Spectroscopy (XTAS) and theoretical calculations
are used to study CCl$_4^+$ prepared by 800 nm strong-field ionization. XTAS
simultaneously probes atoms at the carbon K-edge (280-300 eV) and chlorine
L-edge (195-220 eV). Comparison of experiment to X-ray spectra computed by
orbital-optimized density functional theory (OO-DFT) indicates that after
ionization, CCl$_4^+$ undergoes symmetry breaking driven by Jahn-Teller
distortion away from the initial tetrahedral structure (T$_d$) in 6$\pm$2 fs.
The resultant symmetry-broken covalently bonded form subsequently separates to
a noncovalently bound complex between CCl$_3^+$ and Cl over 90$\pm$10 fs, which
is again predicted by theory. Finally, after more than 800 fs, L-edge signals
for atomic Cl are observed, indicating dissociation to free CCl$_3^+$ and Cl.
The results for Jahn-Teller distortion to the symmetry-broken form of CCl$_4^+$
and formation of the Cl -- CCl$_3^+$ complex characterize previously unobserved
new species along the route to dissociation.


title:
Density And Velocity Fluctuations of Alpha Particles in Magnetic
  Switchbacks

abstract:
  Magnetic switchbacks, or sudden reversals in the magnetic field's radial
direction, are one of the more striking observations of Parker Solar Probe
(PSP) thus far in its mission. While their precise production mechanisms are
still unknown, the two main theories are via interchange reconnection events
and in-situ generation. In this work density and abundance variations of alpha
particles are studied inside and outside individual switchbacks. We find no
consistent compositional differences in the alpha particle abundance ratio,
$n_{\alpha p}$, inside vs outside, nor do we observe any signature when
separating the switchbacks according to $V_{\alpha p}/V_{pw}$, the ratio of
alpha-proton differential speed to the wave phase speed (speed the switchback
is travelling). We argue these measurements cannot be used to rule in favour of
one production mechanism over the other, due to the distance between PSP and
the postulated interchange reconnection events. In addition we examine the 3D
velocity fluctuations of protons and alpha particles within individual
switchbacks. While switchbacks are always associated with increases in proton
velocity, alpha velocities may be enhanced, unchanged, or decrease. This is due
to the interplay between $V_{pw}$ and $V_{\alpha p}$, with the Alfv\'enic
motion of the alpha particles vanishing as the difference $|V_{pw} - V_{\alpha
p}|$ decreases. We show how the Alfv\'enic motion of both the alphas and the
protons through switchbacks can be understood as approximately rigid arm
rotation about the location of the wave frame, and illustrate that the wave
frame can therefore be estimated using particle measurements alone, via sphere
fitting.


title: BILP-Q: Quantum Coalition Structure Generation

abstract:
  Quantum AI is an emerging field that uses quantum computing to solve typical
complex problems in AI. In this work, we propose BILP-Q, the first-ever general
quantum approach for solving the Coalition Structure Generation problem (CSGP),
which is notably NP-hard. In particular, we reformulate the CSGP in terms of a
Quadratic Binary Combinatorial Optimization (QUBO) problem to leverage existing
quantum algorithms (e.g., QAOA) to obtain the best coalition structure. Thus,
we perform a comparative analysis in terms of time complexity between the
proposed quantum approach and the most popular classical baselines.
Furthermore, we consider standard benchmark distributions for coalition values
to test the BILP-Q on small-scale experiments using the IBM Qiskit environment.
Finally, since QUBO problems can be solved operating with quantum annealing, we
run BILP-Q on medium-size problems using a real quantum annealer (D-Wave).


title:
Development and validation of an astronomy self-efficacy instrument for
  understanding and doing

abstract:
  This paper presents a new astronomy self efficacy instrument, composed of two
factors, one relating to learning astronomy content, which we call astronomy
personal self efficacy, and the other relating to the use of astronomical
instrumentation, specifically the use of remote robotic telescopes for data
collection. The latter is referred to as the astronomy instrumental self
efficacy factor. The instrument has been tested for reliability and construct
validity. Reliability testing showed that factor 1 had a Cronbachs alpha of
0.901 and factor 2 had a Cronbachs alpha of 0.937. Construct validity was
established by computing one way analyses of variances, with the p value
suitably protected, using independent variables peripherally related to the
constructs. These analyses demonstrate that both scales possess high construct
validity. The development of this astronomy specific instrument is an important
step in evaluating self efficacy as a precursor to investigating the construct
of science identity in the field of astronomy.


title: Critical localization with Van der Waals interactions

abstract:
  I discuss the quantum dynamics of strongly disordered quantum systems with
critically long range interactions, decaying as $1/r^{2d}$ in $d$ spatial
dimensions. I argue that, contrary to expectations, localization in such
systems is stable at low orders in perturbation theory, giving rise to an
unusual `critically many body localized regime.' I discuss the phenomenology of
this critical MBL regime, which includes distinctive signatures in
entanglement, charge statistics, noise, and transport. Experimentally, such a
critically localized regime can be realized in three dimensional systems with
Van der Waals interactions, such as Rydberg atoms, and in one dimensional
systems with $1/r^2$ interactions, such as trapped ions. I estimate timescales
on which high order perturbative and non-perturbative (avalanche) phenomena may
destabilize this critically MBL regime, and conclude that the avalanche sets
the limiting timescale, in the limit of strong disorder / weak interactions.

categories: cond-mat.dis-nn cond-mat.quant-gas cond-mat.stat-mech cond-mat.str-el


title:
Investigating writing style as a contributor to gender gaps in science
  and technology

abstract:
  While universalism is a foundational principle of science, a growing stream
of research finds that scientific contributions are evaluated differently
depending on the gender of the author, with women tending to receive fewer
citations relative to men, even for work of comparable quality. Strikingly,
research also suggests that these gender gaps are visible even under blinded
review, wherein the evaluator is not aware of the gender of the author. In this
article, we consider whether gender differences in writing styles -- how men
and women communicate their work -- may contribute to these observed gender
gaps. We ground our investigation in a previously established framework for
characterizing the linguistic style of written text, which distinguishes
between two sets of features -- informational (i.e., features that emphasize
facts) and involved (i.e., features that emphasize relationships). Using a
large, matched sample of academic papers and patents, we find significant
differences in writing style by gender; women use more involved features in
their writing, a pattern that holds universally across fields. The magnitude of
the effect varies across fields, with larger gender differences observed in the
social sciences and arts humanities and smaller gaps in the physical sciences
and technology. Subsequently, we show that gender differences in writing style
may have parallels in reading preferences; papers and patents with more
informational features tend to be cited more by men, while those with more
involved features tend to be cited more by women, even after controlling for
the gender of the author, inventor, and patent attorney. Our findings suggest
that formal written text is not devoid of personal character, which could
contribute to bias in evaluation, thereby compromising the norm of
universalism.


title: Practical Considerations in Direct Detection Under Tukey Signalling

abstract:
  The deliberate introduction of controlled intersymbol interference (ISI) in
Tukey signalling enables the recovery of signal amplitude and (in part) signal
phase under direct detection, giving rise to significant spectral efficiency
improvements compared to intensity modulation with direct detection (IMDD). The
use of an integrate-and-dump detector makes precise waveform shaping
unnecessary, thereby equipping the scheme with a high degree of robustness to
nonlinear signal distortions introduced by practical modulators. Signal
sequences drawn from star quadrature amplitude modulation (SQAM) formats admit
an efficient trellis description that facilitates codebook design and
low-complexity near maximum-likelihood sequence detection in the presence of
both shot noise and thermal noise. Under the practical (though suboptimal)
allocation of a 50% duty cycle between ISI-free and ISI-present signalling
segments, at a symbol rate of 50 Gbaud and a launch power of -10 dBm the Tukey
scheme has a maximum theoretically achievable throughput of 225 Gb/s with a
(13,4)-SQAM constellation, while a comparable IMDD scheme achieves about 145
Gb/s using PAM-8.


title: A very preliminary analysis of DALL-E 2

abstract:
  The DALL-E 2 system generates original synthetic images corresponding to an
input text as caption. We report here on the outcome of fourteen tests of this
system designed to assess its common sense, reasoning and ability to understand
complex texts. All of our prompts were intentionally much more challenging than
the typical ones that have been showcased in recent weeks. Nevertheless, for 5
out of the 14 prompts, at least one of the ten images fully satisfied our
requests. On the other hand, on no prompt did all of the ten images satisfy our
requests.


title:
Analysing the Influence of Attack Configurations on the Reconstruction
  of Medical Images in Federated Learning

abstract:
  The idea of federated learning is to train deep neural network models
collaboratively and share them with multiple participants without exposing
their private training data to each other. This is highly attractive in the
medical domain due to patients' privacy records. However, a recently proposed
method called Deep Leakage from Gradients enables attackers to reconstruct data
from shared gradients. This study shows how easy it is to reconstruct images
for different data initialization schemes and distance measures. We show how
data and model architecture influence the optimal choice of initialization
scheme and distance measure configurations when working with single images. We
demonstrate that the choice of initialization scheme and distance measure can
significantly increase convergence speed and quality. Furthermore, we find that
the optimal attack configuration depends largely on the nature of the target
image distribution and the complexity of the model architecture.


title:
Deep Learning-based Automatic Player Identification and Logging in
  American Football Videos

abstract:
  American football games attract significant worldwide attention every year.
Game analysis systems generate crucial information that can help analyze the
games by providing fans and coaches with a convenient means to track and
evaluate player performance. Identifying participating players in each play is
also important for the video indexing of player participation per play.
Processing football game video presents challenges such as crowded setting,
distorted objects, and imbalanced data for identifying players, especially
jersey numbers. In this work, we propose a deep learning-based football video
analysis system to automatically track players and index their participation
per play. It is a multi-stage network design to highlight area of interest and
identify jersey number information with high accuracy. First, we utilize an
object detection network, a detection transformer, to tackle the player
detection problem in crowded context. Second, we identify players using jersey
number recognition with a secondary convolutional neural network, then
synchronize it with a game clock subsystem. Finally, the system outputs a
complete log in a database for play indexing. We demonstrate the effectiveness
and reliability of player identification and the logging system by analyzing
the qualitative and quantitative results on football videos. The proposed
system shows great potential for implementation in and analysis of football
broadcast video.


title:
Emergent SU(2)$_1$ conformal symmetry in the spin-1/2 Kitaev-Gamma chain
  with a Dzyaloshinskii-Moriya interaction

abstract:
  We study the one-dimensional spin-1/2 Kitaev-Gamma model with a
bond-dependent Dzyaloshinskii-Moriya (DM) interaction, which can be induced by
an electric field applied in the third direction where the first and second
directions refer to the two bond directions in the model. By a combination of
field theory and symmetry analysis, an extended gapless phase with an emergent
SU(2)$_1$ conformal symmetry is found in the phase diagram of the spin-1/2
Kitaev-Gamma-DM chain. The analytic predictions are in good agreements with
numerical results obtained from density matrix renormalization group
simulations.


title: Integral field spectroscopy with the solar gravitational lens

abstract:
  The prospect of combining integral field spectroscopy with the solar
gravitational lens (SGL) to spectrally and spatially resolve the surfaces and
atmospheres of extrasolar planets is investigated. The properties of hyperbolic
orbits visiting the focal region of the SGL are calculated analytically,
demonstrating trade offs between departure velocity and time of arrival, as
well as gravity assist maneuvers and heliocentric angular velocity. Numerical
integration of the solar barycentric motion demonstrates that navigational
acceleration of $\textrm{d}v \lesssim 80 \frac{\textrm{m}}{\textrm{s}} + 6.7
\frac{\textrm{m}}{\textrm{s}} \frac{t}{\textrm{year}}$ is needed to obtain and
maintain alignment. Obtaining target ephemerides of sufficient precision is an
open problem. The optical properties of an oblate gravitational lens are
reviewed, including calculations of the magnification and the point-spread
function that forms inside a telescope. Image formation for extended,
incoherent sources is discussed when the projected image is smaller than,
approximately equal to, and larger than the critical caustic. Sources of
contamination which limit observational SNR are considered in detail, including
the sun, the solar corona, the host star, and potential background objects. A
noise mitigation strategy of spectrally and spatially separating the light
using integral field spectroscopy is emphasized. A pseudoinverse-based image
reconstruction scheme demonstrates that direct reconstruction of an Earth-like
source from \textit{single} measurements of the Einstein ring is possible when
the critical caustic and observed SNR are sufficiently large. In this
arrangement, a mission would not require multiple telescopes or navigational
symmetry breaking, enabling continuous monitoring of the atmospheric
composition and dynamics on other planets.


title:
Visualization and Optimization Techniques for High Dimensional Parameter
  Spaces

abstract:
  High dimensional parameter space optimization is crucial in many
applications. The parameters affecting this performance can be both numerical
and categorical in their type. The existing techniques of black-box
optimization and visual analytics are good in dealing with numerical parameters
but analyzing categorical variables in context of the numerical variables are
not well studied. Hence, we propose a novel approach, to create an auto-tuning
framework for storage systems optimization combining both direct optimization
techniques and visual analytics research. While the optimization algorithm will
be the core of the system, visual analytics will provide a guideline with the
help of an external agent (expert) to provide crucial hints to narrow down the
large search space for the optimization engine. As part of the initial step
towards creating an auto-tuning engine for storage systems optimization, we
created an Interactive Configuration Explorer \textit{ICE}, which directly
addresses the need of analysts to learn how the dependent numerical variable is
affected by the parameter settings given multiple optimization objectives. No
information is lost as ICE shows the complete distribution and statistics of
the dependent variable in context with each categorical variable. Analysts can
interactively filter the variables to optimize for certain goals such as
achieving a system with maximum performance, low variance, etc. Our system was
developed in tight collaboration with a group of systems performance
researchers and its final effectiveness was evaluated with expert interviews, a
comparative user study, and two case studies. We also discuss our research plan
for creating an efficient auto-tuning framework combining black-box
optimization and visual analytics for storage systems performance optimization.


title:
Existence and uniqueness of global solutions for time fractional
  Keller-Segel system and fractional dissipation

abstract:
  This paper is concerned with the fractional Keller-Segel system in the
temporal and spatial variables. We consider fractional dissipation for the
physical variables including a fractional dissipation mechanism for the
chemotactic diffusion, as well as a time fractional variation assumed in the
Caputo sense. We prove new results concerning to the existence of global
solutions in critical homogeneous Besov spaces without using auxiliary norms,
including the existence of self-similar solutions, which constitutes a
persistence result in this framework. In addition, we prove a uniqueness result
without assuming any smallness condition of the initial data.


title:
An Online Ensemble Learning Model for Detecting Attacks in Wireless
  Sensor Networks

abstract:
  In today's modern world, the usage of technology is unavoidable and the rapid
advances in the Internet and communication fields have resulted to expand the
Wireless Sensor Network (WSN) technology. A huge number of sensing devices
collect and/or generate numerous sensory data throughout time for a wide range
of fields and applications. However, WSN has been proven to be vulnerable to
security breaches, the harsh and unattended deployment of these networks,
combined with their constrained resources and the volume of data generated
introduce a major security concern. WSN applications are extremely critical, it
is essential to build reliable solutions that involve fast and continuous
mechanisms for online data stream analysis enabling the detection of attacks
and intrusions. In this context, our aim is to develop an intelligent,
efficient, and updatable intrusion detection system by applying an important
machine learning concept known as ensemble learning in order to improve
detection performance. Although ensemble models have been proven to be useful
in offline learning, they have received less attention in streaming
applications. In this paper, we examine the application of different
homogeneous and heterogeneous online ensembles in sensory data analysis, on a
specialized wireless sensor network-detection system (WSN-DS) dataset in order
to classify four types of attacks: Blackhole attack, Grayhole, Flooding, and
Scheduling among normal network traffic. Among the proposed novel online
ensembles, both the heterogeneous ensemble consisting of an Adaptive Random
Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the
homogeneous ensemble HAT made up of 10 models achieved higher detection rates
of 96.84% and 97.2%, respectively. The above models are efficient and effective
in dealing with concept drift, while taking into account the resource
constraints of WSNs.


title: Controlling for Latent Confounding with Triple Proxies

abstract:
  We apply results in Hu and Schennach (2008) to achieve nonparametric
identification of causal effects using noisy proxies for unobserved
confounders. We call this the `triple proxy' approach because it requires three
proxies that are jointly independent conditional on unobservables. We consider
three different choices for the third proxy: it may be an outcome, a vector of
treatments, or a collection of auxiliary variables. We compare to an
alternative identification strategy introduced by Miao et. al. (2018) in which
causal effects are identified using two conditionally independent proxies. We
refer to this as the `double proxy' approach. We show that the conditional
independence assumptions in the double and triple proxy approaches are
non-nested, which suggests that either of the two identification strategies may
be appropriate depending on the particular setting.


title:
Reflected spectroscopy of small exoplanets II: characterization of
  terrestrial exoplanets

abstract:
  A space telescope capable of high-contrast imaging has been recognized as the
avenue toward finding terrestrial planets around nearby Sun-like stars and
characterizing their potential habitability. It is thus essential to quantify
the capability of reflected light spectroscopy obtained through direct imaging
for terrestrial exoplanets, and existing work focused on planetary analogs of
modern Earth. Here we go beyond Earth analogs and use a Bayesian retrieval
algorithm, ExoReL$^\Re$, to determine what we could learn about terrestrial
exoplanets from their reflected light spectra. Recognizing the potential
diversity of terrestrial exoplanets, our focus is to distinguish atmospheric
scenarios without any a priori knowledge of the dominant gas. We find that,
while a moderate-resolution spectrum in the optical band ($0.4-1.0\ \mu$m) may
sufficiently characterize a modern Earth analog, it would likely result in
incorrect interpretation for planets similar to Archean Earth or having
CO$_2$-dominated atmospheres. Including observations in the near-infrared bands
($1.0-1.8\ \mu$m) can prevent this error, determine the main component (N$_2$,
O$_2$, or CO$_2$), and quantify trace gases (H$_2$O, O$_3$, and CH$_4$) of the
atmosphere. These results are useful to define the science requirements and
design the wavelength bandwidth and observation plans of exoplanet direct
imaging missions in the future.


title: A remark on the Rost Nilpotence Principle

abstract:
  We consider a weak version of the Rost Nilpotence Principle. For
characteristic zero fields k, we show that if it holds for all smooth
projective schemes over k, then the Rost Nilpotence Principle does also. We
also correct the proof of a lemma in arXiv:1807.08163(3).


title: On the integral Hodge conjecture for varieties with trivial Chow group

abstract:
  We obtain examples of smooth projective varieties over $\mathbb{C}$ that
violate the integral Hodge conjecture and for which the total Chow group is of
finite rank. Moreover, we show that there exist such examples defined over
number fields.


title: Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers

abstract:
  Convolutional Neural Networks (CNNs) are one of the most studied family of
deep learning models for signal classification, including modulation,
technology, detection, and identification. In this work, we focus on technology
classification based on raw I/Q samples collected from multiple synchronized
receivers. As an example use case, we study protocol identification of Wi-Fi,
LTE-LAA, and 5G NR-U technologies that coexist over the 5 GHz Unlicensed
National Information Infrastructure (U-NII) bands. Designing and training
accurate CNN classifiers involve significant time and effort that goes into
fine-tuning a model's architectural settings and determining the appropriate
hyperparameter configurations, such as learning rate and batch size. We tackle
the former by defining architectural settings themselves as hyperparameters. We
attempt to automatically optimize these architectural parameters, along with
other preprocessing (e.g., number of I/Q samples within each classifier input)
and learning hyperparameters, by forming a Hyperparameter Optimization
(HyperOpt) problem, which we solve in a near-optimal fashion using the
Hyperband algorithm. The resulting near-optimal CNN (OCNN) classifier is then
used to study classification accuracy for OTA as well as simulations datasets,
considering various SNR values. We show that the number of receivers to
construct multi-channel inputs for CNNs should be defined as a preprocessing
hyperparameter to be optimized via Hyperband. OTA results reveal that our OCNN
classifiers improve classification accuracy by 24.58% compared to manually
tuned CNNs. We also study the effect of min-max normalization of I/Q samples
within each classifier's input on generalization accuracy over simulated
datasets with SNRs other than training set's SNR and show an average of 108.05%
improvement when I/Q samples are normalized.


title:
Turing and wave instabilities in hyperbolic reaction-diffusion systems:
  The role of second-order time derivatives and cross-diffusion terms on
  pattern formation

abstract:
  Hyperbolic reaction-diffusion equations have recently attracted attention
both for their application to a variety of biological and chemical phenomena,
and for their distinct features in terms of propagation speed and novel
instabilities not present in classical two-species reaction-diffusion systems.
We explore the onset of diffusive instabilities and resulting pattern formation
for such systems. Starting with a rather general formulation of the problem, we
obtain necessary and sufficient conditions for the Turing and wave
instabilities in such systems, thereby classifying parameter spaces for which
these diffusive instabilities occur. We find that the additional temporal terms
do not strongly modify the Turing patterns which form or parameters which admit
them, but only their regions of existence. This is in contrast to the case of
additional space derivatives, where past work has shown that resulting
patterned structures are sensitive to second-order cross-diffusion and
first-order advection. We also show that additional temporal terms are
necessary for the emergence of spatiotemporal patterns under the wave
instability. We find that such wave instabilities exist for parameters which
are mutually exclusive to those parameters leading to stationary Turing
patterns. This implies that wave instabilities may occur in cases where the
activator diffuses faster than the inhibitor, leading to routes to spatial
symmetry breaking in reaction-diffusion systems which are distinct from the
well studied Turing case.


title:
A Neural Network-enhanced Reproducing Kernel Particle Method for
  Modeling Strain Localization

abstract:
  Modeling the localized intensive deformation in a damaged solid requires
highly refined discretization for accurate prediction, which significantly
increases the computational cost. Although adaptive model refinement can be
employed for enhanced effectiveness, it is cumbersome for the traditional
mesh-based methods to perform while modeling the evolving localizations. In
this work, neural network-enhanced reproducing kernel particle method (NN-RKPM)
is proposed, where the location, orientation, and shape of the solution
transition near a localization is automatically captured by the NN
approximation via a block-level neural network optimization. The weights and
biases in the blocked parametrization network control the location and
orientation of the localization. The designed basic four-kernel NN block is
capable of capturing a triple junction or a quadruple junction topological
pattern, while more complicated localization topological patters are captured
by the superposition of multiple four-kernel NN blocks. The standard RK
approximation is then utilized to approximate the smooth part of the solution,
which permits a much coarser discretization than the high-resolution
discretization needed to capture sharp solution transitions with the
conventional methods. A regularization of the neural network approximation is
additionally introduced for discretization-independent material responses. The
effectiveness of the proposed NN-RKPM is verified by a series of numerical
verifications.

categories: cs.CE cs.LG cs.NA math.NA physics.comp-ph


title:
HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge
  Streams

abstract:
  Sequences of group interactions, such as emails, online discussions, and
co-authorships, are ubiquitous; and they are naturally represented as a stream
of hyperedges. Despite their broad potential applications, anomaly detection in
hypergraphs (i.e., sets of hyperedges) has received surprisingly little
attention, compared to that in graphs. While it is tempting to reduce
hypergraphs to graphs and apply existing graph-based methods, according to our
experiments, taking higher-order structures of hypergraphs into consideration
is worthwhile. We propose HashNWalk, an incremental algorithm that detects
anomalies in a stream of hyperedges. It maintains and updates a constant-size
summary of the structural and temporal information about the stream. Using the
summary, which is the form of a proximity matrix, HashNWalk measures the
anomalousness of each new hyperedge as it appears. HashNWalk is (a) Fast: it
processes each hyperedge in near real-time and billions of hyperedges within a
few hours, (b) Space Efficient: the size of the maintained summary is a
predefined constant, (c) Effective: it successfully detects anomalous
hyperedges in real-world hypergraphs.


title:
Partial regularity of suitable weak solutions of the model arising in
  amorphous molecular beam epitaxy

abstract:
  In this paper, we are concerned with the precise relationship between the
Hausdorff dimension of possible singular point set $\mathcal{S}$ of suitable
weak solutions and the parameter $\alpha$ in the nonlinear term in the
following parabolic equation $$h_t+h_{xxxx}+\partial_{xx}|h_x|^\alpha=f.$$ It
is shown that when $5/3\leq\alpha<7/3$, the
$\frac{3\alpha-5}{\alpha-1}$-dimensional parabolic Hausdorff measure of
$\mathcal{S}$ is zero, which generalizes the recent corresponding work of
Oz\'anski and Robinson in [31,SIAM J. Math. Anal. 51: 228--255, 2019] for
$\alpha=2$ and $f=0$. The same result is valid for a 3D modified Navier-Stokes
system.


title: Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series

abstract:
  We consider the problem of inferring the conditional independence graph (CIG)
of a high-dimensional stationary multivariate Gaussian time series. A
sparse-group lasso based frequency-domain formulation of the problem has been
considered in the literature where the objective is to estimate the sparse
inverse power spectral density (PSD) of the data. The CIG is then inferred from
the estimated inverse PSD. In this paper we investigate use of a sparse-group
log-sum penalty (LSP) instead of sparse-group lasso penalty. An alternating
direction method of multipliers (ADMM) approach for iterative optimization of
the non-convex problem is presented. We provide sufficient conditions for local
convergence in the Frobenius norm of the inverse PSD estimators to the true
value. This results also yields a rate of convergence. We illustrate our
approach using numerical examples utilizing both synthetic and real data.


title:
Node-based uniform strain virtual elements for compressible and nearly
  incompressible plane elasticity

abstract:
  We propose a displacement-based approach to solve problems in compressible
and nearly incompressible plane elasticity that combines a nodal integration
technique and the virtual element method (VEM), wherein the strain is averaged
around nodes from the strain of surrounding virtual elements. For the strain
averaging procedure, a nodal averaging operator is constructed using a
generalization to virtual elements of the node-based uniform strain approach
for finite elements. We refer to these new elements as node-based uniform
strain virtual elements (NVEM). A salient feature of the NVEM is that the
stresses and strains become nodal variables just like displacements, which can
be exploited in nonlinear simulations thereby providing room for further
development of this novel approach. Through several benchmark problems in plane
elasticity, we demonstrate that the NVEM is accurate and optimally convergent,
and devoid of volumetric locking in the nearly incompressible limit.


title: Extreme values of derivatives of zeta and $L$-functions

abstract:
  It is proved that as $T \to \infty$, uniformly for all positive integers
$\ell \leqslant (\log_3 T) / (\log_4 T)$, we have \begin{equation*}
\max_{T\leqslant t\leqslant 2T}\left|\zeta^{(\ell)}\Big(1+it\Big)\right|
\geqslant \big(\mathbf Y_{\ell}+ o\left(1\right)\big)\left(\log_2 T
\right)^{\ell+1} \,, \end{equation*} where $\mathbf Y_{\ell} = \int_0^{\infty}
u^{\ell} \rho (u) du$. Here $\rho(u)$ is the Dickman function. We have $\mathbf
Y_{\ell} > e^{\gamma}/(\ell + 1)$ and $ \log\, \mathbf Y_{\ell} = \left(1 +
o\left(1\right) \right) \ell \log \ell$ when $ \ell \to \infty $, which
significantly improves previous results in [14, 28]. Similar results are
established for Dirichlet $L$-functions.


title: PRETRUST: A Framework for Fast Payments in Blockchain System

abstract:
  Decentralized cryptocurrencies based on blockchains hold an advantage over
the conventional methods in digital payments. However, many current
decentralized cryptocurrencies based on blockchains cannot manage to satisfy
the requests for fast payments. In fact, most time cost on blockchains is the
time waiting for records to be verified. The efficiency of addressing
transactions will be greatly improved if this part of the time can be saved.
One motivation is to scale out the capacity by reducing interactions with the
public chain while keeping its original structure, which is a mainstream
strategy of improving efficiency. In the paper, we propose PRETRUST, a new
framework to address the problem above. PRETRUST is based on consortium
blockchains thoughts, supporting fast payments. In order to make parties
address transactions and scale-out the capacity, the main strategy is based on
the guarantee mechanism and shard technology with a random arrangement of
transactions and consensus groups. In PRETRUST, the guarantee process replaces
the verification of a transaction on-chain. And the model of a trusted
execution environment is utilized in the interactions between PRETRUST and
public systems. Throughout the paper, we discuss several transaction scenarios
and analyze the security, which shows that PRETRUST can guarantee security.


title:
Designing for Responsible Trust in AI Systems: A Communication
  Perspective

abstract:
  Current literature and public discourse on "trust in AI" are often focused on
the principles underlying trustworthy AI, with insufficient attention paid to
how people develop trust. Given that AI systems differ in their level of
trustworthiness, two open questions come to the fore: how should AI
trustworthiness be responsibly communicated to ensure appropriate and equitable
trust judgments by different users, and how can we protect users from deceptive
attempts to earn their trust? We draw from communication theories and
literature on trust in technologies to develop a conceptual model called MATCH,
which describes how trustworthiness is communicated in AI systems through
trustworthiness cues and how those cues are processed by people to make trust
judgments. Besides AI-generated content, we highlight transparency and
interaction as AI systems' affordances that present a wide range of
trustworthiness cues to users. By bringing to light the variety of users'
cognitive processes to make trust judgments and their potential limitations, we
urge technology creators to make conscious decisions in choosing reliable
trustworthiness cues for target users and, as an industry, to regulate this
space and prevent malicious use. Towards these goals, we define the concepts of
warranted trustworthiness cues and expensive trustworthiness cues, and propose
a checklist of requirements to help technology creators identify appropriate
cues to use. We present a hypothetical use case to illustrate how practitioners
can use MATCH to design AI systems responsibly, and discuss future directions
for research and industry efforts aimed at promoting responsible trust in AI.


title:
Understanding the impact of image and input resolution on deep digital
  pathology patch classifiers

abstract:
  We consider annotation efficient learning in Digital Pathology (DP), where
expert annotations are expensive and thus scarce. We explore the impact of
image and input resolution on DP patch classification performance. We use two
cancer patch classification datasets PCam and CRC, to validate the results of
our study. Our experiments show that patch classification performance can be
improved by manipulating both the image and input resolution in
annotation-scarce and annotation-rich environments. We show a positive
correlation between the image and input resolution and the patch classification
accuracy on both datasets. By exploiting the image and input resolution, our
final model trained on < 1% of data performs equally well compared to the model
trained on 100% of data in the original image resolution on the PCam dataset.


title:
Solution formula for generalized two-phase Stokes equations and its
  applications to maximal regularity; model problems

abstract:
  In this paper we give a solution formula for the two-phase Stokes equations
with and without surface tension and gravity in the whole space with flat
interface. The solution formula has already considered by Shibata-Shimizu.
However we reconstruct the formula so that we are able to prove resolvent
estimate and maximal regularity estimate. In the previous work, they needed to
assume additional conditions on normal components. We also take care of normal
components, while the assumption becomes weaker than before. The method is
based on an $H^\infty$ calculus which has already used for the Stokes problems
with various boundary conditions in the half space.


title: Average Redundancy of Variable-Length Balancing Schemes \`{a} la Knuth

abstract:
  We study and propose schemes that map messages onto constant-weight codewords
using variable-length prefixes. We provide polynomial-time computable formulas
that estimate the average number of redundant bits incurred by our schemes. In
addition to the exact formulas, we also perform an asymptotic analysis and
demonstrate that our scheme uses $\frac12 \log n+O(1)$ redundant bits to encode
messages into length-$n$ words with weight $(n/2)+{\sf q}$ for constant ${\sf
q}$.


title:
Efficient Algorithms for Monotone Non-Submodular Maximization with
  Partition Matroid Constraint

abstract:
  In this work, we study the problem of monotone non-submodular maximization
with partition matroid constraint. Although a generalization of this problem
has been studied in literature, our work focuses on leveraging properties of
partition matroid constraint to (1) propose algorithms with theoretical bound
and efficient query complexity; and (2) provide better analysis on theoretical
performance guarantee of some existing techniques. We further investigate those
algorithms' performance in two applications: Boosting Influence Spread and
Video Summarization. Experiments show our algorithms return comparative results
to the state-of-the-art algorithms while taking much fewer queries.


title: On a class of semigroup products

abstract:
  This paper concerns a class of semigroups that arise as products $US$,
associated to what we call `action pairs'. Here $U$ and $S$ are subsemigroups
of a common monoid and, roughly speaking, $S$ has an action on the monoid
completion $U^1$ that is suitably compatible with the product in the
over-monoid.
  The semigroups encapsulated by the action pair construction include many
natural classes such as inverse semigroups and (left) restriction semigroups,
as well as many important concrete examples such as transformational wreath
products, linear monoids, (partial) endomorphism monoids of independence
algebras, and the singular ideals of many of these. Action pairs provide a
unified framework for systematically studying such semigroups, within which we
build a suite of tools to ensure a comprehensive understanding of them. We then
apply our abstract results to many special cases of interest.
  The first part of the paper constitutes a detailed structural analysis of
semigroups arising from action pairs. We show that any such semigroup $US$ is a
quotient of a semidirect product $U\rtimes S$, and we classify all congruences
on semidirect products that correspond to action pairs. We also prove several
covering and embedding theorems, each of which naturally extends celebrated
results of McAlister on proper (a.k.a. $E$-unitary) inverse semigroups.
  The second part of the paper concerns presentations by generators and
relations for semigroups arising from action pairs. We develop a substantial
body of general results and techniques that allow us to build presentations for
$US$ out of presentations for the constituents $U$ and $S$ in many cases, and
then apply these to several examples, including those listed above. Due to the
broad applicability of the action pair construction, many results in the
literature are special cases of our more general ones.


title: Stability Experiments: The Overlooked Dual of Memory Experiments

abstract:
  Topological quantum computations are built on a foundation of two basic
tasks: preserving logical observables through time and moving logical
observables through space. Memory experiments, which check how well logical
observables are preserved through time, are a well established benchmark.
Strangely, there is no corresponding well established benchmark for moving
logical observables through space. This paper tries to fill that gap with
"stability experiments", which check how well a quantum error correction system
can determine the product of a large region of stabilizers. Stability
experiments achieve this by testing on a region that is locally a normal code
but globally has a known product of stabilizers.


title: Quantum Dictionaries without QRAM

abstract:
  This paper presents an efficient gate-level implementation of a quantum
dictionary: a data structure that can store a superposition of mappings from
keys to values. The dictionary is stored as a fixed-length list of sorted
address-value pairs, where the length of the list is the maximum number of
entries that can be put in the dictionary. An addressed value can be extracted
from (or injected into) the dictionary using $C \cdot (V + 2.5A) + O(V + A +
C)$ expected Toffoli gates and $O(V + A)$ auxiliary qubits (where $C$ is the
maximum capacity, $A$ is the address width, and $V$ is the value width).


title: Ancient solutions and translators of Lagrangian mean curvature flow

abstract:
  Suppose that $\mathcal{M}$ is an almost calibrated, exact, ancient solution
of Lagrangian mean curvature flow in $\mathbb{C}^n$. We show that if
$\mathcal{M}$ has a blow-down given by the static union of two Lagrangian
subspaces with distinct Lagrangian angles that intersect along a line, then
$\mathcal{M}$ is a translator. In particular in $\mathbb{C}^2$, all
zero-Maslov, exact, ancient solutions of Lagrangian mean curvature flow with
entropy less than 3 are special Lagrangian, a union of planes, or translators.


title:
Linear stability of black holes with static scalar hair in full
  Horndeski theories: generic instabilities and surviving models

abstract:
  In full Horndeski theories, we show that the static and spherically symmetric
black hole (BH) solutions with a static scalar field $\phi$ whose kinetic term
$X$ is nonvanishing on the BH horizon are generically prone to ghost/Laplacian
instabilities. We then search for asymptotically flat hairy BH solutions with a
vanishing $X$ on the horizon free from ghost/Laplacian instabilities. We show
that models with regular coupling functions of $\phi$ and $X$ result in no-hair
Schwarzschild BHs in general. On the other hand, the presence of a coupling
between the scalar field and the Gauss-Bonnet (GB) term $R_{\rm GB}^2$, even
with the coexistence of other regular coupling functions, leads to the
realization of asymptotically flat hairy BH solutions without ghost/Laplacian
instabilities. Finally, we find that hairy BH solutions in power-law $F(R_{\rm
GB}^2)$ gravity are plagued by ghost instabilities. These results imply that
the GB coupling of the form $\xi(\phi)R_{\rm GB}^2$ plays a prominent role for
the existence of asymptotically flat hairy BH solutions free from
ghost/Laplacian instabilities.


title:
Noise-reducing attention cross fusion learning transformer for
  histological image classification of osteosarcoma

abstract:
  The degree of malignancy of osteosarcoma and its tendency to
metastasize/spread mainly depend on the pathological grade (determined by
observing the morphology of the tumor under a microscope). The purpose of this
study is to use artificial intelligence to classify osteosarcoma histological
images and to assess tumor survival and necrosis, which will help doctors
reduce their workload, improve the accuracy of osteosarcoma cancer detection,
and make a better prognosis for patients. The study proposes a typical
transformer image classification framework by integrating noise reduction
convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to
classify osteosarcoma histological images. Noise reduction convolutional
autoencoder could well denoise histological images of osteosarcoma, resulting
in more pure images for osteosarcoma classification. Moreover, we introduce
feature cross fusion learning, which integrates two scale image patches, to
sufficiently explore their interactions by using additional classification
tokens. As a result, a refined fusion feature is generated, which is fed to the
residual neural network for label predictions. We conduct extensive experiments
to evaluate the performance of the proposed approach. The experimental results
demonstrate that our method outperforms the traditional and deep learning
approaches on various evaluation metrics, with an accuracy of 99.17% to support
osteosarcoma diagnosis.


title: A suite of diagnostic metrics for characterizing selection schemes

abstract:
  Evolutionary algorithms are effective general-purpose techniques for solving
optimization problems. Understanding how each component of an evolutionary
algorithm influences its problem-solving success improves our ability to target
particular problem domains. Our work focuses on evaluating selection schemes,
which choose individuals to contribute genetic material to the next generation.
We introduce four diagnostic search spaces for testing the strengths and
weaknesses of selection schemes: the exploitation rate diagnostic, ordered
exploitation rate diagnostic, contradictory objectives diagnostic, and the
multi-path exploration diagnostic. Each diagnostic is handcrafted to isolate
and measure the relative exploitation and exploration characteristics of
selection schemes. In this study, we use our diagnostics to evaluate six
population selection methods: truncation selection, tournament selection,
fitness sharing, lexicase selection, nondominated sorting, and novelty search.
Expectedly, tournament and truncation selection excelled in gradient
exploitation but poorly explored search spaces, and novelty search excelled at
exploration but failed to exploit fitness gradients. Fitness sharing performed
poorly across all diagnostics, suggesting poor overall exploitation and
exploration abilities. Nondominated sorting was best for maintaining
populations comprised of individuals with different trade-offs of multiple
objectives, but struggled to effectively exploit fitness gradients. Lexicase
selection balanced search space exploration with exploitation, generally
performing well across diagnostics. Our work demonstrates the value of
diagnostic search spaces for building a deeper understanding of selection
schemes, which can then be used to improve or develop new selection methods.


title: Geometrical effective actions for a partially massless spin-2 field

abstract:
  We consider nonlinear effective actions for a spin-2 field, whose
"decoupling" limit gives Fierz-Pauli action in D dimensional maximally
symmetric spacetime. We find, especially, the effective action for a partially
massless field can take a concise geometrical form. The exact solution for time
evolution of the background metric in the model using the effective action is
also studied.


title: An Extensive Data Processing Pipeline for MIMIC-IV

abstract:
  An increasing amount of research is being devoted to applying machine
learning methods to electronic health record (EHR) data for various clinical
tasks. This growing area of research has exposed the limitation of
accessibility of EHR datasets for all, as well as the reproducibility of
different modeling frameworks. One reason for these limitations is the lack of
standardized pre-processing pipelines. MIMIC is a freely available EHR dataset
in a raw format that has been used in numerous studies. The absence of
standardized pre-processing steps serves as a major barrier to the wider
adoption of the dataset. It also leads to different cohorts being used in
downstream tasks, limiting the ability to compare the results among similar
studies. Contrasting studies also use various distinct performance metrics,
which can greatly reduce the ability to compare model results. In this work, we
provide an end-to-end fully customizable pipeline to extract, clean, and
pre-process data; and to predict and evaluate the fourth version of the MIMIC
dataset (MIMIC-IV) for ICU and non-ICU-related clinical time-series prediction
tasks.


title:
A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig
  Workers against AI Inequality

abstract:
  The growing inequality in gig work between workers and platforms has become a
critical social issue as gig work plays an increasingly prominent role in the
future of work. The AI inequality is caused by (1) the technology divide in who
has access to AI technologies in gig work; and (2) the data divide in who owns
the data in gig work leads to unfair working conditions, growing pay gap,
neglect of workers' diverse preferences, and workers' lack of trust in the
platforms. In this position paper, we argue that a bottom-up approach that
empowers individual workers to access AI-enabled work planning support and
share data among a group of workers through a network of end-user-programmable
intelligent assistants is a practical way to bridge AI inequality in gig work
under the current paradigm of privately owned platforms. This position paper
articulates a set of research challenges, potential approaches, and community
engagement opportunities, seeking to start a dialogue on this important
research topic in the interdisciplinary CHIWORK community.


title:
VPNets: Volume-preserving neural networks for learning source-free
  dynamics

abstract:
  We propose volume-preserving networks (VPNets) for learning unknown
source-free dynamical systems using trajectory data. We propose three modules
and combine them to obtain two network architectures, coined R-VPNet and
LA-VPNet. The distinct feature of the proposed models is that they are
intrinsic volume-preserving. In addition, the corresponding approximation
theorems are proved, which theoretically guarantee the expressivity of the
proposed VPNets to learn source-free dynamics. The effectiveness,
generalization ability and structure-preserving property of the VP-Nets are
demonstrated by numerical experiments.


title: User-controllable Recommendation Against Filter Bubbles

abstract:
  Recommender systems usually face the issue of filter bubbles:
overrecommending homogeneous items based on user features and historical
interactions. Filter bubbles will grow along the feedback loop and
inadvertently narrow user interests. Existing work usually mitigates filter
bubbles by incorporating objectives apart from accuracy such as diversity and
fairness. However, they typically sacrifice accuracy, hurting model fidelity
and user experience. Worse still, users have to passively accept the
recommendation strategy and influence the system in an inefficient manner with
high latency, e.g., keeping providing feedback (e.g., like and dislike) until
the system recognizes the user intention.
  This work proposes a new recommender prototype called UserControllable
Recommender System (UCRS), which enables users to actively control the
mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are
deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands
for users to mitigate the bubbles at different granularities. 3) UCRS can
respond to the controls and adjust the recommendations on the fly. The key to
adjusting lies in blocking the effect of out-of-date user representations on
recommendations, which contains historical information inconsistent with the
control commands. As such, we develop a causality-enhanced User-Controllable
Inference (UCI) framework, which can quickly revise the recommendations based
on user controls in the inference stage and utilize counterfactual inference to
mitigate the effect of out-of-date user representations. Experiments on three
datasets validate that the UCI framework can effectively recommend more desired
items based on user controls, showing promising performance w.r.t. both
accuracy and diversity.


title: GenDR: A Generalized Differentiable Renderer

abstract:
  In this work, we present and study a generalized family of differentiable
renderers. We discuss from scratch which components are necessary for
differentiable rendering and formalize the requirements for each component. We
instantiate our general differentiable renderer, which generalizes existing
differentiable renderers like SoftRas and DIB-R, with an array of different
smoothing distributions to cover a large spectrum of reasonable settings. We
evaluate an array of differentiable renderer instantiations on the popular
ShapeNet 3D reconstruction benchmark and analyze the implications of our
results. Surprisingly, the simple uniform distribution yields the best overall
results when averaged over 13 classes; in general, however, the optimal choice
of distribution heavily depends on the task.


title:
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive
  Learning

abstract:
  Graph contrastive learning has gained significant progress recently. However,
existing works have rarely explored non-aligned node-node contrasting. In this
paper, we propose a novel graph contrastive learning method named RoSA that
focuses on utilizing non-aligned augmented views for node-level representation
learning. First, we leverage the earth mover's distance to model the minimum
effort to transform the distribution of one view to the other as our
contrastive objective, which does not require alignment between views. Then we
introduce adversarial training as an auxiliary method to increase sampling
diversity and enhance the robustness of our model. Experimental results show
that RoSA outperforms a series of graph contrastive learning frameworks on
homophilous, non-homophilous and dynamic graphs, which validates the
effectiveness of our work. To the best of our awareness, RoSA is the first work
focuses on the non-aligned node-node graph contrastive learning problem. Our
codes are available at:
\href{https://github.com/ZhuYun97/RoSA}{\texttt{https://github.com/ZhuYun97/RoSA}}


title:
CATNet: Cross-event Attention-based Time-aware Network for Medical Event
  Prediction

abstract:
  Medical event prediction (MEP) is a fundamental task in the medical domain,
which needs to predict medical events, including medications, diagnosis codes,
laboratory tests, procedures, outcomes, and so on, according to historical
medical records. The task is challenging as medical data is a type of complex
time series data with heterogeneous and temporal irregular characteristics.
Many machine learning methods that consider the two characteristics have been
proposed for medical event prediction. However, most of them consider the two
characteristics separately and ignore the correlations among different types of
medical events, especially relations between historical medical events and
target medical events. In this paper, we propose a novel neural network based
on attention mechanism, called cross-event attention-based time-aware network
(CATNet), for medical event prediction. It is a time-aware, event-aware and
task-adaptive method with the following advantages: 1) modeling heterogeneous
information and temporal information in a unified way and considering temporal
irregular characteristics locally and globally respectively, 2) taking full
advantage of correlations among different types of events via cross-event
attention. Experiments on two public datasets (MIMIC-III and eICU) show CATNet
can be adaptive with different MEP tasks and outperforms other state-of-the-art
methods on various MEP tasks. The source code of CATNet will be released after
this manuscript is accepted.


title:
Repro: An Open-Source Library for Improving the Reproducibility and
  Usability of Publicly Available Research Code

abstract:
  We introduce Repro, an open-source library which aims at improving the
reproducibility and usability of research code. The library provides a
lightweight Python API for running software released by researchers within
Docker containers which contain the exact required runtime configuration and
dependencies for the code. Because the environment setup for each package is
handled by Docker, users do not have to do any configuration themselves. Once
Repro is installed, users can run the code for the 30+ papers currently
supported by the library. We hope researchers see the value provided to others
by including their research code in Repro and consider adding support for their
own research code.


title:
Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with
  Application to Sim2Real Pneumonia Lesion Detection

abstract:
  A computer-aided detection (CAD) system based on machine learning is expected
to assist radiologists in making a diagnosis. It is desirable to build CAD
systems for the various types of diseases accumulating daily in a hospital. An
obstacle in developing a CAD system for a disease is that the number of medical
images is typically too small to improve the performance of the machine
learning model. In this paper, we aim to explore ways to address this problem
through a sim2real transfer approach in medical image fields. To build a
platform to evaluate the performance of sim2real transfer methods in the field
of medical imaging, we construct a benchmark dataset that consists of $101$
chest X-images with difficult-to-identify pneumonia lesions judged by an
experienced radiologist and a simulator based on fractal Perlin noise and the
X-ray principle for generating pseudo pneumonia lesions. We then develop a
novel domain randomization method, called Goldilocks-curriculum domain
randomization (GDR) and evaluate our method in this platform.


title: AoI-Aware Markov Decision Policies for Caching

abstract:
  We consider a scenario that utilizes road side units (RSUs) as distributed
caches in connected vehicular networks. The goal of the use of caches in our
scenario is for rapidly providing contents to connected vehicles under various
traffic conditions. During this operation, due to the rapidly changed road
environment and user mobility, the concept of age-of-information (AoI) is
considered for (1) updating the cached information as well as (2) maintaining
the freshness of cached information. The frequent updates of cached information
maintain the freshness of the information at the expense of network resources.
Here, the frequent updates increase the number of data transmissions between
RSUs and MBS; and thus, it increases system costs, consequently. Therefore, the
tradeoff exists between the AoI of cached information and the system costs.
Based on this observation, the proposed algorithm in this paper aims at the
system cost reduction which is fundamentally required for content delivery
while minimizing the content AoI, based on Markov Decision Process (MDP) and
Lyapunov optimization.


title:
COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19
  Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex
  Ultrasound Augmentation Learning

abstract:
  As the global population continues to face significant negative impact by the
on-going COVID-19 pandemic, there has been an increasing usage of point-of-care
ultrasound (POCUS) imaging as a low-cost and effective imaging modality of
choice in the COVID-19 clinical workflow. A major barrier with widespread
adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert
clinicians that can interpret POCUS examinations, leading to considerable
interest in deep learning-driven clinical decision support systems to tackle
this challenge. A major challenge to building deep neural networks for COVID-19
screening using POCUS is the heterogeneity in the types of probes used to
capture ultrasound images (e.g., convex vs. linear probes), which can lead to
very different visual appearances. In this study, we explore the impact of
leveraging extended linear-convex ultrasound augmentation learning on producing
enhanced deep neural networks for COVID-19 assessment, where we conduct data
augmentation on convex probe data alongside linear probe data that have been
transformed to better resemble convex probe data. Experimental results using an
efficient deep columnar anti-aliased convolutional neural network designed via
a machined-driven design exploration strategy (which we name COVID-Net US-X)
show that the proposed extended linear-convex ultrasound augmentation learning
significantly increases performance, with a gain of 5.1% in test accuracy and
13.6% in AUC.


title:
H2H: Heterogeneous Model to Heterogeneous System Mapping with
  Computation and Communication Awareness

abstract:
  The complex nature of real-world problems calls for heterogeneity in both
machine learning (ML) models and hardware systems. The heterogeneity in ML
models comes from multi-sensor perceiving and multi-task learning, i.e.,
multi-modality multi-task (MMMT), resulting in diverse deep neural network
(DNN) layers and computation patterns. The heterogeneity in systems comes from
diverse processing components, as it becomes the prevailing method to integrate
multiple dedicated accelerators into one system. Therefore, a new problem
emerges: heterogeneous model to heterogeneous system mapping (H2H). While
previous mapping algorithms mostly focus on efficient computations, in this
work, we argue that it is indispensable to consider computation and
communication simultaneously for better system efficiency. We propose a novel
H2H mapping algorithm with both computation and communication awareness; by
slightly trading computation for communication, the system overall latency and
energy consumption can be largely reduced. The superior performance of our work
is evaluated based on MAESTRO modeling, demonstrating 15%-74% latency reduction
and 23%-64% energy reduction compared with existing computation-prioritized
mapping algorithms.


title:
Detecting Textual Adversarial Examples Based on Distributional
  Characteristics of Data Representations

abstract:
  Although deep neural networks have achieved state-of-the-art performance in
various machine learning tasks, adversarial examples, constructed by adding
small non-random perturbations to correctly classified inputs, successfully
fool highly expressive deep classifiers into incorrect predictions. Approaches
to adversarial attacks in natural language tasks have boomed in the last five
years using character-level, word-level, phrase-level, or sentence-level
textual perturbations. While there is some work in NLP on defending against
such attacks through proactive methods, like adversarial training, there is to
our knowledge no effective general reactive approaches to defence via detection
of textual adversarial examples such as is found in the image processing
literature. In this paper, we propose two new reactive methods for NLP to fill
this gap, which unlike the few limited application baselines from NLP are based
entirely on distribution characteristics of learned representations: we adapt
one from the image processing literature (Local Intrinsic Dimensionality
(LID)), and propose a novel one (MultiDistance Representation Ensemble Method
(MDRE)). Adapted LID and MDRE obtain state-of-the-art results on
character-level, word-level, and phrase-level attacks on the IMDB dataset as
well as on the later two with respect to the MultiNLI dataset. For future
research, we publish our code.