title: From inconsistency to incompatibility

abstract:
  The aim of this article is to generalize logics of formal inconsistency
($\textbf{LFI}$s) to systems dealing with the concept of incompatibility,
expressed by means of a binary connective. The basic idea is that having two
incompatible formulas to hold trivializes a deduction, and as a special case, a
formula becomes consistent (in the sense of $\textbf{LFI}$s) when it is
incompatible with its own negation. We show how this notion extends that of
consistency in a non-trivial way, presenting conservative translations for many
simple $\textbf{LFI}$s into some of the most basic logics of incompatibility,
what evidences in a precise way how the notion of incompatibility generalizes
that of consistency. We provide semantics for the new logics, as well as
decision procedures, based on restricted non-deterministic matrices. The use of
non-deterministic semantics with restrictions is justified by the fact that, as
proved here, these systems are not algebraizable according to Blok-Pigozzi nor
are they characterizable by finite Nmatrices. Finally, we briefly compare our
logics to other systems focused on treating incompatibility, specially those
pioneered by Brandom and further developed by Peregrin.


title:
Online Learning for Orchestration of Inference in Multi-User
  End-Edge-Cloud Networks

abstract:
  Deep-learning-based intelligent services have become prevalent in
cyber-physical applications including smart cities and health-care. Deploying
deep-learning-based intelligence near the end-user enhances privacy protection,
responsiveness, and reliability. Resource-constrained end-devices must be
carefully managed in order to meet the latency and energy requirements of
computationally-intensive deep learning services. Collaborative end-edge-cloud
computing for deep learning provides a range of performance and efficiency that
can address application requirements through computation offloading. The
decision to offload computation is a communication-computation co-optimization
problem that varies with both system parameters (e.g., network condition) and
workload characteristics (e.g., inputs). On the other hand, deep learning model
optimization provides another source of tradeoff between latency and model
accuracy. An end-to-end decision-making solution that considers such
computation-communication problem is required to synergistically find the
optimal offloading policy and model for deep learning services. To this end, we
propose a reinforcement-learning-based computation offloading solution that
learns optimal offloading policy considering deep learning model selection
techniques to minimize response time while providing sufficient accuracy. We
demonstrate the effectiveness of our solution for edge devices in an
end-edge-cloud system and evaluate with a real-setup implementation using
multiple AWS and ARM core configurations. Our solution provides 35% speedup in
the average response time compared to the state-of-the-art with less than 0.9%
accuracy reduction, demonstrating the promise of our online learning framework
for orchestrating DL inference in end-edge-cloud systems.


title:
Cell-Free Massive MIMO with Finite Fronthaul Capacity: A Stochastic
  Geometry Perspective

abstract:
  In this work, we analyze the downlink performance of a cell-free massive
multiple-input-multiple-output system with finite capacity fronthaul links
between the centralized baseband units and the access point (APs). Conditioned
on the user and AP locations, we first derive an achievable rate for a randomly
selected user in the network that captures the effect of finite fronthaul
capacity. Further, we present the performance analysis for two different types
of network architecture, namely the traditional and the user-centric. For the
traditional architecture, where each user is served by all the APs in the
network, we derive the user rate coverage using statistical properties of the
binomial point process. For the user-centric architecture, where each user is
served by a specified number of its nearest APs, we derive the rate coverage
for the typical user using statistical properties of the Poisson point process.
In addition, we statistically characterize the number of users per AP that is
necessary for coverage analysis. From the system analyses, we conclude that for
the traditional architecture the average system sum-rate is a quasi-concave
function of the number of users. Further, for the user-centric architecture,
there exists an optimal number of serving APs that maximizes the average user
rate.


title:
Analysing Security and Privacy Threats in the Lockdown Periods of
  COVID-19 Pandemic: Twitter Dataset Case Study

abstract:
  The COVID-19 pandemic will be remembered as a uniquely disruptive period that
altered the lives of billions of citizens globally, resulting in new-normal for
the way people live and work. With the coronavirus pandemic, everyone had to
adapt to the "work or study from home" operating model that has transformed our
online lives and exponentially increased the use of cyberspace. Concurrently,
there has been a huge spike in social media platforms such as Facebook and
Twitter during the COVID-19 lockdown periods. These lockdown periods have
resulted in a set of new cybercrimes, thereby allowing attackers to victimise
users of social media platforms in times of fear, uncertainty, and doubt. The
threats range from running phishing campaigns and malicious domains to
extracting private information about victims for malicious purposes. This
research paper performs a large-scale study to investigate the impact of
lockdown periods during the COVID-19 pandemic on the security and privacy of
social media users. We analyse 10.6 Million COVID-related tweets from 533 days
of data crawling and investigate users' security and privacy behaviour in three
different periods (i.e., before, during, and after lockdown). Our study shows
that users unintentionally share more personal identifiable information when
writing about the pandemic situation in their tweets. The privacy risk reaches
100% if a user posts three or more sensitive tweets about the pandemic. We
investigate the number of suspicious domains shared in social media during
different pandemic phases. Our analysis reveals an increase in suspicious
domains during the lockdown compared to other lockdown phases. We observe that
IT, Search Engines, and Businesses are the top three categories that contain
suspicious domains. Our analysis reveals that adversaries' strategies to
instigate malicious activities change with the country's pandemic situation.


title:
Exploration of the spin-phonon coupling in the noncollinear
  antiferromagnetic antiperovskite Mn$_3$NiN

abstract:
  Antiferromagnetic antiperovskites, of the form Mn$_3B$N ($B$ = Ni, Cu, Zn,
Sn, Ir, and Pt), have shown an outstanding behavior in which, giant negative
thermal expansion and chiral magnetic structures are intertwined. As such,
aiming to shed light on the magnetostructural behavior, related to the magnetic
ordering and structure of this type of materials, we studied by theoretical
first-principles calculations the spin-phonon coupling in the Manganese-based
antiperovskite Mn$_3$NiN, as a prototype in the Mn$_3B$N family. We found a
strong spin-lattice coupling by means of the understanding of the
phonon-dispersion curves obtained when including the chiral noncollinear
magnetic structure. To do so, we highlight the importance of the
exchange-correlation scheme selected and its influence on the electronic,
structural, and vibrational degrees of freedom. Finally, we found two unstable
vibrational modes at the $M$- and $R$-points when the magnetic ordering is
switched to ferromagnetic from the chiral $\Gamma_{4g}$ and $\Gamma_{5g}$. The
latter coupling was observed as a key signature of the strong spin-lattice
interaction.


title:
ReViVD: Exploration and Filtering of Trajectories in an Immersive
  Environment using 3D Shapes

abstract:
  We present ReViVD, a tool for exploring and filtering large trajectory-based
datasets using virtual reality. ReViVD's novelty lies in using simple 3D shapes
-- such as cuboids, spheres and cylinders -- as queries for users to select and
filter groups of trajectories. Building on this simple paradigm, more complex
queries can be created by combining previously made selection groups through a
system of user-created Boolean operations. We demonstrate the use of ReViVD in
different application domains, from GPS position tracking to simulated data
(e.g., turbulent particle flows and traffic simulation). Our results show the
ease of use and expressiveness of the 3D geometric shapes in a broad range of
exploratory tasks. ReViVD was found to be particularly useful for progressively
refining selections to isolate outlying behaviors. It also acts as a powerful
communication tool for conveying the structure of normally abstract datasets to
an audience.


title:
Privacy Leakage of Adversarial Training Models in Federated Learning
  Systems

abstract:
  Adversarial Training (AT) is crucial for obtaining deep neural networks that
are robust to adversarial attacks, yet recent works found that it could also
make models more vulnerable to privacy attacks. In this work, we further reveal
this unsettling property of AT by designing a novel privacy attack that is
practically applicable to the privacy-sensitive Federated Learning (FL)
systems. Using our method, the attacker can exploit AT models in the FL system
to accurately reconstruct users' private training images even when the training
batch size is large. Code is available at
https://github.com/zjysteven/PrivayAttack_AT_FL.


title: Multilayer Random Sequential Adsorption

abstract:
  In this work, we present a variant of the multilayer random sequential
adsorption (RSA) process that is inspired by orthogonal resource sharing in
wireless communication networks. In the one-dimensional (1D) version of this
variant, the deposition of overlapping rods is allowed only if they are
assigned two different colors, where colors are symbolic of orthogonal
resources, such as frequency bands, in communication networks. Owing to a
strong spatial coupling among the deposited rods of different colors, finding
an exact solution for the density of deposited rods of a given color as a
function of time seems intractable. Hence, we propose two useful approximations
to obtain the time-varying density of rods of a given color. The first
approximation is based on the recursive use of the known monolayer RSA result
for the indirect estimation of the density of rods for the multilayer version.
The second approximation, which is more accurate but computationally intensive,
involves accurate characterization of the time evolution of the gap density
function. This gap density function is subsequently used to estimate the
density of rods of a given color. We also consider the two-dimensional (2D)
version of this problem, where we estimate the time-varying density of
deposited circles of a given color as a function of time by extending the first
approximation approach developed for the 1D case. The accuracy of all the
results is validated through extensive Monte Carlo simulations.


title:
Communication-Efficient Algorithms for Solving Pressure Poisson Equation
  for Multiphase Flows using Parallel Computers

abstract:
  Numerical solution of partial differential equations on parallel computers
using domain decomposition usually requires synchronization and communication
among the processors. These operations often have a significant overhead in
terms of time and energy. In this paper, we propose communication-efficient
parallel algorithms for solving partial differential equations that alleviate
this overhead. First, we describe an asynchronous algorithm that removes the
requirement of synchronization and checks for termination in a distributed
fashion while maintaining the provision to restart iterations if necessary.
Then, we build on the asynchronous algorithm to propose an event-triggered
communication algorithm that communicates the boundary values to neighboring
processors only at certain iterations, thereby reducing the number of messages
while maintaining similar accuracy of solution. We demonstrate our algorithms
on a successive over-relaxation solver for the Pressure Poisson equation
arising from variable density incompressible multiphase flows in 3-D and show
that our algorithms improve time and energy efficiency.


title:
Linking stability of nonlinear sampled-data systems and their
  continuous-time limits

abstract:
  Discrete-time (DT) models of sampled-data systems can be regarded as
predictions of the future state value given current values for the state, input
and sampling period. A DT model is exact if its prediction coincides with the
true value and is otherwise approximate. Conditions under which Semiglobal
Exponential Stability under nonuniform sampling (SES-VSR) is preserved among
different (exact or approximate) DT models when fed back with the same
sampling-period dependent control law exist. The current paper proves that the
exact DT model is SES-VSR if and only if its corresponding continuous-time (CT)
limit (for infinitesimally small sampling period) is Globally Exponentially
Stable (GES), which is restrictive. The contribution of this note consists in
extending and relaxing the assumptions of previous results, as follows: (i)
only mild conditions are given in order to link stability properties between DT
models fed back with \emph{different} control laws, and (ii) the DT model
properties making the CT limit Globally Asymptotically and Locally
Exponentially Stable (GALES), instead of GES, are provided.


title:
Imbalanced Classification via Explicit Gradient Learning From Augmented
  Data

abstract:
  Learning from imbalanced data is one of the most significant challenges in
real-world classification tasks. In such cases, neural networks performance is
substantially impaired due to preference towards the majority class. Existing
approaches attempt to eliminate the bias through data re-sampling or
re-weighting the loss in the learning process. Still, these methods tend to
overfit the minority samples and perform poorly when the structure of the
minority class is highly irregular. Here, we propose a novel deep meta-learning
technique to augment a given imbalanced dataset with new minority instances.
These additional data are incorporated in the classifier's deep-learning
process, and their contributions are learned explicitly. The advantage of the
proposed method is demonstrated on synthetic and real-world datasets with
various imbalance ratios.


title: Geometry-Aware Planar Embedding of Treelike Structures

abstract:
  The growing complexity of spatial and structural information in 3D data makes
data inspection and visualization a challenging task. We describe a method to
create a planar embedding of 3D treelike structures using their skeleton
representations. Our method maintains the original geometry, without overlaps,
to the best extent possible, allowing exploration of the topology within a
single view. We present a novel camera view generation method which maximizes
the visible geometric attributes (segment shape and relative placement between
segments). Camera views are created for individual segments and are used to
determine local bending angles at each node by projecting them to 2D. The final
embedding is generated by minimizing an energy function (the weights of which
are user adjustable) based on branch length and the 2D angles, while avoiding
intersections. The user can also interactively modify segment placement within
the 2D embedding, and the overall embedding will update accordingly. A global
to local interactive exploration is provided using hierarchical camera views
that are created for subtrees within the structure. We evaluate our method both
qualitatively and quantitatively and demonstrate our results by constructing
planar visualizations of line data (traced neurons) and volume data (CT
vascular and bronchial data


title: Qualitative Behavior of a Metabolic Pathway with Hybrid Feedback

abstract:
  We study the qualitative behavior of a model to represent local regulation in
a metabolic network. The model is based on the end-product control structure
introduced in [A. Goelzer, F. Bekkal Brikci, I. Martin-Verstraete et al., BMC
Syst Biol 2 (2008), pp. 1--18]. In this class of regulation, the metabolite
effector is the end-product of a metabolic pathway. We suppose the input to the
pathway to switch between zero and a positive value according to the
concentration of the metabolite effector. Considering the switched system as a
differential inclusion, we prove that it converges to a globally uniformly
asymptotically stable equilibrium point, reaches the sliding mode or oscillates
around the sliding mode depending on the positive value of the input. Finally,
we show that in any case the solution of the switched system is the limit of
solutions of equation sequences with smooth or piecewise linear inputs.


title:
Guidelines and evaluation for clinical explainable AI on medical image
  analysis

abstract:
  Explainable artificial intelligence (XAI) is essential for enabling clinical
users to get informed decision support from AI and comply with evidence-based
medical practice. Applying XAI in clinical settings requires proper evaluation
criteria to ensure the explanation technique is both technically sound and
clinically useful, but specific support is lacking to achieve this goal. To
bridge the research gap, we propose the Clinical XAI Guidelines that consist of
five criteria a clinical XAI needs to be optimized for. The guidelines
recommend choosing an explanation form based on Guideline 1 (G1)
Understandability and G2 Clinical relevance. For the chosen explanation form,
its specific XAI technique should be optimized for G3 Truthfulness, G4
Informative plausibility, and G5 Computational efficiency.
  Following the guidelines, we conducted a systematic evaluation on a novel
problem of multi-modal medical image explanation with two clinical tasks, and
proposed new evaluation metrics accordingly. The evaluated 16 commonly-used
heatmap XAI techniques were not suitable for clinical use due to their failure
in \textbf{G3} and \textbf{G4}. Our evaluation demonstrated the use of Clinical
XAI Guidelines to support the design and evaluation for clinically viable XAI.


title:
Ensemble Learning techniques for object detection in high-resolution
  satellite images

abstract:
  Ensembling is a method that aims to maximize the detection performance by
fusing individual detectors. While rarely mentioned in deep-learning articles
applied to remote sensing, ensembling methods have been widely used to achieve
high scores in recent data science com-petitions, such as Kaggle. The few
remote sensing articles mentioning ensembling mainly focus on mid resolution
images and earth observation applications such as land use classification, but
never on Very High Resolution (VHR) images for defense-related applications or
object detection.This study aims at reviewing the most relevant ensembling
techniques to be used for object detection on very high resolution imagery and
shows an example of the value of such techniques on a relevant operational
use-case (vehicle detection in desert areas).


title:
Effective Training Strategies for Deep-learning-based Precipitation
  Nowcasting and Estimation

abstract:
  Deep learning has been successfully applied to precipitation nowcasting. In
this work, we propose a pre-training scheme and a new loss function for
improving deep-learning-based nowcasting. First, we adapt U-Net, a widely-used
deep-learning model, for the two problems of interest here: precipitation
nowcasting and precipitation estimation from radar images. We formulate the
former as a classification problem with three precipitation intervals and the
latter as a regression problem. For these tasks, we propose to pre-train the
model to predict radar images in the near future without requiring ground-truth
precipitation, and we also propose the use of a new loss function for
fine-tuning to mitigate the class imbalance problem. We demonstrate the
effectiveness of our approach using radar images and precipitation datasets
collected from South Korea over seven years. It is highlighted that our
pre-training scheme and new loss function improve the critical success index
(CSI) of nowcasting of heavy rainfall (at least 10 mm/hr) by up to 95.7% and
43.6%, respectively, at a 5-hr lead time. We also demonstrate that our approach
reduces the precipitation estimation error by up to 10.7%, compared to the
conventional approach, for light rainfall (between 1 and 10 mm/hr). Lastly, we
report the sensitivity of our approach to different resolutions and a detailed
analysis of four cases of heavy rainfall.


title:
Nano-Trap Engineering in MOF Microenvironment for Ultratrace Iodine
  Sensors

abstract:
  Ultra-sensitive and highly selective iodine gas sensors play a crucial role
during the nuclear radiation leak for a timely detection and mitigation of
pollution, ensuring the safety of a vast number of operators and subsequent
integrity of the facility. Herein, we rationally designed a metal-organic
framework (MOF) that exhibits an outstanding performance with an almost
billion-fold enhancement in the electrical response due to its optimized
hydrophobicity, which allows the easy migration of iodine molecules though the
channels and the presence of suitable interaction sites, temporarily anchoring
the target molecule for ultra-trace sensing. The prototype sensor tested in
demanding environments demonstrates its high selectivity, ultra-trace parts per
billion (ppb)-level sensitivity, good reversibility, and a very fast response
time even at high frequencies compared to existing adsorbents, including
commercially available materials. Further, the iodine sensing at the atomic
level was studied in detail by measuring the electrical response of a single
crystal and, the optimal thickness of the MOF layer was identified for an
industrially-viable prototype sensor by using inkjet printing. In a wider
perspective, we propose a general strategy towards electrically efficient
sensing materials with hybrid functionalities for engineering high-sensitivity
iodine sensors for a safe and sustainable future.


title:
Temperature-independent optical cavities for laser frequency
  stabilization

abstract:
  We propose a method for thermal expansion compensation of reference
monolithic optical cavities for laser frequency stabilization. Two schemes of
optical cavities are considered: a Fabry-Perot interferometer with a crimp ring
and a whispering-gallery-mode cavity with a clamp. In each scheme, thermal
expansion compensation is achieved due to the strained connection of the cavity
with an element made of a material with a high coefficient of thermal
expansion. The temperature region of the cavities' optical length stabilization
is estimated.


title:
Hierarchical Deep Generative Models for Design Under Free-Form Geometric
  Uncertainty

abstract:
  Deep generative models have demonstrated effectiveness in learning compact
and expressive design representations that significantly improve geometric
design optimization. However, these models do not consider the uncertainty
introduced by manufacturing or fabrication. Past work that quantifies such
uncertainty often makes simplifying assumptions on geometric variations, while
the "real-world", "free-form" uncertainty and its impact on design performance
are difficult to quantify due to the high dimensionality. To address this
issue, we propose a Generative Adversarial Network-based Design under
Uncertainty Framework (GAN-DUF), which contains a deep generative model that
simultaneously learns a compact representation of nominal (ideal) designs and
the conditional distribution of fabricated designs given any nominal design.
This opens up new possibilities of 1)~building a universal uncertainty
quantification model compatible with both shape and topological designs,
2)~modeling free-form geometric uncertainties without the need to make any
assumptions on the distribution of geometric variability, and 3)~allowing fast
prediction of uncertainties for new nominal designs. We can combine the
proposed deep generative model with robust design optimization or
reliability-based design optimization for design under uncertainty. We
demonstrated the framework on two real-world engineering design examples and
showed its capability of finding the solution that possesses better
performances after fabrication.


title:
Magnetic field induced corrections to the NJL model coupling constant
  from vacuum polarization

abstract:
  Magnetic field dependent corrections for the coupling constant of the
Nambu-Jona-Lasinio model are calculated by considering the one-loop background
field method. These coupling constants turn out to break chiral and flavor
symmetries and they lead to a slight improvement of the numerical values of the
up and down quark condensates when compared to results from lattice QCD. The
corresponding magnetic field dependencies of the neutral pion and kaon masses
are also presented and compared with available lattice QCD calculations. The
resulting magnetic field correction to the $\eta-\eta'$ mixing angle is also
estimated.


title: Moment Matching Deep Contrastive Latent Variable Models

abstract:
  In the contrastive analysis (CA) setting, machine learning practitioners are
specifically interested in discovering patterns that are enriched in a target
dataset as compared to a background dataset generated from sources of variation
irrelevant to the task at hand. For example, a biomedical data analyst may seek
to understand variations in genomic data only present among patients with a
given disease as opposed to those also present in healthy control subjects.
Such scenarios have motivated the development of contrastive latent variable
models to isolate variations unique to these target datasets from those shared
across the target and background datasets, with current state of the art models
based on the variational autoencoder (VAE) framework. However, previously
proposed models do not explicitly enforce the constraints on latent variables
underlying CA, potentially leading to the undesirable leakage of information
between the two sets of latent variables. Here we propose the moment matching
contrastive VAE (MM-cVAE), a reformulation of the VAE for CA that uses the
maximum mean discrepancy to explicitly enforce two crucial latent variable
constraints underlying CA. On three challenging CA tasks we find that our
method outperforms the previous state-of-the-art both qualitatively and on a
set of quantitative metrics.


title:
Approximations of the Set of Trajectories and Integral Funnel of the
  Nonlinear Control Systems with Limited Control Resources

abstract:
  In this paper approximations of the set of trajectories and integral funnel
of the control system described by nonlinear ordinary differential equation
with integral constraint on the control functions are considered. The set of
admissible control functions is replaced by a set, consisting of a finite
number of piecewise-constant control functions. It is shown that the set of
trajectories generated by a finite number of piecewise-constant control
functions is an internal approximation of the set of trajectories. Further,
each trajectory generated by a piecewise-constant control function is
substituted by appropriate Euler's broken line and it is proved that the set
consisting of a finite number of Euler's broken lines is an approximation of
the set of trajectories of given control system. An approximation of the
system's integral funnel by a set consisting of a finite number of points is
obtained.


title:
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement
  Simulator

abstract:
  With the prevalence of wearable devices, inertial measurement unit (IMU) data
has been utilized in monitoring and assessment of human mobility such as human
activity recognition (HAR). Training deep neural network (DNN) models for these
tasks require a large amount of labeled data, which are hard to acquire in
uncontrolled environments. To mitigate the data scarcity problem, we design
CROMOSim, a cross-modality sensor simulator that simulates high fidelity
virtual IMU sensor data from motion capture systems or monocular RGB cameras.
It utilizes a skinned multi-person linear model (SMPL) for 3D body pose and
shape representations, to enable simulation from arbitrary on-body positions. A
DNN model is trained to learn the functional mapping from imperfect trajectory
estimations in a 3D SMPL body tri-mesh due to measurement noise, calibration
errors, occlusion and other modeling artifacts, to IMU data. We evaluate the
fidelity of CROMOSim simulated data and its utility in data augmentation on
various HAR datasets. Extensive experiment results show that the proposed model
achieves a 6.7% improvement over baseline methods in a HAR task.


title:
Heuristic Sensing Schemes for Four-Target Detection in Time-Constrained
  Vector Poisson and Gaussian Channels

abstract:
  In this work, we investigate the different sensing schemes for the detection
of four targets as observed through a vector Poisson and Gaussian channels when
the sensing time resource is limited and the source signals can be observed
through a variety of sum combinations during that fixed time. For this purpose,
we can maximize the mutual information or the detection probability with
respect to the time allocated to different sum combinations, for a given total
fixed time. It is observed that for both Poisson and Gaussian channels; mutual
information and Bayes risk with $0-1$ cost are not necessarily consistent with
each other. Concavity of mutual information between input and output, for
certain sensing schemes, in Poisson channel and Gaussian channel is shown to be
concave w.r.t given times as linear time constraint is imposed. No optimal
sensing scheme for any of the two channels is investigated in this work.


title: Human-in-the-loop Machine Learning: A Macro-Micro Perspective

abstract:
  Though technical advance of artificial intelligence and machine learning has
enabled many promising intelligent systems, many computing tasks are still not
able to be fully accomplished by machine intelligence. Motivated by the
complementary nature of human and machine intelligence, an emerging trend is to
involve humans in the loop of machine learning and decision-making. In this
paper, we provide a macro-micro review of human-in-the-loop machine learning.
We first describe major machine learning challenges which can be addressed by
human intervention in the loop. Then we examine closely the latest research and
findings of introducing humans into each step of the lifecycle of machine
learning. Finally, we analyze current research gaps and point out future
research directions.


title:
T-METASET: Task-Aware Generation of Metamaterial Datasets by
  Diversity-Based Active Learning

abstract:
  Inspired by the recent success of deep learning in diverse domains,
data-driven metamaterials design has emerged as a compelling design paradigm to
unlock the potential of multiscale architecture. However, existing
model-centric approaches lack principled methodologies dedicated to
high-quality data generation. Resorting to space-filling design in shape
descriptor space, existing metamaterial datasets suffer from property
distributions that are either highly imbalanced or at odds with design tasks of
interest. To this end, we propose t-METASET: an intelligent data acquisition
framework for task-aware dataset generation. We seek a solution to a
commonplace yet frequently overlooked scenario at early design stages: when a
massive ($~\sim O(10^4)$) shape library has been prepared with no properties
evaluated. The key idea is to exploit a data-driven shape descriptor learned
from generative models, fit a sparse regressor as the start-up agent, and
leverage diversity-related metrics to drive data acquisition to areas that help
designers fulfill design goals. We validate the proposed framework in three
hypothetical deployment scenarios, which encompass general use, task-aware use,
and tailorable use. Two large-scale shape-only mechanical metamaterial datasets
are used as test datasets. The results demonstrate that t-METASET can
incrementally grow task-aware datasets. Applicable to general design
representations, t-METASET can boost future advancements of not only
metamaterials but data-driven design in other domains.


title: Efficient Massive Machine Type Communication (mMTC) via AMP

abstract:
  We propose efficient and low complexity multiuser detection (MUD) algorithms
for Gaussian multiple access channel (G-MAC) for short-packet transmission in
massive machine type communications. We formulate the problem of MUD for the G-
MAC as a compressive sensing problem with virtual sparsity induced by one-hot
vector transmission of devices in code domain. Our proposed MUD algorithms rely
on block and non-block approximate message passing (AMP) for soft decoding of
the transmitted packets. Taking into account the induced sparsity leads to a
known prior distribution on the transmit vector; hence, the optimal minimum
mean squared error (MMSE) denoiser can be employed. We consider both separable
and non-separable MMSE denoisers for AMP soft decoding. The effectiveness of
the proposed MUD algorithms for a large number of devices is supported by
simulation results. For packets of 8 information bits, while the state of the
art AMP with soft-threshold denoising achieves 5/100 of the converse bound at
Eb/N0 = 4 dB, the proposed algorithms reach 4/9 and 1/2 of the converse bound.


title: Excitation of absorbing exceptional points in the time domain

abstract:
  The response of physical systems to waves is usually analyzed using
eigenmodes and their eigenfrequencies, which, in electrodynamics, are
associated with lasing and perfect absorption phenomena. Non-Hermitian
degeneracies with coalescing eigenvalues and eigenmodes, known as exceptional
points (EPs), lead to a qualitatively different response, which has been shown
to enhance the sensitivity of detectors, among other properties. However, so
far most of the research focus has been on the changes of the frequency
spectrum at and near an EP and the spatial properties of the eigenmodes. Here
we analyze the time-domain excitation properties of systems at an EP that
supports zero scattering, both at real and complex frequencies. We show that
the signature of the EP for such systems consists in perfect capture of
spatially non-uniform inputs of $te^{i\omega t}$, as well spatially uniform
harmonic signals, $e^{i\omega t}$. For lossy systems, the EP can occur at real
$\omega$, corresponding to coherent perfect absorption for $e^{i\omega t}$ and
a combination of absorption and energy storage for $te^{i\omega t}.$ For
lossless systems, an EP of this type is possible at complex $\omega$, in which
case the inputs will be exponentially growing in time and all the input energy
will be stored until the signal is turned off. We analytically derive the main
features of the temporal excitation of these EPs, and demonstrate them for
coupled planar slabs and ring resonators. We also identify interesting
conversion processes that occur at reflection zeros. Finally, we highlight
superior impedance matching enabled by these singularities.


title:
Relations among equicontinuity, characteristic $0$ property,
  periodicity, and their variants

abstract:
  Recurrence is one of the most fundamental properties of dynamics. It is
generalized into several concepts (e.g. equicontinuity, variants of almost
periodicity, distal property, characteristic $0$ property) for actions of
groups and semi-groups. Some concepts for group actions also are introduced for
decomposition (e.g. foliated spaces). Though the studies of decomposition can
be applied to foliated spaces and group actions, they can not be applied to
semi-group actions in general because the set of orbits of a semi-group need
not be a decomposition of the base space. This paper introduces
semi-decomposition to analyze semi-group actions and decompositions in a
unified manner. Then we show that characteristic $0$ properties and
$R$-closedness (i.e. closedness of the orbit element relation) coincide for
semi-decompositions on Hausdorff spaces. Each of weak equicontinuity and weak
almost periodicity for semi-decompositions on metrizable spaces implies
characteristic $0$ property and Hausdorff separation axiom for the class space.
In addition, the weak almost periodicity implies weak upper semi-continuity for
the class decomposition.


title:
Geostatistical Model Resolution Enhancement in the Context of
  Multiple-Point Statistics

abstract:
  Current multiple-point based simulations implementations generate
geostatistical models at the scale of the training image; there is an
assumption that the categories are exclusive at smaller scales. The goal of
this paper is to generate models with multiple-point statistics (MPS) at a
higher resolution than that of the available training image. This paper
addresses model resolution enhancement by studying the scale-dependence of
spatial structure in MPS based models -- extrapolating the smaller scale MPS
from the larger scale MPS, and (2) rescaling the training image directly to the
smaller scale. The first approach investigates the MPS probabilities. A number
of challenges in characterizing smaller scale variability using high-order
statistics are documented. The paper concludes by advocating the direct
rescaling of the training image to generate models at higher resolution.


title:
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn
  Infants

abstract:
  Precise monitoring of respiratory rate in premature infants is essential to
initiate medical interventions as required. Wired technologies can be invasive
and obtrusive to the patients. We propose a Deep Learning enabled wearable
monitoring system for premature newborn infants, where respiratory cessation is
predicted using signals that are collected wirelessly from a non-invasive
wearable Bellypatch put on infant's body. We propose a five-stage design
pipeline involving data collection and labeling, feature scaling, model
selection with hyperparameter tuning, model training and validation, model
testing and deployment. The model used is a 1-D Convolutional Neural Network
(1DCNN) architecture with 1 convolutional layer, 1 pooling layer and 3
fully-connected layers, achieving 97.15% accuracy. To address energy
limitations of wearable processing, several quantization techniques are
explored and their performance and energy consumption are analyzed. We propose
a novel Spiking-Neural-Network(SNN) based respiratory classification solution,
which can be implemented on event-driven neuromorphic hardware. We propose an
approach to convert the analog operations of our baseline 1DCNN to their
spiking equivalent. We perform a design-space exploration using the parameters
of the converted SNN to generate inference solutions having different accuracy
and energy footprints. We select a solution that achieves 93.33% accuracy with
18 times lower energy compared with baseline 1DCNN model. Additionally the
proposed SNN solution achieves similar accuracy but with 4 times less energy.


title:
Generating Videos with Dynamics-aware Implicit Generative Adversarial
  Networks

abstract:
  In the deep learning era, long video generation of high-quality still remains
challenging due to the spatio-temporal complexity and continuity of videos.
Existing prior works have attempted to model video distribution by representing
videos as 3D grids of RGB values, which impedes the scale of generated videos
and neglects continuous dynamics. In this paper, we found that the recent
emerging paradigm of implicit neural representations (INRs) that encodes a
continuous signal into a parameterized neural network effectively mitigates the
issue. By utilizing INRs of video, we propose dynamics-aware implicit
generative adversarial network (DIGAN), a novel generative adversarial network
for video generation. Specifically, we introduce (a) an INR-based video
generator that improves the motion dynamics by manipulating the space and time
coordinates differently and (b) a motion discriminator that efficiently
identifies the unnatural motions without observing the entire long frame
sequences. We demonstrate the superiority of DIGAN under various datasets,
along with multiple intriguing properties, e.g., long video synthesis, video
extrapolation, and non-autoregressive video generation. For example, DIGAN
improves the previous state-of-the-art FVD score on UCF-101 by 30.7% and can be
trained on 128 frame videos of 128x128 resolution, 80 frames longer than the 48
frames of the previous state-of-the-art method.


title:
Ghost projection. II. Beam shaping using realistic spatially-random
  masks

abstract:
  The spatial light modulator and optical data projector both rely on precisely
configurable optical elements to shape a light beam. Here we explore an
image-projection approach which does not require a configurable beam-shaping
element. We term this approach {\em ghost projection} on account of its
conceptual relation to computational ghost imaging. Instead of a configurable
beam shaping element, the method transversely displaces a single illuminated
mask, such as a spatially-random screen, to create specified distributions of
radiant exposure. The method has potential applicability to image projection
employing a variety of radiation and matter wave fields, such as hard x rays,
neutrons, muons, atomic beams and molecular beams. Building on our previous
theoretical and computational studies, we here seek to understand the effects,
sensitivity, and tolerance of some key experimental limitations of the method.
Focusing on the case of hard x rays, we employ experimentally acquired masks to
numerically study the deleterious effects of photon shot noise, inaccuracies in
random-mask exposure time, and inaccuracies in mask positioning, as well as
adapting to spatially non-uniform illumination. Understanding the influence of
these factors will assist in optimizing experimental design and work towards
achieving ghost projection in practice.


title: Deep Iterative Phase Retrieval for Ptychography

abstract:
  One of the most prominent challenges in the field of diffractive imaging is
the phase retrieval (PR) problem: In order to reconstruct an object from its
diffraction pattern, the inverse Fourier transform must be computed. This is
only possible given the full complex-valued diffraction data, i.e. magnitude
and phase. However, in diffractive imaging, generally only magnitudes can be
directly measured while the phase needs to be estimated. In this work we
specifically consider ptychography, a sub-field of diffractive imaging, where
objects are reconstructed from multiple overlapping diffraction images. We
propose an augmentation of existing iterative phase retrieval algorithms with a
neural network designed for refining the result of each iteration. For this
purpose we adapt and extend a recently proposed architecture from the speech
processing field. Evaluation results show the proposed approach delivers
improved convergence rates in terms of both iteration count and algorithm
runtime.


title:
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation
  in Two-sided Markets

abstract:
  The two-sided markets such as ride-sharing companies often involve a group of
subjects who are making sequential decisions across time and/or location. With
the rapid development of smart phones and internet of things, they have
substantially transformed the transportation landscape of human beings. In this
paper we consider large-scale fleet management in ride-sharing companies that
involve multiple units in different areas receiving sequences of products (or
treatments) over time. Major technical challenges, such as policy evaluation,
arise in those studies because (i) spatial and temporal proximities induce
interference between locations and times; and (ii) the large number of
locations results in the curse of dimensionality. To address both challenges
simultaneously, we introduce a multi-agent reinforcement learning (MARL)
framework for carrying policy evaluation in these studies. We propose novel
estimators for mean outcomes under different products that are consistent
despite the high-dimensionality of state-action space. The proposed estimator
works favorably in simulation experiments. We further illustrate our method
using a real dataset obtained from a two-sided marketplace company to evaluate
the effects of applying different subsidizing policies. A Python implementation
of the proposed method is available at
https://github.com/RunzheStat/CausalMARL.


title: Characterizing Error in Noncommutative Geometric Gait Analysis

abstract:
  A key problem in robotic locomotion is in finding optimal shape changes to
effectively displace systems through the world. Variational techniques for gait
optimization require estimates of body displacement per gait cycle; however,
these estimates introduce error due to unincluded high order terms. In this
paper, we formulate existing estimates for displacement, and describe the
contribution of low order terms to these estimates. We additionally describe
the magnitude of higher (third) order effects, and identify that choice of body
coordinate, gait diameter, and starting phase influence these effects. We
demonstrate that variation of such parameters on two example systems (the
differential drive car and Purcell swimmer) effectively manages third order
contributions.


title:
Unveiling the Main Sequence to Starburst Transition Region with a Sample
  of Intermediate Redshift Luminous Infrared Galaxies

abstract:
  We present a CO(3-2) study of four systems composed of six (ultra) luminous
infrared galaxies (U/LIRGs), located at 0.28 < z < 0.44, that straddle the
transition region between regular star forming galaxies and starbursts. These
galaxies benefit from previous multi-wavelength analysis allowing in depth
exploration of an understudied population of U/LIRGs at a time when the
universe is experiencing a rapid decline in star formation rate density. We
detect CO(3-2) emission in four targets and these galaxies fall between the
loci of regular star forming galaxies and starbursts on the Kennicutt-Schmidtt
relation. Compared to low luminosity LIRGs and high luminosity ULIRGs at
similar redshifts, we find they all have similar molecular gas budgets with the
difference in their star formation rates (SFR) driven by the star formation
efficiency (SFE). This suggests that at these redshifts large molecular gas
reservoirs must coincide with an increased SFE to transition a galaxy into the
starburst regime. We studied the structure and kinematics and found our four
detections are either interacting or have disturbed morphology which may be
driving the SFE. One of the CO(3-2) non-detections has a strong continuum
detection, and has been previously observed in H$\alpha$, suggesting an unusual
interstellar medium for a ULIRG. We conclude that our sample of transitioning
U/LIRGs fill the gap between regular star forming galaxies and starbursts,
suggest a continuous change in SFE between these two populations and the
increased SFE may be driven by morphology and differing stages of interaction.


title:
Quantifying the role of antiferromagnetic fluctuations in the
  superconductivity of the doped Hubbard model

abstract:
  We study the contribution of the electron-spin fluctuation coupling to the
superconducting state of the two dimensional Hubbard model within dynamical
cluster approximation (DCA) using a numerical exact continuous time Monte Carlo
solver. By analyzing the frequency dependence of the self energy, we show that
only about half of the superconductivity can be attributed to a "pairing glue"
arising from treating spin fluctuations as a pairing boson in the standard
one-loop theory.


title:
Solutions of Poisson's Equation for Stochastically Monotone Markov
  Chains

abstract:
  Stochastically monotone Markov chains arise in many applied domains,
especially in the setting of queues and storage systems. Poisson's equation is
a key tool for analyzing additive functionals of such models, such as
cumulative sums of waiting times or sums of rewards. In this paper, we show
that when the reward function for such a Markov chain is monotone, the solution
of Poisson's equation is monotone. This implies that the value function
associated with infinite horizon average reward is monotone in the state when
the reward is monotone.


title:
Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud
  Registration

abstract:
  With current trends in sensors (cheaper, more volume of data) and
applications (increasing affordability for new tasks, new ideas in what 3D data
could be useful for); there is corresponding increasing interest in the ability
to automatically, reliably, and cheaply, register together individual point
clouds. The volume of data to handle, and still elusive need to have the
registration occur fully reliably and fully automatically, mean there is a need
to innovate further. One largely untapped area of innovation is that of
exploiting the {\em semantic information} of the points in question. Points on
a tree should match points on a tree, for example, and not points on car.
Moreover, such a natural restriction is clearly human-like - a human would
generally quickly eliminate candidate regions for matching based on semantics.
Employing semantic information is not only efficient but natural. It is also
timely - due to the recent advances in semantic classification capabilities.
This paper advances this theme by demonstrating that state of the art
registration techniques, in particular ones that rely on "preservation of
length under rigid motion" as an underlying matching consistency constraint,
can be augmented with semantic information. Semantic identity is of course also
preserved under rigid-motion, but also under wider motions present in a scene.
We demonstrate that not only the potential obstacle of cost of semantic
segmentation, and the potential obstacle of the unreliability of semantic
segmentation; are both no impediment to achieving both speed and accuracy in
fully automatic registration of large scale point clouds.


title:
Benchmarking missing-values approaches for predictive models on health
  databases

abstract:
  BACKGROUND: As databases grow larger, it becomes harder to fully control
their collection, and they frequently come with missing values: incomplete
observations. These large databases are well suited to train machine-learning
models, for instance for forecasting or to extract biomarkers in biomedical
settings. Such predictive approaches can use discriminative -- rather than
generative -- modeling, and thus open the door to new missing-values
strategies. Yet existing empirical evaluations of strategies to handle missing
values have focused on inferential statistics. RESULTS: Here we conduct a
systematic benchmark of missing-values strategies in predictive models with a
focus on large health databases: four electronic health record datasets, a
population brain imaging one, a health survey and two intensive care ones.
Using gradient-boosted trees, we compare native support for missing values with
simple and state-of-the-art imputation prior to learning. We investigate
prediction accuracy and computational time. For prediction after imputation, we
find that adding an indicator to express which values have been imputed is
important, suggesting that the data are missing not at random. Elaborate
missing values imputation can improve prediction compared to simple strategies
but requires longer computational time on large data. Learning trees that model
missing values-with missing incorporated attribute-leads to robust, fast, and
well-performing predictive modeling. CONCLUSIONS: Native support for missing
values in supervised machine learning predicts better than state-of-the-art
imputation with much less computational cost. When using imputation, it is
important to add indicator columns expressing which values have been imputed.


title: Unleashing the Power of Transformer for Graphs

abstract:
  Despite recent successes in natural language processing and computer vision,
Transformer suffers from the scalability problem when dealing with graphs. The
computational complexity is unacceptable for large-scale graphs, e.g.,
knowledge graphs. One solution is to consider only the near neighbors, which,
however, will lose the key merit of Transformer to attend to the elements at
any distance. In this paper, we propose a new Transformer architecture, named
dual-encoding Transformer (DET). DET has a structural encoder to aggregate
information from connected neighbors and a semantic encoder to focus on
semantically useful distant nodes. In comparison with resorting to multi-hop
neighbors, DET seeks the desired distant neighbors via self-supervised
training. We further find these two encoders can be incorporated to boost each
others' performance. Our experiments demonstrate DET has achieved superior
performance compared to the respective state-of-the-art methods in dealing with
molecules, networks and knowledge graphs with various sizes.


title:
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in
  Eliminating Data Bias

abstract:
  Backdoor attack is a new AI security risk that has emerged in recent years.
Drawing on the previous research of adversarial attack, we argue that the
backdoor attack has the potential to tap into the model learning process and
improve model performance. Based on Clean Accuracy Drop (CAD) in backdoor
attack, we found that CAD came out of the effect of pseudo-deletion of data. We
provided a preliminary explanation of this phenomenon from the perspective of
model classification boundaries and observed that this pseudo-deletion had
advantages over direct deletion in the data debiasing problem. Based on the
above findings, we proposed Debiasing Backdoor Attack (DBA). It achieves SOTA
in the debiasing task and has a broader application scenario than
undersampling.


title: MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

abstract:
  Reinforcement learning competitions advance the field by providing
appropriate scope and support to develop solutions toward a specific problem.
To promote the development of more broadly applicable methods, organizers need
to enforce the use of general techniques, the use of sample-efficient methods,
and the reproducibility of the results. While beneficial for the research
community, these restrictions come at a cost -- increased difficulty. If the
barrier for entry is too high, many potential participants are demoralized.
With this in mind, we hosted the third edition of the MineRL ObtainDiamond
competition, MineRL Diamond 2021, with a separate track in which we permitted
any solution to promote the participation of newcomers. With this track and
more extensive tutorials and support, we saw an increased number of
submissions. The participants of this easier track were able to obtain a
diamond, and the participants of the harder track progressed the generalizable
solutions in the same task.


title:
DeepSketch: A New Machine Learning-Based Reference Search Technique for
  Post-Deduplication Delta Compression

abstract:
  Data reduction in storage systems is becoming increasingly important as an
effective solution to minimize the management cost of a data center. To
maximize data-reduction efficiency, existing post-deduplication
delta-compression techniques perform delta compression along with traditional
data deduplication and lossless compression. Unfortunately, we observe that
existing techniques achieve significantly lower data-reduction ratios than the
optimal due to their limited accuracy in identifying similar data blocks.
  In this paper, we propose DeepSketch, a new reference search technique for
post-deduplication delta compression that leverages the learning-to-hash method
to achieve higher accuracy in reference search for delta compression, thereby
improving data-reduction efficiency. DeepSketch uses a deep neural network to
extract a data block's sketch, i.e., to create an approximate data signature of
the block that can preserve similarity with other blocks. Our evaluation using
eleven real-world workloads shows that DeepSketch improves the data-reduction
ratio by up to 33% (21% on average) over a state-of-the-art post-deduplication
delta-compression technique.


title: Variational Neural Temporal Point Process

abstract:
  A temporal point process is a stochastic process that predicts which type of
events is likely to happen and when the event will occur given a history of a
sequence of events. There are various examples of occurrence dynamics in the
daily life, and it is important to train the temporal dynamics and solve two
different prediction problems, time and type predictions. Especially, deep
neural network based models have outperformed the statistical models, such as
Hawkes processes and Poisson processes. However, many existing approaches
overfit to specific events, instead of learning and predicting various event
types. Therefore, such approaches could not cope with the modified
relationships between events and fail to predict the intensity functions of
temporal point processes very well. In this paper, to solve these problems, we
propose a variational neural temporal point process (VNTPP). We introduce the
inference and the generative networks, and train a distribution of latent
variable to deal with stochastic property on deep neural network. The intensity
functions are computed using the distribution of latent variable so that we can
predict event types and the arrival times of the events more accurately. We
empirically demonstrate that our model can generalize the representations of
various event types. Moreover, we show quantitatively and qualitatively that
our model outperforms other deep neural network based models and statistical
processes on synthetic and real-world datasets.


title:
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series
  Forecasting

abstract:
  Time series forecasting plays a key role in a variety of domains. In a lot of
real-world scenarios, there exist multiple forecasting entities (e.g. power
station in the solar system, stations in the traffic system). A straightforward
forecasting solution is to mine the temporal dependency for each individual
entity through 1d-CNN, RNN, transformer, etc. This approach overlooks the
relations between these entities and, in consequence, loses the opportunity to
improve performance using spatial-temporal relation. However, in many
real-world scenarios, beside explicit relation, there could exist crucial yet
implicit relation between entities. How to discover the useful implicit
relation between entities and effectively utilize the relations for each entity
under various circumstances is crucial. In order to mine the implicit relation
between entities as much as possible and dynamically utilize the relation to
improve the forecasting performance, we propose an attentional multi-graph
neural network with automatic graph learning (A2GNN) in this work.
Particularly, a Gumbel-softmax based auto graph learner is designed to
automatically capture the implicit relation among forecasting entities. We
further propose an attentional relation learner that enables every entity to
dynamically pay attention to its preferred relations. Extensive experiments are
conducted on five real-world datasets from three different domains. The results
demonstrate the effectiveness of A2GNN beyond several state-of-the-art methods.


title: Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer

abstract:
  Machine learning, especially deep learning, has greatly advanced molecular
studies in the biochemical domain. Most typically, modeling for most molecular
tasks have converged to several paradigms. For example, we usually adopt the
prediction paradigm to solve tasks of molecular property prediction. To improve
the generation and interpretability of purely data-driven models, researchers
have incorporated biochemical domain knowledge into these models for molecular
studies. This knowledge incorporation has led to a rising trend of paradigm
transfer, which is solving one molecular learning task by reformulating it as
another one. In this paper, we present a literature review towards
knowledge-informed molecular learning in perspective of paradigm transfer,
where we categorize the paradigms, review their methods and analyze how domain
knowledge contributes. Furthermore, we summarize the trends and point out
interesting future directions for molecular learning.


title:
Cyber Loss Model Risk Translates to Premium Mispricing and Risk
  Sensitivity

abstract:
  We focus on model risk and risk sensitivity when addressing the insurability
of cyber risk. The standard statistical approaches to assessment of
insurability and potential mispricing are enhanced in several aspects involving
consideration of model risk. Model risk can arise from model uncertainty, and
parameters uncertainty. We demonstrate how to quantify the effect of model risk
in this analysis by incorporating various robust estimators for key model
parameter estimates that apply in both marginal and joint cyber risk loss
process modelling. We contrast these robust techniques with standard methods
previously used in studying insurabilty of cyber risk. This allows us to
accurately assess the critical impact that robust estimation can have on tail
index estimation for heavy tailed loss models, as well as the effect of robust
dependence analysis when quantifying joint loss models and insurance portfolio
diversification. We argue that the choice of such methods is akin to a form of
model risk and we study the risk sensitivity that arise from choices relating
to the class of robust estimation adopted and the impact of the settings
associated with such methods on key actuarial tasks such as premium calculation
in cyber insurance. Through this analysis we are able to address the question
that, to the best of our knowledge, no other study has investigated in the
context of cyber risk: is model risk present in cyber risk data, and how does
is it translate into premium mispricing? We believe our findings should
complement existing studies seeking to explore insurability of cyber losses. In
order to ensure our findings are based on realistic industry informed loss
data, we have utilised one of the leading industry cyber loss datasets obtained
from Advisen, which represents a comprehensive data set on cyber monetary
losses, from which we form our analysis and conclusions.


title:
Off-Policy Confidence Interval Estimation with Confounded Markov
  Decision Process

abstract:
  This paper is concerned with constructing a confidence interval for a target
policy's value offline based on a pre-collected observational data in infinite
horizon settings. Most of the existing works assume no unmeasured variables
exist that confound the observed actions. This assumption, however, is likely
to be violated in real applications such as healthcare and technological
industries. In this paper, we show that with some auxiliary variables that
mediate the effect of actions on the system dynamics, the target policy's value
is identifiable in a confounded Markov decision process. Based on this result,
we develop an efficient off-policy value estimator that is robust to potential
model misspecification and provide rigorous uncertainty quantification. Our
method is justified by theoretical results, simulated and real datasets
obtained from ridesharing companies.


title: Online Caching with Optimistic Learning

abstract:
  The design of effective online caching policies is an increasingly important
problem for content distribution networks, online social networks and edge
computing services, among other areas. This paper proposes a new algorithmic
toolbox for tackling this problem through the lens of optimistic online
learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework
which is developed further here to include predictions for the file requests,
and we design online caching algorithms for bipartite networks with fixed-size
caches or elastic leased caches subject to time-average budget constraints. The
predictions are provided by a content recommendation system that influences the
users viewing activity, and hence can naturally reduce the caching network's
uncertainty about future requests. We prove that the proposed optimistic
learning caching policies can achieve sub-zero performance loss (regret) for
perfect predictions, and maintain the best achievable regret bound $O(\sqrt T)$
even for arbitrary-bad predictions. The performance of the proposed algorithms
is evaluated with detailed trace-driven numerical tests.


title: Weyl Laws for Open Quantum Maps

abstract:
  We find Weyl upper bounds for the quantum open baker's map in the
semiclassical limit. For the number of eigenvalues in an annulus, we derive the
asymptotic upper bound $\mathcal O(N^\delta)$ where $\delta$ is the dimension
of the trapped set of the baker's map and $(2 \pi N)^{-1}$ is the semiclassical
parameter, which improves upon the previous result of $\mathcal O(N^{\delta +
\epsilon})$. Furthermore, we derive a Weyl upper bound with explicit dependence
on the inner radius of the annulus for quantum open baker's maps with Gevrey
cutoffs.


title:
A Strong Minimum principle and Large Time Asymptotics for viscosity
  solutions to a class of doubly nonlinear possibly degenerate parabolic
  equations

abstract:
  We study a version of the strong minimum principle, and large time
asymptotics of positive viscosity solutions to classes of doubly nonlinear
parabolic equations of the form $$ H(Du,D^2u)-u^{k-1}u_t=0,\;\;k\geq
1,\quad\mbox{in $\Omega\times [0,T)$},$$ where $\Omega\subset \mathbb{R}^n$ is
a bounded domain and $0<T\leq \infty$. The spatial operator $H$ is homogeneous
with power $k$.


title:
VADOI:Voice-Activity-Detection Overlapping Inference For End-to-end
  Long-form Speech Recognition

abstract:
  While end-to-end models have shown great success on the Automatic Speech
Recognition task, performance degrades severely when target sentences are
long-form. The previous proposed methods, (partial) overlapping inference are
shown to be effective on long-form decoding. For both methods, word error rate
(WER) decreases monotonically when overlapping percentage decreases. Setting
aside computational cost, the setup with 50% overlapping during inference can
achieve the best performance. However, a lower overlapping percentage has an
advantage of fast inference speed. In this paper, we first conduct
comprehensive experiments comparing overlapping inference and partial
overlapping inference with various configurations. We then propose
Voice-Activity-Detection Overlapping Inference to provide a trade-off between
WER and computation cost. Results show that the proposed method can achieve a
20% relative computation cost reduction on Librispeech and Microsoft Speech
Language Translation long-form corpus while maintaining the WER performance
when comparing to the best performing overlapping inference algorithm. We also
propose Soft-Match to compensate for similar words mis-aligned problem.


title:
Adversarial Attacks on Speech Recognition Systems for Mission-Critical
  Applications: A Survey

abstract:
  A Machine-Critical Application is a system that is fundamentally necessary to
the success of specific and sensitive operations such as search and recovery,
rescue, military, and emergency management actions. Recent advances in Machine
Learning, Natural Language Processing, voice recognition, and speech processing
technologies have naturally allowed the development and deployment of
speech-based conversational interfaces to interact with various
machine-critical applications. While these conversational interfaces have
allowed users to give voice commands to carry out strategic and critical
activities, their robustness to adversarial attacks remains uncertain and
unclear. Indeed, Adversarial Artificial Intelligence (AI) which refers to a set
of techniques that attempt to fool machine learning models with deceptive data,
is a growing threat in the AI and machine learning research community, in
particular for machine-critical applications. The most common reason of
adversarial attacks is to cause a malfunction in a machine learning model. An
adversarial attack might entail presenting a model with inaccurate or
fabricated samples as it's training data, or introducing maliciously designed
data to deceive an already trained model. While focusing on speech recognition
for machine-critical applications, in this paper, we first review existing
speech recognition techniques, then, we investigate the effectiveness of
adversarial attacks and defenses against these systems, before outlining
research challenges, defense recommendations, and future work. This paper is
expected to serve researchers and practitioners as a reference to help them in
understanding the challenges, position themselves and, ultimately, help them to
improve existing models of speech recognition for mission-critical
applications. Keywords: Mission-Critical Applications, Adversarial AI, Speech
Recognition Systems.


title:
Statistical Mechanical theory for spatio-temporal evolution of
  Intra-tumor heterogeneity in cancers: Analysis of Multiregion sequencing data

abstract:
  Heterogeneity in characteristics from one region (sub-population) to another,
commonly observed in complex systems, such as glasses and a collection of
cells, is hard to describe theoretically. In the context of cancer, intra-tumor
heterogeneity (ITH), characterized by cells with genetic and phenotypic
variability that co-exist within a single tumor, is often the cause of
ineffective therapy and recurrence of cancer. Multi-region sequencing (M-Seq),
obtained by sampling multiple regions of a single tumor, has vividly
demonstrated the pervasive nature of ITH, raising the need for a theory that
accounts for evolution of tumor heterogeneity. Here, we develop a statistical
mechanical theory to quantify ITH, using the Hamming distance, between genetic
mutations in distinct regions within a single tumor. An analytic expression for
ITH, expressed in terms of cell division probability ($\alpha$) and mutation
probability ($p$), is validated using cellular-automaton type simulations.
Application of the theory successfully captures ITH extracted from M-seq data
in patients with exogenous cancers (melanoma and lung). The theory, based on
punctuated evolution at the early stages of the tumor followed by neutral
evolution, is accurate provided the spatial variation in the tumor mutation
burden is not large. We show that there are substantial variations in ITH in
distinct regions of a single solid tumor, which supports the notion that
distinct subclones could co-exist. The simulations show that there are
substantial variations in the sub-populations, with the ITH increasing as the
distance between the regions increases. The analytical and simulation framework
developed here could be used in the quantitative analyses of the experimental
(M-Seq) data. More broadly, our theory is likely to be useful in analyzing
dynamic heterogeneity in complex systems such as super-cooled liquids.

categories: cond-mat.soft cond-mat.stat-mech physics.bio-ph q-bio.PE q-bio.TO


title:
Constrained multibody kinematics and dynamics in absolute coordinates: a
  discussion of three approaches to representing rigid body rotation

abstract:
  We compare three approaches to posing the index 3 set of differential
algebraic equations (DAEs) associated with the constrained multibody dynamics
problem formulated in absolute coordinates. The first approach works directly
with the orientation matrix and therefore eschews the need for generalized
coordinates used to produce the orientation matrix $ \mathbf{A} $. The approach
is informed by the fact that rotation matrices belong to the SO(3) Lie matrix
group. The second approach employs Euler parameters, while the third uses Euler
angles. In all cases, the index 3 DAE problem is solved via a first order
implicit numerical integrator. We note a roughly twofold speedup of $
\mathbf{rA} $ over $ \mathbf{r\epsilon} $, and a 1.2 -- 1.3 times speedup of $
\mathbf{r\epsilon} $ over $ \mathbf{rp} $. The tests were carried out in
conjunction with four 3D mechanisms. The improvements in simulation speed of
the $ \mathbf{rA} $ approach are traced back to a simpler form of the equations
of motion and more concise Jacobians that enter the numerical solution. The
contributions made herein are twofold. First, we provide first order variations
of all the quantities that enter the $ \mathbf{rA} $ formulation when used in
the context of implicit integration; i.e., sensitivity of the kinematic
constraints for all lower pair joints, as well as the sensitivity of the
constraint reaction forces. Second, to the best of our knowledge, there is no
other contribution that compares head to head the solution efficiency of $
\mathbf{rA} $, $ \mathbf{rp} $, and $ \mathbf{r\epsilon} $ in the context of
the multibody dynamics problem posed in absolute coordinates.


title:
On the monodromy manifold of $q$-Painlev\'e VI and its Riemann-Hilbert
  problem

abstract:
  We study the sixth $q$-difference Painlev\'e equation
($q{\textrm{P}_{\textrm{VI}}}$) through its associated Riemann-Hilbert problem
(RHP) and show that the RHP is always solvable for irreducible monodromy data.
This enables us to identify the solution space of $q{\textrm{P}_{\textrm{VI}}}$
with a monodromy manifold for generic parameter values. We deduce this manifold
explicitly and show it is a smooth and affine algebraic surface when it does
not contain reducible monodromy. Furthermore, we describe the RHP for reducible
monodromy data and show that, when solvable, its solution is given explicitly
in terms of certain orthogonal polynomials yielding special function solutions
of $q{\textrm{P}_{\textrm{VI}}}$.


title: A frequency resolved atlas of the sky in continuous gravitational waves

abstract:
  We present the first atlas of the continuous gravitational wave sky, produced
using LIGO O3a public data. For each 0.045 Hz frequency band and every point on
the sky the atlas provides upper limits, signal-to-noise ratios (SNR) and
frequencies where the search measures the maximum SNR. The results presented in
the atlas are produced with the Falcon pipeline and cover nearly monochromatic
gravitational wave signals in the 500-1000 Hz band, with up to +/-5e-11 Hz/s
frequency derivative. Compared to the most sensitive results previously
published (also produced with the Falcon pipeline) our upper limits are 50%
more constraining. Neutron stars with ellipticity of 1e-8 can be detected up to
150 pc away, while allowing for a large fraction of the stars' energy to be
lost through non-gravitational channels.


title: Delta Potentials Supported by Hybrid Geometries and Their Deformations

abstract:
  We study the hybrid type of delta potentials in $\mathbb{R}^2$ and
$\mathbb{R}^3$, where the delta potentials supported by a circle/sphere are
considered together with the delta potentials supported by a point outside of
the circle/sphere. The construction of the self-adjoint Hamiltonian operator
associated with the formal expressions for the circle and point delta
potentials is explicitly given. The bound state energies and scattering
properties for each problem are also studied. Finally, we consider the delta
potentials supported by deformed circle/sphere and show that the first order
change in the bound state energies under small deformations of circle/sphere
has a simple geometric interpretation.


title:
Myriad: a real-world testbed to bridge trajectory optimization and deep
  learning

abstract:
  We present Myriad, a testbed written in JAX for learning and planning in
real-world continuous environments. The primary contributions of Myriad are
threefold. First, Myriad provides machine learning practitioners access to
trajectory optimization techniques for application within a typical automatic
differentiation workflow. Second, Myriad presents many real-world optimal
control problems, ranging from biology to medicine to engineering, for use by
the machine learning community. Formulated in continuous space and time, these
environments retain some of the complexity of real-world systems often
abstracted away by standard benchmarks. As such, Myriad strives to serve as a
stepping stone towards application of modern machine learning techniques for
impactful real-world tasks. Finally, we use the Myriad repository to showcase a
novel approach for learning and control tasks. Trained in a fully end-to-end
fashion, our model leverages an implicit planning module over neural ordinary
differential equations, enabling simultaneous learning and planning with
complex environment dynamics.


title:
Quantum Gaussian process model of potential energy surface for a
  polyatomic molecule

abstract:
  With gates of a quantum computer designed to encode multi-dimensional
vectors, projections of quantum computer states onto specific qubit states can
produce kernels of reproducing kernel Hilbert spaces. We show that quantum
kernels obtained with a fixed ansatz implementable on current quantum computers
can be used for accurate regression models of global potential energy surfaces
(PES) for polyatomic molecules. To obtain accurate regression models, we apply
Bayesian optimization to maximize marginal likelihood by varying the parameters
of the quantum gates. This yields Gaussian process models with quantum kernels.
We illustrate the effect of qubit entanglement in the quantum kernels and
explore the generalization performance of quantum Gaussian processes by
extrapolating global six-dimensional PES in the energy domain.


title: Optimization under Connected Uncertainty

abstract:
  Robust optimization methods have shown practical advantages in a wide range
of decision-making applications under uncertainty. Recently, their efficacy has
been extended to multi-period settings. Current approaches model uncertainty
either independent of the past or in an implicit fashion by budgeting the
aggregate uncertainty. In many applications, however, past realizations
directly influence future uncertainties. For this class of problems, we develop
a modeling framework that explicitly incorporates this dependence via connected
uncertainty sets, whose parameters at each period depend on previous
uncertainty realizations. To find optimal here-and-now solutions, we
reformulate robust and distributionally robust constraints for popular set
structures and demonstrate this modeling framework numerically on broadly
applicable knapsack and portfolio-optimization problems.


title: Disentangling Light Fields for Super-Resolution and Disparity Estimation

abstract:
  Light field (LF) cameras record both intensity and directions of light rays,
and encode 3D scenes into 4D LF images. Recently, many convolutional neural
networks (CNNs) have been proposed for various LF image processing tasks.
However, it is challenging for CNNs to effectively process LF images since the
spatial and angular information are highly inter-twined with varying
disparities. In this paper, we propose a generic mechanism to disentangle these
coupled information for LF image processing. Specifically, we first design a
class of domain-specific convolutions to disentangle LFs from different
dimensions, and then leverage these disentangled features by designing
task-specific modules. Our disentangling mechanism can well incorporate the LF
structure prior and effectively handle 4D LF data. Based on the proposed
mechanism, we develop three networks (i.e., DistgSSR, DistgASR and DistgDisp)
for spatial super-resolution, angular super-resolution and disparity
estimation. Experimental results show that our networks achieve
state-of-the-art performance on all these three tasks, which demonstrates the
effectiveness, efficiency, and generality of our disentangling mechanism.
Project page: https://yingqianwang.github.io/DistgLF/.


title: I4/mcm-Si$_{48}$: An Ideal Topological Nodal-Line Semimetal

abstract:
  Topological semimetals (TSMs) have attracted numerous attention due to their
exotic physical properties and great application potentials. Silicon-based TMSs
are of particularly importance because of their high abundance, nontoxicity and
natural compatibility with current semiconductor industry. In this work, an
ideal low-energy topological nodal-line semimetal (TNLSM) silicon
(I4/mcm-Si$_{48}$) with a clean band crossing at Fermi level is screened from
thousands of silicon allotropes by the transferable tight-binding and DFT-HSE
calculations. The results of formation energy, phonon dispersion, ab initio
molecular dynamics and elastic constants show that I4/mcm-Si48 possesses good
stability and is more stable than several synthetized silicon structures. By
analyzing the symmetry, it reveals that the topological nodal-line of
I4/mcm-Si48 is protected by mirror symmetry and inversion, time-reversal and
SU(2) spin-rotation symmetries, and the nearly flat drumhead-like surface
spectrum is observed. Furthermore, I4/mcm-Si48 exhibits exotic photoelectric
properties and the Dirac fermions with high Fermi velocity
(3.4$\sim$4.36$\times$10$^5$ m/s) can be excited by low energy photons. Our
study provides a promising topological nodal-line semimetal for fundamental
research and potential practical applications in semiconductor-compatible
high-speed photoelectric devices.


title: Space Layout of Low-entropy Hydration Shells Guides Protein Binding

abstract:
  Protein-protein binding enables orderly and lawful biological
self-organization, and is therefore considered a miracle of nature.
Protein-protein binding is steered by electrostatic forces, hydrogen bonding,
van der Waals force, and hydrophobic interactions. Among these physical forces,
only the hydrophobic interactions can be considered as long-range
intermolecular attractions between proteins in intracellular and extracellular
fluid. Low-entropy regions of hydration shells around proteins drive
hydrophobic attraction among them that essentially coordinate protein-protein
docking in rotational-conformational space of mutual orientations at the
guidance stage of the binding. Here, an innovative method was developed for
identifying the low-entropy regions of hydration shells of given proteins, and
we discovered that the largest low-entropy regions of hydration shells on
proteins typically cover the binding sites. According to an analysis of
determined protein complex structures, shape matching between the largest
low-entropy hydration shell region of a protein and that of its partner at the
binding sites is revealed as a regular pattern. Protein-protein binding is thus
found to be mainly guided by hydrophobic collapse between the shape-matched
low-entropy hydration shells that is verified by bioinformatics analyses of
hundreds of structures of protein complexes. A simple algorithm is developed to
precisely predict protein binding sites.


title: No-Regret Learning in Partially-Informed Auctions

abstract:
  Auctions with partially-revealed information about items are broadly employed
in real-world applications, but the underlying mechanisms have limited
theoretical support. In this work, we study a machine learning formulation of
these types of mechanisms, presenting algorithms that are no-regret from the
buyer's perspective. Specifically, a buyer who wishes to maximize his utility
interacts repeatedly with a platform over a series of $T$ rounds. In each
round, a new item is drawn from an unknown distribution and the platform
publishes a price together with incomplete, "masked" information about the
item. The buyer then decides whether to purchase the item. We formalize this
problem as an online learning task where the goal is to have low regret with
respect to a myopic oracle that has perfect knowledge of the distribution over
items and the seller's masking function. When the distribution over items is
known to the buyer and the mask is a SimHash function mapping $\mathbb{R}^d$ to
$\{0,1\}^{\ell}$, our algorithm has regret $\tilde
{\mathcal{O}}((Td\ell)^{\frac{1}{2}})$. In a fully agnostic setting when the
mask is an arbitrary function mapping to a set of size $n$, our algorithm has
regret $\tilde {\mathcal{O}}(T^{\frac{3}{4}}n^{\frac{1}{2}})$. Finally, when
the prices are stochastic, the algorithm has regret $\tilde
{\mathcal{O}}((Tn)^{\frac{1}{2}})$.


title: Stability of heteroclinic cycles in rings of coupled oscillators

abstract:
  Networks of interacting nodes connected by edges arise in almost every branch
of scientific enquiry. The connectivity structure of the network can force the
existence of invariant subspaces, which would not arise in generic dynamical
systems. These invariant subspaces can result in the appearance of robust
heteroclinic cycles, which would otherwise be structurally unstable. Typically,
the dynamics near a stable heteroclinic cycle is non-ergodic: mean residence
times near the fixed points in the cycle are undefined, and there is a
persistent slowing down. In this paper, we examine ring networks with
nearest-neighbour or nearest-m-neighbour coupling, and show that there exist
classes of heteroclinic cycles in the phase space of the dynamics. We show that
there is always at least one heteroclinic cycle which can be asymptotically
stable, and thus the attracting dynamics of the network are expected to be
non-ergodic. We conjecture that much of this behaviour persists in less
structured networks and as such, non-ergodic behaviour is somehow typical.


title:
It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum
  Generation

abstract:
  We are interested in training general-purpose reinforcement learning agents
that can solve a wide variety of goals. Training such agents efficiently
requires automatic generation of a goal curriculum. This is challenging as it
requires (a) exploring goals of increasing difficulty, while ensuring that the
agent (b) is exposed to a diverse set of goals in a sample efficient manner and
(c) does not catastrophically forget previously solved goals. We propose
Curriculum Self Play (CuSP), an automated goal generation framework that seeks
to satisfy these desiderata by virtue of a multi-player game with four agents.
We extend the asymmetric curricula learning in PAIRED (Dennis et al., 2020) to
a symmetrized game that carefully balances cooperation and competition between
two off-policy student learners and two regret-maximizing teachers. CuSP
additionally introduces entropic goal coverage and accounts for the
non-stationary nature of the students, allowing us to automatically induce a
curriculum that balances progressive exploration with anti-catastrophic
exploitation. We demonstrate that our method succeeds at generating an
effective curricula of goals for a range of control tasks, outperforming other
methods at zero-shot test-time generalization to novel out-of-distribution
goals.


title: Phase-space stochastic quantum hydrodynamics for interacting Bose gases

abstract:
  Hydrodynamic theories offer successful approaches that are capable of
simulating the otherwise difficult-to-compute dynamics of quantum many-body
systems. In this work we derive, within the positive-P phase-space formalism, a
new stochastic hydrodynamic method for the description of interacting Bose
gases. It goes beyond existing hydrodynamic approaches, such as superfluid
hydrodynamics or generalized hydrodynamics, in its capacity to simulate the
full quantum dynamics of these systems: it possesses the ability to compute
non-equilibrium quantum correlations, even for short-wavelength phenomena.
Using this description, we derive a linearized stochastic hydrodynamic scheme
which is able to simulate such non-equilibrium situations for longer times than
the full positive-P approach, at the expense of approximating the treatment of
quantum fluctuations, and show that this linearized scheme can be directly
connected with existing Bogoliubov approaches. Furthermore, we go on to
demonstrate the usefulness and advantages of this formalism by exploring the
correlations that arise in a quantum shock wave scenario and comparing its
predictions to other established quantum many-body approaches.

categories: cond-mat.quant-gas physics.comp-ph physics.flu-dyn quant-ph


title: Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

abstract:
  Question answering (QA) over real-world knowledge bases (KBs) is challenging
because of the diverse (essentially unbounded) types of reasoning patterns
needed. However, we hypothesize in a large KB, reasoning patterns required to
answer a query type reoccur for various entities in their respective subgraph
neighborhoods. Leveraging this structural similarity between local
neighborhoods of different subgraphs, we introduce a semiparametric model with
(i) a nonparametric component that for each query, dynamically retrieves other
similar $k$-nearest neighbor (KNN) training queries along with query-specific
subgraphs and (ii) a parametric component that is trained to identify the
(latent) reasoning patterns from the subgraphs of KNN queries and then apply it
to the subgraph of the target query. We also propose a novel algorithm to
select a query-specific compact subgraph from within the massive knowledge
graph (KG), allowing us to scale to full Freebase KG containing billions of
edges. We show that our model answers queries requiring complex reasoning
patterns more effectively than existing KG completion algorithms. The proposed
model outperforms or performs competitively with state-of-the-art models on
several KBQA benchmarks.


title: Universal 1-Bit Compressive Sensing for Bounded Dynamic Range Signals

abstract:
  A {\em universal 1-bit compressive sensing (CS)} scheme consists of a
measurement matrix $A$ such that for all signals $x$ belonging to a particular
class, $x$ can be approximately recovered from $\textrm{sign}(Ax)$. 1-bit CS
models extreme quantization effects where only one bit of information is
revealed per measurement. We focus on universal support recovery for 1-bit CS
in the case of {\em sparse} signals with bounded {\em dynamic range}.
Specifically, a vector $x \in \mathbb{R}^n$ is said to have sparsity $k$ if it
has at most $k$ nonzero entries, and dynamic range $R$ if the ratio between its
largest and smallest nonzero entries is at most $R$ in magnitude. Our main
result shows that if the entries of the measurement matrix $A$ are
i.i.d.~Gaussians, then under mild assumptions on the scaling of $k$ and $R$,
the number of measurements needs to be $\tilde{\Omega}(Rk^{3/2})$ to recover
the support of $k$-sparse signals with dynamic range $R$ using $1$-bit CS. In
addition, we show that a near-matching $O(R k^{3/2} \log n)$ upper bound
follows as a simple corollary of known results. The $k^{3/2}$ scaling contrasts
with the known lower bound of $\tilde{\Omega}(k^2 \log n)$ for the number of
measurements to recover the support of arbitrary $k$-sparse signals.


title:
A Decentralized Communication Framework based on Dual-Level Recurrence
  for Multi-Agent Reinforcement Learning

abstract:
  We propose a model enabling decentralized multiple agents to share their
perception of environment in a fair and adaptive way. In our model, both the
current message and historical observation are taken into account, and they are
handled in the same recurrent model but in different forms. We present a
dual-level recurrent communication framework for multi-agent systems, in which
the first recurrence occurs in the communication sequence and is used to
transmit communication data among agents, while the second recurrence is based
on the time sequence and combines the historical observations for each agent.
The developed communication flow separates communication messages from memories
but allows agents to share their historical observations by the dual-level
recurrence. This design makes agents adapt to changeable communication objects,
while the communication results are fair to these agents. We provide a
sufficient discussion about our method in both partially observable and fully
observable environments. The results of several experiments suggest our method
outperforms the existing decentralized communication frameworks and the
corresponding centralized training method.


title:
Gaussian Processes and Statistical Decision-making in Non-Euclidean
  Spaces

abstract:
  Bayesian learning using Gaussian processes provides a foundational framework
for making decisions in a manner that balances what is known with what could be
learned by gathering data. In this dissertation, we develop techniques for
broadening the applicability of Gaussian processes. This is done in two ways.
Firstly, we develop pathwise conditioning techniques for Gaussian processes,
which allow one to express posterior random functions as prior random functions
plus a dependent update term. We introduce a wide class of efficient
approximations built from this viewpoint, which can be randomly sampled once in
advance, and evaluated at arbitrary locations without any subsequent
stochasticity. This key property improves efficiency and makes it simpler to
deploy Gaussian process models in decision-making settings. Secondly, we
develop a collection of Gaussian process models over non-Euclidean spaces,
including Riemannian manifolds and graphs. We derive fully constructive
expressions for the covariance kernels of scalar-valued Gaussian processes on
Riemannian manifolds and graphs. Building on these ideas, we describe a
formalism for defining vector-valued Gaussian processes on Riemannian
manifolds. The introduced techniques allow all of these models to be trained
using standard computational methods. In total, these contributions make
Gaussian processes easier to work with and allow them to be used within a wider
class of domains in an effective and principled manner. This, in turn, makes it
possible to potentially apply Gaussian processes to novel decision-making
settings.


title:
Upsilon invariant for graphs and the homology cobordism group of
  homology cylinders

abstract:
  Upsilon is a homomorphism on the smooth concordance group of knots defined by
Ozsv\'{a}th, Stipsicz and Szab\'{o}. In this paper, we define a generalization
of upsilon for a family of embedded graphs in rational homolog spheres. We show
that our invariant will induce a homomorphism on the homology cobordism group
of homology cylinders, and present some applications. To define this invariant,
we use tangle Floer homology. We lift relative gradings on tangle Floer
homology to absolute gradings (for certain tangles) and prove a concatenation
formula for it.


title: Order-Optimal Error Bounds for Noisy Kernel-Based Bayesian Quadrature

abstract:
  In this paper, we study the sample complexity of {\em noisy Bayesian
quadrature} (BQ), in which we seek to approximate an integral based on noisy
black-box queries to the underlying function. We consider functions in a {\em
Reproducing Kernel Hilbert Space} (RKHS) with the Mat\'ern-$\nu$ kernel,
focusing on combinations of the parameter $\nu$ and dimension $d$ such that the
RKHS is equivalent to a Sobolev class. In this setting, we provide
near-matching upper and lower bounds on the best possible average error.
Specifically, we find that when the black-box queries are subject to Gaussian
noise having variance $\sigma^2$, any algorithm making at most $T$ queries
(even with adaptive sampling) must incur a mean absolute error of
$\Omega(T^{-\frac{\nu}{d}-1} + \sigma T^{-\frac{1}{2}})$, and there exists a
non-adaptive algorithm attaining an error of at most $O(T^{-\frac{\nu}{d}-1} +
\sigma T^{-\frac{1}{2}})$. Hence, the bounds are order-optimal, and establish
that there is no adaptivity gap in terms of scaling laws.

categories: stat.ML cs.IT cs.LG math.IT math.ST stat.TH


title: Isotopy classes of involutions of del Pezzo surfaces

abstract:
  Let $M_n := \mathbb{CP}^2 \# n\overline{\mathbb{CP}^2}$ for $0 \leq n \leq 8$
be the underlying smooth manifold of a degree $9-n$ del Pezzo surface. We prove
three results about the mapping class group $\text{Mod}(M_n) :=
\pi_0(\text{Homeo}^+(M_n))$:
  1. the classification of, and a structure theorem for, all involutions in
$\text{Mod}(M_n)$,
  2. a positive solution to the smooth Nielsen realization problem for
involutions of $M_n$, and
  3. a purely topological characterization of three remarkable types of
involutions on certain $M_n$ coming from birational geometry: de Jonqui\'eres
involutions, Geiser involutions, and Bertini involutions.
  One main ingredient is the theory of hyperbolic reflection groups.


title:
An Ensemble Learning Framework for Vehicle Trajectory Prediction in
  Interactive Scenarios

abstract:
  Precisely modeling interactions and accurately predicting trajectories of
surrounding vehicles are essential to the decision-making and path-planning of
intelligent vehicles. This paper proposes a novel framework based on ensemble
learning to improve the performance of trajectory predictions in interactive
scenarios. The framework is termed Interactive Ensemble Trajectory Predictor
(IETP). IETP assembles interaction-aware trajectory predictors as base learners
to build an ensemble learner. Firstly, each base learner in IETP observes
historical trajectories of vehicles in the scene. Then each base learner
handles interactions between vehicles to predict trajectories. Finally, an
ensemble learner is built to predict trajectories by applying two ensemble
strategies on the predictions from all base learners. Predictions generated by
the ensemble learner are final outputs of IETP. In this study, three
experiments using different data are conducted based on the NGSIM dataset.
Experimental results show that IETP improves the predicting accuracy and
decreases the variance of errors compared to base learners. In addition, IETP
exceeds baseline models with 50% of the training data, indicating that IETP is
data-efficient. Moreover, the implementation of IETP is publicly available at
https://github.com/BIT-Jack/IETP.