title: Interpretable Concept-based Prototypical Networks for Few-Shot Learning

abstract:
  Few-shot learning aims at recognizing new instances from classes with limited
samples. This challenging task is usually alleviated by performing
meta-learning on similar tasks. However, the resulting models are black-boxes.
There has been growing concerns about deploying black-box machine learning
models and FSL is not an exception in this regard. In this paper, we propose a
method for FSL based on a set of human-interpretable concepts. It constructs a
set of metric spaces associated with the concepts and classifies samples of
novel classes by aggregating concept-specific decisions. The proposed method
does not require concept annotations for query samples. This interpretable
method achieved results on a par with six previously state-of-the-art black-box
FSL methods on the CUB fine-grained bird classification dataset.


title:
The Equation of State of Neutron-Rich Matter at Fourth Order of Chiral
  Effective Field Theory and the Radius of a Medium-Mass Neutron Star

abstract:
  We report neutron star predictions based on our most recent equations of
state. These are derived from chiral effective field theory, which allows for a
systematic development of nuclear forces, order by order. We utilize
high-quality two-nucleon interactions and include all three-nucleon forces up
to fourth order in the chiral expansion. Our ab initio predictions are
restricted to the domain of applicability of chiral effective field theory.
However, stellar matter in the interior of neutron stars can be up to several
times denser than normal nuclear matter at saturation, and its composition is
essentially unknown. Following established practices, we extend our microscopic
predictions to higher densities matching piecewise polytropes. The radius of
the average-size neutron star, about 1.4 solar masses, is sensitive to the
pressure at normal densities, and thus it is suitable to constrain ab initio
theories of the equation of state. For this reason, we focus on the radius of
medium-mass stars. We compare our results with other theoretical predictions
and recent constraints.


title: The heterogeneous helicoseir

abstract:
  We study the rotations of a heavy string (helicoseir) about a vertical axis
with one free endpoint and with arbitrary density, under the action of the
gravitational force. We show that the problem can be transformed into a
nonlinear eigenvalue equation, as in the uniform case. The eigenmodes of this
equation represent equilibrium configurations of the rotating string in which
the shape of the string doesn't change with time. As previously proved by
Kolodner for the homogenous case, the occurrence of new modes of the nonlinear
equation is tied to the spectrum of the corresponding linear equation. We have
been able to generalize this result to a class of densities $\rho(s) = \gamma
(1-s)^{\gamma-1}$, which includes the homogenous string as a special case
($\gamma=1$).
  We also show that the solutions to the nonlinear eigenvalue equation (NLE)
for an arbitrary density are orthogonal and that a solution of this equation
with a given number of nodes contains solutions of a different helicoseir, with
a smaller number of nodes. Both properties hold also for the homogeneous case
and had not been established before.


title: Superspreading and Heterogeneity in Epidemics

abstract:
  Epidemic disease spreading is conventionally often modelled and analyzed by
means of rate and diffusion equations, following the paradigms of
well-controlled chemical reactions and diffusive dynamics in a test tube. Yet,
serious worries that this suggestive and appealing similarity might be a false
friend were already voiced by the pioneers of mathematical epidemiology. A
century later, we can draw on cross-fertilizations from network and game theory
and the emerging field of eco-evolutionary dynamics to substantiate them.
Epidemiological spreading is thereby revealed as a fundamentally heterogeneous
and erratic process that shares certain properties with more unwieldy
phenomena, such as earthquakes, hurricanes, traffic jams, and stock crashes.
They are all characterised by high tail risks that materialize very rarely but
fatally. That they arise from bursts of unlikely chains of localized random
"superspreader" events, by which micro-scale fluctuations and uncertainties may
get heftily magnified, makes their accurate prediction and control
intrinsically and notoriously hard. That epidemic disease spreading is moreover
closely intertwined with equally heterogeneous genetic drift and information
feedback adds new challenges -- and chances.


title: Coset topologies on $\mathbb{Z}$ and arithmetic applications

abstract:
  We provide a construction which covers as special cases many of the
topologies on integers one can find in the literature. Moreover, our analysis
of the Golomb and Kirch topologies inserts them in a family of connected,
Hausdorff topologies on $\mathbb{Z}$, obtained from closed sets of the
profinite completion $\hat{\mathbb{Z}}$. We also discuss various applications
to number theory.


title: Robustness of quantum Hall interferometry

abstract:
  Fabry-P\'{e}rot interferometry has emerged as a tool to probe anyon
statistics in the quantum Hall effect. The interference phase is interpreted as
a combination of a quantized statistical phase and an Aharonov-Bohm phase,
proportional to the device area and the charge of the anyons propagating along
the device edge. This interpretation faces two challenges. First, the edge
states have a finite width and hence the device area is ill-defined. Second,
multiple localized anyons may be present in states that overlap with the edge,
and it may not be clear whether a second anyon traveling along the edge will go
inside or outside the region with a localized anyon and therefore whether or
not it should pick up a statistical phase. We show how one may overcome both
challenges. In a case where only one chiral edge mode passes through the
constrictions defining the interferometer, as when electrons in a constriction
are in a Laughlin state with $\nu=1/(2n+1)$ or the integer state at $\nu=1$, we
show that the interference phase can be directly related to the total electron
charge contained in the interferometer. This holds for arbitrary
electron-electron interactions and holds even if the bulk of the interferometer
has a higher electron density than the region of the constrictions. In contrast
to the device area or to the number of anyons inside a propagating edge
channel, the total charge is well-defined. We examine, at the microscopic
level, how the relation between charge and phase is maintained when there is a
soft confining potential and disorder near the edge of the interferometer, and
we discuss briefly the complications that can occur when multiple chiral modes
can pass through the constriction.


title:
A Bibliometric Horizon Scanning Methodology for Identifying Emerging
  Topics in the Scientific Literature

abstract:
  A bibliometric methodology for scanning for emerging science and technology
areas is described, where topics in the science, technology and innovation
enterprise are discovered using Latent Dirichlet Allocation, their growth rates
are modeled using first-order rate kinetics, and research specialization of
various entities in these topics is measured using the location quotient.
Multiple interactive visualization interfaces that integrate these results
together to assist human analysts are developed. This methodology is
demonstrated by analyzing the last five years of publications, patents and
grants (~ 14 million documents) showing, for example, that deep learning for
machine vision is the fastest growing area, and that China has a stronger focus
than the U.S. in this area.


title:
PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable
  Multi-GPU Inference Servers

abstract:
  In cloud machine learning (ML) inference systems, providing low latency to
end-users is of utmost importance. However, maximizing server utilization and
system throughput is also crucial for ML service providers as it helps lower
the total-cost-of-ownership. GPUs have oftentimes been criticized for ML
inference usages as its massive compute and memory throughput is hard to be
fully utilized under low-batch inference scenarios. To address such limitation,
NVIDIA's recently announced Ampere GPU architecture provides features to
"reconfigure" one large, monolithic GPU into multiple smaller "GPU partitions".
Such feature provides cloud ML service providers the ability to utilize the
reconfigurable GPU not only for large-batch training but also for small-batch
inference with the potential to achieve high resource utilization. In this
paper, we study this emerging GPU architecture with reconfigurability to
develop a high-performance multi-GPU ML inference server. Our first proposition
is a sophisticated partitioning algorithm for reconfigurable GPUs that
systematically determines a heterogeneous set of multi-granular GPU partitions,
best suited for the inference server's deployment. Furthermore, we co-design an
elastic scheduling algorithm tailored for our heterogeneously partitioned GPU
server which effectively balances low latency and high GPU utilization.


title:
Causal Domain Adaptation with Copula Entropy based Conditional
  Independence Test

abstract:
  Domain Adaptation (DA) is a typical problem in machine learning that aims to
transfer the model trained on source domain to target domain with different
distribution. Causal DA is a special case of DA that solves the problem from
the view of causality. It embeds the probabilistic relationships in multiple
domains in a larger causal structure network of a system and tries to find the
causal source (or intervention) on the system as the reason of distribution
drifts of the system states across domains. In this sense, causal DA is
transformed as a causal discovery problem that finds invariant representation
across domains through the conditional independence between the state variables
and observable state of the system given interventions. Testing conditional
independence is the corner stone of causal discovery. Recently, a copula
entropy based conditional independence test was proposed with a rigorous theory
and a non-parametric estimation method. In this paper, we first present a
mathemetical model for causal DA problem and then propose a method for causal
DA that finds the invariant representation across domains with the copula
entropy based conditional independence test. The effectiveness of the method is
verified on two simulated data. The power of the proposed method is then
demonstrated on two real-world data: adult census income data and gait
characteristics data.


title: Out of Hypervisor (OoH): When Nested Virtualization Becomes Practical

abstract:
  This paper introduces Out of Hypervisor (OoH), a new research axis close to
nested virtualization. Instead of emulating a full virtual hardware inside a VM
to support a hypervisor, the OoH principle is to individually expose current
hypervisor-oriented hardware virtualization features to the guest OS so that
its processes could also take benefit from those features. In fact, several
hardware virtualization features such as Intel PML, SPP, CAT, and EPT which
currently can only be used by the hypervisor also be beneficial for processes
that run inside the VM. We illustrate OoH with Intel PML (Page Modification
Logging), a feature which allows efficient dirty page tracking for improving VM
live migration. According to the fact that dirty page tracking is at the heart
of process checkpointing (CRIU) and concurrent garbage collection (Boehm), we
present two OoH PML designs namely Shadow PML (SPML) and Extended PML (EPML).
The former requires no hardware changes but incurs significant overhead,
justifying EPML which extends PML. We evaluated and compared SPML and EPML with
/proc and userfaultfd,t wo default solutions in Linux. We do this using a
key-value store database as the benchmark. The results show that EPML reduces
CRIU checkpointing time by about 14% while leading to a negligible overhead (of
about 0.5%) compared to SPML, /proc, and userfaultfd.


title:
On Problems Related to Unbounded SubsetSum: A Unified Combinatorial
  Approach

abstract:
  Unbounded SubsetSum is a classical textbook problem: given integers
$w_1,w_2,\cdots,w_n\in [1,u],~c,u$, we need to find if there exists
$m_1,m_2,\cdots,m_n\in \mathbb{N}$ satisfying $c=\sum_{i=1}^n w_im_i$. In its
all-target version, $t\in \mathbb{Z}_+$ is given and answer for all integers
$c\in[0,t]$ is required. In this paper, we study three generalizations of this
simple problem: All-Target Unbounded Knapsack, All-Target CoinChange and
Residue Table. By new combinatorial insights into the structures of solutions,
we present a novel two-phase approach for such problems. As a result, we
present the first near-linear algorithms for CoinChange and Residue Table,
which runs in $\tilde{O}(u+t)$ and $\tilde{O}(u)$ time deterministically. We
also show if we can compute $(\min,+)$ convolution for $n$-length arrays in
$T(n)$ time, then All-Target Unbounded Knapsack can be solved in
$\tilde{O}(T(u)+t)$ time, thus establishing sub-quadratic equivalence between
All-Target Unbounded Knapsack and $(\min,+)$ convolution.


title: Pareto-Rational Verification

abstract:
  We study the rational verification problem which consists in verifying the
correctness of a system executing in an environment that is assumed to behave
rationally. We consider the model of rationality in which the environment only
executes behaviors that are Pareto-optimal with regards to its set of
objectives, given the behaviour of the system (which is committed in advance of
any interaction). When the objectives are $\omega$-regular, we prove that the
Pareto-rational verification problem is co-NP-complete and fixed-parameter
tractable (FPT) in the number of objectives of the environment. When the
objectives are described by LTL formulas, the problem is PSPACE-complete as the
classical LTL model-checking problem. In order to evaluate the applicability of
our results in practice, we have implemented and evaluated two variations of
our FPT algorithm on our running example and on randomly generated instances.


title:
Architectural Optimization and Feature Learning for High-Dimensional
  Time Series Datasets

abstract:
  As our ability to sense increases, we are experiencing a transition from
data-poor problems, in which the central issue is a lack of relevant data, to
data-rich problems, in which the central issue is to identify a few relevant
features in a sea of observations. Motivated by applications in
gravitational-wave astrophysics, we study the problem of predicting the
presence of transient noise artifacts in a gravitational wave detector from a
rich collection of measurements from the detector and its environment.
  We argue that feature learning--in which relevant features are optimized from
data--is critical to achieving high accuracy. We introduce models that reduce
the error rate by over 60\% compared to the previous state of the art, which
used fixed, hand-crafted features. Feature learning is useful not only because
it improves performance on prediction tasks; the results provide valuable
information about patterns associated with phenomena of interest that would
otherwise be undiscoverable. In our application, features found to be
associated with transient noise provide diagnostic information about its origin
and suggest mitigation strategies.
  Learning in high-dimensional settings is challenging. Through experiments
with a variety of architectures, we identify two key factors in successful
models: sparsity, for selecting relevant variables within the high-dimensional
observations; and depth, which confers flexibility for handling complex
interactions and robustness with respect to temporal variations. We illustrate
their significance through systematic experiments on real detector data. Our
results provide experimental corroboration of common assumptions in the
machine-learning community and have direct applicability to improving our
ability to sense gravitational waves, as well as to many other problem settings
with similarly high-dimensional, noisy, or partly irrelevant data.


title:
Point Label Aware Superpixels for Multi-species Segmentation of
  Underwater Imagery

abstract:
  Monitoring coral reefs using underwater vehicles increases the range of
marine surveys and availability of historical ecological data by collecting
significant quantities of images. Analysis of this imagery can be automated
using a model trained to perform semantic segmentation, however it is too
costly and time-consuming to densely label images for training supervised
models. In this letter, we leverage photo-quadrat imagery labeled by ecologists
with sparse point labels. We propose a point label aware method for propagating
labels within superpixel regions to obtain augmented ground truth for training
a semantic segmentation model. Our point label aware superpixel method utilizes
the sparse point labels, and clusters pixels using learned features to
accurately generate single-species segments in cluttered, complex coral images.
Our method outperforms prior methods on the UCSD Mosaics dataset by 3.62% for
pixel accuracy and 8.35% for mean IoU for the label propagation task.
Furthermore, our approach reduces computation time reported by previous
approaches by 76%. We train a DeepLabv3+ architecture and outperform
state-of-the-art for semantic segmentation by 2.91% for pixel accuracy and
9.65% for mean IoU on the UCSD Mosaics dataset and by 4.19% for pixel accuracy
and 14.32% mean IoU for the Eilat dataset.


title:
Generic Gelfand-Tsetlin Modules of Quantized and Classical Orthogonal
  Algebras

abstract:
  We construct infinite-dimensional analogues of finite-dimensional simple
modules of the nonstandard $q$-deformed enveloping algebra
$U_q'(\mathfrak{so}_n)$ defined by Gavrilik and Klimyk, and we do the same for
the classical universal enveloping algebra $U(\mathfrak{so}_n)$. In this paper
we only consider the case when $q$ is not a root of unity, and $q\to 1$ for the
classical case. Extending work by Mazorchuk on $\mathfrak{so}_n$, we provide
rational matrix coefficients for these infinite-dimensional modules of both
$U_q'(\mathfrak{so}_n)$ and $U(\mathfrak{so}_n)$. We use these modules with
rationalized formulas to embed the respective algebras into skew group algebras
of shift operators. Casimir elements of $U_q'(\mathfrak{so}_n)$ were given by
Gavrilik and Iorgov, and we consider the commutative subalgebra $\Gamma\subset
U_q'(\mathfrak{so}_n)$ generated by these elements and the corresponding
subalgebra $\Gamma_1\subset U(\mathfrak{so}_n)$. The images of $\Gamma$ and
$\Gamma_1$ under their respective embeddings into skew group algebras are equal
to invariant algebras under certain group actions. We use these facts to show
$\Gamma$ is a Harish-Chandra subalgebra of $U_q'(\mathfrak{so}_n)$ and
$\Gamma_1$ is a Harish-Chandra subalgebra of $U(\mathfrak{so}_n)$.


title:
Textual analysis of clinical notes on pathology request forms to
  determine sensitivity and specificity of Hepatitis B and C virus infection
  status

abstract:
  Background: It is not established whether clinical notes provided on
pathology request forms are useful as decision support data when assessing
Hepatitis B and C viral infection status. Objective: To determine sensitivity,
specificity, and predictive value of clinical notes for identifying infection
status of Hepatitis B and C. Methods: The study comprises 179 cases and 166
cases tested for HBsAg and anti-HCV serological markers, respectively, and
accompanied by a written description (clinical note) provided on pathology
request forms by the clinician on duty. The clinical note sensitivity,
specificity, positive (PPV) and negative (NPV) predictive values were
calculated using serological HBsAg and anti-HCV tests as gold standards.
Results: The sensitivity and specificity of clinical notes for Hepatitis B
infection status were 90 percent and 56 percent, respectively. The sensitivity
and specificity of clinical notes for Hepatitis C infection status were 86
percent and 21 percent, respectively. Conclusions: Clinical note information
identifies moderate-to-high sensitivity with regards to Hepatitis B and C viral
infection status, however, given low specificity in both groups, the clinical
note is not favourable for ruling disease in, possibly due to high rate of
false positives.


title: Limitations of Deep Learning for Inverse Problems on Digital Hardware

abstract:
  Deep neural networks have seen tremendous success over the last years. Since
the training is performed on digital hardware, in this paper, we analyze what
actually can be computed on current hardware platforms modeled as Turing
machines, which would lead to inherent restrictions of deep learning. For this,
we focus on the class of inverse problems, which, in particular, encompasses
any task to reconstruct data from measurements. We prove that
finite-dimensional inverse problems are not Banach-Mazur computable for small
relaxation parameters. In fact, our result even holds for Borel-Turing
computability., i.e., there does not exist an algorithm which performs the
training of a neural network on digital hardware for any given accuracy. This
establishes a conceptual barrier on the capabilities of neural networks for
finite-dimensional inverse problems given that the computations are performed
on digital hardware.


title:
Sparsity-aware neural user behavior modeling in online interaction
  platforms

abstract:
  Modern online platforms offer users an opportunity to participate in a
variety of content-creation, social networking, and shopping activities. With
the rapid proliferation of such online services, learning data-driven user
behavior models is indispensable to enable personalized user experiences.
Recently, representation learning has emerged as an effective strategy for user
modeling, powered by neural networks trained over large volumes of interaction
data. Despite their enormous potential, we encounter the unique challenge of
data sparsity for a vast majority of entities, e.g., sparsity in ground-truth
labels for entities and in entity-level interactions (cold-start users, items
in the long-tail, and ephemeral groups).
  In this dissertation, we develop generalizable neural representation learning
frameworks for user behavior modeling designed to address different sparsity
challenges across applications. Our problem settings span transductive and
inductive learning scenarios, where transductive learning models entities seen
during training and inductive learning targets entities that are only observed
during inference. We leverage different facets of information reflecting user
behavior (e.g., interconnectivity in social networks, temporal and attributed
interaction information) to enable personalized inference at scale. Our
proposed models are complementary to concurrent advances in neural
architectural choices and are adaptive to the rapid addition of new
applications in online platforms.


title:
Well-posedness of a modified degenerate Cahn-Hilliard model for surface
  diffusion

abstract:
  We study the well-posedness of a modified degenerate Cahn-Hilliard type model
for surface diffusion. With degenerate phase-dependent diffusion mobility and
additional stabilizing function, this model is able to give the correct sharp
interface limit. We introduce a notion of weak solutions for the nonlinear
model. The existence result is obtained by approximations of the proposed model
with nondegenerate mobilities. We also employ this method to prove existence of
weak solutions to a related model where the chemical potential contains a
nonlocal term originated from self-climb of dislocations in crystalline
materials.


title:
PetroFit: A Python Package for Computing Petrosian Radii and Fitting
  Galaxy Light Profiles

abstract:
  PetroFit is an open-source Python package, based on Astropy and Photutils,
that can calculate Petrosian profiles and fit galaxy images. It offers
end-to-end tools for making accurate photometric measurements, estimating
morphological properties, and fitting 2D models to galaxy images. Petrosian
metric radii can be used for model parameter estimation and aperture photometry
to provide accurate total fluxes. Correction tools are provided for improving
Petrosian radii estimates affected by galaxy morphology. PetroFit also provides
tools for sampling Astropy-based models (including custom profiles and
multi-component models) onto image grids and enables PSF convolution to account
for the effects of seeing. These capabilities provide a robust means of
modeling and fitting galaxy light profiles. We have made the PetroFit package
publicly available on GitHub (PetroFit/petrofit) and PyPi (pip install
petrofit).


title: Hybrid Molecular Beam Epitaxy of Ge-based Oxides

abstract:
  Germanium-based oxides such as rutile GeO2 are garnering attention owing to
their wide band gaps and the prospects for ambipolar doping for application in
high-power devices. Here, we present the use of germanium tetraisopropoxide
(GTIP) (an organometallic chemical precursor) as a source of Ge for the
demonstration of hybrid molecular beam epitaxy (MBE) for Ge-containing
compounds. We use Sn1-xGexO2 and SrSn1-xGexO3 as model systems to demonstrate
this new synthesis method. A combination of high-resolution X-ray diffraction,
scanning transmission electron microscopy, and X-ray photoelectron spectroscopy
confirms the successful growth of epitaxial rutile Sn1-xGexO2 on TiO2(001)
substrates up to x = 0.54 and coherent perovskite SrSn1-xGexO3 on GdScO3(110)
substrates up to x = 0.16. Characterization and first-principles calculations
corroborate that Ge preferentially occupies the Sn site, as opposed to the Sr
site. These findings confirm the viability of the GTIP precursor for the growth
of germanium-containing oxides by hybrid MBE, and thus open the door to
high-quality perovskite germanate films.


title:
An empirical comparison of machine learning models for student's mental
  health illness assessment

abstract:
  Student's mental health problems have been explored previously in higher
education literature in various contexts including empirical work involving
quantitative and qualitative methods. Nevertheless, comparatively few research
could be found, aiming for computational methods that learn information
directly from data without relying on set parameters for a predetermined
equation as an analytical method. This study aims to investigate the
performance of Machine learning (ML) models used in higher education. ML models
considered are Naive Bayes, Support Vector Machine, K-Nearest Neighbor,
Logistic regression, Stochastic Gradient Descent, Decision Tree, Random Forest,
XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural)
algorithm. Considering the factors of mental health illness among students, we
follow three phases of data processing: segmentation, feature extraction, and
classification. We evaluate these ML models against classification performance
metrics such as accuracy, precision, recall, F1 score, and predicted run time.
The empirical analysis includes two contributions: 1. It examines the
performance of various ML models on a survey-based educational dataset,
inferring a significant classification performance by a tree-based XGBoost
algorithm; 2. It explores the feature importance [variables] from the datasets
to infer the significant importance of social support, learning environment,
and childhood adversities on a student's mental health illness.


title:
Machine learning techniques to identify antibiotic resistance in
  patients diagnosed with various skin and soft tissue infections

abstract:
  Skin and soft tissue infections (SSTIs) are among the most frequently
observed diseases in ambulatory and hospital settings. Resistance of diverse
bacterial pathogens to antibiotics is a significant cause of severe SSTIs, and
treatment failure results in morbidity, mortality, and increased cost of
hospitalization. Therefore, antimicrobial surveillance is essential to predict
antibiotic resistance trends and monitor the results of medical interventions.
To address this, we developed machine learning (ML) models (deep and
conventional algorithms) to predict antimicrobial resistance using antibiotic
susceptibility testing (ABST) data collected from patients clinically diagnosed
with primary and secondary pyoderma over a period of one year. We trained an
individual ML algorithm on each antimicrobial family to determine whether a
Gram-Positive Cocci (GPC) or Gram-Negative Bacilli (GNB) bacteria will resist
the corresponding antibiotic. For this purpose, clinical and demographic
features from the patient and data from ABST were employed in training. We
achieved an Area Under the Curve (AUC) of 0.68-0.98 in GPC and 0.56-0.93 in GNB
bacteria, depending on the antimicrobial family. We also conducted a
correlation analysis to determine the linear relationship between each feature
and antimicrobial families in different bacteria. ML techniques suggest that a
predictable nonlinear relationship exists between patients'
clinical-demographic characteristics and antibiotic resistance; however, the
accuracy of this prediction depends on the type of the antimicrobial family.


title:
Zariski dense orbits for endomorphisms of a power of the additive group
  scheme defined over finite fields

abstract:
  We prove the Zariski dense orbit conjecture in positive characteristic for
endomorphisms of $\mathbb{G}_a^N$ defined over $\overline{\mathbb{F}_p}$.


title: Deconfined Quantum Criticality among Grand Unified Theories

abstract:
  This article overviews the recent developments in applying the idea of
deconfined quantum criticality in condensed matter physics to understand
quantum phase transitions among grand unified theories in high energy physics
in the 4-dimensional spacetime. In particular, dictated by a mod 2 class
nonperturbative global mixed gauge-gravitational anomaly, there can be a
gapless deconfined quantum critical region between Georgi-Glashow and
Pati-Salam models -- not only the Standard Model occurs as a neighbor phase,
but also beyond the Standard Model phenomena (such as deconfined dark gauge
force and fractionalized/fragmentary fermionic partons) emerge near the
critical region.


title:
A remark on the essential self-adjointness for Klein-Gordon type
  operators

abstract:
  Here we discuss a new simplified proof of the essential self-adjointness for
formally self-adjoint differential operators of real principal type, previously
proved by Vasy (2020) and Nakamura-Taira (2021). For simplicity, here we
discuss the second order cases, i.e., Klein-Gordon type operators only.


title:
The Golden Circle: Creating Socio-technical Alignment in Content
  Moderation

abstract:
  This paper outlines a conceptual framework titled The Golden Circle that
describes the roles of actors at individual, organizational, and societal
levels, and their dynamics in the content moderation ecosystem. Centering harm
reduction and context moderation, it argues that the ML community must attend
to multimodal content moderation solutions, align their work with their
organizations' goals and values, and pay attention to the ever changing social
contexts in which their sociotechnical systems are embedded. This is done by
accounting for the why, how, and what of content moderation from a sociological
and technical lens.


title: Globally Optimal Boresight Alignment of UAV-LiDAR Systems

abstract:
  In airborne light detection and ranging (LiDAR) systems, misalignments
between the LiDAR-scanner and the inertial navigation system (INS) mounted on
an unmanned aerial vehicle (UAV)'s frame can lead to inaccurate 3D point
clouds. Determining the orientation offset, or boresight error is key to many
LiDAR-based applications. In this work, we introduce a mixed-integer
quadratically constrained quadratic program (MIQCQP) that can globally solve
this misalignment problem. We also propose a nested spatial branch and bound
(nsBB) algorithm that improves computational performance. The nsBB relies on
novel preprocessing steps that progressively reduce the problem size. In
addition, an adaptive grid search (aGS) allowing us to obtain quick heuristic
solutions is presented. Our algorithms are open-source, multi-threaded and
multi-machine compatible.


title:
ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for
  Hyperspectral Image Classification

abstract:
  Hyperspectral image (HSI) classification is a topic of active research. One
of the main challenges of HSI classification is the lack of reliable labelled
samples. Various semi-supervised and unsupervised classification methods are
proposed to handle the low number of labelled samples. Chief among them are
graph convolution networks (GCN) and their variants. These approaches exploit
the graph structure for semi-supervised and unsupervised classification. While
several of these methods implicitly construct edge-weights, to our knowledge,
not much work has been done to estimate the edge-weights explicitly. In this
article, we estimate the edge-weights explicitly and use them for the
downstream classification tasks - both semi-supervised and unsupervised. The
proposed edge-weights are based on two key insights - (a) Ensembles reduce the
variance and (b) Classes in HSI datasets and feature similarity have only
one-sided implications. That is, while same classes would have similar
features, similar features do not necessarily imply the same classes.
Exploiting these, we estimate the edge-weights using an aggregate of ensembles
of watersheds over subsamples of features. These edge weights are evaluated for
both semi-supervised and unsupervised classification tasks. The evaluation for
semi-supervised tasks uses Random-Walk based approach. For the unsupervised
case, we use a simple filter using a graph convolution network (GCN). In both
these cases, the proposed edge weights outperform the traditional approaches to
compute edge-weights - Euclidean distances and cosine similarities.
Fascinatingly, with the proposed edge-weights, the simplest GCN obtained
results comparable to the recent state-of-the-art.


title: Variational Interpretable Learning from Multi-view Data

abstract:
  The main idea of canonical correlation analysis (CCA) is to map different
views onto a common latent space with maximum correlation. We propose a deep
interpretable variational canonical correlation analysis (DICCA) for multi-view
learning. The developed model extends the existing latent variable model for
linear CCA to nonlinear models through the use of deep generative networks.
DICCA is designed to disentangle both the shared and view-specific variations
for multi-view data. To further make the model more interpretable, we place a
sparsity-inducing prior on the latent weight with a structured variational
autoencoder that is comprised of view-specific generators. Empirical results on
real-world datasets show that our methods are competitive across domains.


title:
Estimating Galaxy Redshift in Radio-Selected Datasets using Machine
  Learning

abstract:
  All-sky radio surveys are set to revolutionise the field with new
discoveries. However, the vast majority of the tens of millions of radio
galaxies won't have the spectroscopic redshift measurements required for a
large number of science cases. Here, we evaluate techniques for estimating
redshifts of galaxies from a radio-selected survey. Using a radio-selected
sample with broadband photometry at infrared and optical wavelengths, we test
the k-Nearest Neighbours (kNN) and Random Forest machine learning algorithms,
testing them both in their regression and classification modes. Further, we
test different distance metrics used by the kNN algorithm, including the
standard Euclidean distance, the Mahalanobis distance and a learned distance
metric for both the regression mode (the Metric Learning for Kernel Regression
metric) and the classification mode (the Large Margin Nearest Neighbour
metric). We find that all regression-based modes fail on galaxies at a redshift
$z > 1$. However, below this range, the kNN algorithm using the Mahalanobis
distance metric performs best, with an $\eta_{0.15}$ outlier rate of 5.85\%. In
the classification mode, the kNN algorithm using the Mahalanobis distance
metric also performs best, with an $\eta_{0.15}$ outlier rate of 5.85\%,
correctly placing 74\% of galaxies in the top $z > 1.02$ bin. Finally, we also
tested the effect of training in one field and applying the trained algorithm
to similar data from another field and found that variation across fields does
not result in statistically significant differences in predicted redshifts.
Importantly, we find that while we may not be able to predict a continuous
value for high-redshift radio sources, we can identify the majority of them
using the classification modes of existing techniques.


title:
Cyber Mobility Mirror: A Deep Learning-based Real-World Object
  Perception Platform Using Roadside LiDAR

abstract:
  Object perception plays a fundamental role in Cooperative Driving Automation
(CDA) which is regarded as a revolutionary promoter for the next-generation
transportation systems. However, the vehicle-based perception may suffer from
the limited sensing range and occlusion as well as low penetration rates in
connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a
next-generation real-time traffic surveillance system for 3D object perception
and reconstruction, to explore the potential of roadside sensors for enabling
CDA in the real world. The CMM system consists of six main components: 1) the
data pre-processor to retrieve and preprocess the raw data; 2) the roadside 3D
object detector to generate 3D detection results; 3) the multi-object tracker
to identify detected objects; 4) the global locator to map positioning
information from the LiDAR coordinate to geographic coordinate using coordinate
transformation; 5) the cloud-based communicator to transmit perception
information from roadside sensors to equipped vehicles, and 6) the onboard
advisor to reconstruct and display the real-time traffic conditions via
Graphical User Interface (GUI). In this study, a field-operational system is
deployed at a real-world intersection, University Avenue and Iowa Avenue in
Riverside, California to assess the feasibility and performance of our CMM
system. Results from field tests demonstrate that our CMM prototype system can
provide satisfactory perception performance with 96.99% precision and 83.62%
recall. High-fidelity real-time traffic conditions (at the object level) can be
geo-localized with an average error of 0.14m and displayed on the GUI of the
equipped vehicle with a frequency of 3-4 Hz.


title:
Keyword Optimization in Sponsored Search Advertising: A Multi-Level
  Computational Framework

abstract:
  In sponsored search advertising, keywords serve as an essential bridge
linking advertisers, search users and search engines. Advertisers have to deal
with a series of keyword decisions throughout the entire lifecycle of search
advertising campaigns. This paper proposes a multi-level and closed-form
computational framework for keyword optimization (MKOF) to support various
keyword decisions. Based on this framework, we develop corresponding
optimization strategies for keyword targeting, keyword assignment and keyword
grouping at different levels (e.g., market, campaign and adgroup). With two
real-world datasets obtained from past search advertising campaigns, we conduct
computational experiments to evaluate our keyword optimization framework and
instantiated strategies. Experimental results show that our method can approach
the optimal solution in a steady way, and it outperforms two baseline keyword
strategies commonly used in practice. The proposed MKOF framework also provides
a valid experimental environment to implement and assess various keyword
strategies in sponsored search advertising.


title: Hamiltonian Extended Affine Lie Algebras and its representation theory

abstract:
  We introduce a new class of extended affine Lie algebras called Hamiltonian
Extended Lie Algebras(HEALAs). They are so called because the corresponding
derivation algebra is the classical Hamiltonian algebra. We classify the
irreducible integrable modules for the HEALA based on the classification of
irreducible Jet modules for the Hamiltonian algebra (both zero level and
non-zero level.


title: Nitrogen decoration of basal plane dislocations in 4H-SiC

abstract:
  Basal plane dislocations (BPDs) pose great challenge to the reliabilities of
4H silicon carbide (4H-SiC) based bipolar power devices. It is well established
that heavy nitrogen (N) doping promotes the conversion of BPDs to threading
edge dislocations (TEDs) and improves the reliability of 4H-SiC based bipolar
devices. However, the interaction between N and BPDs, as well as its effect on
the electronic properties is still ambiguous, which significantly hinder the
understanding on the electron-transport mechanism of 4H-SiC based power
devices. By combining molten-alkali etching and the Kelvin probe force
microscopy (KPFM) analysis, we demonstrate that BPDs create acceptor-like
states in undoped 4H-SiC, while act as donors in N-doped 4H-SiC.
First-principles calculations verify that BPDs create occupied defect states
above the valance band maximum (VBM) and unoccupied defect states under the
conduction band minimum (CBM) of undoped 4H-SiC. The electron transfer from the
defect states of intrinsic defects/native impurities to the unoccupied defect
states of BPDs gives rise to the acceptor-like behavior of BPDs in undoped
4H-SiC. Defect formation energies demonstrate that N atoms can spontaneously
decorate BPDs during the N doping of 4H-SiC. The binding between N and the BPD
is strong against decomposition. The accumulation of N dopants at the core of
BPDs results in the accumulation of donor-like states at the core of BPDs in
N-doped 4H-SiC. Furthermore, the N decoration of BPDs gives rise to two
distinct defect levels under the CBM of 4H-SiC, which may serve as electron
transfer channels in the Poole-Frenkel (PF) emission of N-doped 4H-SiC, and
increase the reverse leakage current. Our work not only enriches the
understanding on the electronic behavior of BPDs in N-doped 4H-SiC, but also
paves the way for understanding the electron transport mechanism in 4H-SiC
based power devices.


title: Evaluating High-Order Predictive Distributions in Deep Learning

abstract:
  Most work on supervised learning research has focused on marginal
predictions. In decision problems, joint predictive distributions are essential
for good performance. Previous work has developed methods for assessing
low-order predictive distributions with inputs sampled i.i.d. from the testing
distribution. With low-dimensional inputs, these methods distinguish agents
that effectively estimate uncertainty from those that do not. We establish that
the predictive distribution order required for such differentiation increases
greatly with input dimension, rendering these methods impractical. To
accommodate high-dimensional inputs, we introduce \textit{dyadic sampling},
which focuses on predictive distributions associated with random \textit{pairs}
of inputs. We demonstrate that this approach efficiently distinguishes agents
in high-dimensional examples involving simple logistic regression as well as
complex synthetic and empirical data.


title: Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

abstract:
  Autonomous Cyber-Physical Systems must often operate under uncertainties like
sensor degradation and shifts in the operating conditions, which increases its
operational risk. Dynamic Assurance of these systems requires designing runtime
safety components like Out-of-Distribution detectors and risk estimators, which
require labeled data from different operating modes of the system that belong
to scenes with adverse operating conditions, sensors, and actuator faults.
Collecting real-world data of these scenes can be expensive and sometimes not
feasible. So, scenario description languages with samplers like random and grid
search are available to generate synthetic data from simulators, replicating
these real-world scenes. However, we point out three limitations in using these
conventional samplers. First, they are passive samplers, which do not use the
feedback of previous results in the sampling process. Second, the variables to
be sampled may have constraints that are often not included. Third, they do not
balance the tradeoff between exploration and exploitation, which we hypothesize
is necessary for better search space coverage. We present a scene generation
approach with two samplers called Random Neighborhood Search (RNS) and Guided
Bayesian Optimization (GBO), which extend the conventional random search and
Bayesian Optimization search to include the limitations. Also, to facilitate
the samplers, we use a risk-based metric that evaluates how risky the scene was
for the system. We demonstrate our approach using an Autonomous Vehicle example
in CARLA simulation. To evaluate our samplers, we compared them against the
baselines of random search, grid search, and Halton sequence search. Our
samplers of RNS and GBO sampled a higher percentage of high-risk scenes of 83%
and 92%, compared to 56%, 66% and 71% of the grid, random and Halton samplers,
respectively.


title: Efficient Massively Parallel Join Optimization for Large Queries

abstract:
  Modern data analytical workloads often need to run queries over a large
number of tables. An optimal query plan for such queries is crucial for being
able to run these queries within acceptable time bounds. However, with queries
involving many tables, finding the optimal join order becomes a bottleneck in
query optimization. Due to the exponential nature of join order optimization,
optimizers resort to heuristic solutions after a threshold number of tables.
Our objective is two fold: (a) reduce the optimization time for generating
optimal plans; and (b) improve the quality of the heuristic solution. In this
paper, we propose a new massively parallel algorithm, MPDP, that can
efficiently prune the large search space (via a novel plan enumeration
technique) while leveraging the massive parallelism offered by modern hardware
(Eg: GPUs). When evaluated on real-world benchmark queries with PostgreSQL,
MPDP is at least an order of magnitude faster compared to state-of-the-art
techniques for large analytical queries. As a result, we are able to increase
the heuristic-fall-back limit from 12 relations to 25 relations with same time
budget in PostgreSQL. Also, in order to handle queries with even larger number
of tables, we augment MPDP to a well known heuristic, IDP$_2$ (iterative DP
version 2) and a novel heuristic UnionDP. By systematically exploring a much
larger search space, these heuristics provides query plans that are up to 7
times cheaper as compared to the state-of-the-art techniques while being faster
to compute.


title: On a General Class of A-functions

abstract:
  Expanding upon recent work, a new class of $A$-functions is introduced that
can be viewed as an appropriate generalization of the class of regular
$A$-functions, the class of structured $A$-functions, and the class of perfect
$A$-functions. Algebraic results are discussed and analysis is performed to
deduce new results concerning rational arithmetical functions.


title:
Aggressive Racecar Drifting Control Using Onboard Cameras and Inertial
  Measurement Unit

abstract:
  Complex autonomous driving, such as drifting, requires high-precision and
high-frequency pose information to ensure accuracy and safety, which is notably
difficult when using only onboard sensors. In this paper, we propose a drift
controller with two feedback control loops: sideslip controller that stabilizes
the sideslip angle by tuning the front wheel steering angle, and circle
controller that maintains a stable trajectory radius and circle center by
controlling the wheel rotational speed. We use an extended Kalman filter to
estimate the state. A robustified KASA algorithm is further proposed to
accurately estimate the parameters of the circle (i.e., the center and radius)
that best fits into the current trajectory. On the premise of the uniform
circular motion of the vehicle in the process of stable drift, we use angle
information instead of acceleration to describe the dynamic of the vehicle. We
implement our method on a 1/10 scale race car. The car drifts stably with a
given center and radius, which illustrates the effectiveness of our method.


title: StrongSORT: Make DeepSORT Great Again

abstract:
  Existing Multi-Object Tracking (MOT) methods can be roughly classified as
tracking-by-detection and joint-detection-association paradigms. Although the
latter has elicited more attention and demonstrates comparable performance
relative to the former, we claim that the tracking-by-detection paradigm is
still the optimal solution in terms of tracking accuracy. In this paper, we
revisit the classic tracker DeepSORT and upgrade it from various aspects, i.e.,
detection, embedding and association. The resulting tracker, called StrongSORT,
sets new HOTA and IDF1 records on MOT17 and MOT20. We also present two
lightweight and plug-and-play algorithms to further refine the tracking
results. Firstly, an appearance-free link model (AFLink) is proposed to
associate short tracklets into complete trajectories. To the best of our
knowledge, this is the first global link model without appearance information.
Secondly, we propose Gaussian-smoothed interpolation (GSI) to compensate for
missing detections. Instead of ignoring motion information like linear
interpolation, GSI is based on the Gaussian process regression algorithm and
can achieve more accurate localizations. Moreover, AFLink and GSI can be
plugged into various trackers with a negligible extra computational cost (591.9
and 140.9 Hz, respectively, on MOT17). By integrating StrongSORT with the two
algorithms, the final tracker StrongSORT++ ranks first on MOT17 and MOT20 in
terms of HOTA and IDF1 metrics and surpasses the second-place one by 1.3 - 2.2.
Code will be released soon.


title: Semiconductor nanowire metamaterial for broadband near-unity absorption

abstract:
  The realization of a semiconductor near-unity absorber in the infrared will
provide new capabilities to transform applications in sensing, health, imaging,
and quantum information science, especially where portability is required.
Typically, commercially available portable single-photon detectors in the
infrared are made from bulk semiconductors and have efficiencies well below
unity. Here, we design a novel semiconductor nanowire metamaterial, and show
that by carefully arranging an InGaAs nanowire array and by controlling their
shape, we demonstrate near-unity absorption efficiency at room temperature. We
experimentally show an average measured efficiency of 93% (simulated average
efficiency of 97%) over an unprecedented wavelength range from 900 nm to 1500
nm. We further show that the near-unity absorption results from the collective
response of the nanowire metamaterial, originating from both coupling into
leaky resonant waveguide and transverse modes. These coupling mechanisms cause
light to be absorbed directly from the top and indirectly as light scatters
from one nanowire to neighbouring ones. This work leads to the possible
development of a new generation of quantum detectors with unprecedented
broadband near-unity absorption in the infrared, while operating near room
temperature for a wider range of applications.


title: Topological band structure via twisted photons in a degenerate cavity

abstract:
  Synthetic dimensions based on particles' internal degrees of freedom, such as
frequency, spatial modes and arrival time, have attracted significant
attention. They offer ideal large-scale lattices to simulate nontrivial
topological phenomena. Exploring more synthetic dimensions is one of the paths
toward higher dimensional physics. In this work, we design and experimentally
control the coupling among synthetic dimensions consisting of the intrinsic
photonic orbital angular momentum and spin angular momentum degrees of freedom
in a degenerate optical resonant cavity, which generates a periodically driven
spin-orbital coupling system. We directly characterize the system's properties,
including the density of states, energy band structures and topological
windings, through the transmission intensity measurements. Our work
demonstrates a novel mechanism for exploring the spatial modes of twisted
photons as the synthetic dimension, which paves the way to design rich
topological physics in a highly compact platform.


title:
CTformer: Convolution-free Token2Token Dilated Vision Transformer for
  Low-dose CT Denoising

abstract:
  Low-dose computed tomography (LDCT) denoising is an important problem in CT
research. Compared to the normal dose CT (NDCT), LDCT images are subjected to
severe noise and artifacts. Recently in many studies, vision transformers have
shown superior feature representation ability over convolutional neural
networks (CNNs). However, unlike CNNs, the potential of vision transformers in
LDCT denoising was little explored so far. To fill this gap, we propose a
Convolution-free Token2Token Dilated Vision Transformer for low-dose CT
denoising. The CTformer uses a more powerful token rearrangement to encompass
local contextual information and thus avoids convolution. It also dilates and
shifts feature maps to capture longer-range interaction. We interpret the
CTformer by statically inspecting patterns of its internal attention maps and
dynamically tracing the hierarchical attention flow with an explanatory graph.
Furthermore, an overlapped inference mechanism is introduced to effectively
eliminate the boundary artifacts that are common for encoder-decoder-based
denoising models. Experimental results on Mayo LDCT dataset suggest that the
CTformer outperforms the state-of-the-art denoising methods with a low
computation overhead.


title:
Inverse design of ultracompact multi-focal optical devices by
  diffractive neural networks

abstract:
  We propose an efficient inverse design approach for multifunctional optical
elements based on adaptive deep diffractive neural networks (a-D$^2$NNs).
Specifically, we introduce a-D$^2$NNs and design two-layer diffractive devices
that can selectively focus incident radiation over two well-separated spectral
bands at desired distances. We investigate focusing efficiencies at two
wavelengths and achieve targeted spectral lineshapes and spatial point-spread
functions (PSFs) with optimal focusing efficiency. In particular, we
demonstrate control of the spectral bandwidths at separate focal positions
beyond the theoretical limit of single-lens devices with the same aperture
size. Finally, we demonstrate devices that produce super-oscillatory focal
spots at desired wavelengths. The proposed method is compatible with current
diffractive optics and doublet metasurface technology for ultracompact
multispectral imaging and lensless microscopy applications.


title: PartAfford: Part-level Affordance Discovery from 3D Objects

abstract:
  Understanding what objects could furnish for humans-namely, learning object
affordance-is the crux to bridge perception and action. In the vision
community, prior work primarily focuses on learning object affordance with
dense (e.g., at a per-pixel level) supervision. In stark contrast, we humans
learn the object affordance without dense labels. As such, the fundamental
question to devise a computational model is: What is the natural way to learn
the object affordance from visual appearance and geometry with humanlike sparse
supervision? In this work, we present a new task of part-level affordance
discovery (PartAfford): Given only the affordance labels per object, the
machine is tasked to (i) decompose 3D shapes into parts and (ii) discover how
each part of the object corresponds to a certain affordance category. We
propose a novel learning framework for PartAfford, which discovers part-level
representations by leveraging only the affordance set supervision and geometric
primitive regularization, without dense supervision. The proposed approach
consists of two main components: (i) an abstraction encoder with slot attention
for unsupervised clustering and abstraction, and (ii) an affordance decoder
with branches for part reconstruction, affordance prediction, and cuboidal
primitive regularization. To learn and evaluate PartAfford, we construct a
part-level, cross-category 3D object affordance dataset, annotated with 24
affordance categories shared among >25, 000 objects. We demonstrate that our
method enables both the abstraction of 3D objects and part-level affordance
discovery, with generalizability to difficult and cross-category examples.
Further ablations reveal the contribution of each component.


title: Anti-Malware Sandbox Games

abstract:
  We develop a game theoretic model of malware protection using the
state-of-the-art sandbox method, to characterize and compute optimal defense
strategies for anti-malware. We model the strategic interaction between
developers of malware (M) and anti-malware (AM) as a two player game, where AM
commits to a strategy of generating sandbox environments, and M responds by
choosing to either attack or hide malicious activity based on the environment
it senses. We characterize the condition for AM to protect all its machines,
and identify conditions under which an optimal AM strategy can be computed
efficiently. For other cases, we provide a quadratically constrained quadratic
program (QCQP)-based optimization framework to compute the optimal AM strategy.
In addition, we identify a natural and easy to compute strategy for AM, which
as we show empirically, achieves AM utility that is close to the optimal AM
utility, in equilibrium.


title:
Scalable Simulation and Demonstration of Jumping Piezoelectric 2-D Soft
  Robots

abstract:
  Soft robots have drawn great interest due to their ability to take on a rich
range of shapes and motions, compared to traditional rigid robots. However, the
motions, and underlying statics and dynamics, pose significant challenges to
forming well-generalized and robust models necessary for robot design and
control. In this work, we demonstrate a five-actuator soft robot capable of
complex motions and develop a scalable simulation framework that reliably
predicts robot motions. The simulation framework is validated by comparing its
predictions to experimental results, based on a robot constructed from
piezoelectric layers bonded to a steel-foil substrate. The simulation framework
exploits the physics engine PyBullet, and employs discrete rigid-link elements
connected by motors to model the actuators. We perform static and AC analyses
to validate a single-unit actuator cantilever setup and observe close agreement
between simulation and experiments for both the cases. The analyses are
extended to the five-actuator robot, where simulations accurately predict the
static and AC robot motions, including shapes for applied DC voltage inputs,
nearly-static "inchworm" motion, and jumping (in vertical as well as vertical
and horizontal directions). These motions exhibit complex non-linear behavior,
with forward robot motion reaching ~1 cm/s. Our open-source code can be found
at: https://github.com/zhiwuz/sfers.


title:
Pursuit-evasion differential games of players with different speeds in
  spaces of different dimensions

abstract:
  We study pursuit-evasion differential games between a faster pursuer moving
in 3D space and an evader moving in a plane. We first extend the well-known
Apollonius circle to 3D space, by which we construct the isochron for the
considered two players. Then both cases with and without a static target are
considered and the corresponding optimal strategies are derived using the
concept of isochron. In order to guarantee the optimality of the proposed
strategies, the value functions are given and are further proved to be the
solution of Hamilton-Jacobi-Isaacs equation. Simulations with comparison
between the proposed strategies and other classical strategies are carried out
and the results show the optimality of the proposed strategies.


title: Nanopores in Bernal-Stacked Hexagonal Boron Nitride

abstract:
  When single-layer h-BN is subjected to a high-energy electron beam,
triangular pores with nitrogen edges are formed. Because of the broken sp2
bonds, these pores are known to possess magnetic states. We report on the
magnetism and electronic structure of triangular pores as a function of their
size. Moreover, in the Bernal-stacked h-BN (AB-h-BN), multilayer pores with
parallel edges can be created, which is not possible in the commonly fabricated
multilayer AA'-h-BN. Given that these pores can be manufactured in a
well-controlled fashion using an electron beam, it is important to understand
the interactions of pores in neighboring layers. We find that in certain
configurations, the edges of the neighboring pores remain open and retain their
magnetism, and in others, they form interlayer bonds. We present a
comprehensive report on these configurations for small nanopores. We find that
at low temperatures, these pores have near degenerate magnetic configurations,
and may be utilized in magnetoresistance and spintronics applications. In the
process of forming larger multilayer nanopores, interlayer bonds can form,
reducing the magnetization. Yet, unbonded parallel multilayer edges remain
available at all sizes. Understanding these pores is also helpful in a
multitude of applications such as DNA sequencing and quantum emission.


title:
Towards Class-agnostic Tracking Using Feature Decorrelation in Point
  Clouds

abstract:
  Single object tracking in point clouds has been attracting more and more
attention owing to the presence of LiDAR sensors in 3D vision. However, the
existing methods based on deep neural networks focus mainly on training
different models for different categories, which makes them unable to perform
well in real-world applications when encountering classes unseen during the
training phase. In this work, we investigate a more challenging task in the
LiDAR point clouds, class-agnostic tracking, where a general model is supposed
to be learned for any specified targets of both observed and unseen categories.
In particular, we first investigate the class-agnostic performances of the
state-of-the-art trackers via exposing the unseen categories to them during
testing, finding that a key factor for class-agnostic tracking is how to
constrain fused features between the template and search region to maintain
generalization when the distribution is shifted from observed to unseen
classes. Therefore, we propose a feature decorrelation method to address this
problem, which eliminates the spurious correlations of the fused features
through a set of learned weights and further makes the search region consistent
among foreground points and distinctive between foreground and background
points. Experiments on the KITTI and NuScenes demonstrate that the proposed
method can achieve considerable improvements by benchmarking against the
advanced trackers P2B and BAT, especially when tracking unseen objects.


title: Gradient-free Multi-domain Optimization for Autonomous Systems

abstract:
  Autonomous systems are composed of several subsystems such as mechanical,
propulsion, perception, planning and control. These are traditionally designed
separately which makes performance optimization of the integrated system a
significant challenge. In this paper, we study the problem of using
gradient-free optimization methods to jointly optimize the multiple domains of
an autonomous system to find the set of optimal architectures for both hardware
and software. We specifically perform multi-domain, multi-parameter
optimization on an autonomous vehicle to find the best decision-making process,
motion planning and control algorithms, and the physical parameters for
autonomous racing. We detail the multi-domain optimization scheme, benchmark
with different core components, and provide insights for generalization to new
autonomous systems. In addition, this paper provides a benchmark of the
performances of six different gradient-free optimizers in three different
operating environments.
  Our approach is validated with a case study where we describe the autonomous
vehicle system architecture, optimization methods, and finally, provide an
argument on gradient-free optimization being a powerful choice to improve the
performance of autonomous systems in an integrated manner.


title:
Sparse Graph Learning with Eigen-gap for Spectral Filter Training in
  Graph Convolutional Networks

abstract:
  It is now known that the expressive power of graph convolutional neural nets
(GCN) does not grow infinitely with the number of layers. Instead, the GCN
output approaches a subspace spanned by the first eigenvector of the normalized
graph Laplacian matrix with the convergence rate characterized by the
"eigen-gap": the difference between the Laplacian's first two distinct
eigenvalues. To promote a deeper GCN architecture with sufficient
expressiveness, in this paper, given an empirical covariance matrix $\bar{C}$
computed from observable data, we learn a sparse graph Laplacian matrix $L$
closest to $\bar{C}^{-1}$ while maintaining a desirable eigen-gap that slows
down convergence. Specifically, we first define a sparse graph learning problem
with constraints on the first eigenvector (the most common signal) and the
eigen-gap. We solve the corresponding dual problem greedily, where a locally
optimal eigen-pair is computed one at a time via a fast approximation of a
semi-definite programming (SDP) formulation. The computed $L$ with the desired
eigen-gap is normalized spectrally and used for supervised training of GCN for
a targeted task. Experiments show that our proposal produced deeper GCNs and
smaller errors compared to a competing scheme without explicit eigen-gap
optimization.


title:
Origin of Enhanced Skin Friction at the Onset of Boundary-Layer
  Transition

abstract:
  Boundary-layer transition is accompanied by a significant increase in skin
friction whose origin is rigorously explained using the stochastic Lagrangian
formulation of the Navier-Stokes equations. This formulation permits the exact
analysis of vorticity dynamics in individual realizations of a viscous
incompressible fluid flow. The Lagrangian reconstruction formula for vorticity
is here extended for the first time to Neumann boundary conditions (Lighthill
source). We can thus express the wall vorticity, and therefore the wall stress,
as the expectation of a stochastic Cauchy invariant in backward time, with
contributions from (a) wall-vorticity flux (Lighthill source) and (b) interior
vorticity that has been evolved by nonlinear advection, viscous diffusion,
vortex stretching and tilting. We consider the origin of stress maxima in the
transitional region, examining a sufficient number of events to represent the
increased skin friction. The stochastic Cauchy analysis is applied to each
event to trace the origin of the wall vorticity. We find that the Lighthill
source, vortex tilting, diffusion and advection of outer vorticity make minor
contributions. They are less important than spanwise stretching of near-wall
spanwise vorticity, which is the dominant source of skin-friction increase
during laminar-to-turbulent transition. Our analysis should assist more
generally in understanding drag generation and reduction strategies and flow
separation in terms of near-wall vorticity dynamics.


title:
On the validity of quasilinear theory applied to the electron
  bump-on-tail instability

abstract:
  The accuracy of quasilinear theory applied to the electron bump-on-tail
instability, a classic model problem, is explored with conservative high-order
discontinuous Galerkin methods applied to both the quasilinear equations and to
a direct simulation of the Vlasov-Poisson equations. The initial condition is
chosen in the regime of beam parameters for which quasilinear theory should be
applicable. Quasilinear diffusion is initially in good agreement with the
direct simulation but later underestimates the turbulent momentum flux. The
direct simulation corrects from quasilinear evolution by quenching the
instability in a finite time and producing a robust state of oscillation. Flux
enhancement above quasilinear levels occurs as the phase space eddy turnover
time in the largest amplitude wavepackets becomes comparable to the transit
time of resonant phase fluid through wavepacket potentials. In this regime
eddies effectively turn over during wavepacket transit so that phase fluid
predominantly disperses by eddy phase mixing rather than by randomly phased
waves. The enhanced turbulent flux of resonant phase fluid leads in turn,
through energy conservation, to an increase in non-resonant turbulent flux and
thus to an enhanced heating of the main thermal body above quasilinear
predictions. These findings shed light on the kinetic turbulence fluctuation
spectrum and support the theory that collisionless momentum diffusion beyond
the quasilinear approximation can be understood through the dynamics of phase
space eddies (or clumps and granulations).


title:
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification
  and Reasoning Abilities of Language Models

abstract:
  Previous works show the great potential of pre-trained language models (PLMs)
for storing a large amount of factual knowledge. However, to figure out whether
PLMs can be reliable knowledge sources and used as alternative knowledge bases
(KBs), we need to further explore some critical features of PLMs. Firstly,
knowledge memorization and identification abilities: traditional KBs can store
various types of entities and relationships; do PLMs have a high knowledge
capacity to store different types of knowledge? Secondly, reasoning ability: a
qualified knowledge source should not only provide a collection of facts, but
support a symbolic reasoner. Can PLMs derive new knowledge based on the
correlations between facts? To evaluate these features of PLMs, we propose a
benchmark, named Knowledge Memorization, Identification, and Reasoning test
(KMIR). KMIR covers 3 types of knowledge, including general knowledge,
domain-specific knowledge, and commonsense, and provides 184,348 well-designed
questions. Preliminary experiments with various representative pre-training
language models on KMIR reveal many interesting phenomenons: 1) The
memorization ability of PLMs depends more on the number of parameters than
training schemes. 2) Current PLMs are struggling to robustly remember the
facts. 3) Model compression technology retains the amount of knowledge well,
but hurts the identification and reasoning abilities. We hope KMIR can
facilitate the design of PLMs as better knowledge sources.


title: Conjoined Charge Density Waves in the Kagome Superconductor CsV3Sb5

abstract:
  The intricate interplay between novel lattice geometry and spontaneous
symmetry-breaking states is at the forefront of contemporary research on
quantum materials. Recently, the observation of unconventional charge and
pairing density waves in a kagome metal CsV3Sb5 brings out a new showcase for
intertwined orders. While electronic instabilities in CsV3Sb5 are widely
believed to originate from the V 3d-electrons residing on the 2-dimensional
kagome sublattice, the pivotal role of Sb 5p-electrons for 3-dimensional orders
is yet to be understood. Here, using resonant tender x-ray scattering and
high-pressure X-ray scattering, we report a rare realization of conjoined
charge density waves (CDW) in CsV3Sb5. At ambient pressure, we discover a
resonant enhancement at Sb L1-edge (2s-5p) at the 2$\times$2$\times$2 CDW
wavevectors. The resonance, however, is absent at the 2$\times$2 CDW
wavevectors. Applying hydrostatic pressure, we find the CDW transition
temperatures to separate, where the 2$\times$2$\times$2 CDW emerges 4 K above
the 2$\times$2 CDW at 1GPa. Our results establish the coexistence of the
2$\times$2 CDW and the 5p-electron assisted 2$\times$2$\times$2 CDW in CsV3Sb5.
The evolution of the conjoined CDWs under pressure suggests the joint
importance of electronic and phononic fluctuations for the double dome
superconductivity.


title:
Demonstration of Parametric Instability suppression through optical
  feedback

abstract:
  We demonstrate the suppression of parametric instability using through
optical actuation in an electro-optical feedback loop, stabilising the high
order optical mode content in an 80 metre long Fabry-Perot cavity. The loop
suppression of the high order mode is achieved by injecting a high order mode
with the same frequency and opposite phase. Frequency matching is achieved by
measuring the beat note signal between the fundamental and high order mode in
transmission of the cavity and applying that signal to an electro-optical
modulator to create the required frequency sideband. Spatial mode matching of
the sideband to the high order mode is accomplished through the inherent mode
overlap between the input injected beam and the high order mode of the cavity.
The paper presents the theoretical analysis and experimental demonstration of
parametric instability suppression, for an instability which would normally
ring up with a parametric gain of approximately 2.5.


title:
Analysis of tensor-product discontinous Galerkin operators for
  Vlasov-Poisson simulations and GPU implementation on Python

abstract:
  The discontinuous Galerkin (DG) finite element method is conservative, lends
itself well to parallelization, and is high-order accurate due to its close
affinity with the theory of quadrature and orthogonal polynomials. When applied
with an orthogonal discretization (\textit{i.e.} a rectilinear grid) the DG
method may be efficiently implemented on a GPU in just a few lines of
high-level language such as Python. This work demonstrates such an
implementation by writing the DG semi-discrete equation in a tensor-product
form and then computing the products using open source GPU libraries. The
results are illustrated by simulating a problem in plasma physics, namely an
instability in the magnetized Vlasov-Poisson system. Further, as DG is closely
related to spectral methods through its orthogonal basis it is possible to
calculate a transformation to an alternative set of global eigenfunctions for
purposes of analysis or to perform additional operations. This transformation
is also posed as a tensor product and may be GPU-accelerated. In this work a
Fourier series is computed for example (although this does not beat discrete
Fourier transform), and is used to solve the Poisson part of the Vlasov-Poisson
system to $\mathcal{O}(\Delta x^{n+1/2})$-accuracy.


title: Dilaton invading from infinitesimal extra dimension

abstract:
  We show that the Wess-Zumino action for the spontaneously broken Weyl (or
conformal) symmetry, a.k.a dilaton effective action, in even $D$ dimensions can
be obtained from the Kaluza-Klein dimensional reduction of the Lovelock action
in $D+\epsilon$ dimensions by taking the $\epsilon \to 0$ limit, where the
dilaton is identified with the metric in the extra dimension. The construction
gives an explicit form of the dilaton effective action in any even dimensions.


title: Real-space density functional perturbation theory for phonons

abstract:
  We present a real-space formulation and implementation of density functional
perturbation theory for the calculation of phonons. Specifically, employing a
local form for the electrostatics, we derive expressions for the dynamical
matrix and associated Sternheimer equation at any arbitrary phonon wavevector
in the framework of real-space Kohn-Sham density functional theory. In
particular, the formulation is applicable to insulating as well as metallic
systems of any dimensionality, for both orthogonal and non-orthogonal cells, in
the context of local exchange-correlation functionals and the Kleinman-Bylander
representation for nonlocal pseudopotentials. We also develop an implementation
of the proposed formulation within the real-space high-order finite-difference
method. Through representative examples, we verify the accuracy of the
developed formulation and implementation, showing excellent agreement with
established planewave results.


title:
Maximising Wrenches for Kinematically Redundant Systems with Experiments
  on UVMS

abstract:
  This paper presents methods for finding optimal configurations and actuator
forces/torques to maximise contact wrenches in a desired direction for
Underwater Vehicles Manipulator Systems (UVMS). The wrench maximisation problem
is formulated as a linear programming problem, and the optimal configuration is
solved as a bi-level optimisation in the parameterised redundancy space. We
additionally consider the cases of one or more manipulators with multiple
contact forces, maximising wrench capability while tracking a trajectory, and
generating large wrench impulses using dynamic motions. We look at the specific
cases of maximising force to lift a heavy load, and maximising torque during a
valve turning operation. Extensive experimental results are presented using an
underwater robotic platform equipped with a 4DOF manipulator, and show
significant increases in wrench capability compared to existing methods for
UVMS.


title:
LobsDICE: Offline Imitation Learning from Observation via Stationary
  Distribution Correction Estimation

abstract:
  We consider the problem of imitation from observation (IfO), in which the
agent aims to mimic the expert's behavior from the state-only demonstrations by
experts. We additionally assume that the agent cannot interact with the
environment but has access to the action-labeled transition data collected by
some agent with unknown quality. This offline setting for IfO is appealing in
many real-world scenarios where the ground-truth expert actions are
inaccessible and the arbitrary environment interactions are costly or risky. In
this paper, we present LobsDICE, an offline IfO algorithm that learns to
imitate the expert policy via optimization in the space of stationary
distributions. Our algorithm solves a single convex minimization problem, which
minimizes the divergence between the two state-transition distributions induced
by the expert and the agent policy. On an extensive set of offline IfO tasks,
LobsDICE shows promising results, outperforming strong baseline algorithms.


title: Casimir effect in kinetic theory

abstract:
  We study Casimir effect in equilibrium and non-equilibrium photon gas in the
frame of quantum kinetic theory for $U(1)$ gauge field. We first derive the
transport, constraint and gauge fixing equations for the photon number
distribution from Maxwell's equations, and then calculate the energy variation
and Casimir force for a finite system by considering boundary condition on the
surface of the system. We find that, while the Casimir force is suppressed in
equilibrium state by the thermal motion of photons and vanishes in high
temperature limit, it oscillates with time in non-equilibrium state due to the
anisotropic motion of photons and approaches to the vacuum value in the end.


title:
Algorithm and System Co-design for Efficient Subgraph-based Graph
  Representation Learning

abstract:
  Subgraph-based graph representation learning (SGRL) has been recently
proposed to deal with some fundamental challenges encountered by canonical
graph neural networks (GNNs), and has demonstrated advantages in many important
data science applications such as link, relation and motif prediction. However,
current SGRL approaches suffer from a scalability issue since they require
extracting subgraphs for each training and testing query. Recent solutions that
scale up canonical GNNs may not apply to SGRL. Here, we propose a novel
framework SUREL for scalable SGRL by co-designing the learning algorithm and
its system support. SUREL adopts walk-based decomposition of subgraphs and
reuses the walks to form subgraphs, which substantially reduces the redundancy
of subgraph extraction and supports parallel computation. Experiments over
seven homogeneous, heterogeneous and higher-order graphs with millions of nodes
and edges demonstrate the effectiveness and scalability of SUREL. In
particular, compared to SGRL baselines, SUREL achieves 10$\times$ speed-up with
comparable or even better prediction performance; while compared to canonical
GNNs, SUREL achieves 50% prediction accuracy improvement. SUREL is also applied
to the brain vessel prediction task. SUREL significantly outperforms the
state-of-the-art baseline in both prediction accuracy and efficiency.


title:
Use of Transmission and Reflection Complex Time Delays to Reveal
  Scattering Matrix Poles and Zeros: Example of the Ring Graph

abstract:
  We identify the poles and zeros of the scattering matrix of a simple quantum
graph by means of systematic measurement and analysis of Wigner, transmission,
and reflection complex time delays. We examine the ring graph because it
displays both shape and Feshbach resonances, the latter of which arises from an
embedded eigenstate on the real frequency axis. Our analysis provides an
unified understanding of the so-called shape, Feshbach,
electromagnetically-induced transparency, and Fano resonances, on the basis of
the distribution of poles and zeros of the scattering matrix in the complex
frequency plane. It also provides a first-principles understanding of sharp
resonant scattering features, and associated large time delay, in a variety of
practical devices, including photonic microring resonators, microwave ring
resonators, and mesoscopic ring-shaped conductor devices. Our analysis is the
first use of reflection time difference, as well as the first comprehensive use
of complex time delay, to analyze experimental scattering data.


title:
Instant Loans Can Lift Subjective Well-Being: A Randomized Evaluation of
  Digital Credit in Nigeria

abstract:
  Digital loans have exploded in popularity across low and middle income
countries, providing short term, high interest credit via mobile phones. This
paper reports the results of a randomized evaluation of a digital loan product
in Nigeria. Being randomly approved for digital credit (irrespective of credit
score) substantially increases subjective well-being after an average of three
months. For those who are approved, being randomly offered larger loans has an
insignificant effect. Neither treatment significantly impacts other measures of
welfare. We rule out large short-term impacts either positive or negative: on
income and expenditures, resilience, and women's economic empowerment.


title: Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)

abstract:
  We propose a deep learning methodology for multivariate regression that is
based on pattern recognition that triggers fast learning over sensor data. We
used a conversion of sensors-to-image which enables us to take advantage of
Computer Vision architectures and training processes. In addition to this data
preparation methodology, we explore the use of state-of-the-art architectures
to generate regression outputs to predict agricultural crop continuous yield
information. Finally, we compare with some of the top models reported in
MLCAS2021. We found that using a straightforward training process, we were able
to accomplish an MAE of 4.394, RMSE of 5.945, and R^2 of 0.861.


title: Seven non-standard models coupling quantum matter and gravity

abstract:
  We review seven models, which consistently couple quantum matter and
(Newtonian) gravity in a non standard way. For each of them we present the
underlying motivations, the main equations and, when available, a comparison
with experimental data.


title:
A new convective parameterization applied to Jupiter: implications for
  water abundance near the $24^\circ$ N region

abstract:
  Jupiter's atmosphere features a variety of clouds that are formed from the
interplay of chemistry and atmospheric dynamics, from the deep red color of the
Great Red Spot to the high altitude white ammonia clouds present in the zones
(bright bands in Jupiter's atmosphere). Beneath these upper level clouds, water
condensation occurs, and sporadically leads to the formation of towering
convective storms, driven by the release of large amounts of latent heat. These
storms result in a widespread disruption of the cloud and dynamical structure
of the atmosphere at the latitude where they form, making the study of these
events paramount in understanding the dynamics at depth, and the role of water
in the jovian atmosphere. In this work, we use the Explicit Planetary
hybrid-Isentropic Coordinate (EPIC) General Circulation Model (GCM) to study
the jovian atmosphere, with a focus on moist convective storm formation from
water condensation. We present the addition of a sub-grid scale moist
convective module to model convective water cloud formation. We focus on the
$24^\circ$ N latitude, the location of a high speed jetstream, where convective
upwellings have been observed every 4-5 years. We find that the potential of
convection, and vertical mass and energy flux of the atmosphere is strongly
correlated with the amount of water, and we determine an upper limit of the
amount of water in the the region surrounding the jet as twice the solar [O/H]
ratio.


title:
A Simple Estimator for Estimating Treatment Effects Using Observational
  Data and Experimental Data

abstract:
  This short paper discusses some generalizations of the ideas of combining
experimental data and observational data to estimate treatment effects in the
literature.


title: Personalized Subsidy Rules

abstract:
  Subsidies are commonly used to encourage behaviors that can lead to short- or
long-term benefits. Typical examples include subsidized job training programs
and provisions of preventive health products, in which both behavioral
responses and associated gains can exhibit heterogeneity. This study uses the
marginal treatment effect (MTE) framework to study personalized assignments of
subsidies based on individual characteristics. First, we derive the optimality
condition for a welfare-maximizing subsidy rule by showing that the welfare can
be represented as a function of the MTE. Next, we show that subsidies generally
result in better welfare than directly mandating the encouraged behavior
because subsidy rules implicitly target individuals through unobserved
heterogeneity in the behavioral response. When there is positive selection,
that is, when individuals with higher returns are more likely to select the
encouraged behavior, the optimal subsidy rule achieves the first-best welfare,
which is the optimal welfare if a policy-maker can observe individuals' private
information. We then provide methods to (partially) identify the optimal
subsidy rule when the MTE is identified and unidentified. Particularly,
positive selection allows for the point identification of the optimal subsidy
rule even when the MTE curve is not. As an empirical application, we study the
optimal wage subsidy using the experimental data from the Jordan New
Opportunities for Women pilot study.


title: Single-shot self-supervised particle tracking

abstract:
  Particle tracking is a fundamental task in digital microscopy. Recently,
machine-learning approaches have made great strides in overcoming the
limitations of more classical approaches. The training of state-of-the-art
machine-learning methods almost universally relies on either vast amounts of
labeled experimental data or the ability to numerically simulate realistic
datasets. However, the data produced by experiments are often challenging to
label and cannot be easily reproduced numerically. Here, we propose a novel
deep-learning method, named LodeSTAR (Low-shot deep Symmetric Tracking And
Regression), that learns to tracks objects with sub-pixel accuracy from a
single unlabeled experimental image. This is made possible by exploiting the
inherent roto-translational symmetries of the data. We demonstrate that
LodeSTAR outperforms traditional methods in terms of accuracy. Furthermore, we
analyze challenging experimental data containing densely packed cells or noisy
backgrounds. We also exploit additional symmetries to extend the measurable
particle properties to the particle's vertical position by propagating the
signal in Fourier space and its polarizability by scaling the signal strength.
Thanks to the ability to train deep-learning models with a single unlabeled
image, LodeSTAR can accelerate the development of high-quality microscopic
analysis pipelines for engineering, biology, and medicine.

categories: eess.IV cond-mat.soft cs.AI physics.app-ph q-bio.QM


title:
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional
  Network

abstract:
  Graph Convolutional Network (GCN) plays pivotal roles in many real-world
applications. Despite the successes of GCN deployment, GCN often exhibits
performance disparity with respect to node degrees, resulting in worse
predictive accuracy for low-degree nodes. We formulate the problem of
mitigating the degree-related performance disparity in GCN from the perspective
of the Rawlsian difference principle, which is originated from the theory of
distributive justice. Mathematically, we aim to balance the utility between
low-degree nodes and high-degree nodes while minimizing the task-specific loss.
Specifically, we reveal the root cause of this degree-related unfairness by
analyzing the gradients of weight matrices in GCN. Guided by the gradients of
weight matrices, we further propose a pre-processing method RawlsGCN-Graph and
an in-processing method RawlsGCN-Grad that achieves fair predictive accuracy in
low-degree nodes without modification on the GCN architecture or introduction
of additional parameters. Extensive experiments on real-world graphs
demonstrate the effectiveness of our proposed RawlsGCN methods in significantly
reducing degree-related bias while retaining comparable overall performance.


title: Two-block substitutions and morphic words

abstract:
  The Oldenburger Kolakoski sequence is fixed point of a two-block
substitution. We consider in general two-block substitutions and their fixed
points. We prove that some of them have a simple structure: their fixed points
are morphic sequences. We further introduce the Thue-Morse two-block
substitution whose fixed points behave like the Kolakoski sequence.


title: Machine Learning Empowered Intelligent Data Center Networking: A Survey

abstract:
  To support the needs of ever-growing cloud-based services, the number of
servers and network devices in data centers is increasing exponentially, which
in turn results in high complexities and difficulties in network optimization.
To address these challenges, both academia and industry turn to artificial
intelligence technology to realize network intelligence. To this end, a
considerable number of novel and creative machine learning-based (ML-based)
research works have been put forward in recent few years. Nevertheless, there
are still enormous challenges faced by the intelligent optimization of data
center networks (DCNs), especially in the scenario of online real-time dynamic
processing of massive heterogeneous services and traffic data. To best of our
knowledge, there is a lack of systematic and original comprehensively
investigations with in-depth analysis on intelligent DCN. To this end, in this
paper, we comprehensively investigate the application of machine learning to
data center networking, and provide a general overview and in-depth analysis of
the recent works, covering flow prediction, flow classification, load
balancing, resource management, routing optimization, and congestion control.
In order to provide a multi-dimensional and multi-perspective comparison of
various solutions, we design a quality assessment criteria called REBEL-3S to
impartially measure the strengths and weaknesses of these research works.
Moreover, we also present unique insights into the technology evolution of the
fusion of data center network and machine learning, together with some
challenges and potential future research opportunities.


title: Nonarchimedean Lyapunov exponents of polynomials

abstract:
  Let $K$ be an algebraically closed and complete nonarchimedean field with
characteristic $0$ and let $f\in K[z]$ be a polynomial of degree $d\ge 2$. We
study the Lyapunov exponent $L(f,\mu)$ of $f$ with respect to an $f$-invariant
and ergodic Radon probability measure $\mu$ on the Berkovich Julia set of $f$
and the lower Lyapunov exponent $L_f^{-}(f(c))$ of $f$ at a critical value
$f(c)$. Under an integrability assumption, we show $L(f,\mu)$ has a lower bound
only depending on $d$ and $K$. In particular, if $f$ is tame and has no
wandering nonclassical Julia points, then $L(f,\mu)$ is nonnegative; moreover,
if in addition $f$ possesses a unique Julia critical point $c_0$, we show
$L_f^{-}(f(c_0))$ is also nonnegative.


title:
Fluctuating hydrodynamics of dilute electrolyte solutions: systematic
  perturbation calculation of effective transport coefficients governing
  large-scale dynamics

abstract:
  We study the transport properties of dilute electrolyte solutions on the
basis of the fluctuating hydrodynamic equation, which is a set of nonlinear
Langevin equations for the ion densities and flow velocity. The nonlinearity of
the Langevin equations generally leads to effective kinetic coefficients for
the deterministic dynamics of the average ion densities and flow velocity; the
effective coefficients generally differ from the counterparts in the Langevin
equations and is frequency-dependent. Using the path-integral formalism
involving auxiliary fields, we perform systematic perturbation calculations of
the effective kinetic coefficients for ion diffusion, shear viscosity, and
electrical conductivity, which govern the dynamics on the large length scales.
In particular, we study the frequency dependence of the viscosity and
conductivity in the one-loop approximation; the zero-frequency limits of the
complex electrical conductivity and effective shear viscosity coincide with the
classical limiting laws for dilute solutions, derived in different means by
Debye, Falkenhagen, and Onsager. As for the effective kinetic coefficients for
slow ion diffusions in large length scales, our straightforward calculation
yields the cross kinetic coefficient between cations and anions. Further, we
discuss the possibility of extending the present study to more concentrated
solutions.


title:
Effect of Choices of Boundary Conditions on the Numerical Efficiency of
  Direct Solutions of Finite Difference frequency Domain Systems with Perfectly
  Matched Layers

abstract:
  Direct solvers are a common method for solving finite difference frequency
domain (FDFD) systems that arise in numerical solutions of Maxwell's equations.
In a direct solver, one factorizes the system matrix. Since the system matrix
is typically very sparse, the fill-in of these factors is the single most
important computational consideration in terms of time complexity and memory
requirements. As a result, it is of great interest to determine ways in which
this fill-in can be systematically reduced. In this paper, we show that in the
context of commonly used perfectly matched boundary layer methods, the choice
of boundary condition behind the perfectly matched boundary layer can be
exploited to reduce fill-in incurred during the factorization, leading to
significant gains of up to 40 percent in the efficiency of the factorization
procedure. We illustrate our findings by solving linear systems and eigenvalue
problems associated with the FDFD method.


title:
Towards A Device-Independent Deep Learning Approach for the Automated
  Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and
  Multi-Device Validation

abstract:
  Quality assessment of prenatal ultrasonography is essential for the screening
of fetal central nervous system (CNS) anomalies. The interpretation of fetal
brain structures is highly subjective, expertise-driven, and requires years of
training experience, limiting quality prenatal care for all pregnant mothers.
With recent advancement in Artificial Intelligence (AI), specifically deep
learning (DL), assistance in precise anatomy identification through semantic
segmentation essential for the reliable assessment of growth and
neurodevelopment, and detection of structural abnormalities have been proposed.
However, existing works only identify certain structures (e.g., cavum septum
pellucidum, lateral ventricles, cerebellum) from either of the axial views
(transventricular, transcerebellar), limiting the scope for a thorough
anatomical assessment as per practice guidelines necessary for the screening of
CNS anomalies. Further, existing works do not analyze the generalizability of
these DL algorithms across images from multiple ultrasound devices and centers,
thus, limiting their real-world clinical impact. In this study, we propose a DL
based segmentation framework for the automated segmentation of 10 key fetal
brain structures from 2 axial planes from fetal brain USG images (2D). We
developed a custom U-Net variant that uses inceptionv4 block as a feature
extractor and leverages custom domain-specific data augmentation.
Quantitatively, the mean (10 structures; test sets 1/2/3/4) Dice-coefficients
were: 0.827, 0.802, 0.731, 0.783. Irrespective of the USG device/center, the DL
segmentations were qualitatively comparable to their manual segmentations. The
proposed DL system offered a promising and generalizable performance
(multi-centers, multi-device) and also presents evidence in support of
device-induced variation in image quality (a challenge to generalizibility) by
using UMAP analysis.


title:
A Machine Learning Method for Material Property Prediction: Example
  Polymer Compatibility

abstract:
  Prediction of material property is a key problem because of its significance
to material design and screening. We present a brand-new and general machine
learning method for material property prediction. As a representative example,
polymer compatibility is chosen to demonstrate the effectiveness of our method.
Specifically, we mine data from related literature to build a specific database
and give a prediction based on the basic molecular structures of blending
polymers and, as auxiliary, the blending composition. Our model obtains at
least 75% accuracy on the dataset consisting of thousands of entries. We
demonstrate that the relationship between structure and properties can be
learned and simulated by machine learning method.