title: The strong Spector-Gandy Theorem for the higher analytical pointclasses

abstract:
  Assuming projective determinacy, we extend Spector's strong version of the
Spector-Gandy Theorem to all odd levels of the projective hierarchy:
  Theorem. For every space $X$ which is a finite product of the natural numbers
$N$ and Baire space $N^N$ and for every n, if $P$ is a $\Pi^1_{2n+1}$ subset of
$X$, then there is a $\Pi^1_{2n}$ set $Q$ such that
  $P(x) \Longleftrightarrow (\exists!\alpha)Q(x,\alpha) \Longleftrightarrow
(\exists\alpha\in\Delta^1_{2n+1}(x))Q(x,\alpha)$.


title: Online Optimization with Untrusted Predictions

abstract:
  We examine the problem of online optimization, where a decision maker must
sequentially choose points in a general metric space to minimize the sum of
per-round, non-convex hitting costs and the costs of switching decisions
between rounds. The decision maker has access to a black-box oracle, such as a
machine learning model, that provides untrusted and potentially inaccurate
predictions of the optimal decision in each round. The goal of the decision
maker is to exploit the predictions if they are accurate, while guaranteeing
performance that is not much worse than the hindsight optimal sequence of
decisions, even when predictions are inaccurate. We impose the standard
assumption that hitting costs are globally $\alpha$-polyhedral. We propose a
novel algorithm, Adaptive Online Switching (AOS), and prove that, for any
desired $\delta > 0$, it is $(1+2\delta)$-competitive if predictions are
perfect, while also maintaining a uniformly bounded competitive ratio of
$2^{\tilde{\mathcal{O}}(1/(\alpha \delta))}$ even when predictions are
adversarial. Further, we prove that this trade-off is necessary and nearly
optimal in the sense that any deterministic algorithm which is
$(1+\delta)$-competitive if predictions are perfect must be at least
$2^{\tilde{\Omega}(1/(\alpha \delta))}$-competitive when predictions are
inaccurate.


title:
Stakeholder utility measures for declarative processes and their use in
  process comparisons

abstract:
  We present a method for calculating and analyzing stakeholder utilities of
processes that arise in, but are not limited to, the social sciences. These
areas include business process analysis, healthcare workflow analysis and
policy process analysis. This method is quite general and applicable to any
situation in which declarative-type constraints of a modal and/or temporal
nature play a part.
  A declarative process is a process in which activities may freely happen
while respecting a set of constraints. For such a process, anything may happen
so long as it is not explicitly forbidden. Declarative processes have been used
and studied as models of business and healthcare workflows by several authors.
In considering a declarative process as a model of some system it is natural to
consider how the process behaves with respect to stakeholders. We derive a
measure for stakeholder utility that can be applied in a very general setting.
This derivation is achieved by listing a collection a properties which we argue
such a stakeholder utility function ought to satisfy, and then using these to
show a very specific form must hold for such a utility. The utility measure
depends on the set of unique traces of the declarative process, and calculating
this set requires a combinatorial analysis of the declarative graph that
represents the process.
  This builds on previous work of the author wherein the combinatorial
diversity metrics for declarative processes were derived for use in policy
process analysis. The collection of stakeholder utilities can themselves then
be used to form a metric with which we can compare different declarative
processes to one another. These are illustrated using several examples of
declarative processes that already exist in the literature.


title:
High D/H ratios in water and alkanes in comet 67P/Churyumov-Gerasimenko
  measured with the Rosetta/ROSINA DFMS

abstract:
  Isotopic abundances in comets are key to understanding the history and origin
of material in the Solar System. Deuterium-to-hydrogen (D/H) ratios in water
are available for several comets. However, no long-term studies of the D/H
ratio in water of a comet during its passage around the Sun have been reported.
Linear alkanes are important organic molecules, which have been found on
several Solar System bodies, including comets. To date, only upper limits of
isotopic ratios for D/H and 13C/12C in linear alkanes are available.
  The aim of this work is a detailed analysis of the D/H ratio in water during
the whole Rosetta mission. In addition, a first determination of the D/H and
13C/12C ratios in the first four linear alkanes in the coma of
67P/Churyumov-Gerasimenko is provided. We analysed in situ measurements from
the Rosetta/ROSINA Double Focusing Mass Spectrometer (DFMS).
  The D/H ratio from HDO/H2O and the 16O/17O ratio from H216O/H217O did not
change during 67P's passage around the Sun between 2014 and 2016. All D/H ratio
measurements were compatible, within 1$\sigma$, with the mean value of
$5.01\times10^{-4}$ and its relative variation of 2.0%. This suggests that the
D/H ratio in 67P's coma is independent of heliocentric distance, level of
cometary activity, as well as spacecraft location with respect to the nucleus.
Additionally, the 16O/17O ratio could be determined with a higher accuracy than
previously possible, yielding a value of 2347 with a relative variation of
2.3%. For the alkanes, the D/H ratio is between 4.1 and 4.8 times higher than
in H2O, while the 13C/12C ratio is compatible, within uncertainties, with data
for other Solar System objects. The relatively high D/H ratio in alkanes is in
line with other cometary organic molecules and suggests that these organics may
be inherited from the presolar molecular cloud from which the Solar System
formed.


title:
Slight excess at 130 GeV in search for a charged Higgs boson decaying to
  a charm quark and a bottom quark at the Large Hadron Collider

abstract:
  Searches for a charged Higgs boson ($H^\pm$) decaying to a charm quark and a
bottom quark ($H^\pm \to cb$) have been carried out at the Large Hadron
Collider (LHC) in the decay of top quarks ($t\to H^\pm b$). In a recent search
by the ATLAS collaboration (with all Run II data, 139 fb$^{-1}$) a local excess
of around $3\sigma$ has been observed, which is best fitted by a charged Higgs
boson with a mass ($m_{H^\pm}$) of around 130 GeV and a product of branching
ratios (BRs) given by BR$(t\to H^\pm b)\times{\rm BR}(H^\pm\to cb)=0.16\%\pm
0.06\%$. In the context of Two-Higgs-Doublet Models (2HDM) with independent
Yukawa couplings for $H^\pm$ we present the parameter space for which this
excess (assuming it to be genuine) can be accommodated, taking into account the
limits from LHC searches for $H^\pm\to cs$ and $H^\pm\to \tau\nu$ at
$m_{H^\pm}$=130 GeV and the constraint from $b\to s\gamma$. It is then shown
that such an excess cannot be explained in 2HDMs with natural flavour
conservation, but can be accommodated in the flipped Three-Higgs-Doublet Model
(3HDM) and in the aligned 2HDM (A2HDM). Upcoming searches with 139 fb$^{-1}$ in
the channels $H^\pm\to cb$ (CMS), $H^\pm \to cs$ (ATLAS/CMS) and $H^\pm \to
\tau\nu$ (ATLAS/CMS) will determine if the excess is the first sign of an
$H^\pm$ with $m_{H^\pm}=130$ GeV.


title: Resource Marginal Problems

abstract:
  We introduce the resource marginal problems, which concern the possibility of
having a resource-free target subsystem compatible with a given collection of
marginal density matrices. By identifying an appropriate choice of resource $R$
and target subsystem T, our problems reduce, respectively, to the well-known
marginal problems for quantum states and the problem of determining if a given
quantum system is a resource. More generally, we define a set of marginal
states to be resource-free incompatible with a target subsystem T if all global
states compatible with this set must result in a resourceful state in T. We
show that this incompatibility induces a resource theory that can be quantified
by a monotone. We derive necessary and sufficient conditions for this monotone
to be a finite conic program and further show, via the corresponding witnesses,
that resource-free incompatibility is equivalent to an operational advantage in
some subchannel discrimination task. Via our framework, a clear connection can
be established between any marginal problem (that involves some notion of
incompatibility) for quantum states and a resource theory for quantum states.
In addition, the universality of our framework leads, for example, to an
immediate, quantitative understanding of the incompatibility associated with
the recently-proposed entanglement marginal problems. As further applications,
we provide the first example showing a form of transitivity of nonlocality
(steerability) for quantum states. We discuss also the analogous framework in
the dynamical regime and establish a set of theoretical results that closely
mirror those obtained for a resource theory for quantum states. Again, they
allow us to recover a wide variety of results related to the studies of channel
marginal problems, channel resource theories, etc.


title:
Finite-Sum Optimization: A New Perspective for Convergence to a Global
  Solution

abstract:
  Deep neural networks (DNNs) have shown great success in many machine learning
tasks. Their training is challenging since the loss surface of the network
architecture is generally non-convex, or even non-smooth. How and under what
assumptions is guaranteed convergence to a \textit{global} minimum possible? We
propose a reformulation of the minimization problem allowing for a new
recursive algorithmic framework. By using bounded style assumptions, we prove
convergence to an $\varepsilon$-(global) minimum using
$\mathcal{\tilde{O}}(1/\varepsilon^3)$ gradient computations. Our theoretical
foundation motivates further study, implementation, and optimization of the new
algorithmic framework and further investigation of its non-standard bounded
style assumptions. This new direction broadens our understanding of why and
under what circumstances training of a DNN converges to a global minimum.


title: Nesterov Accelerated Shuffling Gradient Method for Convex Optimization

abstract:
  In this paper, we propose Nesterov Accelerated Shuffling Gradient (NASG), a
new algorithm for the convex finite-sum minimization problems. Our method
integrates the traditional Nesterov's acceleration momentum with different
shuffling sampling schemes. We show that our algorithm has an improved rate of
$\mathcal{O}(1/T)$ using unified shuffling schemes, where $T$ is the number of
epochs. This rate is better than that of any other shuffling gradient methods
in convex regime. Our convergence analysis does not require an assumption on
bounded domain or a bounded gradient condition. For randomized shuffling
schemes, we improve the convergence bound further. When employing some initial
condition, we show that our method converges faster near the small neighborhood
of the solution. Numerical simulations demonstrate the efficiency of our
algorithm.


title:
Computation of lattice isomorphisms and the integral matrix similarity
  problem

abstract:
  Let $K$ be a number field, let $A$ be a finite-dimensional $K$-algebra, let
$\mathrm{J}(A)$ denote the Jacobson radical of $A$, and let $\Lambda$ be an
$\mathcal{O}_{K}$-order in $A$. Suppose that each simple component of the
semisimple $K$-algebra $A/{\mathrm{J}(A)}$ is isomorphic to a matrix ring over
a field. Under this hypothesis on $A$, we give an efficient algorithm that
given two $\Lambda$-lattices $X$ and $Y$, determines whether $X$ and $Y$ are
isomorphic, and if so, computes an explicit isomorphism $X \rightarrow Y$. As
an application, we give an efficient algorithm for the following long-standing
problem: given a number field $K$, a positive integer $n$ and two matrices $A,B
\in \mathrm{Mat}_{n}(\mathcal{O}_{K})$, determine whether $A$ and $B$ are
similar over $\mathcal{O}_{K}$, and if so, return a matrix $C \in
\mathrm{GL}_{n}(\mathcal{O}_{K})$ such that $B= CAC^{-1}$. We give explicit
examples that show that the implementation of the latter algorithm for
$\mathcal{O}_{K}=\mathbb{Z}$ vastly outperforms implementations of all previous
algorithms, as predicted by our complexity analysis.


title: Integrated Multiscale Domain Adaptive YOLO

abstract:
  The area of domain adaptation has been instrumental in addressing the domain
shift problem encountered by many applications. This problem arises due to the
difference between the distributions of source data used for training in
comparison with target data used during realistic testing scenarios. In this
paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO)
framework that employs multiple domain adaptation paths and corresponding
domain classifiers at different scales of the recently introduced YOLOv4 object
detector. Building on our baseline multiscale DAYOLO framework, we introduce
three novel deep learning architectures for a Domain Adaptation Network (DAN)
that generates domain-invariant features. In particular, we propose a
Progressive Feature Reduction (PFR), a Unified Classifier (UC), and an
Integrated architecture. We train and test our proposed DAN architectures in
conjunction with YOLOv4 using popular datasets. Our experiments show
significant improvements in object detection performance when training YOLOv4
using the proposed MS-DAYOLO architectures and when tested on target data for
autonomous driving applications. Moreover, MS-DAYOLO framework achieves an
order of magnitude real-time speed improvement relative to Faster R-CNN
solutions while providing comparable object detection performance.


title: TACTiS: Transformer-Attentional Copulas for Time Series

abstract:
  The estimation of time-varying quantities is a fundamental component of
decision making in fields such as healthcare and finance. However, the
practical utility of such estimates is limited by how accurately they quantify
predictive uncertainty. In this work, we address the problem of estimating the
joint predictive distribution of high-dimensional multivariate time series. We
propose a versatile method, based on the transformer architecture, that
estimates joint distributions using an attention-based decoder that provably
learns to mimic the properties of non-parametric copulas. The resulting model
has several desirable properties: it can scale to hundreds of time series,
supports both forecasting and interpolation, can handle unaligned and
non-uniformly sampled data, and can seamlessly adapt to missing data during
training. We demonstrate these properties empirically and show that our model
produces state-of-the-art predictions on several real-world datasets.


title: An Anticipative Markov Modulated Market

abstract:
  A Markovian modulation captures the trend in the market and influences the
market coefficients accordingly. The different scenarios presented by the
market are modeled as the distinct states of a discrete-time Markov chain. In
our paper, we assume the existence of such modulation in a market and, as a
novelty, we assume that it can be anticipative with respect to the future of
the Brownian motion that drives the dynamics of the risky asset. By employing
these own techniques of enlargement of filtrations, we solve an optimal
portfolio utility problem in both a complete and an incomplete market. Many
examples of anticipative Markov chains are presented for which we compute the
additional gain of the investor who has a more accurate information.


title:
Finite-Volume Pionless Effective Field Theory for Few-Nucleon Systems
  with Differentiable Programming

abstract:
  Finite-volume pionless effective field theory provides an efficient framework
for the extrapolation of nuclear spectra and matrix elements calculated at
finite volume in lattice QCD to infinite volume, and to nuclei with larger
atomic number. In this work, it is demonstrated how this framework may be
implemented via a set of correlated Gaussian wavefunctions optimised using
differentiable programming and via solution of a generalised eigenvalue
problem. This approach is shown to be significantly more efficient than a
stochastic implementation of the variational method based on the same form of
correlated Gaussian wavefunctions, yielding comparably accurate representations
of the ground-state wavefunctions with an order of magnitude fewer terms. The
efficiency of representation allows such calculations to be extended to larger
systems than in previous work. The method is demonstrated through calculations
of the binding energies of nuclei with atomic number $A\in\{2,3,4\}$ in finite
volume, matched to lattice QCD calculations at quark masses corresponding to
$m_\pi=806$ MeV, and infinite-volume effective field theory calculations of
$A\in\{2,3,4,5,6\}$ systems based on this matching.


title:
Estimands and their Estimators for Clinical Trials Impacted by the
  COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on
  Unplanned Clinical Trial Disruptions

abstract:
  The COVID-19 pandemic continues to affect the conduct of clinical trials
globally. Complications may arise from pandemic-related operational challenges
such as site closures, travel limitations and interruptions to the supply chain
for the investigational product, or from health-related challenges such as
COVID-19 infections. Some of these complications lead to unforeseen
intercurrent events in the sense that they affect either the interpretation or
the existence of the measurements associated with the clinical question of
interest. In this article, we demonstrate how the ICH E9(R1) Addendum on
estimands and sensitivity analyses provides a rigorous basis to discuss
potential pandemic-related trial disruptions and to embed these disruptions in
the context of study objectives and design elements. We introduce several
hypothetical estimand strategies and review various causal inference and
missing data methods, as well as a statistical method that combines unbiased
and possibly biased estimators for estimation. To illustrate, we describe the
features of a stylized trial, and how it may have been impacted by the
pandemic. This stylized trial will then be re-visited by discussing the changes
to the estimand and the estimator to account for pandemic disruptions. Finally,
we outline considerations for designing future trials in the context of
unforeseen disruptions.


title: MINER: Multiscale Implicit Neural Representations

abstract:
  We introduce a new neural signal representation designed for the efficient
high-resolution representation of large-scale signals. The key innovation in
our multiscale implicit neural representation (MINER) is an internal
representation via a Laplacian pyramid, which provides a sparse multiscale
representation of the signal that captures orthogonal parts of the signal
across scales. We leverage the advantages of the Laplacian pyramid by
representing small disjoint patches of the pyramid at each scale with a tiny
MLP. This enables the capacity of the network to adaptively increase from
coarse to fine scales, and only represent parts of the signal with strong
signal energy. The parameters of each MLP are optimized from coarse-to-fine
scale which results in faster approximations at coarser scales, thereby
ultimately an extremely fast training process. We apply MINER to a range of
large-scale signal representation tasks, including gigapixel images and very
large point clouds, and demonstrate that it requires fewer than 25% of the
parameters, 33% of the memory footprint, and 10% of the computation time of
competing techniques such as ACORN to reach the same representation error.


title:
Causal inference from treatment-control studies having an additional
  factor with unknown assignment mechanism

abstract:
  Consider a situation with two treatments, the first of which is randomized
but the second is not, and the multifactor version of this. Interest is in
treatment effects, defined using standard factorial notation. We define
estimators for the treatment effects and explore their properties when there is
information about the nonrandomized treatment assignment and when there is no
information on the assignment of the nonrandomized treatment. We show when and
how hidden treatments can bias estimators and inflate their sampling variances.


title:
Metagenomic Analysis using Phylogenetic Placement -- A Review of the
  First Decade

abstract:
  Phylogenetic placement refers to a family of tools and methods to analyze,
visualize, and interpret the tsunami of metagenomic sequencing data generated
by high-throughput sequencing. Compared to alternative (e. g.,
similarity-based) methods, it puts metabarcoding sequences into a phylogenetic
context using a set of known reference sequences and taking evolutionary
history into account. Thereby, one can increase the accuracy of metagenomic
surveys and eliminate the requirement for having exact or close matches with
existing sequence databases. Phylogenetic placement constitutes a valuable
analysis tool per se, but also entails a plethora of downstream tools to
interpret its results. A common use case is to analyze species communities
obtained from metagenomic sequencing, for example via taxonomic assignment,
diversity quantification, sample comparison, and identification of correlations
with environmental variables. In this review, we provide an overview over the
methods developed during the first ten years. In particular, the goals of this
review are (i) to motivate the usage of phylogenetic placement and illustrate
some of its use cases, (ii) to outline the full workflow, from raw sequences to
publishable figures, including best practices, (iii) to introduce the most
common tools and methods and their capabilities, (iv) to point out common
placement pitfalls and misconceptions,(v) to showcase typical placement-based
analyses, and how they can help to analyze, visualize, and interpret
phylogenetic placement data.


title: Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably

abstract:
  We investigate the role of noise in optimization algorithms for learning
over-parameterized models. Specifically, we consider the recovery of a rank one
matrix $Y^*\in R^{d\times d}$ from a noisy observation $Y$ using an
over-parameterization model. We parameterize the rank one matrix $Y^*$ by
$XX^\top$, where $X\in R^{d\times d}$. We then show that under mild conditions,
the estimator, obtained by the randomly perturbed gradient descent algorithm
using the square loss function, attains a mean square error of $O(\sigma^2/d)$,
where $\sigma^2$ is the variance of the observational noise. In contrast, the
estimator obtained by gradient descent without random perturbation only attains
a mean square error of $O(\sigma^2)$. Our result partially justifies the
implicit regularization effect of noise when learning over-parameterized
models, and provides new understanding of training over-parameterized neural
networks.


title:
Commentary on Four New DFIT Papers: (a) In-Situ Measurements from EGS
  Collab; (b) Comparison of Techniques at the Bedretto Project; (c) Statistical
  Review of 62 DFITs; and (d) An Article Advocating the Tangent Method

abstract:
  This post provides commentary on recent four papers on diagnostic fracture
injection testing (DFIT). The first paper uses in-situ deformation measurements
to directly observe fractures opening and closing during fracture
injection-falloff tests (Guglielmi et al., 2022). The second compares various
stress measurement techniques in a series of fracture/injection tests from the
Bedretto project (Broker and Ma, 2022). The third statistically reviews results
from applying the interpretation procedure from McClure et al. (2019) to 62
DFITs across nine different shale plays (McClure et al., 2022). The fourth is
an op-ed written in JPT (Journal of Petroleum Technology) by an advocate of the
tangent method for estimating DFIT closure stress (Buijs, 2021; 2022).


title:
Short to Intermediate-range Structure , Transport , and Thermophysical
  Properties of LiF-NaF-ZrF4 Molten Salts

abstract:
  LiF-NaF-ZrF4 multicomponent molten salts are identified as promising
candidates for coolant salts in molten salt reactors and advanced
high-temperature reactors. Herein, ab-initio molecular dynamics (AIMD)
calculations were performed and compared with available experimental data to
assess the ability of polarizable ion models (PIM) to reproduce short to
intermediate-range structure, transport and thermophysical properties of the
LiF-NaF-ZrF4 salt mixtures. This study signifies the importance of accurate
salt structure generation for accurate prediction of transport and
thermophysical properties of multicomponent molten salts.
  Keywords: Multicomponent Molten salts, Ab-initio molecular dynamics,
Polarizable ion model, Fluorozirconate chains, Neoteric liquids simulation,
Ionic liquids, Thermophysical properties, Diffusion coefficients.


title: Negative energy states in pionic hydrogen

abstract:
  Probabilities of finding an antiparticle in an atom or ion containing a
particle of spin 1/2 or spin 0 are determined. The spin 1/2 case was previously
solved by Hans Bethe and his work is summarized. The spin 0 case is treated
numerically for an arbitrary atomic number and analytically for small atomic
numbers. The main tool for the spin 0 case is the Feshbach-Villars
representation of the Klein-Gordon equation.


title: Structured Time Series Prediction without Structural Prior

abstract:
  Time series prediction is a widespread and well studied problem with
applications in many domains (medical, geoscience, network analysis, finance,
econometry etc.). In the case of multivariate time series, the key to good
performances is to properly capture the dependencies between the variates.
Often, these variates are structured, i.e. they are localised in an abstract
space, usually representing an aspect of the physical world, and prediction
amounts to a form of diffusion of the information across that space over time.
Several neural network models of diffusion have been proposed in the
literature. However, most of the existing proposals rely on some a priori
knowledge on the structure of the space, usually in the form of a graph
weighing the pairwise diffusion capacity of its points. We argue that this
piece of information can often be dispensed with, since data already contains
the diffusion capacity information, and in a more reliable form than that
obtained from the usually largely hand-crafted graphs. We propose instead a
fully data-driven model which does not rely on such a graph, nor any other
prior structural information. We conduct a first set of experiments to measure
the impact on performance of a structural prior, as used in baseline models,
and show that, except at very low data levels, it remains negligible, and
beyond a threshold, it may even become detrimental. We then investigate,
through a second set of experiments, the capacity of our model in two respects:
treatment of missing data and domain adaptation.


title:
SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos
  using Convolutional Neural Networks

abstract:
  With the increasing number of online learning material in the web, search for
specific content in lecture videos can be time consuming. Therefore, automatic
slide extraction from the lecture videos can be helpful to give a brief
overview of the main content and to support the students in their studies. For
this task, we propose a deep learning method to detect slide transitions in
lectures videos. We first process each frame of the video by a heuristic-based
approach using a 2-D convolutional neural network to predict transition
candidates. Then, we increase the complexity by employing two 3-D convolutional
neural networks to refine the transition candidates. Evaluation results
demonstrate the effectiveness of our method in finding slide transitions.


title:
On Continuous Integration / Continuous Delivery for Automated Deployment
  of Machine Learning Models using MLOps

abstract:
  Model deployment in machine learning has emerged as an intriguing field of
research in recent years. It is comparable to the procedure defined for
conventional software development. Continuous Integration and Continuous
Delivery (CI/CD) have been shown to smooth down software advancement and speed
up businesses when used in conjunction with development and operations
(DevOps). Using CI/CD pipelines in an application that includes Machine
Learning Operations (MLOps) components, on the other hand, has difficult
difficulties, and pioneers in the area solve them by using unique tools, which
is typically provided by cloud providers. This research provides a more
in-depth look at the machine learning lifecycle and the key distinctions
between DevOps and MLOps. In the MLOps approach, we discuss tools and
approaches for executing the CI/CD pipeline of machine learning frameworks.
Following that, we take a deep look into push and pull-based deployments in
Github Operations (GitOps). Open exploration issues are also identified and
added, which may guide future study.


title:
Bijections between planar maps and planar linear normal $\lambda$-terms
  with connectivity condition

abstract:
  The enumeration of linear $\lambda$-terms has attracted quite some attention
recently, partly due to their link to combinatorial maps. Zeilberger and
Giorgetti (2015) gave a recursive bijection between planar linear normal
$\lambda$-terms and planar maps, which, when restricted to 2-connected
$\lambda$-terms (i.e., without closed sub-terms), leads to bridgeless planar
maps. Inspired by this restriction, Zeilberger and Reed (2019) conjectured that
3-connected planar linear normal $\lambda$-terms have the same counting formula
as bipartite planar maps. In this article, we settle this conjecture by giving
a direct bijection between these two families. Furthermore, using a similar
approach, we give a direct bijection between planar linear normal
$\lambda$-terms and planar maps, whose restriction to 2-connected
$\lambda$-terms leads to loopless planar maps. This bijection seems different
from that of Zeilberger and Giorgetti, even after taking the map dual. We also
explore enumerative consequences of our bijections.


title:
Self-Supervised Representation Learning for Speech Using Visual
  Grounding and Masked Language Modeling

abstract:
  In this paper, we describe our submissions to the ZeroSpeech 2021 Challenge
and SUPERB benchmark. Our submissions are based on the recently proposed
FaST-VGS model, which is a Transformer-based model that learns to associate raw
speech waveforms with semantically related images, all without the use of any
transcriptions of the speech. Additionally, we introduce a novel extension of
this model, FaST-VGS+, which is learned in a multi-task fashion with a masked
language modeling objective in addition to the visual grounding objective. On
ZeroSpeech 2021, we show that our models perform competitively on the ABX task,
outperform all other concurrent submissions on the Syntactic and Semantic
tasks, and nearly match the best system on the Lexical task. On the SUPERB
benchmark, we show that our models also achieve strong performance, in some
cases even outperforming the popular wav2vec2.0 model.


title: LwPosr: Lightweight Efficient Fine-Grained Head Pose Estimation

abstract:
  This paper presents a lightweight network for head pose estimation (HPE)
task. While previous approaches rely on convolutional neural networks, the
proposed network \textit{LwPosr} uses mixture of depthwise separable
convolutional (DSC) and transformer encoder layers which are structured in two
streams and three stages to provide fine-grained regression for predicting head
poses. The quantitative and qualitative demonstration is provided to show that
the proposed network is able to learn head poses efficiently while using less
parameter space. Extensive ablations are conducted using three open-source
datasets namely 300W-LP, AFLW2000, and BIWI datasets. To our knowledge, (1)
\textit{LwPosr} is the lightest network proposed for estimating head poses
compared to both keypoints-based and keypoints-free approaches; (2) it sets a
benchmark for both overperforming the previous lightweight network on mean
absolute error and on reducing number of parameters; (3) it is first of its
kind to use mixture of DSCs and transformer encoders for HPE. This approach is
suitable for mobile devices which require lightweight networks.


title:
Eigenstates of two-level systems in a single-mode quantum field: from
  quantum Rabi model to $N$-atom Dicke model

abstract:
  In the present paper we show that the Hamiltonian describing the resonant
interaction of $N$ two-level systems with a single-mode electromagnetic quantum
field in the Coulomb gauge can be diagonalized with a high degree of accuracy
using a simple basis set of states. This allows one to find an analytical
approximation for the eigenvectors and eigenvalues of the system, which
interpolates the numerical solution in a broad range of the coupling constant
values. In addition, the introduced basis states provide a regular way of
calculating the corrections and estimating the convergence to the exact
numerical solution. The obtained results are valid for both quantum Rabi model
($N = 1$) and the Dicke model for $N \geq 2$ atoms.


title:
Condensation transition in large deviations of self-similar Gaussian
  processes with stochastic resetting

abstract:
  We study the fluctuations of the area $A(t)= \int_0^t x(\tau)\, d\tau$ under
a self-similar Gaussian process (SGP) $x(\tau)$ with Hurst exponent $H>0$
(e.g., standard or fractional Brownian motion, or the random acceleration
process) that stochastically resets to the origin at rate $r$. Typical
fluctuations of $A(t)$ scale as $\sim \sqrt{t}$ for large $t$ and on this scale
the distribution is Gaussian, as one would expect from the central limit
theorem. Here our main focus is on atypically large fluctuations of $A(t)$. In
the long-time limit $t\to\infty$, we find that the full distribution of the
area takes the form
$P_{r}\left(A|t\right)\sim\exp\left[-t^{\alpha}\Phi\left(A/t^{\beta}\right)\right]$
with anomalous exponents $\alpha=1/(2H+2)$ and $\beta = (2H+3)/(4H+4)$ in the
regime of moderately large fluctuations, and a different anomalous scaling form
$P_{r}\left(A|t\right)\sim\exp\left[-t\Psi\left(A/t^{\left(2H+3\right)/2}\right)\right]$
in the regime of very large fluctuations. The associated rate functions
$\Phi(y)$ and $\Psi(w)$ depend on $H$ and are found exactly. Remarkably,
$\Phi(y)$ has a singularity that we interpret as a first-order dynamical
condensation transition, while $\Psi(w)$ exhibits a second-order dynamical
phase transition above which the number of resetting events ceases to be
extensive. The parabolic behavior of $\Phi(y)$ around the origin $y=0$
correctly describes the typical, Gaussian fluctuations of $A(t)$. Despite these
anomalous scalings, we find that all of the cumulants of the distribution
$P_{r}\left(A|t\right)$ grow linearly in time, $\langle A^n\rangle_c\approx c_n
\, t$, in the long-time limit. For the case of reset Brownian motion
(corresponding to $H=1/2$), we develop a recursive scheme to calculate the
coefficients $c_n$ exactly and use it to calculate the first 6 nonvanishing
cumulants.


title: Detecting Majorana Zero Modes via Strong Field Dynamics

abstract:
  We propose a protocol to detect topological phase transitions of
one-dimensional $p$-wave superconductors from their harmonic emission spectrum
in strong fields. Specifically, we identify spectral features due to radiating
edge modes, which characterize the spectrum and the density of states in the
topological phase, and which are absent in the trivial phase. These features
allow us to define an order parameter, obtained from emission measurements,
that unambiguously differentiates between the two phases. Local probing
provides insight to the localized and topologically protected nature of the
modes. The presented results establish that high harmonic spectroscopy can be
used as a novel all-optical tool for the detection of Majorana zero modes.


title:
HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer
  Encoders

abstract:
  In this paper, HeadPosr is proposed to predict the head poses using a single
RGB image. \textit{HeadPosr} uses a novel architecture which includes a
transformer encoder. In concrete, it consists of: (1) backbone; (2) connector;
(3) transformer encoder; (4) prediction head. The significance of using a
transformer encoder for HPE is studied. An extensive ablation study is
performed on varying the (1) number of encoders; (2) number of heads; (3)
different position embeddings; (4) different activations; (5) input channel
size, in a transformer used in HeadPosr. Further studies on using: (1)
different backbones, (2) using different learning rates are also shown. The
elaborated experiments and ablations studies are conducted using three
different open-source widely used datasets for HPE, i.e., 300W-LP, AFLW2000,
and BIWI datasets. Experiments illustrate that \textit{HeadPosr} outperforms
all the state-of-art methods including both the landmark-free and the others
based on using landmark or depth estimation on the AFLW2000 dataset and BIWI
datasets when trained with 300W-LP. It also outperforms when averaging the
results from the compared datasets, hence setting a benchmark for the problem
of HPE, also demonstrating the effectiveness of using transformers over the
state-of-the-art.


title:
$\Gamma$-convergence of some nonlocal perimeters in bounded subsets of
  $\mathbb{R}^n$ with general boundary conditions

abstract:
  We establish the $\Gamma$-convergence of some energy functionals describing
nonlocal attractive interactions in bounded domains. The interaction potential
solves an elliptic equation (local or nonlocal) in the bounded domain and the
primary interest of our results is to identify the effects that the boundary
conditions imposed on the potential have on the limiting functional. We
consider general Robin boundary conditions, which include Dirichlet and Neumann
conditions as particular cases. Depending on the order of the elliptic operator
the limiting functional involves the usual perimeter or some fractional
perimeter.
  We also consider the $\Gamma$-convergence of a related energy functional
combining the usual perimeter functional and the nonlocal repulsive interaction
energy.


title:
On Deformation Space Analogies between Kleinian Reflection Groups and
  Antiholomorphic Rational Maps

abstract:
  In a previous paper, we constructed an explicit dynamical correspondence
between certain Kleinian reflection groups and certain anti-holomorphic
rational maps on the Riemann sphere. In this paper, we show that their
deformation spaces share many striking similarities. We establish an analogue
of Thurston's compactness theorem for critically fixed anti-rational maps. We
also characterize how deformation spaces interact with each other and study the
monodromy representations of the union of all deformation spaces.


title:
Alpha-proton Differential Flow of the Young Solar Wind: Parker Solar
  Probe Observations

abstract:
  The velocity of alpha particles relative to protons can vary depending on the
solar wind type and distance from the Sun (Marsch 2012). Measurements from the
previous spacecraft provided the alpha-proton's differential velocities down to
0.3 au. Parker Solar Probe (PSP) now enables insights into differential flows
of newly accelerated solar wind closer to the Sun for the first time. Here, we
study the difference between proton and alpha bulk velocities near PSP
perihelia of Encounters 3-7 when the core solar wind is in the field of view of
the Solar Probe Analyzer for Ions (SPAN-I) instrument. As previously reported
at larger heliospheric distances, the alpha-proton differential speed observed
by PSP is greater for fast wind than the slow solar wind. We compare PSP
observations with various spacecraft measurements and present the radial and
temporal evolution of the alpha-proton differential speed. The differential
flow decreases as the solar wind propagates from the Sun, consistent with
previous observations. While Helios showed a small radial dependence of
differential flow for the slow solar wind, PSP clearly showed this dependency
for the young slow solar wind down to 0.09 au. Our analysis shows that the
alpha-proton differential speed's magnitude is mainly below the local Alfv\'en
speed. Moreover, alpha particles usually move faster than protons close to the
Sun. PSP crossed the Alfv\'en surface during its eighth Encounter and may cross
it in future Encounters, enabling us to investigate the differential flow very
close to the solar wind acceleration source region for the first time.


title:
Broadband Resonance-Enhanced Frequency Generation by Four-Wave Mixing in
  a Silicon Floquet Topological Photonic Insulator

abstract:
  Floquet topological photonic insulators, whose light transport properties are
dictated by the periodic drive sequence of the lattice, provide more
flexibility for controlling and trapping light than undriven topological
insulators, which can enable novel nonlinear optics applications in topological
photonics. Here, we employ a novel resonance effect called Floquet Defect Mode
Resonance in a 2D silicon Floquet microring lattice to demonstrate
resonance-enhanced frequency generation by four-wave mixing of Floquet bulk
modes in the presence of Kerr nonlinearity. The compact, cavity-less resonance
mode, induced through a periodic perturbation of the lattice drive sequence,
has the largest reported Q-factor for a topological resonator of ~10^5 with low
group velocity dispersion, which enables efficient broadband frequency
generation over several Floquet-Brillouin zones of the Floquet topological
insulator. We achieved wavelength conversion over 10.1 nm spectral range with
an average enhancement of 12.5 dB in the conversion efficiency due to the
Floquet Defect Mode Resonance. Our work could lead to robust light sources
generated directly on a topologically-protected photonic platform.


title:
Revisiting wind wave growth with fully-coupled direct numerical
  simulations

abstract:
  We investigate wind wave growth by direct numerical simulations solving for
the two phase Navier-Stokes equations. We consider ratio of the wave speed $c$
to wind friction velocity $u_*$ from $c/u_*=$ 2 to 8, i.e. in the slow to
intermediate wave regime; and wave steepness $ak$ from 0.1 to 0.25; the two
being varied independently. The turbulent wind and the travelling, nearly
monochromatic waves are fully coupled without any subgrid scale models, with a
turbulent boundary layer friction Reynolds number of 720. We observe wave
growth from wind energy input, in quantitative agreement with that computed
from the extracted surface pressure distribution, which confirms the leading
role of the wind pressure forcing. The phase shift and the amplitude of the
surface pressure distribution are systematically reported. We find that the
wave drag force is not a strong function of $c/u_*$ but closely related to
$ak$. The wave growth rate we obtain agree with previous experimental and
numerical studies. We make an effort to clarify various common underlying
assumptions, and to reconcile the scattering of the data between different
experimental results and the theories, as we revisit this longstanding problem
with new numerical evidence.


title: The trace property in preenveloping classes

abstract:
  We develop the theory of trace modules up to isomorphism and explore the
relationship between preenveloping classes of modules and the property of being
a trace module, guided by the question of whether a given module is trace in a
given preenvelope. As a consequence we identify new examples of trace ideals
and trace modules, and characterize several classes of rings with a focus on
the Gorenstein and regular properties.


title:
data2vec: A General Framework for Self-supervised Learning in Speech,
  Vision and Language

abstract:
  While the general idea of self-supervised learning is identical across
modalities, the actual algorithms and objectives differ widely because they
were developed with a single modality in mind. To get us closer to general
self-supervised learning, we present data2vec, a framework that uses the same
learning method for either speech, NLP or computer vision. The core idea is to
predict latent representations of the full input data based on a masked view of
the input in a self-distillation setup using a standard Transformer
architecture. Instead of predicting modality-specific targets such as words,
visual tokens or units of human speech which are local in nature, data2vec
predicts contextualized latent representations that contain information from
the entire input. Experiments on the major benchmarks of speech recognition,
image classification, and natural language understanding demonstrate a new
state of the art or competitive performance to predominant approaches.


title:
Valid confidence intervals for $\mu , \sigma $ when there is only one
  observation available

abstract:
  Portnoy (2019) considered the problem of constructing an optimal confidence
interval for the mean based on a single observation $\, X \sim {\cal{N}}(\mu ,
\, \sigma^2) \,$. Here we extend this result to obtaining 1-sample confidence
intervals for $\, \sigma \,$ and to cases of symmetric unimodal distributions
and of distributions with compact support. Finally, we extend the multivariate
result in Portnoy (2019) to allow a sample of size $\, m \,$ from a
multivariate normal distribution where $m$ may be less than the dimension.


title:
Two-phase compressible/incompressible Navier--Stokes system with
  inflow-outflow boundary conditions

abstract:
  We prove the existence of a weak solution to the compressible Navier--Stokes
system with singular pressure that explodes when density achieves its
congestion level. This is a quantity whose initial value evolves according to
the transport equation. We then prove that the "stiff pressure" limit gives
rise to the two-phase compressible/incompressible system with congestion
constraint describing the free interface. We prescribe of the velocity at the
boundary and the value of density at the inflow part of the boundary of a
general bounded $C^2$ domain. There are no restrictions on the size of the
boundary conditions.


title: Evaluating Robustness of Cooperative MARL: A Model-based Approach

abstract:
  In recent years, a proliferation of methods were developed for cooperative
multi-agent reinforcement learning (c-MARL). However, the robustness of c-MARL
agents against adversarial attacks has been rarely explored. In this paper, we
propose to evaluate the robustness of c-MARL agents via a model-based approach.
Our proposed formulation can craft stronger adversarial state perturbations of
c-MARL agents(s) to lower total team rewards more than existing model-free
approaches. In addition, we propose the first victim-agent selection strategy
which allows us to develop even stronger adversarial attack. Numerical
experiments on multi-agent MuJoCo benchmarks illustrate the advantage of our
approach over other baselines. The proposed model-based attack consistently
outperforms other baselines in all tested environments.


title: FLUKA cross sections for cosmic-ray interactions with the DRAGON2 code

abstract:
  Secondary particles produced in spallation reactions of cosmic rays with the
interstellar gas provide valuable information that allow us to investigate the
injection and transport of charged particles in the Galaxy. A good
understanding of the cross sections of production of these particles is crucial
to correctly interpret our models, although the existing experimental data is
very scarce and uncertain. We have developed a new set of cross sections, both
inelastic and inclusive, computed with the {\tt FLUKA} Monte Carlo nuclear code
and tested its compatibility with CR data. Inelastic and inclusive cross
sections have been compared to the most up-to-date data and parameterisations
finding a general good agreement. Then, these cross sections have been
implemented in the {\tt DRAGON2} code to characterize the spectra of CR nuclei
up to $Z=26$ and the secondary-to-primary ratios of B, Be and Li.
Interestingly, we find that the FLUKA cross sections allow us to predict an
energy-dependence of the B, Be and Li flux ratios which is compatible with
AMS-02 data and to reproduce simultaneously these flux ratios with a scaling
lower than $20\%$. Finally, we implement the cross sections of production of
gamma rays, calculated with {\tt FLUKA}, in the {\tt Gammasky} code and compute
diffuse gamma-ray sky maps and the local HI emissivity spectrum, finding a very
good agreement with Fermi Large Area Telescope data.


title:
Constructing Large Nonstationary Spatio-Temporal Covariance Models via
  Compositional Warpings

abstract:
  Understanding and predicting environmental phenomena often requires the
construction of spatio-temporal statistical models, which are typically
Gaussian processes. A common assumption made on Gausian processes is that of
covariance stationarity, which is unrealistic in many geophysical applications.
In this article, we introduce a new approach to construct descriptive
nonstationary spatio-temporal models by modeling stationary processes on warped
spatio-temporal domains. The warping functions we use are constructed using
several simple injective warping units which, when combined through
composition, can induce complex warpings. A stationary spatio-temporal
covariance function on the warped domain induces covariance nonstationarity on
the original domain. Sparse linear algebraic methods are used to reduce the
computational complexity when fitting the model in a big data setting. We show
that our proposed nonstationary spatio-temporal model can capture covariance
nonstationarity in both space and time, and provide better probabilistic
predictions than conventional stationary models in both simulation studies and
on a real-world data set.


title: Normal forms of $\omega$-Hamiltonian vector fields with symmetries

abstract:
  In this paper, we present algebraic tools to obtain normal forms of
$\omega$-Hamiltonian vector fields under a semisymplectic action of a Lie
group, by taking into account the symmetries and reversing symmetries of the
vector field. The normal forms resulting from the process preserve the
Hamiltonian condition and the types of symmetries of the original vector field.
Our techniques combine the classical method of normal forms of Hamiltonian
vector fields with the invariant theory of groups.


title:
Optically induced anisotropy in time-resolved scattering: Imaging
  molecular scale structure and dynamics in disordered media with experiment
  and theory

abstract:
  Time-resolved scattering experiments enable imaging of materials at the
molecular scale with femtosecond time resolution. However, in disordered media
they provide access to just one radial dimension thus limiting the study of
orientational structure and dynamics. Here we introduce a rigorous and
practical theoretical framework for predicting and interpreting experiments
combining optically induced anisotropy and time-resolved scattering. Using
impulsive nuclear Raman and ultrafast X-ray scattering experiments of
chloroform and simulations, we demonstrate that this framework can accurately
predict and elucidate both the spatial and temporal features of these
experiments.

categories: physics.chem-ph cond-mat.dis-nn cond-mat.stat-mech physics.optics


title:
Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning
  with Pairwise Alignment

abstract:
  Atlas building and image registration are important tasks for medical image
analysis. Once one or multiple atlases from an image population have been
constructed, commonly (1) images are warped into an atlas space to study
intra-subject or inter-subject variations or (2) a possibly probabilistic atlas
is warped into image space to assign anatomical labels. Atlas estimation and
nonparametric transformations are computationally expensive as they usually
require numerical optimization. Additionally, previous approaches for atlas
building often define similarity measures between a fuzzy atlas and each
individual image, which may cause alignment difficulties because a fuzzy atlas
does not exhibit clear anatomical structures in contrast to the individual
images. This work explores using a convolutional neural network (CNN) to
jointly predict the atlas and a stationary velocity field (SVF)
parameterization for diffeomorphic image registration with respect to the
atlas. Our approach does not require affine pre-registrations and utilizes
pairwise image alignment losses to increase registration accuracy. We evaluate
our model on 3D knee magnetic resonance images (MRI) from the OAI-ZIB dataset.
Our results show that the proposed framework achieves better performance than
other state-of-the-art image registration algorithms, allows for end-to-end
training, and for fast inference at test time.


title: Accurate super-resolution low-field brain MRI

abstract:
  The recent introduction of portable, low-field MRI (LF-MRI) into the clinical
setting has the potential to transform neuroimaging. However, LF-MRI is limited
by lower resolution and signal-to-noise ratio, leading to incomplete
characterization of brain regions. To address this challenge, recent advances
in machine learning facilitate the synthesis of higher resolution images
derived from one or multiple lower resolution scans. Here, we report the
extension of a machine learning super-resolution (SR) algorithm to synthesize 1
mm isotropic MPRAGE-like scans from LF-MRI T1-weighted and T2-weighted
sequences. Our initial results on a paired dataset of LF and high-field (HF,
1.5T-3T) clinical scans show that: (i) application of available automated
segmentation tools directly to LF-MRI images falters; but (ii) segmentation
tools succeed when applied to SR images with high correlation to gold standard
measurements from HF-MRI (e.g., r = 0.85 for hippocampal volume, r = 0.84 for
the thalamus, r = 0.92 for the whole cerebrum). This work demonstrates
proof-of-principle post-processing image enhancement from lower resolution
LF-MRI sequences. These results lay the foundation for future work to enhance
the detection of normal and abnormal image findings at LF and ultimately
improve the diagnostic performance of LF-MRI. Our tools are publicly available
on FreeSurfer (surfer.nmr.mgh.harvard.edu/).


title: Automated Instantiation of Control Flow Tracing Exercises

abstract:
  One of the first steps in learning how to program is reading and tracing
existing code. In order to avoid the error-prone task of generating variations
of a tracing exercise, our tool Tatsu generates instances of a given code
skeleton automatically. This is achieved by a finite unwinding of the program
in the style of bounded model checking and using the SMT solver Z3 to find
models for this unwinded program.


title:
Four Geometry Problems to Introduce Automated Deduction in Secondary
  Schools

abstract:
  The introduction of automated deduction systems in secondary schools face
several bottlenecks, the absence of the subject of rigorous mathematical
demonstrations in the curricula, the lack of knowledge by the teachers about
the subject and the difficulty of tackling the task by automatic means.
  Despite those difficulties we claim that the subject of automated deduction
in geometry can be introduced, by addressing it in particular cases: simple to
manipulate by students and teachers and reasonably easy to be dealt by
automatic deduction tools.
  The subject is discussed by addressing four secondary schools geometry
problems: their rigorous proofs, visual proofs, numeric proofs, algebraic
formal proofs, synthetic formal proofs, or the lack of them. For these problems
we discuss a lesson plan to address them with the help of Information and
Communications Technology, more specifically, automated deduction tools.


title: Teaching Intuitionistic and Classical Propositional Logic Using Isabelle

abstract:
  We describe a natural deduction formalization of intuitionistic and classical
propositional logic in the Isabelle/Pure framework. In contrast to earlier
work, where we explored the pedagogical benefits of using a deep embedding
approach to logical modelling, here we employ a logical framework modelling.
This gives rise to simple and natural teaching examples and we report on the
role it played in teaching our Automated Reasoning course in 2020 and 2021.


title: Evolution of SASyLF 2008-2021

abstract:
  SASyLF was released in 2008 and used as a proof assistant in courses at
several universities. It proved itself useful and has continued to be used, and
each iteration of use has encouraged further development: fixing bugs and
adding enhancements. This paper describes how SASyLF was developed while
keeping true to its purpose. Most notable are making substitutions explicit,
support of "and" and "or," support for mutual and lexicographic induction, and
IDE support.


title: Chiral Electronic Excitations in a Quasi-2D Rashba System BiTeI

abstract:
  The optical transitions between spin-polarized bands of the quasi-two
dimensional Rashba system BiTeI are investigated using polarization resolved
resonant Raman spectroscopy. We detect chiral excitations between states with
opposite helicity and compare spectra to calculations within a three-band
model. Using the resonant Raman excitation profile, we deduce the Rashba
parameters and band gaps of the higher conduction bands near the Fermi level,
and compare the parameters to values obtained by ab initio density function
theory (DFT).


abstract:
  Phase-Stretch Adaptive Gradient-Field Extractor (PAGE) is an edge detection
algorithm that is inspired by physics of electromagnetic diffraction and
dispersion. A computational imaging algorithm, it identifies edges, their
orientations and sharpness in a digital image where the image brightness
changes abruptly. Edge detection is a basic operation performed by the eye and
is crucial to visual perception. PAGE embeds an original image into a set of
feature maps that can be used for object representation and classification. The
algorithm performs exceptionally well as an edge and texture extractor in low
light level and low contrast images. This manuscript is prepared to support the
open-source code which is being simultaneously made available within the GitHub
repository
https://github.com/JalaliLabUCLA/Phase-Stretch-Adaptive-Gradient-field-Extractor/.


title:
Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose
  Maxillofacial CBCT Modeling

abstract:
  Low-dose dental cone beam computed tomography (CBCT) has been increasingly
used for maxillofacial modeling. However, the presence of metallic inserts,
such as implants, crowns, and dental filling, causes severe streaking and
shading artifacts in a CBCT image and loss of the morphological structures of
the teeth, which consequently prevents accurate segmentation of bones. A
two-stage metal artifact reduction method is proposed for accurate 3D low-dose
maxillofacial CBCT modeling, where a key idea is to utilize explicit tooth
shape prior information from intra-oral scan data whose acquisition does not
require any extra radiation exposure. In the first stage, an image-to-image
deep learning network is employed to mitigate metal-related artifacts. To
improve the learning ability, the proposed network is designed to take
advantage of the intra-oral scan data as side-inputs and perform multi-task
learning of auxiliary tooth segmentation. In the second stage, a 3D
maxillofacial model is constructed by segmenting the bones from the dental CBCT
image corrected in the first stage. For accurate bone segmentation, weighted
thresholding is applied, wherein the weighting region is determined depending
on the geometry of the intra-oral scan data. Because acquiring a paired
training dataset of metal-artifact-free and metal artifact-affected dental CBCT
images is challenging in clinical practice, an automatic method of generating a
realistic dataset according to the CBCT physics model is introduced. Numerical
simulations and clinical experiments show the feasibility of the proposed
method, which takes advantage of tooth surface information from intra-oral scan
data in 3D low dose maxillofacial CBCT modeling.


title:
Likelihood-based Inference for Exponential-Family Random Graph Models
  via Linear Programming

abstract:
  This article discusses the problem of determining whether a given point, or
set of points, lies within the convex hull of another set of points in $d$
dimensions. This problem arises naturally in a statistical context when using a
particular approximation to the loglikelihood function for an exponential
family model; in particular, we discuss the application to network models here.
While the convex hull question may be solved via a simple linear program, this
approach is not well known in the statistical literature. Furthermore, this
article details several substantial improvements to the convex hull-testing
algorithm currently implemented in the widely used 'ergm' package for network
modeling.


title:
A Viral Marketing-Based Model For Opinion Dynamics in Online Social
  Networks

abstract:
  Online social networks provide a medium for citizens to form opinions on
different societal issues, and a forum for public discussion. They also expose
users to viral content, such as breaking news articles. In this paper, we study
the interplay between these two aspects: opinion formation and information
cascades in online social networks. We present a new model that allows us to
quantify how users change their opinion as they are exposed to viral content.
Our model is a combination of the popular Friedkin--Johnsen model for opinion
dynamics and the independent cascade model for information propagation. We
present algorithms for simulating our model, and we provide approximation
algorithms for optimizing certain network indices, such as the sum of user
opinions or the disagreement--controversy index; our approach can be used to
obtain insights into how much viral content can increase these indices in
online social networks. Finally, we evaluate our model on real-world datasets.
We show experimentally that marketing campaigns and polarizing contents have
vastly different effects on the network: while the former have only limited
effect on the polarization in the network, the latter can increase the
polarization up to 59% even when only 0.5% of the users start sharing a
polarizing content. We believe that this finding sheds some light into the
growing segregation in today's online media.


title: Structured Prediction Problem Archive

abstract:
  Structured prediction problems are one of the fundamental tools in machine
learning. In order to facilitate algorithm development for their numerical
solution, we collect in one place a large number of datasets in easy to read
formats for a diverse set of problem classes. We provide archival links to
datasets, description of the considered problems and problem formats, and a
short summary of problem characteristics including size, number of instances
etc. For reference we also give a non-exhaustive selection of algorithms
proposed in the literature for their solution. We hope that this central
repository will make benchmarking and comparison to established works easier.
We welcome submission of interesting new datasets and algorithms for inclusion
in our archive.


title:
Deep Reinforcement Learning Assisted Federated Learning Algorithm for
  Data Management of IIoT

abstract:
  The continuous expanded scale of the industrial Internet of Things (IIoT)
leads to IIoT equipments generating massive amounts of user data every moment.
According to the different requirement of end users, these data usually have
high heterogeneity and privacy, while most of users are reluctant to expose
them to the public view. How to manage these time series data in an efficient
and safe way in the field of IIoT is still an open issue, such that it has
attracted extensive attention from academia and industry. As a new machine
learning (ML) paradigm, federated learning (FL) has great advantages in
training heterogeneous and private data. This paper studies the FL technology
applications to manage IIoT equipment data in wireless network environments. In
order to increase the model aggregation rate and reduce communication costs, we
apply deep reinforcement learning (DRL) to IIoT equipment selection process,
specifically to select those IIoT equipment nodes with accurate models.
Therefore, we propose a FL algorithm assisted by DRL, which can take into
account the privacy and efficiency of data training of IIoT equipment. By
analyzing the data characteristics of IIoT equipments, we use MNIST, fashion
MNIST and CIFAR-10 data sets to represent the data generated by IIoT. During
the experiment, we employ the deep neural network (DNN) model to train the
data, and experimental results show that the accuracy can reach more than 97\%,
which corroborates the effectiveness of the proposed algorithm.


title:
Learnability Lock: Authorized Learnability Control Through Adversarial
  Invertible Transformations

abstract:
  Owing much to the revolution of information technology, the recent progress
of deep learning benefits incredibly from the vastly enhanced access to data
available in various digital formats. However, in certain scenarios, people may
not want their data being used for training commercial models and thus studied
how to attack the learnability of deep learning models. Previous works on
learnability attack only consider the goal of preventing unauthorized
exploitation on the specific dataset but not the process of restoring the
learnability for authorized cases. To tackle this issue, this paper introduces
and investigates a new concept called "learnability lock" for controlling the
model's learnability on a specific dataset with a special key. In particular,
we propose adversarial invertible transformation, that can be viewed as a
mapping from image to image, to slightly modify data samples so that they
become "unlearnable" by machine learning models with negligible loss of visual
features. Meanwhile, one can unlock the learnability of the dataset and train
models normally using the corresponding key. The proposed learnability lock
leverages class-wise perturbation that applies a universal transformation
function on data samples of the same label. This ensures that the learnability
can be easily restored with a simple inverse transformation while remaining
difficult to be detected or reverse-engineered. We empirically demonstrate the
success and practicability of our method on visual classification tasks.


title:
Integration of a machine learning model into a decision support tool to
  predict absenteeism at work of prospective employees

abstract:
  Purpose - Inefficient hiring may result in lower productivity and higher
training costs. Productivity losses caused by absenteeism at work cost U.S.
employers billions of dollars each year. Also, employers typically spend a
considerable amount of time managing employees who perform poorly. The purpose
of this study is to develop a decision support tool to predict absenteeism
among potential employees. Design/methodology/approach - We utilized a popular
open-access dataset. In order to categorize absenteeism classes, the data have
been preprocessed, and four methods of machine learning classification have
been applied: Multinomial Logistic Regression (MLR), Support Vector Machines
(SVM), Artificial Neural Networks (ANN), and Random Forests (RF). We selected
the best model, based on several validation scores, and compared its
performance against the existing model; we then integrated the best model into
our proposed web-based for hiring managers. Findings - A web-based decision
tool allows hiring managers to make more informed decisions before hiring a
potential employee, thus reducing time, financial loss and reducing the
probability of economic insolvency. Originality/value - In this paper, we
propose a model that is trained based on attributes that can be collected
during the hiring process. Furthermore, hiring managers may lack experience in
machine learning or do not have the time to spend developing machine learning
algorithms. Thus, we propose a web-based interactive tool that can be used
without prior knowledge of machine learning algorithms.


title: Data driven design of optical resonators

abstract:
  Optical devices lie at the heart of most of the technology we see around us.
When one actually wants to make such an optical device, one can predict its
optical behavior using computational simulations of Maxwell's equations. If one
then asks what the optimal design would be in order to obtain a certain optical
behavior, the only way to go further would be to try out all of the possible
designs and compute the electromagnetic spectrum they produce. When there are
many design parameters, this brute force approach quickly becomes too
computationally expensive. We therefore need other methods to create optimal
optical devices. An alternative to the brute force approach is inverse design.
In this paradigm, one starts from the desired optical response of a material
and then determines the design parameters that are needed to obtain this
optical response. There are many algorithms known in the literature that
implement this inverse design. Some of the best performing, recent approaches
are based on Deep Learning. The central idea is to train a neural network to
predict the optical response for given design parameters. Since neural networks
are completely differentiable, we can compute gradients of the response with
respect to the design parameters. We can use these gradients to update the
design parameters and get an optical response closer to the one we want. This
allows us to obtain an optimal design much faster compared to the brute force
approach. In my thesis, I use Deep Learning for the inverse design of the
Fabry-P\'erot resonator. This system can be described fully analytically and is
therefore ideal to study.


title: Stop Oversampling for Class Imbalance Learning: A Critical Review

abstract:
  For the last two decades, oversampling has been employed to overcome the
challenge of learning from imbalanced datasets. Many approaches to solving this
challenge have been offered in the literature. Oversampling, on the other hand,
is a concern. That is, models trained on fictitious data may fail spectacularly
when put to real-world problems. The fundamental difficulty with oversampling
approaches is that, given a real-life population, the synthesized samples may
not truly belong to the minority class. As a result, training a classifier on
these samples while pretending they represent minority may result in incorrect
predictions when the model is used in the real world. We analyzed a large
number of oversampling methods in this paper and devised a new oversampling
evaluation system based on hiding a number of majority examples and comparing
them to those generated by the oversampling process. Based on our evaluation
system, we ranked all these methods based on their incorrectly generated
examples for comparison. Our experiments using more than 70 oversampling
methods and three imbalanced real-world datasets reveal that all oversampling
methods studied generate minority samples that are most likely to be majority.
Given data and methods in hand, we argue that oversampling in its current forms
and methodologies is unreliable for learning from class imbalanced data and
should be avoided in real-world applications.


title:
Convolutional Neural Networks on Graphs with Chebyshev Approximation,
  Revisited

abstract:
  Designing spectral convolutional networks is a challenging problem in graph
learning. ChebNet, one of the early attempts, approximates the spectral
convolution using Chebyshev polynomials. GCN simplifies ChebNet by utilizing
only the first two Chebyshev polynomials while still outperforming it on
real-world datasets. GPR-GNN and BernNet demonstrate that the Monomial and
Bernstein bases also outperform the Chebyshev basis in terms of learning the
spectral convolution. Such conclusions are counter-intuitive in the field of
approximation theory, where it is established that the Chebyshev polynomial
achieves the optimum convergent rate for approximating a function.
  In this paper, we revisit the problem of approximating the spectral
convolution with Chebyshev polynomials. We show that ChebNet's inferior
performance is primarily due to illegal coefficients learnt by ChebNet
approximating analytic filter functions, which leads to over-fitting. We then
propose ChebNetII, a new GNN model based on Chebyshev interpolation, which
enhances the original Chebyshev polynomial approximation while reducing the
Runge phenomena. We conducted an extensive experimental study to demonstrate
that ChebNetII can learn arbitrary graph spectrum filters and achieve superior
performance in both full- and semi-supervised node classification tasks.


title:
Magnetic charge and indicators of the anomalous Hall effect in
  unconventional magnetic systems

abstract:
  The anomalous Hall effect (AHE) can appear in certain antiferromagnetic
metals when it is allowed by symmetry. Since the net magnetization is usually
small in antiferromagnets, it is useful to have other physical indicators of
the AHE that have the same symmetry properties as the latter and can be
conveniently measured and calculated. Here we propose such indicators named as
electronic chiralization (EC), which are constructed using spatial gradients of
spin and charge densities in general periodic crystals, and can potentially be
measured directly by scattering experiments. Such constructions particularly
reveal the important role of magnetic charge in the AHE in unconventional
magnetic systems with vanishing net magnetization. Guided by the EC we give two
examples of the AHE when magnetic charge is explicitly present: A minimum
honeycomb model inspired by the magnetic-charge-ordered phase of kagome spin
ice, and skew scattering of two-dimensional (2D) Dirac electrons by magnetic
charge.


title: Simulators for Mobile Social Robots:State-of-the-Art and Challenges

abstract:
  The future robots are expected to work in a shared physical space with humans
[1], however, the presence of humans leads to a dynamic environment that is
challenging for mobile robots to navigate. The path planning algorithms
designed to navigate a collision free path in complex human environments are
often tested in real environments due to the lack of simulation frameworks.
This paper identifies key requirements for an ideal simulator for this task,
evaluates existing simulation frameworks and most importantly, it identifies
the challenges and limitations of the existing simulation techniques. First and
foremost, we recognize that the simulators needed for the purpose of testing
mobile robots designed for human environments are unique as they must model
realistic pedestrian behavior in addition to the modelling of mobile robots.
Our study finds that Pedsim_ros [2] and a more recent SocNavBench framework [3]
are the only two 3D simulation frameworks that meet most of the key
requirements defined in our paper. In summary, we identify the need for
developing more simulators that offer an ability to create realistic 3D
pedestrian rich virtual environments along with the flexibility of designing
complex robots and their sensor models from scratch.


title:
Multi-Label Classification of Thoracic Diseases using Dense
  Convolutional Network on Chest Radiographs

abstract:
  Chest X-ray images are one of the most common medical diagnosis techniques to
identify different thoracic diseases. However, identification of pathologies in
X-ray images requires skilled manpower and are often cited as a time-consuming
task with varied level of interpretation, particularly in cases where the
identification of disease only by images is difficult for human eyes. With
recent achievements of deep learning in image classification, its application
in disease diagnosis has been widely explored. This research project presents a
multi-label disease diagnosis model of chest x-rays. Using Dense Convolutional
Neural Network (DenseNet), the diagnosis system was able to obtain high
classification predictions. The model obtained the highest AUC score of 0.896
for condition Cardiomegaly and the lowest AUC score for Nodule, 0.655. The
model also localized the parts of the chest radiograph that indicated the
presence of each pathology using GRADCAM, thus contributing to the model
interpretability of a deep learning algorithm.


title:
Biases in estimates of black hole kicks from the spin distribution of
  binary black holes

abstract:
  A population of more than 50 binary black hole mergers has now been observed
by the LIGO and Virgo gravitational-wave observatories. While neutron stars are
known to have large velocities associated with impulsive kicks imparted to them
at birth in supernovae, whether black holes receive similar kicks, and of what
magnitude, remains an open question. Recently, Callister et al. (2021) analysed
the binary black hole population under the hypothesis that they were all formed
through isolated binary evolution and claimed that large black hole kicks
(greater than 260 km/s at 99% confidence) were required for the spin
distribution of merging binary black holes to match observations. Here we
highlight that a key assumption made by Callister et al. (2021) -- that all
secondary black holes can be tidally spun up -- is not motivated by physical
models, and may lead to a bias in their estimate of the magnitudes of black
hole kicks. We make only minor changes to the Callister et al. (2021) model,
accounting for a population of wider merging binaries where tidal
synchronisation is ineffective. We show that this naturally produces a bimodal
spin distribution for secondary black holes, and that the spin-orbit
misalignments observed in the binary black hole population can be explained by
more typical black hole kicks of order 100 km/s, consistent with kicks inferred
from Galactic X-ray binaries containing black holes. We conclude that the
majority of the binary black hole population is consistent with forming through
isolated binary evolution.


title:
Eisenstein series for $G_2$ and the symmetric cube Bloch--Kato
  conjecture

abstract:
  Let $F$ be a cuspidal eigenform of even weight and trivial nebentypus, let
$p$ be a prime not dividing the level of $F$, and let $\rho_F$ be the $p$-adic
Galois representation attached to $F$. Assume that the $L$-function attached to
the symmetric cube of $\rho_F$ vanishes to odd order at its central point. Then
under some mild hypotheses, and conditional on certain consequences of Arthur's
conjectures, we construct a nontrivial element in the Bloch--Kato Selmer group
of an appropriate twist of the symmetric cube of $\rho_F$, in accordance with
the Bloch--Kato conjectures.
  Our technique is based on the method of Skinner and Urban. We construct a
class in the appropriate Selmer group by $p$-adically deforming Eisenstein
series for the exceptional group $G_2$ in a generically cuspidal family and
then studying a lattice in the corresponding family of $G_2$-Galois
representations. We also make a detailed study of the specific conjectures used
and explain how one might try to prove them.


title: Fair SA: Sensitivity Analysis for Fairness in Face Recognition

abstract:
  As the use of deep learning in high impact domains becomes ubiquitous, it is
increasingly important to assess the resilience of models. One such high impact
domain is that of face recognition, with real world applications involving
images affected by various degradations, such as motion blur or high exposure.
Moreover, images captured across different attributes, such as gender and race,
can also challenge the robustness of a face recognition algorithm. While
traditional summary statistics suggest that the aggregate performance of face
recognition models has continued to improve, these metrics do not directly
measure the robustness or fairness of the models. Visual Psychophysics
Sensitivity Analysis (VPSA) [1] provides a way to pinpoint the individual
causes of failure by way of introducing incremental perturbations in the data.
However, perturbations may affect subgroups differently. In this paper, we
propose a new fairness evaluation based on robustness in the form of a generic
framework that extends VPSA. With this framework, we can analyze the ability of
a model to perform fairly for different subgroups of a population affected by
perturbations, and pinpoint the exact failure modes for a subgroup by measuring
targeted robustness. With the increasing focus on the fairness of models, we
use face recognition as an example application of our framework and propose to
compactly visualize the fairness analysis of a model via AUC matrices. We
analyze the performance of common face recognition models and empirically show
that certain subgroups are at a disadvantage when images are perturbed, thereby
uncovering trends that were not visible using the model's performance on
subgroups without perturbations.


title:
CALM: Contrastive Aligned Audio-Language Multirate and Multimodal
  Representations

abstract:
  Deriving multimodal representations of audio and lexical inputs is a central
problem in Natural Language Understanding (NLU). In this paper, we present
Contrastive Aligned Audio-Language Multirate and Multimodal Representations
(CALM), an approach for learning multimodal representations using contrastive
and multirate information inherent in audio and lexical inputs. The proposed
model aligns acoustic and lexical information in the input embedding space of a
pretrained language-only contextual embedding model. By aligning audio
representations to pretrained language representations and utilizing
contrastive information between acoustic inputs, CALM is able to bootstrap
audio embedding competitive with existing audio representation models in only a
few hours of training time. Operationally, audio spectrograms are processed
using linearized patches through a Spectral Transformer (SpecTran) which is
trained using a Contrastive Audio-Language Pretraining objective to align audio
and language from similar queries. Subsequently, the derived acoustic and
lexical tokens representations are input into a multimodal transformer to
incorporate utterance level context and derive the proposed CALM
representations. We show that these pretrained embeddings can subsequently be
used in multimodal supervised tasks and demonstrate the benefits of the
proposed pretraining steps in terms of the alignment of the two embedding
spaces and the multirate nature of the pretraining. Our system shows 10-25\%
improvement over existing emotion recognition systems including
state-of-the-art three-modality systems under various evaluation objectives.


title:
Representative Scenarios to Capture Renewable Generation Stochasticity
  and Cross-Correlations

abstract:
  Generating representative scenarios for power system planning in which the
stochasticity of renewable generation and cross-correlations between renewables
and load are fully captured, is a challenging problem. Traditional methods for
scenario generation often fail to generate diverse scenarios that include both
seasonal (frequently occurring) and atypical (extreme) days required for
planning purposes. This paper presents a methodical approach to generate
representative scenarios. It also proposes new metrics that are more relevant
for evaluating the generated scenarios from an applications perspective. When
applied to historical data from a power utility, the proposed approach resulted
in scenarios that included a good mix of seasonal and atypical days. The
results also demonstrated pertinence of the proposed cluster validation
metrics. Finally, the paper presents a trade-off for determining optimal number
of scenarios for a given application.


title: Lorentz-covariant sampling theory for fields

abstract:
  Sampling theory is a discipline in communications engineering involved with
the exact reconstruction of continuous signals from discrete sets of sample
points. From a physics perspective, this is interesting in relation to the
question of whether spacetime is continuous or discrete at the Planck scale,
since in sampling theory we have functions which can be viewed as equivalently
residing on a continuous or discrete space. Further, it is possible to
formulate analogues of sampling which yield discreteness without disturbing
underlying spacetime symmetries. In particular, there is a proposal for how
this can be adapted for Minkowski spacetime. Here we will provide a detailed
examination of the extension of sampling theory to this context. We will also
discuss generally how spacetime symmetries manifest themselves in sampling
theory, which at the surface seems in conflict with the fact that the
discreteness of the sampling is not manifestly covariant. Specifically, we will
show how the symmetry of a function space with a sampling property is
equivalent to the existence of a family of possible sampling lattices related
by the symmetry transformations.


title:
Moral Emotions Shape the Virality of COVID-19 Misinformation on Social
  Media

abstract:
  While false rumors pose a threat to the successful overcoming of the COVID-19
pandemic, an understanding of how rumors diffuse in online social networks is -
even for non-crisis situations - still in its infancy. Here we analyze a large
sample consisting of COVID-19 rumor cascades from Twitter that have been
fact-checked by third-party organizations. The data comprises N=10,610 rumor
cascades that have been retweeted more than 24 million times. We investigate
whether COVID-19 misinformation spreads more viral than the truth and whether
the differences in the diffusion of true vs. false rumors can be explained by
the moral emotions they carry. We observe that, on average, COVID-19
misinformation is more likely to go viral than truthful information. However,
the veracity effect is moderated by moral emotions: false rumors are more viral
than the truth if the source tweets embed a high number of other-condemning
emotion words, whereas a higher number of self-conscious emotion words is
linked to a less viral spread. The effects are pronounced both for health
misinformation and false political rumors. These findings offer insights into
how true vs. false rumors spread and highlight the importance of considering
emotions from the moral emotion families in social media content.


title: On some convexity and monotonicity inequalities of Elliott Lieb

abstract:
  A fundamental paper of Elliott Lieb from 1973 has been the basis for much
beautiful work on matrix inequalities by many people over the following years.
We review a well-connected set of these developments. Some new proofs are
provided.


title: Physical symmetries and gauge choices in the Landau problem

abstract:
  Due to a special nature of the Landau problem, in which the magnetic field is
uniformly spreading over the whole two-dimensional plane, there necessarily
exist three conserved quantities, i.e. two conserved momenta and one conserved
orbital angular momentum for the electron, independently of the choice of the
gauge potential. Accordingly, the quantum eigen-functions of the Landau problem
can be obtained by diagonalizing the Landau Hamiltonian together with one of
the above three conserved operators with the result that the quantum mechanical
eigen-functions of the Landau problem can be written down for arbitrary gauge
potential. The purpose of the present paper is to pursue the logical
consequences of this gauge-potential-independent formulation of the Landau
problem, with a particular intention of unraveling the physical significance of
the concept of gauge-invariant-extension of the canonical orbital angular
momentum advocated in recent literature on the nucleon spin decomposition
problem. At the end, our analysis is shown to disclose physically insubstantial
aspects of the gauge symmetry concept.


title: Nonmyopic Multiclass Active Search for Diverse Discovery

abstract:
  Active search is a setting in adaptive experimental design where we aim to
uncover members of rare, valuable class(es) subject to a budget constraint. An
important consideration in this problem is diversity among the discovered
targets -- in many applications, diverse discoveries offer more insight and may
be preferable in downstream tasks. However, most existing active search
policies either assume that all targets belong to a common positive class or
encourage diversity via simple heuristics. We present a novel formulation of
active search with multiple target classes, characterized by a utility function
that naturally induces a preference for label diversity among discoveries via a
diminishing returns mechanism. We then study this problem under the Bayesian
lens and prove a hardness result for approximating the optimal policy. Finally,
we propose an efficient, nonmyopic approximation to the optimal policy and
demonstrate its superior empirical performance across a wide variety of
experimental settings, including drug discovery.


title: Perfectly packing a square by squares of nearly harmonic sidelength

abstract:
  A well known open problem of Meir and Moser asks if the squares of sidelength
$1/n$ for $n \geq 2$ can be packed perfectly into a square of area
$\sum_{n=2}^\infty \frac{1}{n^2} = \frac{\pi^2}{6}-1$. In this paper we show
that for any $1/2 < t < 1$, and any $n_0$ that is sufficiently large depending
on $t$, the squares of sidelength $n^{-t}$ for $n \geq n_0$ can be packed
perfectly into a square of area $\sum_{n=n_0}^\infty \frac{1}{n^{2t}}$. This
was previously known (if one packs a rectangle instead of a square) for $1/2 <
t \leq 2/3$ (in which case one can take $n_0=1$).


title:
Model and predict age and sex in healthy subjects using brain white
  matter features: A deep learning approach

abstract:
  The human brain's white matter (WM) structure is of immense interest to the
scientific community. Diffusion MRI gives a powerful tool to describe the brain
WM structure noninvasively. To potentially enable monitoring of age-related
changes and investigation of sex-related brain structure differences on the
mapping between the brain connectome and healthy subjects' age and sex, we
extract fiber-cluster-based diffusion features and predict sex and age with a
novel ensembled neural network classifier. We conduct experiments on the Human
Connectome Project (HCP) young adult dataset and show that our model achieves
94.82% accuracy in sex prediction and 2.51 years MAE in age prediction. We also
show that the fractional anisotropy (FA) is the most predictive of sex, while
the number of fibers is the most predictive of age and the combination of
different features can improve the model performance.


title:
MOST-Net: A Memory Oriented Style Transfer Network for Face Sketch
  Synthesis

abstract:
  Face sketch synthesis has been widely used in multi-media entertainment and
law enforcement. Despite the recent developments in deep neural networks,
accurate and realistic face sketch synthesis is still a challenging task due to
the diversity and complexity of human faces. Current image-to-image
translation-based face sketch synthesis frequently encounters over-fitting
problems when it comes to small-scale datasets. To tackle this problem, we
present an end-to-end Memory Oriented Style Transfer Network (MOST-Net) for
face sketch synthesis which can produce high-fidelity sketches with limited
data. Specifically, an external self-supervised dynamic memory module is
introduced to capture the domain alignment knowledge in the long term. In this
way, our proposed model could obtain the domain-transfer ability by
establishing the durable relationship between faces and corresponding sketches
on the feature level. Furthermore, we design a novel Memory Refinement Loss (MR
Loss) for feature alignment in the memory module, which enhances the accuracy
of memory slots in an unsupervised manner. Extensive experiments on the CUFS
and the CUFSF datasets show that our MOST-Net achieves state-of-the-art
performance, especially in terms of the Structural Similarity Index(SSIM).


title: Local Explanations for Reinforcement Learning

abstract:
  Many works in explainable AI have focused on explaining black-box
classification models. Explaining deep reinforcement learning (RL) policies in
a manner that could be understood by domain users has received much less
attention. In this paper, we propose a novel perspective to understanding RL
policies based on identifying important states from automatically learned
meta-states. The key conceptual difference between our approach and many
previous ones is that we form meta-states based on locality governed by the
expert policy dynamics rather than based on similarity of actions, and that we
do not assume any particular knowledge of the underlying topology of the state
space. Theoretically, we show that our algorithm to find meta-states converges
and the objective that selects important states from each meta-state is
submodular leading to efficient high quality greedy selection. Experiments on
four domains (four rooms, door-key, minipacman, and pong) and a carefully
conducted user study illustrate that our perspective leads to better
understanding of the policy. We conjecture that this is a result of our
meta-states being more intuitive in that the corresponding important states are
strong indicators of tractable intermediate goals that are easier for humans to
interpret and follow.


title:
A note on domain monotonicity for the Neumann eigenvalues of the
  Laplacian

abstract:
  Given a convex domain and its convex sub-domain we prove a variant of domain
monotonicity for the Neumann eigenvalues of the Laplacian.


title:
Penalizing Gradient Norm for Efficiently Improving Generalization in
  Deep Learning

abstract:
  How to train deep neural networks (DNNs) to generalize well is a central
concern in deep learning, especially for severely overparameterized networks
nowadays. In this paper, we propose an effective method to improve the model
generalization by additionally penalizing the gradient norm of loss function
during optimization. We demonstrate that confining the gradient norm of loss
function could help lead the optimizers towards finding flat minima. We
leverage the first-order approximation to efficiently implement the
corresponding gradient to fit well in the gradient descent framework. In our
experiments, we confirm that when using our methods, generalization performance
of various models could be improved on different datasets. Also, we show that
the recent sharpness-aware minimization method \cite{DBLP:conf/iclr/ForetKMN21}
is a special, but not the best, case of our method, where the best case of our
method could give new state-of-art performance on these tasks.


title:
Multiple Correlated Jammers Nullification using LSTM-based Deep Dueling
  Neural Network

abstract:
  Suppressing the deliberate interference for wireless networks is critical to
guarantee a reliable communication link. However, nullifying the jamming
signals can be problematic when the correlations between transmitted jamming
signals are deliberately varied over time. Specifically, recent studies reveal
that by deliberately varying the correlations among jamming signals, attackers
can effectively vary the jamming channels and thus their nullspace, even when
the physical channels remain unchanged. That makes the beam-forming matrix
derived from the nullspace of the jamming channels unable to suppress the
jamming signals. Most existing solutions only consider unchanged correlations
or heuristically adapt to the time-varying correlation problem by continuously
monitoring the residual jamming signals before updating the beam-forming
matrix. In this paper, we systematically formulate the optimization problem of
the nullspace estimation and data transmission phases. Even ignoring the
unknown strategy of the jammers and the challenging nullspace estimation
process, the resulting problem is an integer programming problem, hence
intractable to obtain its optimal solution. To tackle it and address the
unknown strategy of the jammer, we reformulate the problem using a partially
observable semi-Markov decision process (POSMDP) and then design a deep dueling
Q-learning based framework to tune the duration of the nullspace estimation and
data transmission phases. Extensive simulations demonstrate that the proposed
techniques effectively deal with jamming signals whose correlations vary over
time, and the range of correlations is unknown. Especially, our techniques do
not require continuous monitoring of the residual jamming signals (after the
nullification process) before updating the beam-forming matrix. As such, the
system is more spectral-efficient and has a lower outage probability.


title:
Two-Step Spike Encoding Scheme and Architecture for Highly Sparse
  Spiking-Neural-Network

abstract:
  This paper proposes a two-step spike encoding scheme, which consists of the
source encoding and the process encoding for a high energy-efficient
spiking-neural-network (SNN) acceleration. The eigen-train generation and its
superposition generate spike trains which show high accuracy with low spike
ratio. Sparsity boosting (SB) and spike generation skipping (SGS) reduce the
amount of operations for SNN. Time shrinking multi-level encoding (TS-MLE)
compresses the number of spikes in a train along time axis, and spike-level
clock skipping (SLCS) decreases the processing time. Eigen-train generation
achieves 90.3% accuracy, the same accuracy of CNN, under the condition of 4.18%
spike ratio for CIFAR-10 classification. SB reduces spike ratio by 0.49x with
only 0.1% accuracy loss, and the SGS reduces the spike ratio by 20.9% with 0.5%
accuracy loss. TS-MLE and SLCS increases the throughput of SNN by 2.8x while
decreasing the hardware resource for spike generator by 75% compared with
previous generators.