title: Evaluation of the Gauss integral

abstract:
  The normal or Gaussian distribution plays a prominent role in almost all
fields of science. However, it is well known that the Gauss (or Euler--Poisson)
integral over a finite boundary, as it is necessary for instance for the error
function or the cumulative distribution of the normal distribution, cannot be
expressed by analytic functions. This is proven by the Risch algorithm. Still,
there are proposals for approximate solutions. In this paper, we give a new
solution in terms of normal distributions by applying a geometric procedure
iteratively to the problem.


title:
Design and Characterization of 3D Printed, Open-Source Actuators for
  Legged Locomotion

abstract:
  Impressive animal locomotion capabilities are mediated by the co-evolution of
the skeletal morphology and muscular properties. Legged robot performance would
also likely benefit from the co-optimization of actuators and leg morphology.
However, development of custom actuators for legged robots is often expensive
and time consuming, which discourages roboticists from pursuing performance
gains afforded by application-specific actuator optimization. This paper
presents open-source designs for two quasi-direct-drive actuators with
performance regimes appropriate for an 8--15 kg robot, built completely with
off the shelf and 3D-printed components for less than $200 USD each. The
mechanical, electrical, and thermal properties of each actuator are
characterized and compared to benchmark data. Actuators subjected to 420k
strides of gait data experienced only a 2% reduction in efficiency and 26 mrad
in backlash growth, demonstrating viability for rigorous and sustained research
applications. We present a thermal solution that nearly doubles the
thermally-driven torque limits of our plastic actuator design. The performance
results are comparable to traditional metallic actuators for use in high-speed
legged robots of the same scale. These 3D printed designs demonstrate an
approach for designing and characterizing low-cost, highly customizable, and
highly reproducible actuators, democratizing the field of actuator design and
enabling co-design and optimization of actuators and robot legs.


title:
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and
  Applications

abstract:
  This paper studies stochastic optimization for a sum of compositional
functions, where the inner-level function of each summand is coupled with the
corresponding summation index. We refer to this family of problems as
finite-sum coupled compositional optimization (FCCO). It has broad applications
in machine learning for optimizing non-convex or convex compositional
measures/objectives such as average precision (AP), $p$-norm push, listwise
ranking losses, neighborhood component analysis (NCA), deep survival analysis,
deep latent variable models, softmax functions, and model agnostic
meta-learning, which deserves finer analysis. Yet, existing algorithms and
analysis are restricted in one or other aspects. The contribution of this paper
is to provide a comprehensive analysis of a simple stochastic algorithm for
both non-convex and convex objectives. The key results are {\bf improved oracle
complexities with the parallel speed-up} by the moving-average based stochastic
estimator with mini-batching. Our theoretical analysis also exhibits new
insights for improving the practical implementation by sampling the batches of
equal size for the outer and inner levels. Numerical experiments on AP
maximization and $p$-norm push optimization corroborate some aspects of the
theory.


title:
Time Complexity of Consensus in Dynamic Networks Under Oblivious Message
  Adversaries

abstract:
  Consensus is a most fundamental task in distributed computing. This paper
studies the consensus problem for a set of processes connected by a dynamic
directed network, in which computation and communication is lock-step
synchronous but controlled by an oblivious message adversary. In this basic
model, determining consensus solvability and designing consensus algorithms in
the case where it is possible, has been shown to be surprisingly difficult. We
present an explicit decision procedure to determine if consensus is possible
under a given adversary. This in turn enables us, for the first time, to study
the time complexity of consensus in this model. In particular, we derive time
complexity upper bounds for consensus solvability both for a centralized
decision procedure as well as for solving distributed consensus. We complement
these results with time complexity lower bounds. Intriguingly, we find that
reaching consensus under an oblivious message adversary can take exponentially
longer than broadcasting the input value of some process to all other
processes.


title: Non-linear Hopf manifolds are locally conformally Kahler

abstract:
  A Hopf manifold is a quotient of $C^n\backslash 0$ by the cyclic group
generated by a holomorphic contraction. Hopf manifolds are diffeomorphic to
$S^1\times S^{2n-1}$ and hence do not admit Kahler metrics. It is known that
Hopf manifolds defined by linear contractions (called linear Hopf manifolds)
have locally conformally Kahler (LCK) metrics. In this paper we prove that the
Hopf manifolds defined by non-linear holomorphic contractions admit holomorphic
embeddings into linear Hopf manifolds, and, moreover they admit LCK metrics.


title: Data-Driven Safety Verification for Legged Robots

abstract:
  Planning safe motions for legged robots requires sophisticated safety
verification tools. However, designing such tools for such complex systems is
challenging due to the nonlinear and high-dimensional nature of these systems'
dynamics. In this letter, we present a probabilistic verification framework for
legged systems, which evaluates the safety of planned trajectories by learning
an assessment function from trajectories collected from a closed-loop system.
Our approach does not require an analytic expression of the closed-loop
dynamics, thus enabling safety verification of systems with complex models and
controllers. Our framework consists of an offline stage that initializes a
safety assessment function by simulating a nominal model and an online stage
that adapts the function to address the sim-to-real gap. The performance of the
proposed approach for safety verification is demonstrated using a quadruped
balancing task and a humanoid reaching task. The results demonstrate that our
framework accurately predicts the systems' safety both at the planning phase to
generate robust trajectories and at execution phase to detect unexpected
external disturbances.


title: The rise of the lottery heroes: why zero-shot pruning is hard

abstract:
  Recent advances in deep learning optimization showed that just a subset of
parameters are really necessary to successfully train a model. Potentially,
such a discovery has broad impact from the theory to application; however, it
is known that finding these trainable sub-network is a typically costly
process. This inhibits practical applications: can the learned sub-graph
structures in deep learning models be found at training time? In this work we
explore such a possibility, observing and motivating why common approaches
typically fail in the extreme scenarios of interest, and proposing an approach
which potentially enables training with reduced computational effort. The
experiments on either challenging architectures and datasets suggest the
algorithmic accessibility over such a computational gain, and in particular a
trade-off between accuracy achieved and training complexity deployed emerges.


title: A deterministic algorithm for finding $r$-power divisors

abstract:
  Building on work of Boneh, Durfee and Howgrave-Graham, we present a
deterministic algorithm that provably finds all integers $p$ such that $p^r
\mathrel| N$ in time $O(N^{1/4r+\epsilon})$ for any $\epsilon > 0$. For
example, the algorithm can be used to test squarefreeness of $N$ in time
$O(N^{1/8+\epsilon})$; previously, the best rigorous bound for this problem was
$O(N^{1/6+\epsilon})$, achieved via the Pollard--Strassen method.


title:
Near-exact treatment of seniority-zero ground and excited states with a
  Richardson-Gaudin mean-field

abstract:
  Eigenvectors of the reduced Bardeen-Cooper-Schrieffer Hamiltonian,
Richardson-Gaudin (RG) states, are used as a variational wavefunction Ansatz
for strongly-correlated electronic systems. These states are geminal products
whose coefficients are solutions of non-linear equations. Previous results
showed un-physical behaviour but in this contribution it is shown that with
only the variational solution for the ground state, all the seniority-zero
states are quite well approximated. The difficulty is in choosing the correct
RG state. The systems studied showed a clear choice and we expect it should
always be possible to reason physically which state to choose.


title:
Learning Transferable Reward for Query Object Localization with Policy
  Adaptation

abstract:
  We propose a reinforcement learning based approach to query object
localization, for which an agent is trained to localize objects of interest
specified by a small exemplary set. We learn a transferable reward signal
formulated using the exemplary set by ordinal metric learning. Our proposed
method enables test-time policy adaptation to new environments where the reward
signals are not readily available, and outperforms fine-tuning approaches that
are limited to annotated images. In addition, the transferable reward allows
repurposing the trained agent from one specific class to another class.
Experiments on corrupted MNIST, CU-Birds, and COCO datasets demonstrate the
effectiveness of our approach.


title:
Exploiting Problem Structure in Deep Declarative Networks: Two Case
  Studies

abstract:
  Deep declarative networks and other recent related works have shown how to
differentiate the solution map of a (continuous) parametrized optimization
problem, opening up the possibility of embedding mathematical optimization
problems into end-to-end learnable models. These differentiability results can
lead to significant memory savings by providing an expression for computing the
derivative without needing to unroll the steps of the forward-pass optimization
procedure during the backward pass. However, the results typically require
inverting a large Hessian matrix, which is computationally expensive when
implemented naively. In this work we study two applications of deep declarative
networks -- robust vector pooling and optimal transport -- and show how problem
structure can be exploited to obtain very efficient backward pass computations
in terms of both time and memory. Our ideas can be used as a guide for
improving the computational performance of other novel deep declarative nodes.


title:
Can autonomy make bicycle-sharing systems more sustainable?
  Environmental impact analysis of an emerging mobility technology

abstract:
  Autonomous bicycles have recently been proposed as a new and more efficient
approach to bicycle-sharing systems (BSS), but the corresponding environmental
implications remain unresearched. Conducting environmental impact assessments
at an early technological stage is critical to influencing the design and,
ultimately, environmental impacts of a system. Consequently, this paper aims to
assess the environmental impact of autonomous shared bikes compared with
current station-based and dockless systems under different sets of modeling
hypotheses and mode-shift scenarios. The results indicate that autonomy could
reduce the environmental impact per passenger kilometer traveled of current
station-based and dockless BSS by 33.1 % and 58.0 %. The sensitivity analysis
shows that the environmental impact of autonomous shared bicycles will mainly
depend on vehicle usage rates and the need for infrastructure. Finally, this
study highlights the importance of targeting the mode replacement from more
polluting modes, especially as traditional mobility modes decarbonize and
become more efficient.


title: Cardiac Reentry Modeled by Spatiotemporal Chaos in a Coupled Map Lattice

abstract:
  Arrhythmias are potentially fatal disruptions to the normal heart rhythm, but
their underlying dynamics is still poorly understood. Theoretical modeling is
an important tool to fill this gap. Typical studies often employ detailed
multidimensional conductance-based models. We describe the cardiac muscle with
a three-dimensional map-based membrane potential model in lattices. Although
maps retain the biophysical behavior of cells and generate computationally
efficient tissue models, few studies have used them to understand cardiac
dynamics. Our study captures healthy and pathological behaviors with fewer
parameters and simpler equations than conductance models. We successfully
generalize results obtained previously with reaction-diffusion systems, showing
how chaotic properties result in reentry, a pathological propagation of stimuli
that evolves to arrhythmias with complex spatiotemporal features. The
bifurcation diagram of the single cell is very similar to that obtained in a
detailed conductance-based model. We find torsade de pointes, a clinical
manifestation of some types of tachycardia in the electrocardiogram, using a
generic sampling of the whole network during spiral waves. We also find a novel
type of dynamical pattern, with wavefronts composed of synchronized cardiac
plateaus and bursts. Our study provides the first in-depth look at the use of
map-based models to simulate complex cardiac dynamics.


title:
Gaussian Belief Trees for Chance Constrained Asymptotically Optimal
  Motion Planning

abstract:
  In this paper, we address the problem of sampling-based motion planning under
motion and measurement uncertainty with probabilistic guarantees. We generalize
traditional sampling-based tree-based motion planning algorithms for
deterministic systems and propose belief-$\mathcal{A}$, a framework that
extends any kinodynamical tree-based planner to the belief space for linear (or
linearizable) systems. We introduce appropriate sampling techniques and
distance metrics for the belief space that preserve the probabilistic
completeness and asymptotic optimality properties of the underlying planner. We
demonstrate the efficacy of our approach for finding safe low-cost paths
efficiently and asymptotically optimally in simulation, for both holonomic and
non-holonomic systems.


title: Quantum Error Correction Scheme for Fully Correlated Noise

abstract:
  Quantum error correction schemes for channels on an $n$-qubit system with
error operators of the form $W^{\otimes n}$ (fully-correlated noise) are
studied, where $W$ is an arbitary $2\times 2$ unitary operator. In a previous
paper, a recursive quantum error correction scheme using $k+1$ qubits to
protect $k$ qubits was proposed. In this paper, the error correction scheme is
implemented for 3-qubit and 5-qubit channels on the IBM online quantum
computers using \texttt{qiskit}, and issues arising in implementation are
studied. In particular, it was found that there was an error in the previous
paper concerning the decomposition of the encoding/decoding operator into
elementary quantum gates. In this paper, a modified encoding/decoding operator,
which can be decomposed efficiently, is presented. The scheme is implemented on
different IBM quantum computers, and the results on different machines are
compared. The encoding/decoding operator are further decomposed into basic
gates, i.e., single qubit gates and CNOT gates, used by the IBM quantum
computers to improve the results. Furthermore, the special type of channels
with the error operators of the form $\sigma_x^{\otimes n}, \sigma_y^{\otimes
n}$ and $\sigma_z^{\otimes n}$ are also studied, where $\sigma_x, \sigma_y,
\sigma_z$ are the Pauli matrices. A hybrid quantum error correction scheme
which protects both quantum and classical information are implemented using the
IBM quantum computers. Significant improvements of the numerical experiments in
a recent paper are demonstrated for such channels on $n$-qubits for $n=3,4,5$.


title:
Internet of Nano, Bio-Nano, Biodegradable and Ingestible Things: A
  Survey

abstract:
  In recent years, advances in biotechnology, nanotechnology and materials
science have led to development of revolutionizing applications in Internet of
Things (IoT). In particular, the interconnection of nanomaterials, nanoimplants
and nanobiosensors with existing IoT networks have inspired the concepts of
Internet of Nano Things (IoNT), Internet of Bio-Nano Things (IoBNT), Internet
of Biodegradable Things (IoBDT) and Internet of Ingestible Things (IoIT). To
date, although there are several survey papers that addressed these concepts
separately, there is no current survey covering all studies in IoNT, IoBNT,
IoBDT and IoIT. Therefore, in this paper, we provide a complete overview of all
recent work in these four areas. Furthermore, we emphasize the research
challenges, potential applications, and open research areas.


title:
The fundamental equations for inversion of operator pencils on Banach
  space

abstract:
  We prove that the resolvent of a linear operator pencil is analytic on an
open annulus if and only if the coefficients of the Laurent series satisfy a
system of fundamental equations and are geometrically bounded. Our analysis
extends earlier work on the fundamental equations to include the case where the
resolvent has an isolated essential singularity. We find a closed form for the
resolvent and use the fundamental equations to establish key spectral
separation properties when the resolvent has only a finite number of isolated
singularities. Finally we show that our results can also be applied to
polynomial pencils.


title: TrimBERT: Tailoring BERT for Trade-offs

abstract:
  Models based on BERT have been extremely successful in solving a variety of
natural language processing (NLP) tasks. Unfortunately, many of these large
models require a great deal of computational resources and/or time for
pre-training and fine-tuning which limits wider adoptability. While
self-attention layers have been well-studied, a strong justification for
inclusion of the intermediate layers which follow them remains missing in the
literature. In this work, we show that reducing the number of intermediate
layers in BERT-Base results in minimal fine-tuning accuracy loss of downstream
tasks while significantly decreasing model size and training time. We further
mitigate two key bottlenecks, by replacing all softmax operations in the
self-attention layers with a computationally simpler alternative and removing
half of all layernorm operations. This further decreases the training time
while maintaining a high level of fine-tuning accuracy.


title:
Fourier-Based Augmentations for Improved Robustness and Uncertainty
  Calibration

abstract:
  Diverse data augmentation strategies are a natural approach to improving
robustness in computer vision models against unforeseen shifts in data
distribution. However, the ability to tailor such strategies to inoculate a
model against specific classes of corruptions or attacks -- without incurring
substantial losses in robustness against other classes of corruptions --
remains elusive. In this work, we successfully harden a model against
Fourier-based attacks, while producing superior-to-AugMix accuracy and
calibration results on both the CIFAR-10-C and CIFAR-100-C datasets;
classification error is reduced by over ten percentage points for some
high-severity noise and digital-type corruptions. We achieve this by
incorporating Fourier-basis perturbations in the AugMix image-augmentation
framework. Thus we demonstrate that the AugMix framework can be tailored to
effectively target particular distribution shifts, while boosting overall model
robustness.


title:
Construction of Large-Scale Misinformation Labeled Datasets from Social
  Media Discourse using Label Refinement

abstract:
  Malicious accounts spreading misinformation has led to widespread false and
misleading narratives in recent times, especially during the COVID-19 pandemic,
and social media platforms struggle to eliminate these contents rapidly. This
is because adapting to new domains requires human intensive fact-checking that
is slow and difficult to scale. To address this challenge, we propose to
leverage news-source credibility labels as weak labels for social media posts
and propose model-guided refinement of labels to construct large-scale, diverse
misinformation labeled datasets in new domains. The weak labels can be
inaccurate at the article or social media post level where the stance of the
user does not align with the news source or article credibility. We propose a
framework to use a detection model self-trained on the initial weak labels with
uncertainty sampling based on entropy in predictions of the model to identify
potentially inaccurate labels and correct for them using self-supervision or
relabeling. The framework will incorporate social context of the post in terms
of the community of its associated user for surfacing inaccurate labels towards
building a large-scale dataset with minimum human effort. To provide labeled
datasets with distinction of misleading narratives where information might be
missing significant context or has inaccurate ancillary details, the proposed
framework will use the few labeled samples as class prototypes to separate high
confidence samples into false, unproven, mixture, mostly false, mostly true,
true, and debunk information. The approach is demonstrated for providing a
large-scale misinformation dataset on COVID-19 vaccines.


title:
Detecting change-points in noisy GPS time series with continuous
  piecewise structures

abstract:
  Detecting change-points in noisy data sequences with an underlying continuous
piecewise structure is a challenging problem, especially when prior knowledge
of the exact nature of the structural changes is unknown. One important
application is the automatic detection of slow slip events (SSEs), a type of
slow earthquakes, in GPS measurements of ground deformation. We propose a new
method based on Singular Spectrum Analysis to obscure the deviation from the
piecewise-linear structure, allowing us to apply Isolate-Detect to detect
change-points in SSE data with piecewise-non-linear structures. We demonstrate
its effectiveness in both simulated and real SSE data.


title:
All analytic solutions for geodesic motion in axially symmetric
  space-times

abstract:
  Recent observations of the orbits of star clusters around Sgr $A^\star$,
imaging of black holes and gravitational waveforms of merging compact objects
require a detailed understanding of the general relativistic geodesic motion.
We came up with a method to provide all the possible geodesics in an axially
symmetric space-time. The Kerr metric is explicitly worked out, recovering the
Schwarzschild geodesics in the static limit. We also found the most general
Killing tensor and its associated constant of motion for an axisymmetric
space-time. The relevance of these results is crucial to understanding the
different scenarios and the fundamental nature of the compact object at the
galactic center.


title:
Microgrid Day-Ahead Scheduling Considering Neural Network based Battery
  Degradation Model

abstract:
  Battery energy storage system (BESS) can effectively mitigate the uncertainty
of variable renewable generation. Degradation is un-preventable for batteries
such as the most popular Lithium-ion battery (LiB). The main causes of LiB
degradation are loss of Li-ions, loss of electrolyte, and increase of internal
resistance which are hard to model and predict. In this paper, we propose a
data driven method to predict the battery degradation per a given scheduled
battery operational profile. Particularly, a neural net-work based battery
degradation (NNBD) model is proposed to quantify the battery degradation with
inputs of major battery degradation factors. When incorporating the proposed
NNBD model into microgrid day-ahead scheduling (MDS), we can estab-lish a
battery degradation based MDS (BDMDS) model that can consider the equivalent
battery degradation cost precisely. Since the proposed NNBD model is highly
non-linear and non-convex, BDMDS would be very hard to solve. To address this
issue, a neural network and optimization decoupled heuristic (NNODH) algorithm
is proposed in this paper to effectively solve this neural network embedded
optimization problem. Simulation results demonstrate that the proposed NNODH
algorithm is able to ob-tain the optimal solution with lowest total cost
including normal operation cost and battery degradation cost.


title: Optimal channel selection with discrete QCQP

abstract:
  Reducing the high computational cost of large convolutional neural networks
is crucial when deploying the networks to resource-constrained environments. We
first show the greedy approach of recent channel pruning methods ignores the
inherent quadratic coupling between channels in the neighboring layers and
cannot safely remove inactive weights during the pruning procedure.
Furthermore, due to these inactive weights, the greedy methods cannot guarantee
to satisfy the given resource constraints and deviate with the true objective.
In this regard, we propose a novel channel selection method that optimally
selects channels via discrete QCQP, which provably prevents any inactive
weights and guarantees to meet the resource constraints tightly in terms of
FLOPs, memory usage, and network size. We also propose a quadratic model that
accurately estimates the actual inference time of the pruned network, which
allows us to adopt inference time as a resource constraint option. Furthermore,
we generalize our method to extend the selection granularity beyond channels
and handle non-sequential connections. Our experiments on CIFAR-10 and ImageNet
show our proposed pruning method outperforms other fixed-importance channel
pruning methods on various network architectures.


title:
On the role of the point at infinity in Deny's principle of positivity
  of mass for Riesz potentials

abstract:
  First introduced by J. Deny, the classical principle of positivity of mass
says that if $\kappa_\alpha\mu\leqslant\kappa_\alpha\nu$ everywhere on
$\mathbb{R}^n$, then $\mu(\mathbb{R}^n)\leqslant\nu(\mathbb{R}^n)$. Here
$\mu,\nu$ are positive Radon measures on $\mathbb{R}^n$, $n\geqslant2$, and
$\kappa_\alpha\mu$ is the potential of $\mu$ with respect to the Riesz kernel
$|x-y|^{\alpha-n}$ of order $\alpha\in(0,2]$, $\alpha<n$. We strengthen Deny's
principle by showing that $\mu(\mathbb{R}^n)\leqslant\nu(\mathbb{R}^n)$ still
holds even if $\kappa_\alpha\mu\leqslant\kappa_\alpha\nu$ is fulfilled only on
a proper subset $A$ of $\mathbb{R}^n$ that is not inner $\alpha$-thin at
infinity; and moreover, this condition on $A$ can not in general be improved.
Hence, if $\xi$ is a signed measure on $\mathbb{R}^n$ with $\int1\,d\xi>0$,
then $\kappa_\alpha\xi>0$ everywhere on $\mathbb{R}^n$, except for a subset
which is inner $\alpha$-thin at infinity. The analysis performed is based on
the author's recent theories of inner Riesz balayage and inner Riesz
equilibrium measures (Potential Anal., 2021), the inner equilibrium measure
being understood in an extended sense where both the energy and the total mass
may be infinite.


title:
KinoJGM: A framework for efficient and accurate quadrotor trajectory
  generation and tracking in dynamic environments

abstract:
  Unmapped areas and aerodynamic disturbances render autonomous navigation with
quadrotors extremely challenging. To fly safely and efficiently, trajectory
planners and trackers must be able to navigate unknown environments with
unpredictable aerodynamic effects in real-time. When encountering aerodynamic
effects such as strong winds, most current approaches to quadrotor trajectory
planning and tracking will not attempt to deviate from a determined plan, even
if it is risky, in the hope that any aerodynamic disturbances can be resisted
by a robust controller. This paper presents a novel systematic trajectory
planning and tracking framework for autonomous quadrotors. We propose a
Kinodynamic Jump Space Search (Kino-JSS) to generate a safe and efficient route
in unknown environments with aerodynamic disturbances. A real-time Gaussian
Process is employed to model the effects of aerodynamic disturbances, which we
then integrate with a Model Predictive Controller to achieve efficient and
accurate trajectory optimization and tracking. We demonstrate our system to
improve the efficiency of trajectory generation in unknown environments by up
to 75\% in the cases tested, compared with recent state-of-the-art. We also
demonstrate that our system improves the accuracy of tracking in selected
environments with unpredictable aerodynamic effects.


title:
A sensitivity analysis approach for the causal hazard ratio in
  randomized and observational studies

abstract:
  The Hazard Ratio (HR) is often reported as the main causal effect when
studying survival data. Despite its popularity, the HR suffers from an unclear
causal interpretation. As already pointed out in the literature, there is a
built-in selection bias in the HR, because similarly to the truncation by death
problem, the HR conditions on post-treatment survival. A recently proposed
alternative, inspired by the Survivor Average Causal Effect (SACE), is the
causal HR, defined as the ratio between hazards across treatment groups among
the study participants that would have survived regardless of their treatment
assignment. We discuss the challenge in identifying the causal HR and present a
sensitivity analysis identification approach in randomized controlled trials
utilizing a working frailty model. We further extend our framework to adjust
for potential confounders using inverse probability of treatment weighting. We
present a Cox-based and a flexible non-parametric kernel-based estimation under
right censoring. We study the finite-sample properties of the proposed
estimation method through simulations. We illustrate the utility of our
framework using two real-data examples.


title: History of Solar Neutrino Observations

abstract:
  The first solar neutrino experiment led by Raymond Davis Jr. showed a deficit
of neutrinos relative to the solar model prediction, referred to as the "solar
neutrino problem" since the 1970s. The Kamiokande experiment led by Masatoshi
Koshiba successfully observed solar neutrinos, as first reported in 1989. The
observed flux of solar neutrinos was almost half the prediction and confirmed
the solar neutrino problem. This problem was not resolved for some time due to
possible uncertainties in the solar model. In 2001, it was discovered that the
solar neutrino problem is due to neutrino oscillations by comparing the
Super-Kamiokande and Sudbury Neutrino Observatory results, which was the first
model-independent comparison. Detailed studies of solar neutrino oscillations
have since been performed, and the results of solar neutrino experiments are
consistent with solar model predictions when the effect of neutrino
oscillations are taken into account. In this article, the history of solar
neutrino observations is reviewed with the contributions of Kamiokande and
Super-Kamiokande detailed.

categories: hep-ex astro-ph.HE astro-ph.SR hep-ph physics.hist-ph


title: Standard Deviation-Based Quantization for Deep Neural Networks

abstract:
  Quantization of deep neural networks is a promising approach that reduces the
inference cost, making it feasible to run deep networks on resource-restricted
devices. Inspired by existing methods, we propose a new framework to learn the
quantization intervals (discrete values) using the knowledge of the network's
weight and activation distributions, i.e., standard deviation. Furthermore, we
propose a novel base-2 logarithmic quantization scheme to quantize weights to
power-of-two discrete values. Our proposed scheme allows us to replace
resource-hungry high-precision multipliers with simple shift-add operations.
According to our evaluations, our method outperforms existing work on CIFAR10
and ImageNet datasets and even achieves better accuracy performance with 3-bit
weights and activations when compared to the full-precision models. Moreover,
our scheme simultaneously prunes the network's parameters and allows us to
flexibly adjust the pruning ratio during the quantization process.


title: Applying Polynomial Decoupling Methods to the Polynomial NARX Model

abstract:
  System identification uses measurements of a dynamic system's input and
output to reconstruct a mathematical model for that system. These can be
mechanical, electrical, physiological, among others. Since most of the systems
around us exhibit some form of nonlinear behavior, nonlinear system
identification techniques are the tools that will help us gain a better
understanding of our surroundings and potentially let us improve their
performance. One model that is often used to represent nonlinear systems is the
polynomial NARX model, an equation error model where the output is a polynomial
function of the past inputs and outputs. That said, a major disadvantage with
the polynomial NARX model is that the number of parameters increases rapidly
with increasing polynomial order. Furthermore, the polynomial NARX model is a
black-box model, and is therefore difficult to interpret. This paper discusses
a decoupling algorithm for the polynomial NARX model that substitutes the
multivariate polynomial with a transformation matrix followed by a bank of
univariate polynomials. This decreases the number of model parameters
significantly and also imposes structure on the black-box NARX model. Since a
non-convex optimization is required for this identification technique,
initialization is an important factor to consider. In this paper the decoupling
algorithm is developed in conjunction with several different initialization
techniques. The resulting algorithms are applied to two nonlinear benchmark
problems: measurement data from the Silver-Box and simulation data from the
Bouc-Wen friction model, and the performance is evaluated for different
validation signals in both simulation and prediction.


title:
Four-point functions with multi-cycle fields in symmetric orbifolds and
  the D1-D5 CFT

abstract:
  We study $S_N$-invariant four-point functions with two generic multi-cycle
fields and two twist-2 fields, at the free orbifold point of the D1-D5 CFT. We
derive the explicit factorization of these functions following from the action
of the symmetric group on the composite multi-cycle fields. Apart from
non-trivial symmetry factors that we compute, the function with multi-cycle
operators is reduced to a sum of connected correlators in which the composite
fields have, at most, two cycles. The correlators with two double-cycle and two
single-cycle fields give the leading order contribution in the large-$N$ limit.
We derive explicit formulas for these functions, encompassing a large class of
choices for the single- and the double-cycle fields, including generic Ramond
ground states, NS chiral fields and the marginal deformation operator. We are
thus able to extract important dynamical information from the short-distance
OPEs: conformal dimensions, R-charges and structure constants of families of
BPS and non-BPS fields present in the corresponding light-light and heavy-light
channels. We also discuss properties of generic multi-cycle $Q$-point functions
in $M^N/S_N$ orbifolds, using a technology due to Pakman, Rastelli and Razamat.


title: Mathematical Structures of Cohomological Field Theories

abstract:
  A mathematical definition of Cohomological Lagrangian Field Theories (CoLFTs)
is formulated in the language of graded/bi-graded manifolds. Algebraic
properties of observables in CoLFTs are studied. Methods of constructing
CoLFTs, with or without gauge symmetries, are discussed. In particular, a
generalization of the Mathai-Quillen formalism is given. Examples such as
topological quantum mechanics, topological sigma model, topological M-theory,
and topological Yang-Mills theory can be obtained uniformly using this new
formalism.


title:
Analyzing Human Observer Ability in Morphing Attack Detection -- Where
  Do We Stand?

abstract:
  While several works have studied the vulnerability of automated FRS and have
proposed morphing attack detection (MAD) methods, very few have focused on
studying the human ability to detect morphing attacks. The examiner/observer's
face morph detection ability is based on their observation, domain knowledge,
experience, and familiarity with the problem, and no works report the detailed
findings from observers who check identity documents as a part of their
everyday professional life. This work creates a new benchmark database of
realistic morphing attacks from 48 unique subjects leading to 400 morphed
images presented to the observers in a Differential-MAD (D-MAD) setting. Unlike
the existing databases, the newly created morphed image database has been
created with careful considerations to age, gender and ethnicity to create
realistic morph attacks. Further, unlike the previous works, we also capture
ten images from Automated Border Control (ABC) gates to mimic the realistic
D-MAD setting leading to 400 probe images in border crossing scenarios. The
newly created dataset is further used to study the ability of human observers'
ability to detect morphed images. In addition, a new dataset of 180 morphed
images is also created using the FRGCv2 dataset under the Single Image-MAD
(S-MAD) setting. Further, to benchmark the human ability in detecting morphs, a
new evaluation platform is created to conduct S-MAD and D-MAD analysis. The
benchmark study employs 469 observers for D-MAD and 410 observers for S-MAD who
are primarily governmental employees from more than 40 countries. The analysis
provides interesting insights and points to expert observers' missing
competence and failure to detect a considerable amount of morphing attacks.
Human observers tend to detect morphed images to a lower accuracy as compared
to the automated MAD algorithms evaluated in this work.


title: Efficient Video Segmentation Models with Per-frame Inference

abstract:
  Most existing real-time deep models trained with each frame independently may
produce inconsistent results across the temporal axis when tested on a video
sequence. A few methods take the correlations in the video sequence into
account,e.g., by propagating the results to the neighboring frames using
optical flow or extracting frame representations using multi-frame information,
which may lead to inaccurate results or unbalanced latency. In this work, we
focus on improving the temporal consistency without introducing computation
overhead in inference. To this end, we perform inference at each frame.
Temporal consistency is achieved by learning from video frames with extra
constraints during the training phase. introduced for inference. We propose
several techniques to learn from the video sequence, including a temporal
consistency loss and online/offline knowledge distillation methods. On the task
of semantic video segmentation, weighing among accuracy, temporal smoothness,
and efficiency, our proposed method outperforms keyframe-based methods and a
few baseline methods that are trained with each frame independently, on
datasets including Cityscapes, Camvid, and 300VW-Mask. We further apply our
training method to video instance segmentation on YouTubeVISand develop an
application of portrait matting in video sequences, by segmenting temporally
consistent instance-level trimaps across frames. Experiments show superior
qualitative and quantitative results. Code is available at:
https://git.io/vidseg.


title:
A Straight Lightning Bolt?! Observation of a Predicted Macro Dark Matter
  Signature

abstract:
  Recently, four remarkably straight very brief flashes of light were captured
on video in Perth, Australia within 0.5s of one another. Straight lightning was
recently identified as a prediction of macroscopic dark matter (macros) -- a
broad class of alternative candidates to particle dark matter. A sufficiently
large macro passing through the atmosphere producing a straight column ofplasma
would produce fluorescence, or, under atmospheric conditions conducive to
lightning, seeda very straight lightning strike visible to the naked eye. Other
explanations are considered, but areproblematic.


title: BagPipe: Accelerating Deep Recommendation Model Training

abstract:
  Deep learning based recommendation models (DLRM) are widely used in several
business critical applications. Training such recommendation models efficiently
is challenging primarily because they consist of billions of embedding-based
parameters which are often stored remotely leading to significant overheads
from embedding access. By profiling existing DLRM training, we observe that
only 8.5% of the iteration time is spent in forward/backward pass while the
remaining time is spent on embedding and model synchronization. Our key insight
in this paper is that access to embeddings have a specific structure and
pattern which can be used to accelerate training. We observe that embedding
accesses are heavily skewed, with almost 1% of embeddings represent more than
92% of total accesses. Further, we observe that during training we can
lookahead at future batches to determine exactly which embeddings will be
needed at what iteration in the future. Based on these insight, we propose
Bagpipe, a system for training deep recommendation models that uses caching and
prefetching to overlap remote embedding accesses with the computation. We
designed an Oracle Cacher, a new system component which uses our lookahead
algorithm to generate optimal cache update decisions and provide strong
consistency guarantees. Our experiments using three datasets and two models
shows that our approach provides a speed up of up to 6.2x compared to state of
the art baselines, while providing the same convergence and reproducibility
guarantees as synchronous training.


title:
Koopman Spectral Analysis of Intermittent Dynamics in Complex Systems: A
  Case Study in Pathophysiological Processes of Obstructive Sleep Apnea

abstract:
  Complex systems, such as pathophysiological processes, commonly exhibit
chaotic, nonlinear, and intermittent phenomena. Koopman operator theory and
Hankel alternative view of Koopman (HAVOK) model have been widely used to
decompose the chaos of the complex system dynamics into an intermittent forced
linear system. Although the statistics of the intermittent forcing have been
proposed to characterize intermittencies in the HAVOK model, they were not
adequate to attribute for the mode switching of nonlinear dynamics and the
fat-tailed non-Gaussian distribution originated from high-frequency bursts and
rarely-observed intermittent forcing. The paper proposed a new intermittency
dynamics analysis approach to characterize the intermittent phases, chaotic
bursts, and local spectral-temporal properties of various intermittent dynamics
modes using spectral decomposition and wavelet analysis. To validate our
methods, the intermittency behavior of apneic events in obstructive sleep apnea
disorder was selected as the case, in which heart rate variability (HRV)
features were extracted. Next, we constructed the Hankel matrix from the HRV
features and obtained the last eigen time-delay coordinate by singular value
decomposition of the Hankel matrix, which was modeled as an intermittent
forcing input. The statistics of the forcing in OSA demonstrated the fat-tailed
distribution of the intermittent forcing, which correspond to the intermittency
of the underlying OSA pathophysiological process. The pooled means and standard
deviations of the burst duration and the inter-burst duration across OSA
patients were also calculated to be minutes and minutes. Scalogram amplitude
and spectral decomposition of the wavelet transform exhibited various
predominant frequencies and dynamics modes associated with apneic events.


title: Thompson Sampling with Unrestricted Delays

abstract:
  We investigate properties of Thompson Sampling in the stochastic multi-armed
bandit problem with delayed feedback. In a setting with i.i.d delays, we
establish to our knowledge the first regret bounds for Thompson Sampling with
arbitrary delay distributions, including ones with unbounded expectation. Our
bounds are qualitatively comparable to the best available bounds derived via
ad-hoc algorithms, and only depend on delays via selected quantiles of the
delay distributions. Furthermore, in extensive simulation experiments, we find
that Thompson Sampling outperforms a number of alternative proposals, including
methods specifically designed for settings with delayed feedback.


title:
Angular momentum in the internal SO(3) symmetry space relating the size
  of universe and the Higgs mass

abstract:
  We show that with an internal SO(3) symmetric triplet of Higgs fields, the
conserved quantity associated with this internal SO(3) symmetry leads to
spontaneous symmetry breaking giving the Higgs field a mass.


title: On distinguishability distillation and dilution exponents

abstract:
  In this note, I define error exponents and strong converse exponents for the
tasks of distinguishability distillation and dilution. These are counterparts
to the one-shot distillable distinguishability and the one-shot
distinguishability cost, as previously defined in the resource theory of
asymmetric distinguishability. I show that they can be evaluated by
semi-definite programming, establish a number of their properties, bound them
using R\'enyi relative entropies, and relate them to each other.


title:
First High Resolution Interferometric Observation of a Solar Prominence
  With ALMA

abstract:
  We present the first observation of a solar prominence at $84-116$ GHz using
the high resolution interferometric imaging of ALMA. Simultaneous observations
in H$\alpha$ from Bia{\l}kaw Observatory and with SDO/AIA reveal similar
prominence morphology to the ALMA observation. The contribution functions of 3
mm and H$\alpha$ emission are shown to have significant overlap across a range
of gas pressures. We estimate the maximum millimetre-continuum optical
thickness to be $\tau_\mathrm{3mm}\approx 2$, and the brightness temperature
from the observed H$\alpha$ intensity. The brightness temperature measured by
ALMA is $\sim 6000-7000$ K in the prominence spine, which correlates well with
the estimated brightness temperature for a gas temperature of 8000 K.


title:
Understanding Adversarial Robustness from Feature Maps of Convolutional
  Layers

abstract:
  The adversarial robustness of a neural network mainly relies on two factors,
one is the feature representation capacity of the network, and the other is its
resistance ability to perturbations. In this paper, we study the
anti-perturbation ability of the network from the feature maps of convolutional
layers. Our theoretical analysis discovers that larger convolutional features
before average pooling can contribute to better resistance to perturbations,
but the conclusion is not true for max pooling. Based on the theoretical
findings, we present two feasible ways to improve the robustness of existing
neural networks. The proposed approaches are very simple and only require
upsampling the inputs or modifying the stride configuration of convolution
operators. We test our approaches on several benchmark neural network
architectures, including AlexNet, VGG16, RestNet18 and PreActResNet18, and
achieve non-trivial improvements on both natural accuracy and robustness under
various attacks. Our study brings new insights into the design of robust neural
networks. The code is available at \url{https://github.com/MTandHJ/rcm}.


title:
A Hybrid High-Order scheme for the stationary, incompressible
  magnetohydrodynamics equations

abstract:
  We propose and analyse a hybrid high-order (HHO) scheme for stationary
incompressible magnetohydrodynamics equations. The scheme has an arbitrary
order of accuracy and is applicable on generic polyhedral meshes. For sources
that are small enough, we prove error estimates in energy norm for the velocity
and magnetic field, and $L^2$-norm for the pressure; these estimates are fully
robust with respect to small faces, and of optimal order with respect to the
mesh size. Using compactness techniques, we also prove that the scheme
converges to a solution of the continuous problem, irrespective of the source
being small or large. Finally, we illustrate our theoretical results through 3D
numerical tests on tetrahedral and Voronoi mesh families.


title: Chiral Anomaly in Euler Fluid and Beltrami Flow

abstract:
  We show that the chiral anomaly of quantum field theories with Dirac fermions
subject to an axial background field is an inherent property of kinematics of a
perfect classical fluid. Celebrated Beltrami flows (stationary solutions of
Euler equations with extensive helicity) exhibit the chiral anomaly equivalent
to that known for Dirac fermions. A prominent effect of the chiral anomaly is
the transport electric current at equilibrium. We show that it is also a
property of Beltrami flows.


title: List Locally Surjective Homomorphisms in Hereditary Graph Classes

abstract:
  A locally surjective homomorphism from a graph $G$ to a graph $H$ is an
edge-preserving mapping from $V(G)$ to $V(H)$ that is surjective in the
neighborhood of each vertex in $G$. In the list locally surjective homomorphism
problem, denoted by LLSHom($H$), the graph $H$ is fixed and the instance
consists of a graph $G$ whose every vertex is equipped with a subset of $V(H)$,
called list. We ask for the existence of a locally surjective homomorphism from
$G$ to $H$, where every vertex of $G$ is mapped to a vertex from its list. In
this paper, we study the complexity of the LLSHom($H$) problem in $F$-free
graphs, i.e., graphs that exclude a fixed graph $F$ as an induced subgraph. We
aim to understand for which pairs $(H,F)$ the problem can be solved in
subexponential time.
  We show that for all graphs $H$, for which the problem is NP-hard in general
graphs, it cannot be solved in subexponential time in $F$-free graphs unless
$F$ is a bounded-degree forest or the ETH fails. The initial study reveals that
a natural subfamily of bounded-degree forests $F$ that might lead to some
tractability results is the family $\mathcal S$ consisting of forests whose
every component has at most three leaves. In this case, we exhibit the
following dichotomy theorem: besides the cases that are polynomial-time
solvable in general graphs, the graphs $H \in \{P_3,C_4\}$ are the only
connected ones that allow for a subexponential-time algorithm in $F$-free
graphs for every $F \in \mathcal S$ (unless the ETH fails).


title: Learning Invariant Weights in Neural Networks

abstract:
  Assumptions about invariances or symmetries in data can significantly
increase the predictive power of statistical models. Many commonly used models
in machine learning are constraint to respect certain symmetries in the data,
such as translation equivariance in convolutional neural networks, and
incorporation of new symmetry types is actively being studied. Yet, efforts to
learn such invariances from the data itself remains an open research problem.
It has been shown that marginal likelihood offers a principled way to learn
invariances in Gaussian Processes. We propose a weight-space equivalent to this
approach, by minimizing a lower bound on the marginal likelihood to learn
invariances in neural networks resulting in naturally higher performing models.


title:
On Learning and Testing of Counterfactual Fairness through Data
  Preprocessing

abstract:
  Machine learning has become more important in real-life decision-making but
people are concerned about the ethical problems it may bring when used
improperly. Recent work brings the discussion of machine learning fairness into
the causal framework and elaborates on the concept of Counterfactual Fairness.
In this paper, we develop the Fair Learning through dAta Preprocessing (FLAP)
algorithm to learn counterfactually fair decisions from biased training data
and formalize the conditions where different data preprocessing procedures
should be used to guarantee counterfactual fairness. We also show that
Counterfactual Fairness is equivalent to the conditional independence of the
decisions and the sensitive attributes given the processed non-sensitive
attributes, which enables us to detect discrimination in the original decision
using the processed data. The performance of our algorithm is illustrated using
simulated data and real-world applications.


title:
Long-Term Missing Value Imputation for Time Series Data Using Deep
  Neural Networks

abstract:
  We present an approach that uses a deep learning model, in particular, a
MultiLayer Perceptron (MLP), for estimating the missing values of a variable in
multivariate time series data. We focus on filling a long continuous gap (e.g.,
multiple months of missing daily observations) rather than on individual
randomly missing observations. Our proposed gap filling algorithm uses an
automated method for determining the optimal MLP model architecture, thus
allowing for optimal prediction performance for the given time series. We
tested our approach by filling gaps of various lengths (three months to three
years) in three environmental datasets with different time series
characteristics, namely daily groundwater levels, daily soil moisture, and
hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled
values obtained with our approach to the widely-used R-based time series gap
filling methods ImputeTS and mtsdi. The results indicate that using an MLP for
filling a large gap leads to better results, especially when the data behave
nonlinearly. Thus, our approach enables the use of datasets that have a large
gap in one variable, which is common in many long-term environmental monitoring
observations.


title:
Systematic upper limits on the size of missing pulsar glitches in the
  first UTMOST open data release

abstract:
  A systematic, semi-automated search for pulsar glitches in the first UTMOST
public data release is presented. The search is carried out using a hidden
Markov model which incorporates both glitches and timing noise into the model
of the assumed phase evolution of the pulsar. Glitches are detected through
Bayesian model selection between models with and without glitches present with
minimal human intervention. Nine glitches are detected among seven objects, all
of which have been previously reported. No new glitches were detected.
Injection studies are used to place 90\% frequentist upper limits on the size
of undetected glitches in each of the 282 objects searched. The mean upper
limit obtained is $\Delta f^{90\%}/f = 1.9 \times 10^{-8}$, with a range of
$4.1 \times 10^{-11} \leq \Delta f^{90\%}/f \leq 2.7 \times 10^{-7}$, assuming
step events with no post-glitch recoveries. It is demonstrated that including
glitch recovery has a mild effect, in most cases increasing the upper limit by
a factor of $\lesssim 5$ conservatively assuming complete recovery on a
timescale of $100\,\mathrm{d}$.


title:
Towards an Accountable and Reproducible Federated Learning: A FactSheets
  Approach

abstract:
  Federated Learning (FL) is a novel paradigm for the shared training of models
based on decentralized and private data. With respect to ethical guidelines, FL
is promising regarding privacy, but needs to excel vis-\`a-vis transparency and
trustworthiness. In particular, FL has to address the accountability of the
parties involved and their adherence to rules, law and principles. We introduce
AF^2 Framework, where we instrument FL with accountability by fusing verifiable
claims with tamper-evident facts, into reproducible arguments. We build on AI
FactSheets for instilling transparency and trustworthiness into the AI
lifecycle and expand it to incorporate dynamic and nested facts, as well as
complex model compositions in FL. Based on our approach, an auditor can
validate, reproduce and certify a FL process. This can be directly applied in
practice to address the challenges of AI engineering and ethics.


title:
Superposed Hyperbolic Kink and Pulse Solutions of Coupled $\phi^4$, NLS
  and MKdV Equations

abstract:
  We obtain novel solutions of a coupled $\phi^4$, a coupled nonlinear
Schr\"odinger (NLS) and a coupled modified Korteweg de Vries (MKdV) model which
can be re-expressed as a linear superposition of either the sum or the
difference of two hyperbolic kink or two hyperbolic pulse solutions. These
results demonstrate that the notion of superposed solutions extends to coupled
nonlinear equations as well.


title: Ensemble Method for Estimating Individualized Treatment Effects

abstract:
  In many medical and business applications, researchers are interested in
estimating individualized treatment effects using data from a randomized
experiment. For example in medical applications, doctors learn the treatment
effects from clinical trials and in technology companies, researchers learn
them from A/B testing experiments. Although dozens of machine learning models
have been proposed for this task, it is challenging to determine which model
will be best for the problem at hand because ground-truth treatment effects are
unobservable. In contrast to several recent papers proposing methods to select
one of these competing models, we propose an algorithm for aggregating the
estimates from a diverse library of models. We compare ensembling to model
selection on 43 benchmark datasets, and find that ensembling wins almost every
time. Theoretically, we prove that our ensemble model is (asymptotically) at
least as accurate as the best model under consideration, even if the number of
candidate models is allowed to grow with the sample size.


title: Integrability of pushforward measures by analytic maps

abstract:
  Given a map $\phi:X\rightarrow Y$ between $F$-analytic manifolds over a local
field $F$ of characteristic $0$, we introduce an invariant
$\epsilon_{\star}(\phi)$ which quantifies the integrability of pushforwards of
smooth compactly supported measures by $\phi$. We further define a local
version $\epsilon_{\star}(\phi,x)$ near $x\in X$. These invariants have a
strong connection to the singularities of $\phi$.
  When $Y$ is one-dimensional, we give an explicit formula for
$\epsilon_{\star}(\phi,x)$, and show it is asymptotically equivalent to other
known singularity invariants such as the $F$-log-canonical threshold
$\operatorname{lct}_{F}(\phi-\phi(x);x)$ at $x$.
  In the general case, we show that $\epsilon_{\star}(\phi,x)$ is bounded from
below by the $F$-log-canonical threshold
$\lambda=\operatorname{lct}_{F}(\mathcal{J}_{\phi};x)$ of the Jacobian ideal
$\mathcal{J}_{\phi}$ near $x$. If $\dim Y=\dim X$, equality is attained. If
$\dim Y<\dim X$, the inequality can be strict; however, for $F=\mathbb{C}$, we
establish the upper bound $\epsilon_{\star}(\phi,x)\leq\lambda/(1-\lambda)$,
whenever $\lambda<1$.
  Finally, we specialize to polynomial maps $\varphi:X\rightarrow Y$ between
smooth algebraic $\mathbb{Q}$-varieties $X$ and $Y$. We geometrically
characterize the condition that $\epsilon_{\star}(\varphi_{F})=\infty$ over a
large family of local fields, by showing it is equivalent to $\varphi$ being
flat with fibers of semi-log-canonical singularities.


title:
Bayesian inference for discretely observed continuous time multi-state
  models

abstract:
  Multi-state models are frequently applied for representing processes evolving
through a discrete set of state. Important classes of multi-state models arise
when transitions between states may depend on the time since entry into the
current state or on the time elapsed from the starting of the process. The
former models are called semi-Markov while the latter are known as
inhomogeneous Markov models. Inference for both the models presents
computational difficulties when the process is only observed at discrete time
points with no additional information about the state transitions. Indeed, in
both the cases, the likelihood function is not available in closed form. In
order to obtain Bayesian inference under these two classes of models we
reconstruct the whole unobserved trajectories conditioned on the observed
points via a Metropolis-Hastings algorithm. As proposal density we use that
given by the nested Markov models whose conditioned trajectories can be easily
drawn by the uniformization technique. The resulting inference is illustrated
via simulation studies and the analysis of two benchmark data sets for multi
state models.


title: Deep neural networks for fine-grained surveillance of overdose mortality

abstract:
  Surveillance of drug overdose deaths relies on death certificates for
identification of the substances that caused death. Drugs and drug classes can
be identified through the International Classification of Diseases, 10th
Revision (ICD-10) codes present on death certificates. However, ICD-10 codes do
not always provide high levels of specificity in drug identification. To
achieve more fine-grained identification of substances on a death certificate,
the free-text cause of death section, completed by the medical certifier, must
be analyzed. Current methods for analyzing free-text death certificates rely
solely on look-up tables for identifying specific substances, which must be
frequently updated and maintained. To improve identification of drugs on death
certificates, a deep learning named-entity recognition model was developed,
which achieved an F1-score of 99.13%. This model can identify new drug
misspellings and novel substances that are not present on current surveillance
look-up tables, enhancing the surveillance of drug overdose deaths.


title: Directed Graph Auto-Encoders

abstract:
  We introduce a new class of auto-encoders for directed graphs, motivated by a
direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The
proposed model learns pairs of interpretable latent representations for the
nodes of directed graphs, and uses parameterized graph convolutional network
(GCN) layers for its encoder and an asymmetric inner product decoder.
Parameters in the encoder control the weighting of representations exchanged
between neighboring nodes. We demonstrate the ability of the proposed model to
learn meaningful latent embeddings and achieve superior performance on the
directed link prediction task on several popular network datasets.


title:
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural
  Networks for Detecting Ventricular Arrhythmias based on ECGs

abstract:
  Ventricular arrhythmias (VA) are the main causes of sudden cardiac death.
Developing machine learning methods for detecting VA based on
electrocardiograms (ECGs) can help save people's lives. However, developing
such machine learning models for ECGs is challenging because of the following:
1) group-level diversity from different subjects and 2) individual-level
diversity from different moments of a single subject. In this study, we aim to
solve these problems in the pre-training and fine-tuning stages. For the
pre-training stage, we propose a novel model agnostic meta-learning (MAML) with
curriculum learning (CL) method to solve group-level diversity. MAML is
expected to better transfer the knowledge from a large dataset and use only a
few recordings to quickly adapt the model to a new person. CL is supposed to
further improve MAML by meta-learning from easy to difficult tasks. For the
fine-tuning stage, we propose improved pre-fine-tuning to solve
individual-level diversity. We conduct experiments using a combination of three
publicly available ECG datasets. The results show that our method outperforms
the compared methods in terms of all evaluation metrics. Ablation studies show
that MAML and CL could help perform more evenly, and pre-fine-tuning could
better fit the model to training data.


title: Human-Centered Concept Explanations for Neural Networks

abstract:
  Understanding complex machine learning models such as deep neural networks
with explanations is crucial in various applications. Many explanations stem
from the model perspective, and may not necessarily effectively communicate why
the model is making its predictions at the right level of abstraction. For
example, providing importance weights to individual pixels in an image can only
express which parts of that particular image are important to the model, but
humans may prefer an explanation which explains the prediction by concept-based
thinking. In this work, we review the emerging area of concept based
explanations. We start by introducing concept explanations including the class
of Concept Activation Vectors (CAV) which characterize concepts using vectors
in appropriate spaces of neural activations, and discuss different properties
of useful concepts, and approaches to measure the usefulness of concept
vectors. We then discuss approaches to automatically extract concepts, and
approaches to address some of their caveats. Finally, we discuss some case
studies that showcase the utility of such concept-based explanations in
synthetic settings and real world applications.


title: The outcome of the restabilization process in matching markets

abstract:
  For a many-to-one matching model, we study the matchings obtained through the
restabilization of stable matchings that had been disrupted by a change in the
population. We include a simple representation of the stable matching obtained
in terms of the initial stable matching (i.e., before being disrupted by
changes in the population) and the firm-optimal stable matching. (We used
Lattice Theory to characterize the outcome of the restabilization process.) We
also describe the connection between the original stable matching and the one
obtained after the restabilization process in the new market.


title:
Social Learning under Platform Influence: Consensus and Persistent
  Disagreement

abstract:
  Individuals increasingly rely on social networking platforms to form
opinions. However, these platforms typically aim to maximize engagement, which
may not align with social good. In this paper, we introduce an opinion dynamics
model where agents are connected in a social network, and update their opinions
based on their neighbors' opinions and on the content shown to them by the
platform. We focus on a stochastic block model with two blocks, where the
initial opinions of the individuals in different blocks are different. We prove
that for large and dense enough networks the trajectory of opinion dynamics in
such networks can be approximated well by a simple two-agent system. The latter
admits tractable analytical analysis, which we leverage to provide interesting
insights into the platform's impact on the social learning outcome in our
original two-block model. Specifically, by using our approximation result, we
show that agents' opinions approximately converge to some limiting opinion,
which is either: consensus, where all agents agree, or persistent disagreement,
where agents' opinions differ. We find that when the platform is weak and there
is a high number of connections between agents with different initial opinions,
a consensus equilibrium is likely. In this case, even if a persistent
disagreement equilibrium arises, the polarization in this equilibrium, i.e.,
the degree of disagreement, is low. When the platform is strong, a persistent
disagreement equilibrium is likely and the equilibrium polarization is high. A
moderate platform typically leads to a persistent disagreement equilibrium with
moderate polarization. Lastly, more balanced and less polarized initial
opinions are more likely to lead to persistent disagreement than to consensus.


title:
Chebyshev Polynomials, Sliding Columns, and the $k$-step Fibonacci
  Numbers

abstract:
  We give a direct and intuitive proof (via sliding some columns up and down)
of the following interesting fact: if we write out the Chebyshev polynomials in
a chart and take the sums of coefficients along certain diagonals, we obtain
the Fibonaccis, the Tribonaccis, the Tetranaccis, and so on.


title: Cauchy problem for general time fractional diffusion equation

abstract:
  In the present work, we consider the Cauchy problem for the time fractional
diffusion equation involving the general Caputo-type differential operator
proposed by Kochubei. First, the existence, the positivity and the long time
behavior of solutions of the equation without source term are established by
using the Fourier analysis technique. Then, based on the representation of the
solution of the inhomogenous linear ordinary differential equation with the
general operator, the similar problems for the diffusion equation with source
term are studied.


title:
Prediction of Depression Severity Based on the Prosodic and Semantic
  Features with Bidirectional LSTM and Time Distributed CNN

abstract:
  Depression is increasingly impacting individuals both physically and
psychologically worldwide. It has become a global major public health problem
and attracts attention from various research fields. Traditionally, the
diagnosis of depression is formulated through semi-structured interviews and
supplementary questionnaires, which makes the diagnosis heavily relying on
physicians experience and is subject to bias. Mental health monitoring and
cloud-based remote diagnosis can be implemented through an automated depression
diagnosis system. In this article, we propose an attention-based multimodality
speech and text representation for depression prediction. Our model is trained
to estimate the depression severity of participants using the Distress Analysis
Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset. For the audio modality, we
use the collaborative voice analysis repository (COVAREP) features provided by
the dataset and employ a Bidirectional Long Short-Term Memory Network (Bi-LSTM)
followed by a Time-distributed Convolutional Neural Network (T-CNN). For the
text modality, we use global vectors for word representation (GloVe) to perform
word embeddings and the embeddings are fed into the Bi-LSTM network. Results
show that both audio and text models perform well on the depression severity
estimation task, with best sequence level F1 score of 0.9870 and patient-level
F1 score of 0.9074 for the audio model over five classes (healthy, mild,
moderate, moderately severe, and severe), as well as sequence level F1 score of
0.9709 and patient-level F1 score of 0.9245 for the text model over five
classes. Results are similar for the multimodality fused model, with the
highest F1 score of 0.9580 on the patient-level depression detection task over
five classes. Experiments show statistically significant improvements over
previous works.


title: Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection

abstract:
  We present an end-to-end differentiable neural network architecture to
perform anomaly detection in multivariate time series by incorporating a
Sequential Probability Ratio Test on the prediction residual. The architecture
is a cascade of dynamical systems designed to separate linearly predictable
components of the signal such as trends and seasonality, from the non-linear
ones. The former are modeled by local Linear Dynamic Layers, and their residual
is fed to a generic Temporal Convolutional Network that also aggregates global
statistics from different time series as context for the local predictions of
each one. The last layer implements the anomaly detector, which exploits the
temporal structure of the prediction residuals to detect both isolated point
anomalies and set-point changes. It is based on a novel application of the
classic CUMSUM algorithm, adapted through the use of a variational
approximation of f-divergences. The model automatically adapts to the time
scales of the observed signals. It approximates a SARIMA model at the get-go,
and auto-tunes to the statistics of the signal and its covariates, without the
need for supervision, as more data is observed. The resulting system, which we
call STRIC, outperforms both state-of-the-art robust statistical methods and
deep neural network architectures on multiple anomaly detection benchmarks.


title:
Learning ECG Representations based on Manipulated Temporal-Spatial
  Reverse Detection

abstract:
  Learning representations from electrocardiogram (ECG) serves as a fundamental
step for many downstream machine learning-based ECG analysis tasks. However,
the learning process is always restricted by lack of high-quality labeled data
in reality. Existing methods addressing data deficiency either cannot provide
satisfied representations for downstream tasks or require too much effort to
construct similar and dissimilar pairs to learn informative representations. In
this paper, we propose a straightforward but effective approach to learn ECG
representations. Inspired by the temporal and spatial characteristics of ECG,
we flip the original signals horizontally, vertically, and both horizontally
and vertically. The learning is then done by classifying the four types of
signals including the original one. To verify the effectiveness of the proposed
temporal-spatial (T-S) reverse detection method, we conduct a downstream task
to detect atrial fibrillation (AF) which is one of the most common ECG tasks.
The results show that the ECG representations learned with our method lead to
remarkable performances on the downstream task. In addition, after exploring
the representational feature space and investigating which parts of the ECG
signal contribute to the representations, we conclude that the temporal reverse
is more effective than the spatial reverse for learning ECG representations.


title:
APEACH: Attacking Pejorative Expressions with Analysis on
  Crowd-Generated Hate Speech Evaluation Datasets

abstract:
  Detecting toxic or pejorative expressions in online communities has become
one of the main concerns for preventing the users' mental harm. This led to the
development of large-scale hate speech detection datasets of various domains,
which are mainly built upon web-crawled texts with labels by crowd workers.
However, for languages other than English, researchers might have to rely on
only a small-sized corpus due to the lack of data-driven research of hate
speech detection. This sometimes misleads the evaluation of prevalently used
pretrained language models (PLMs) such as BERT, given that PLMs often share the
domain of pretraining corpus with the evaluation set, resulting in
over-representation of the detection performance. Also, the scope of pejorative
expressions might be restricted if the dataset is built on a single domain
text.
  To alleviate the above problems in Korean hate speech detection, we propose
APEACH,a method that allows the collection of hate speech generated by
unspecified users. By controlling the crowd-generation of hate speech and
adding only a minimum post-labeling, we create a corpus that enables the
generalizable and fair evaluation of hate speech detection regarding text
domain and topic. We Compare our outcome with prior work on an annotation-based
toxic news comment dataset using publicly available PLMs. We check that our
dataset is less sensitive to the lexical overlap between the evaluation set and
pretraining corpus of PLMs, showing that it helps mitigate the unexpected
under/over-representation of model performance. We distribute our dataset
publicly online to further facilitate the general-domain hate speech detection
in Korean.


title: GRB 200826A: A Precursor of a Long GRB with Missing Main Emission

abstract:
  The recently discovered peculiar Gamma-ray Burst, GRB 200826A, poses a
dilemma for the collapsar model. Although all other characteristics of the
burst are consistent with it being a Type II (i.e., collapse of a massive star)
event, the observed duration of the event is only approximately one second,
which is at odds with the predicted allowable timescale range for a collapsar
event. To resolve this dilemma, this Letter proposes that the original burst
could be an intrinsically long GRB comprised of a precursor and a main emission
(ME) phase. However, the ME phase is missed due to either precession of the jet
or the blockage of a companion star, leaving only the precursor observed as a
short-duration GRB 200826A. Interestingly, we found that the temporal and
spectral properties of GRB 200826A broadly resembled those of the bright
precursor observed in GRB 160625B. Furthermore, assuming the prototype burst of
GRB 200826A is similar to GRB 160625B, we found that the observer may indeed
miss its ME because of geometric effects caused either by jet precession or
companion blockage models. Our approach provides a natural explanation for the
GRB 200826A-like bursts and agrees with the rarity of those events.


title: Diffusion equations with general nonlocal time and space derivatives

abstract:
  In the present study, firstly, based on the continuous time random walk
(CTRW) theory, general diffusion equations are derived. The time derivative is
taken as the general Caputo-type derivative introduced by Kochubei and the
spatial derivative is the general Laplacian defined by removing the conditions
(1.5) and (1.6) from the definition of the general fractional Laplacian
proposed in the paper (Servadei and Valdinoci, 2012). Secondly, the existence
of solutions of the Cauchy problem for the general diffusion equation is proved
by extending the domain of the general Laplacian to a general Sobolev space.
The results for positivity and boundedness of the solutions are also obtained.
In the last, the existence result for solutions of the initial boundary value
problem (IBVP) for the general diffusion equation on a bounded domain is
established by using the Friedrichs extension of the general fractional
Laplacian introduced in the book (Bisci, Radulescu and Servadei, 2016).


title: Observation of a Critical Charge Mode in a Strange Metal

abstract:
  Quantum electronic matter has long been understood in terms of two limiting
behaviors of electrons: one of delocalized metallic states, and the other of
localized magnetic states. Understanding the strange metallic behavior which
develops at the brink of localization demands new probes of the underlying
electronic charge dynamics. Using a state-of-the-art technique,
synchrotron-radiation-based Mossbauer spectroscopy, we have studied the
longitudinal charge fluctuations of the strange metal phase of beta-YbAlB4 as a
function of temperature and pressure. We find that the usual single absorption
peak in the Fermi-liquid regime splits into two peaks upon entering the
critical regime. This spectrum is naturally interpreted as a single nuclear
transition, modulated by nearby electronic valence fluctuations whose long
time-scales are further enhanced, due to the formation of charged polarons. Our
results represent a direct observation of critical charge fluctuations as a new
signature of strange metals.


title: Construction of a non ultra-static Ellis wormhole in general relativity

abstract:
  Several traversable wormholes (T-WHs) of the Morris-Thorne type have been
presented as exact solutions of Einstein-nonlinear electrodynamics gravity
(GR-NLED), e.g. \cite{Arellano,Bronnikov2017, Bronnikov_Walia, Canate_Breton,
Canate_Breton_Ortiz, Canate_Magos_Breton}. However, none of these solutions is
support by a nonlinear electrodynamics model satisfying plausible conditions.
In this work, the first traversable wormhole solution of Einstein-nonlinear
electrodynamics gravity coupled to a self-interacting scalar field (GR-NLED-SF)
and with nonlinear electrodynamics model such that in the limit of weak field
becomes the Maxwell electrodynamics, is presented.


title:
Astrophysical Plasma Instabilities induced by Long-Range Interacting
  Dark Matter

abstract:
  If dark matter (DM) is millicharged or darkly charged, collective plasma
processes may dominate momentum exchange over direct particle collisions. In
particular, plasma streaming instabilities can couple the momentum of DM to
counter-streaming baryons or other DM and result in the counter-streaming
fluids coming to rest with each other, just as happens for baryonic
collisionless shocks in astrophysical systems. While electrostatic plasma
instabilities (such as the two stream) are highly suppressed by Landau damping
in the cosmological situations of interest, electromagnetic instabilities such
as the Weibel can couple the momenta. Their growth rates are slower than the
prior assumption that they would grow at the plasma frequency of DM. We find
that the streaming of DM in the pre-Recombination universe is affected more
strongly by direct collisions than collective processes, validating previous
constraints. However, when considering unmagnetized instabilities the
properties of the Bullet Cluster merger would be substantially altered if
$[q_\chi/m_\chi] \gtrsim 10^{-4}$, where $[q_\chi/m_\chi]$ is the
charge-to-mass ratio of DM relative to that of the proton. When a magnetic
field is added consistent with cluster observations, Weibel and Firehose
instabilities result in the constraint $[q_\chi/m_\chi] \gtrsim
10^{-12}-10^{-11}$. The constraints are even stronger in the case of a dark
$U(1)$ charge, ruling out $[q_\chi/m_\chi] \gtrsim 10^{-14}$ in the Bullet
Cluster system. The strongest previous limits on millicharged DM arise from
considering the spin down of galactic disks. We show that plasma instabilities
or tangled background magnetic fields could lead to diffusive propagation of
DM, weakening these spin down limits. Thus, our constraints from considering
plasma instabilities are the most stringent over much of the millicharged and
especially dark-charged parameter space.


abstract:
  In this work, the generalization of Friedel formula and Krein's theorem in
complex potential scattering theory is presented. The consequence of various
symmetry constraints on dynamical system are discussed. In addition,
Muskhelishvili-Omn\`es representation of Krein's theorem is also given and
discussed.


title:
A Data-Driven Column Generation Algorithm For Bin Packing Problem in
  Manufacturing Industry

abstract:
  The bin packing problem exists widely in real logistic scenarios (e.g.,
packing pipeline, express delivery), with its goal to improve the packing
efficiency and reduce the transportation cost. In this NP-hard combinatorial
optimization problem, the position and quantity of each item in the box are
strictly restricted by complex constraints and special customer requirements.
Existing approaches are hard to obtain the optimal solution since rigorous
constraints cannot be handled within a reasonable computation load. In this
paper, for handling this difficulty, the packing knowledge is extracted from
historical data collected from the packing pipeline of Huawei. First, by fully
exploiting the relationship between historical packing records and input
orders(orders to be packed) , the problem is reformulated as a set cover
problem. Then, two novel strategies, the constraint handling and process
acceleration strategies are applied to the classic column generation approach
to solve this set cover problem. The cost of solving pricing problem for
generating new columns is high due to the complex constraints and customer
requirements. The proposed constraints handling strategy exploits the
historical packing records with the most negative value of the reduced cost.
Those constraints have been implicitly satisfied in these historical packing
records so that there is no need to conduct further evaluation on constraints,
thus the computational load is saved. To further eliminate the iteration
process of column generation algorithm and accelerate the optimization process,
a Learning to Price approach called Modified Pointer Network is proposed, by
which we can determine which historical packing records should be selected
directly. Through experiments on realworld datasets, we show our proposed
method can improve the packing success rate and decrease the computation time
simultaneously.


title: An enhanced six-functor formalism for diamonds and v-stacks

abstract:
  This article extends Scholze's six functor formalism for diamonds to a very
general class of stacky morphisms between v-stacks, using $\infty$-categorical
techniques developed by Liu-Zheng.


title:
A residual-based message passing algorithm for constraint satisfaction
  problems

abstract:
  Message passing algorithms, whose iterative nature captures well complicated
interactions among interconnected variables in complex systems and extracts
information from the fixed point of iterated messages, provide a powerful
toolkit in tackling hard computational tasks in optimization, inference, and
learning problems. In the context of constraint satisfaction problems (CSPs),
when a control parameter (such as constraint density) is tuned, multiple
threshold phenomena emerge, signaling fundamental structural transitions in
their solution space. Finding solutions around these transition points is
exceedingly challenging for algorithm design, where message passing algorithms
suffer from a large message fluctuation far from convergence. Here we introduce
a residual-based updating step into message passing algorithms, in which
messages varying large between consecutive steps are given high priority in the
updating process. For the specific example of model RB, a typical prototype of
random CSPs with growing domains, we show that our algorithm improves the
convergence of message updating and increases the success probability in
finding solutions around the satisfiability threshold with a low computational
cost. Our approach to message passing algorithms should be of value for
exploring their power in developing algorithms to find ground-state solutions
and understand the detailed structure of solution space of hard optimization
problems.


title:
Spectrally separable photon-pair generation in dispersion engineered
  thin-film lithium niobate

abstract:
  Existing nonlinear-optic implementations of pure, unfiltered heralded
single-photon sources do not offer the scalability required for densely
integrated quantum networks. Additionally, lithium niobate has hitherto been
unsuitable for such use due to its material dispersion. We engineer the
dispersion and the quasi-phasematching conditions of a waveguide in the rapidly
emerging thin-film lithium niobate platform to generate spectrally separable
photon pairs in the telecommunications band. Such photon pairs can be used as
spectrally pure heralded single-photon sources in quantum networks. We estimate
a heralded-state spectral purity of ${>}94\%$ based on joint spectral intensity
measurements. Further, a joint spectral phase-sensitive measurement of the
unheralded time-integrated second-order correlation function yields a
heralded-state purity of $(86 \pm 5)\%$.


title: Multi-Phase ISM in the z = 5.7 Hyperluminous Starburst SPT0346-52

abstract:
  SPT0346-52 (z=5.7) is the most intensely star-forming galaxy discovered by
the South Pole Telescope, with Sigma_SFR ~ 4200 Msol yr^-1 kpc^-2. In this
paper, we expand on previous spatially-resolved studies, using ALMA
observations of dust continuum, [NII]205 micron, [CII]158 micron, [OI]146
micron, and undetected [NII]122 micron and [OI]63 micron emission to study the
multi-phase interstellar medium (ISM) in SPT0346-52. We use pixelated,
visibility-based lens modeling to reconstruct the source-plane emission. We
also model the source-plane emission using the photoionization code CLOUDY and
find a supersolar metallicity system. We calculate T_dust = 48.3 K and
lambda_peak = 80 micron, and see line deficits in all five lines. The ionized
gas is less dense than comparable galaxies, with n_e < 32 cm^-3, while ~20% of
the [CII]158 emission originates from the ionized phase of the ISM. We also
calculate the masses of several phases of the ISM. We find that molecular gas
dominates the mass of the ISM in SPT0346-52, with the molecular gas mass ~4x
higher than the neutral atomic gas mass and ~100x higher than the ionized gas
mass.


title:
Laboratory observation of plasmoid-dominated magnetic reconnection in
  hybrid collisional-collisionless regime

abstract:
  Magnetic reconnection, breaking and reorganization of magnetic field
topology, is a fundamental process for rapid release of magnetic energy into
plasma particles that occurs pervasively throughout the universe. In most
natural circumstances, the plasma properties on either side of the reconnection
layer are asymmetric, in particular for the collision rates that are associated
with a combination of density and temperature and critically determine the
reconnection mechanism. To date, all laboratory experiments on magnetic
reconnections have been limited to purely collisional or collisionless regimes.
Here, we report a well-designed experimental investigation on asymmetric
magnetic reconnections in a novel hybrid collisional-collisionless regime by
interactions between laser-ablated Cu and CH plasmas. We show that the growth
rate of the tearing instability in such a hybrid regime is still extremely
large, resulting in rapid formation of multiple plasmoids, lower than that in
the purely collisionless regime but much higher than the collisional case. In
addition, we, for the first time, directly observe the topology evolutions of
the whole process of plasmoid-dominated magnetic reconnections by using
highly-resolved proton radiography.


title: Bidding Agent Design in the LinkedIn Ad Marketplace

abstract:
  We establish a general optimization framework for the design of automated
bidding agent in dynamic online marketplaces. It optimizes solely for the
buyer's interest and is agnostic to the auction mechanism imposed by the
seller. As a result, the framework allows, for instance, the joint optimization
of a group of ads across multiple platforms each running its own auction
format. Bidding strategy derived from this framework automatically guarantees
the optimality of budget allocation across ad units and platforms. Common
constraints such as budget delivery schedule, return on investments and
guaranteed results, directly translates to additional parameters in the bidding
formula. We share practical learnings of the deployed bidding system in the
LinkedIn ad marketplace based on this framework.


title:
MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface
  Aided Radar Systems

abstract:
  As a widely used localization and sensing technique, radars will play an
important role in future wireless networks. However, the wireless channels
between the radar and the targets are passively adopted by traditional radars,
which limits the performance of target detection. To address this issue, we
propose to use the reconfigurable intelligent surface (RIS) to improve the
detection accuracy of radar systems due to its capability to customize channel
conditions by adjusting its phase shifts, which is referred to as MetaRadar. In
such a system, it is challenging to jointly optimize both radar waveforms and
RIS phase shifts in order to improve the multi-target detection performance. To
tackle this challenge, we design a waveform and phase shift optimization (WPSO)
algorithm to effectively solve the multi-target detection problem, and also
analyze the performance of the proposed MetaRadar scheme theoretically.
Simulation results show that the detection performance of the MetaRadar scheme
is significantly better than that of the traditional radar schemes.


title:
Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self
  Disentanglement

abstract:
  Cycle reconstruction regularized adversarial training -- e.g., CycleGAN,
DiscoGAN, and DualGAN -- has been widely used for image style transfer with
unpaired training data. Several recent works, however, have shown that local
distortions are frequent, and structural consistency cannot be guaranteed.
Targeting this issue, prior works usually relied on additional segmentation or
consistent feature extraction steps that are task-specific. To counter this,
this work aims to learn a general add-on structural feature extractor, by
explicitly enforcing the structural alignment between an input and its
synthesized image. Specifically, we propose a novel input-output image patches
self-training scheme to achieve a disentanglement of underlying anatomical
structures and imaging modalities. The translator and structure encoder are
updated, following an alternating training protocol. In addition, the
information w.r.t. imaging modality can be eliminated with an asymmetric
adversarial game. We train, validate, and test our network on 1,768, 416, and
1,560 unpaired subject-independent slices of tagged and cine magnetic resonance
imaging from a total of twenty healthy subjects, respectively, demonstrating
superior performance over competing methods.


title:
From spherical to periodic symmetry: the analog of orbital angular
  momentum for semiconductor crystals

abstract:
  The angular momentum formalism provides a powerful way to classify atomic
states. Yet, requiring a spherical symmetry from the very first line, this
formalism cannot be used for periodic systems, even though cubic semiconductor
states are commonly classified according to atomic notations. Although never
noted, it is possible to define the analog of the orbital angular momentum, by
only using the potential felt by the electrons. The spin-orbit interaction for
crystals then takes the $\mathbfcal{\hat{L}}\cdot \hat{\vS}$ form, with
$\mathbfcal{\hat{L}}$ reducing to $\hat{\vL}=\vr\times\hat{\vp}$ for spherical
symmetry. This provides the long-missed support for using the eigenvalues of
$\mathbfcal{\hat{L}}$ and $\mathbfcal{\hat{J}}=\mathbfcal{\hat{L}}+\hat{\vS}$,
as quantum indices to label cubic semiconductor states. Importantly, these
quantum indices also control the phase factor that relates valence electron to
hole operators, in the same way as particle to antiparticle, in spite of the
fact that the hole is definitely not the valence-electron antiparticle. Being
associated with a broader definition, the
($\mathbfcal{\hat{L}},\mathbfcal{\hat{J}}$) analogs of the
$(\hat{\vL},\hat{\vJ})$ angular momenta, must be distinguished by names: we
suggest "spatial momentum" for $\mathbfcal{\hat{L}}$ that acts in the real
space, and "hybrid momentum" for $\mathbfcal{\hat{J}}$ that also acts on spin,
the potential symmetry being specified as "cubic spatial momentum". This would
cast $\hat{\vJ}$ as a "spherical hybrid momentum", a bit awkward for the
concept is novel.


title:
Electroweak Phase Transition in the $Z_3$-invariant NMSSM Implications
  of LHC and Dark matter Searches and Prospects of Detecting the Gravitational
  Waves

abstract:
  We study in detail the viability and the patterns of a strong first-order
electroweak phase transition as a prerequisite to electroweak baryogenesis in
the framework of $Z_3$-invariant Next-to-Minimal Supersymmetric Standard Model
(NMSSM), in the light of recent experimental results from the Higgs sector,
dark matter (DM) searches and those from the searches of the lighter chargino
and neutralinos at the Large Hadron Collider (LHC). For the latter, we
undertake thorough recasts of the relevant, recent LHC analyses. With the help
of a few benchmark scenarios, we demonstrate that while the LHC has started to
eliminate regions of the parameter space with relatively small
$\mu_\mathrm{eff}$, that favors the coveted strong first-order phase
transition, rather steadily, there remains phenomenologically much involved and
compatible regions of the same which are yet not sensitive to the current LHC
analyses. It is further noted that such a region could also be compatible with
all pertinent theoretical and experimental constraints. We then proceed to
analyze the prospects of detecting the stochastic gravitational waves, which
are expected to arise from such a phase transition, at various future/proposed
experiments, within the mentioned theoretical framework and find them to be
somewhat ambitious under the currently projected sensitivities of those
experiments.


title:
HipBone: A performance-portable GPU-accelerated C++ version of the
  NekBone benchmark

abstract:
  We present hipBone, an open source performance-portable proxy application for
the Nek5000 (and NekRS) CFD applications. HipBone is a fully GPU-accelerated
C++ implementation of the original NekBone CPU proxy application with several
novel algorithmic and implementation improvements which optimize its
performance on modern fine-grain parallel GPU accelerators. Our optimizations
include a conversion to store the degrees of freedom of the problem in
assembled form in order to reduce the amount of data moved during the main
iteration and a portable implementation of the main Poisson operator kernel. We
demonstrate near-roofline performance of the operator kernel on three different
modern GPU accelerators from two different vendors. We present a novel
algorithm for splitting the application of the Poisson operator on GPUs which
aggressively hides MPI communication required for both halo exchange and
assembly. Our implementation of nearest-neighbor MPI communication then
leverages several different routing algorithms and GPU-Direct RDMA
capabilities, when available, which improves scalability of the benchmark. We
demonstrate the performance of hipBone on three different clusters housed at
Oak Ridge National Laboratory, namely the Summit supercomputer and the Frontier
early-access clusters, Spock and Crusher. Our tests demonstrate both
portability across different clusters and very good scaling efficiency,
especially on large problems.


title:
GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News
  Detection

abstract:
  Social media in present times has a significant and growing influence. Fake
news being spread on these platforms have a disruptive and damaging impact on
our lives. Furthermore, as multimedia content improves the visibility of posts
more than text data, it has been observed that often multimedia is being used
for creating fake content. A plethora of previous multimodal-based work has
tried to address the problem of modeling heterogeneous modalities in
identifying fake content. However, these works have the following limitations:
(1) inefficient encoding of inter-modal relations by utilizing a simple
concatenation operator on the modalities at a later stage in a model, which
might result in information loss; (2) training very deep neural networks with a
disproportionate number of parameters on small but complex real-life multimodal
datasets result in higher chances of overfitting. To address these limitations,
we propose GAME-ON, a Graph Neural Network based end-to-end trainable framework
that allows granular interactions within and across different modalities to
learn more robust data representations for multimodal fake news detection. We
use two publicly available fake news datasets, Twitter and Weibo, for
evaluations. Our model outperforms on Twitter by an average of 11% and keeps
competitive performance on Weibo, within a 2.6% margin, while using 65% fewer
parameters than the best comparable state-of-the-art baseline.