title: A class of dissimilarity semimetrics for preference relations

abstract:
  We propose a class of semimetrics for preference relations any one of which
is an alternative to the classical Kemeny-Snell-Bogart metric. (We take a
fairly general viewpoint about what constitutes a preference relation, allowing
for any acyclic order to act as one.) These semimetrics are based solely on the
implications of preferences for choice behavior, and thus appear more suitable
in economic contexts and choice experiments. In our main result, we obtain a
fairly simple axiomatic characterization for the class we propose. The
apparently most important member of this class (at least in the case of finite
alternative spaces), which we dub the top-difference semimetric, is
characterized separately. We also obtain alternative formulae for it, and
relative to this metric, compute the diameter of the space of complete
preferences, as well as the best transitive extension of a given acyclic
preference relation. Finally, we prove that our preference metric spaces cannot
be isometically embedded in a Euclidean space.


title:
Survival Prediction of Brain Cancer with Incomplete Radiology,
  Pathology, Genomics, and Demographic Data

abstract:
  Integrating cross-department multi-modal data (e.g., radiological,
pathological, genomic, and clinical data) is ubiquitous in brain cancer
diagnosis and survival prediction. To date, such an integration is typically
conducted by human physicians (and panels of experts), which can be subjective
and semi-quantitative. Recent advances in multi-modal deep learning, however,
have opened a door to leverage such a process to a more objective and
quantitative manner. Unfortunately, the prior arts of using four modalities on
brain cancer survival prediction are limited by a "complete modalities" setting
(i.e., with all modalities available). Thus, there are still open questions on
how to effectively predict brain cancer survival from the incomplete
radiological, pathological, genomic, and demographic data (e.g., one or more
modalities might not be collected for a patient). For instance, should we use
both complete and incomplete data, and more importantly, how to use those data?
To answer the preceding questions, we generalize the multi-modal learning on
cross-department multi-modal data to a missing data setting. Our contribution
is three-fold: 1) We introduce optimal multi-modal learning with missing data
(MMD) pipeline with optimized hardware consumption and computational
efficiency; 2) We extend multi-modal learning on radiological, pathological,
genomic, and demographic data into missing data scenarios; 3) a large-scale
public dataset (with 962 patients) is collected to systematically evaluate
glioma tumor survival prediction using four modalities. The proposed method
improved the C-index of survival prediction from 0.7624 to 0.8053.


title:
Harmonicity Plays a Critical Role in DNN Based Versus in
  Biologically-Inspired Monaural Speech Segregation Systems

abstract:
  Recent advancements in deep learning have led to drastic improvements in
speech segregation models. Despite their success and growing applicability, few
efforts have been made to analyze the underlying principles that these networks
learn to perform segregation. Here we analyze the role of harmonicity on two
state-of-the-art Deep Neural Networks (DNN)-based models- Conv-TasNet and
DPT-Net. We evaluate their performance with mixtures of natural speech versus
slightly manipulated inharmonic speech, where harmonics are slightly frequency
jittered. We find that performance deteriorates significantly if one source is
even slightly harmonically jittered, e.g., an imperceptible 3% harmonic jitter
degrades performance of Conv-TasNet from 15.4 dB to 0.70 dB. Training the model
on inharmonic speech does not remedy this sensitivity, instead resulting in
worse performance on natural speech mixtures, making inharmonicity a powerful
adversarial factor in DNN models. Furthermore, additional analyses reveal that
DNN algorithms deviate markedly from biologically inspired algorithms that rely
primarily on timing cues and not harmonicity to segregate speech.


title: Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction

abstract:
  Multi-agent interactions are important to model for forecasting other agents'
behaviors and trajectories. At a certain time, to forecast a reasonable future
trajectory, each agent needs to pay attention to the interactions with only a
small group of most relevant agents instead of unnecessarily paying attention
to all the other agents. However, existing attention modeling works ignore that
human attention in driving does not change rapidly, and may introduce
fluctuating attention across time steps. In this paper, we formulate an
attention model for multi-agent interactions based on a total variation
temporal smoothness prior and propose a trajectory prediction architecture that
leverages the knowledge of these attended interactions. We demonstrate how the
total variation attention prior along with the new sequence prediction loss
terms leads to smoother attention and more sample-efficient learning of
multi-agent trajectory prediction, and show its advantages in terms of
prediction accuracy by comparing it with the state-of-the-art approaches on
both synthetic and naturalistic driving data. We demonstrate the performance of
our algorithm for trajectory prediction on the INTERACTION dataset on our
website.


title: ProbTA: A sound and complete proof rule for probabilistic verification

abstract:
  We propose a sound and complete proof rule ProbTA for quantitative analysis
of violation probability of probabilistic programs. Our approach extends the
technique of trace abstraction with probability in the control-flow randomness
style, in contrast to previous work of combining trace abstraction and
probabilisitic verification which adopts the data randomness style. In our
method, a program specification is proved or disproved by decomposing the
program into different modules of traces. Precise quantitative analysis is
enabled by novel models proposed to bridge program verification and probability
theory. Based on the proof rule, we propose a new automated algorithm via CEGAR
involving multiple technical issues unprecedented in non-probabilistic trace
abstraction and data randomness-based approach.


title:
Centers of centralizers of nilpotent elements in exceptional Lie
  superalgebras

abstract:
  Let $\mathfrak{g}=\mathfrak{g}_{\bar{0}}\oplus\mathfrak{g}_{\bar{1}}$ be a
finite-dimensional simple Lie superalgebra of type $D(2,1;\alpha)$, $G(3)$ or
$F(4)$ over $\mathbb{C}$. Let $G$ be the simply connected semisimple algebraic
group over $\mathbb{C}$ such that $\mathrm{Lie}(G)=\mathfrak{g}_{\bar{0}}$.
Suppose $e\in\mathfrak{g}_{\bar{0}}$ is nilpotent. We describe the centralizer
$\mathfrak{g}^{e}$ of $e$ in $\mathfrak{g}$ and its centre
$\mathfrak{z}(\mathfrak{g}^{e})$ especially. We also determine the labelled
Dynkin diagram for $e$. We prove theorems relating the dimension of
$\left(\mathfrak{z}(\mathfrak{g}^{e})\right)^{G^{e}}$ and the labelled Dynkin
diagram.


title: SLAM-Supported Self-Training for 6D Object Pose Estimation

abstract:
  Recent progress in learning-based object pose estimation paves the way for
developing richer object-level world representations. However, the estimators,
often trained with out-of-domain data, can suffer performance degradation as
deployed in novel environments. To address the problem, we present a
SLAM-supported self-training procedure to autonomously improve robot object
pose estimation ability during navigation. Combining the network predictions
with robot odometry, we can build a consistent object-level environment map via
pose graph optimization (PGO). Exploiting the state estimates from PGO, we
pseudo-label robot-collected RGB images to fine-tune the pose estimators.
Unfortunately, it is difficult to quantify the uncertainty of the estimator
predictions. The unmodeled data uncertainty used for PGO can result in
low-quality object pose estimates. An automatic covariance tuning method is
developed for robust PGO by allowing the measurement uncertainty models to
change as part of the optimization process. The formulation permits a
straightforward alternating minimization procedure that re-scales covariances
analytically and component-wise, enabling more flexible noise modeling for
learning-based measurements. We test our method with the deep object pose
estimator (DOPE) on the YCB video dataset and in real-world robot experiments.
The method can achieve significant performance gain in pose estimation, and in
return facilitates the success of object SLAM.


title: The Extended Tidal Tails of NGC 7089 (M2)

abstract:
  Using photometry and proper motions from Gaia Early Data Release 3, we detect
a 45 degree-long trailing stellar debris stream associated with the old,
metal-poor globular cluster NGC 7089. With a width on the order of 100 pc, the
extended stream appears to be as dynamically cold as the coldest known streams
found to date. There is some evidence for an extended leading tail extending
between 28 and 37 degrees from the cluster, though the greater distance of this
tail, combined with proper motions that are virtually indistinguishable from
those of foreground stars, make the detection much less certain. The proper
motion profile and the path on the sky of the trailing tail are not well
matched using a simple Galactic potential comprised purely of a disk, bulge,
and spherical halo. However, the addition of a moving, massive (M = 1.88 x
10^(11) solar masses) Large Magellanic Cloud brings the model predictions into
much better agreement with the observables. We provide tables of the most
highly ranked candidate stream stars for follow-up by ongoing and future
spectroscopic surveys.


title: Efficient quantum gate decomposition via adaptive circuit compression

abstract:
  In this work, we report on a novel quantum gate approximation algorithm based
on the application of parametric two-qubit gates in the synthesis process. The
utilization of these parametric two-qubit gates in the circuit design allows us
to transform the discrete combinatorial problem of circuit synthesis into an
optimization problem over continuous variables. The circuit is then compressed
by a sequential removal of two-qubit gates from the design, while the remaining
building blocks are continuously adapted to the reduced gate structure by
iterated learning cycles. We implemented the developed algorithm in the
SQUANDER software package and benchmarked it against several state-of-the-art
quantum gate synthesis tools. Our numerical experiments revealed outstanding
circuit compression capabilities of our compilation algorithm providing the
most optimal gate count in the majority of the addressed quantum circuits.


title: Analyzing the Security of the Business Collaboration Platform App Model

abstract:
  Business Collaboration Platforms like Microsoft Teams and Slack enable
teamwork by supporting text chatting and third-party resource integration. A
user can access online file storage, make video calls, and manage a code
repository, all from within the platform, thus making them a hub for sensitive
communication and resources. The key enabler for these productivity features is
a third-party application model. We contribute an experimental security
analysis of this model and the third-party apps. Performing this analysis is
challenging because commercial platforms and their apps are closed-source
systems. Our analysis methodology is to systematically investigate different
types of interactions possible between apps and users. We discover that the
access control model in these systems violates two fundamental security
principles: least privilege and complete mediation. These violations enable a
malicious app to exploit the confidentiality and integrity of user messages and
third-party resources connected to the platform. We construct proof-of-concept
attacks that can: (1) eavesdrop on user messages without having permission to
read those messages; (2) launch fake video calls; (3) automatically merge code
into repositories without user approval or involvement. Finally, we provide an
analysis of countermeasures that systems like Slack and Microsoft Teams can
adopt today.


title:
DeepSE-WF: Unified Security Estimation for Website Fingerprinting
  Defenses

abstract:
  Website fingerprinting (WF) attacks, usually conducted with the help of a
machine learning-based classifier, enable a network eavesdropper to pinpoint
which web page a user is accessing through the inspection of traffic patterns.
These attacks have been shown to succeed even when users browse the Internet
through encrypted tunnels, e.g., through Tor or VPNs. To assess the security of
new defenses against WF attacks, recent works have proposed feature-dependent
theoretical frameworks that estimate the Bayes error of an adversary's features
set or the mutual information leaked by manually-crafted features.
Unfortunately, as state-of-the-art WF attacks increasingly rely on deep
learning and latent feature spaces, security estimations based on simpler (and
less informative) manually-crafted features can no longer be trusted to assess
the potential success of a WF adversary in defeating such defenses. In this
work, we propose DeepSE-WF, a novel WF security estimation framework that
leverages specialized kNN-based estimators to produce Bayes error and mutual
information estimates from learned latent feature spaces, thus bridging the gap
between current WF attacks and security estimation methods. Our evaluation
reveals that DeepSE-WF produces tighter security estimates than previous
frameworks, reducing the required computational resources to output security
estimations by one order of magnitude.


title: Generating Stellar Obliquity in Systems with Broken Protoplanetary Disks

abstract:
  Recent advances in sub-millimeter observations of young circumstellar nebulae
have opened an unprecedented window into the structure of protoplanetary disks,
which has revealed the surprising ubiquity of broken and misaligned disks. In
this work, we demonstrate that such disks are capable of torquing the spin axis
of their host star, representing a hitherto unexplored pathway by which stellar
obliquities may be generated. The basis of this mechanism is a crossing of the
stellar spin precession and inner disk regression frequencies, resulting in
adiabatic excitation of the stellar obliquity. We derive analytical expressions
for the characteristic frequencies of the inner disk and star as a function of
the disk gap boundaries, and place an approximate limit on the disk
architectures for which frequency crossing and resulting obliquity excitation
are expected, thereby illustrating the efficacy of this model. Cumulatively,
our results support the emerging concensus that significant spin-orbit
misalignments are an expected outcome of planet formation.


title:
The Impact of Heavy-Duty Vehicle Electrification on Large Power Grids: a
  Synthetic Texas Case Study

abstract:
  The electrification of heavy-duty vehicles (HDEVs) is a nascent and rapidly
emerging avenue for decarbonization of the transportation sector. In this
paper, we examine the impacts of increased vehicle electrification on the power
grid infrastructure, with particular focus on HDEVs. We utilize a synthetic
representation of the 2000-bus Texas transmission grid, and realistic
representations of multiple distribution grids in Travis county, Texas, as well
as transit data pertaining to HDEVs, to uncover the consequences of HDEV
electrification, and expose the limitations imposed by existing electric grid
infrastructure. Our analysis reveals that grid-wide voltage problems that are
spatiotemporally correlated with the mobility of HDEVs may occur even at modest
penetration levels. In fact, we find that as little as 11% of heavy duty
vehicles in Texas charging simultaneously can lead to significant voltage
violations on the transmission network that compromise grid reliability.
Furthermore, we find that just a few dozen EVs charging simultaneously can lead
to voltage violations at the distribution level.


title: UX Personas for defining robot's character and personality

abstract:
  This paper describes the use of UX Personas for defining a stable character
for the Pepper robot. This approach generated from the first analysis of the
social interactions that occurred between Pepper and a group of children with
autism working with the robot in a therapeutic laboratory on autonomy.


title:
Variational Inference with Locally Enhanced Bounds for Hierarchical
  Models

abstract:
  Hierarchical models represent a challenging setting for inference algorithms.
MCMC methods struggle to scale to large models with many local variables and
observations, and variational inference (VI) may fail to provide accurate
approximations due to the use of simple variational families. Some variational
methods (e.g. importance weighted VI) integrate Monte Carlo methods to give
better accuracy, but these tend to be unsuitable for hierarchical models, as
they do not allow for subsampling and their performance tends to degrade for
high dimensional models. We propose a new family of variational bounds for
hierarchical models, based on the application of tightening methods (e.g.
importance weighting) separately for each group of local random variables. We
show that our approach naturally allows the use of subsampling to get unbiased
gradients, and that it fully leverages the power of methods that build tighter
lower bounds by applying them independently in lower dimensional spaces,
leading to better results and more accurate posterior approximations than
relevant baselines.


title: Islands in FRW Cosmologies

abstract:
  We search for candidate island regions in FRW cosmologies supported by
radiation, cosmological constant $\Lambda$, and non-zero spatial curvature. The
radiation is assumed to be in a thermal state. We apply the necessary
conditions introduced in [arXiv:2008.01022]. Both for the open and closed
universes with $\Lambda<0$, the Friedmann equation admits recollapsing
solutions with a time-symmetric slice. In the case of closed universes, there
is always an island that is the whole Cauchy slice. However, for $\Lambda<0$,
we also find another finite-sized candidate island region, in the middle of the
spacetime, at the turnaround time. In the case of the open universes, we only
find a candidate island region for $\Lambda<0$, that appears at the turnaround
time. It starts from a finite value of the radial coordinate and extends to
infinity. Looking at both open and closed universes, we conclude that the key
ingredient that allows the existence of islands is the negative cosmological
constant. We provide analytic results along the time-symmetric slices and
support our results with numerics in the whole spacetime.


title:
Foothold Evaluation Criterion for Dynamic Transition Feasibility for
  Quadruped Robots

abstract:
  To traverse complex scenarios reliably a legged robot needs to move its base
aided by the ground reaction forces, which can only be generated by the legs
that are momentarily in contact with the ground. A proper selection of
footholds is crucial for maintaining balance. In this paper, we propose a
foothold evaluation criterion that considers the transition feasibility for
both linear and angular dynamics to overcome complex scenarios. We devise
convex and nonlinear formulations as a direct extension of the Continuous
Convex Resolution of Centroidal Dynamic Trajectories (C-CROC) in a
receding-horizon fashion to grant dynamic feasibility for future behaviours.
The criterion is integrated with a Vision-based Foothold Adaptation (VFA)
strategy that takes into account the robot kinematics, leg collisions and
terrain morphology. We verify the validity of the selected footholds and the
generated trajectories in simulation and experiments with the 90kg quadruped
robot HyQ.


title: Inflation from Dynamical Projective Connections

abstract:
  We show how the recently developed string-inspired, projectively-invariant
gravitational model Thomas-Whitehead gravity (TW gravity) naturally gives rise
to a field acting as the inflaton. In the formulation of TW gravity, a field
$\mathcal{D}_{ab}$ is introduced into the projective connection components and
is related to a rank-two tensor field $\mathcal{P}_{ab}$. Through the dynamical
action of TW gravity, in terms of projective curvature, the tensor field
$\mathcal{P}_{ab}$ acquires dynamics. By decomposing $\mathcal{P}_{ab}$ into
its trace and traceless degrees of freedom, and choosing the connection to be
Levi-Civita, we demonstrate that TW gravity contains a non-minimally coupled
scalar field with a specific potential. Considering only the trace degrees of
freedom, we demonstrate that the scalar field acts as an inflaton in the slow
roll approximation. We find a range of values for the parameters introduced by
TW gravity that fit the experimental constraints of the most recent
cosmological data.


title: Morphisms represented by monomorphisms with n-torsionfree cokernel

abstract:
  We introduce and study a new class of morphisms which includes morphisms
represented by monomorphisms in the sense of Auslander and Bridger. As an
application, we give not only an extension of Kato's theorem on morphisms
represented by monomorphisms, but also a common generalization of several
results due to Auslander and Bridger that describe relationships between
torsionfreeness and the grades of Ext modules.


title: The Flag Median and FlagIRLS

abstract:
  Finding prototypes (e.g., mean and median) for a dataset is central to a
number of common machine learning algorithms. Subspaces have been shown to
provide useful, robust representations for datasets of images, videos and more.
Since subspaces correspond to points on a Grassmann manifold, one is led to
consider the idea of a subspace prototype for a Grassmann-valued dataset. While
a number of different subspace prototypes have been described, the calculation
of some of these prototypes has proven to be computationally expensive while
other prototypes are affected by outliers and produce highly imperfect
clustering on noisy data. This work proposes a new subspace prototype, the flag
median, and introduces the FlagIRLS algorithm for its calculation. We provide
evidence that the flag median is robust to outliers and can be used effectively
in algorithms like Linde-Buzo-Grey (LBG) to produce improved clusterings on
Grassmannians. Numerical experiments include a synthetic dataset, the MNIST
handwritten digits dataset, the Mind's Eye video dataset and the UCF YouTube
action dataset. The flag median is compared the other leading algorithms for
computing prototypes on the Grassmannian, namely, the $\ell_2$-median and to
the flag mean. We find that using FlagIRLS to compute the flag median converges
in $4$ iterations on a synthetic dataset. We also see that Grassmannian LBG
with a codebook size of $20$ and using the flag median produces at least a
$10\%$ improvement in cluster purity over Grassmannian LBG using the flag mean
or $\ell_2$-median on the Mind's Eye dataset.


title:
Fate of the false vacuum in string-inspired infinite-derivative
  non-local field theory

abstract:
  We study Coleman bounce in infinite-derivative non-local theories which are
motivated from string field theory. The kinetic term is extended via infinite
series of high-order derivatives, which comes into play at an energy scale $M$,
without introducing any new states or ghosts in the mass spectrum. We calculate
the bubble nucleation in thin-wall approximation treating the system in
semi-classical manner. We find that the effect of non-local scale $M$ in the
theory is to suppress the vacuum tunneling rate from false to true vacuum
compared the standard local bouncing scenario. We show that as we move further
away from the bubble wall, the effects of non-locality gets reduced and this
suppression is significant only around the wall of the nucleated bubble. From
our investigations we conclude that the main effect is due to the fact that the
non-locality smears the solution of the local bubble profile. However the
energy of the bubble wall remains unaffected by the microscopic non-local
behavior of the theory in the thin-wall approximation. We also discuss the
cases for Lee-Wick theories and applications of our result to cosmology.


title: $\mathrm{SO}(2)$-Equivariant Reinforcement Learning

abstract:
  Equivariant neural networks enforce symmetry within the structure of their
convolutional layers, resulting in a substantial improvement in sample
efficiency when learning an equivariant or invariant function. Such models are
applicable to robotic manipulation learning which can often be formulated as a
rotationally symmetric problem. This paper studies equivariant model
architectures in the context of $Q$-learning and actor-critic reinforcement
learning. We identify equivariant and invariant characteristics of the optimal
$Q$-function and the optimal policy and propose equivariant DQN and SAC
algorithms that leverage this structure. We present experiments that
demonstrate that our equivariant versions of DQN and SAC can be significantly
more sample efficient than competing algorithms on an important class of
robotic manipulation problems.


title: Pointillism: Accurate 3D bounding box estimation with multi-radars

abstract:
  Autonomous perception requires high-quality environment sensing in the form
of 3D bounding boxes of dynamic objects. The primary sensors used in automotive
systems are light-based cameras and LiDARs. However, they are known to fail in
adverse weather conditions. Radars can potentially solve this problem as they
are barely affected by adverse weather conditions. However, specular
reflections of wireless signals cause poor performance of radar point clouds.
We introduce Pointillism, a system that combines data from multiple spatially
separated radars with an optimal separation to mitigate these problems. We
introduce a novel concept of Cross Potential Point Clouds, which uses the
spatial diversity induced by multiple radars and solves the problem of noise
and sparsity in radar point clouds. Furthermore, we present the design of
RP-net, a novel deep learning architecture, designed explicitly for radar's
sparse data distribution, to enable accurate 3D bounding box estimation. The
spatial techniques designed and proposed in this paper are fundamental to
radars point cloud distribution and would benefit other radar sensing
applications.


title:
Automatic differentiation for the direct SCF approach to the
  Hartree-Fock method

abstract:
  Automatic differentiation has become an important tool for optimization
problems in computational science, and it has been applied to the Hartree-Fock
method. However, eigenvalue calculation in the self-consistent field method has
impeded the use of efficient reverse-mode automatic differentiation. Here, we
propose a method to directly minimize Hartree-Fock energy under the
orthonormality constraint of the molecular orbitals using reverse-mode
automatic differentiation by avoiding eigenvalue calculation. According to our
validation, the method was more stable than the conventional self-consistent
field method and achieved comparable accuracy.


title:
Theory of solar oscillations in the inertial frequency range: Linear
  modes of the convection zone

abstract:
  On the one hand, several types of global-scale inertial modes of oscillation
have been observed on the Sun. They include the equatorial Rossby modes,
critical-latitude modes, and high-latitude modes. On the other hand, the
columnar convective modes (predicted by simulations; also known as banana cells
or thermal Rossby waves) remain elusive. We aim to investigate the influence of
turbulent diffusivities, non-adiabatic stratification, differential rotation,
and a latitudinal entropy gradient on the linear global modes of the rotating
solar convection zone. We solve numerically for the eigenmodes of a rotating
compressible fluid inside a spherical shell. We identify modes in the inertial
frequency range including the columnar convective modes, as well as modes of
mixed character. The corresponding mode dispersion relations and eigenfunctions
are computed for azimuthal orders $m \leq 16$. The three main results are as
follows. Firstly, we find that, for $m \gtrsim 5$, the radial dependence of the
equatorial Rossby modes with no radial node ($n=0$) is radically changed from
the traditional expectation ($r^m$) for turbulent diffusivities $\gtrsim
10^{12}$ cm$^2$ s$^{-1}$. Secondly, we find mixed modes, i.e. modes that share
properties of the equatorial Rossby modes with one radial node ($n=1$) and the
columnar convective modes. Thirdly, we show that the $m=1$ high-latitude mode
in the model is consistent with the solar observations when the latitudinal
entropy gradient corresponding to a thermal wind balance is included
(baroclinally unstable mode). To our knowledge, this work is the first
realistic eigenvalue calculation of the global modes of the rotating solar
convection zone. This calculation reveals a rich spectrum of modes in the
inertial frequency range, which can be directly compared to the observations.
In turn, the observed modes can inform us about the solar convection zone.


title:
Estimating the Uncertainty in Emotion Class Labels with
  Utterance-Specific Dirichlet Priors

abstract:
  Emotion recognition is a key attribute for artificial intelligence systems
that need to naturally interact with humans. However, the task definition is
still an open problem due to inherent ambiguity of emotions. In this paper, a
novel Bayesian training loss based on per-utterance Dirichlet prior
distributions is proposed for verbal emotion recognition, which models the
uncertainty in one-hot labels created when human annotators assign the same
utterance to different emotion classes. An additional metric is used to
evaluate the performance by detecting test utterances with high labelling
uncertainty. This removes a major limitation that emotion classification
systems only consider utterances with majority labels.Furthermore, a
frequentist approach is studied to leverage the continuous-valued "soft" labels
obtained by averaging the one-hot labels. We propose a two-branch model
structure for emotion classification on a per-utterance basis. Experiments with
the widely used IEMOCAP dataset demonstrate that the two-branch structure
achieves state-of-the-art classification results with all common IEMOCAP test
setups. Based on this, uncertainty estimation experiments were performed. The
best performance in terms of the area under the precision-recall curve when
detecting utterances with high uncertainty was achieved by interpolating the
Bayesian training loss with the Kullback-Leibler divergence training loss for
the soft labels.


title: Reproducible Subjective Evaluation

abstract:
  Human perceptual studies are the gold standard for the evaluation of many
research tasks in machine learning, linguistics, and psychology. However, these
studies require significant time and cost to perform. As a result, many
researchers use objective measures that can correlate poorly with human
evaluation. When subjective evaluations are performed, they are often not
reported with sufficient detail to ensure reproducibility. We propose
Reproducible Subjective Evaluation (ReSEval), an open-source framework for
quickly deploying crowdsourced subjective evaluations directly from Python.
ReSEval lets researchers launch A/B, ABX, Mean Opinion Score (MOS) and MUltiple
Stimuli with Hidden Reference and Anchor (MUSHRA) tests on audio, image, text,
or video data from a command-line interface or using one line of Python, making
it as easy to run as objective evaluation. With ReSEval, researchers can
reproduce each other's subjective evaluations by sharing a configuration file
and the audio, image, text, or video files.


title:
Self-Supervision, Remote Sensing and Abstraction: Representation
  Learning Across 3 Million Locations

abstract:
  Self-supervision based deep learning classification approaches have received
considerable attention in academic literature. However, the performance of such
methods on remote sensing imagery domains remains under-explored. In this work,
we explore contrastive representation learning methods on the task of
imagery-based city classification, an important problem in urban computing. We
use satellite and map imagery across 2 domains, 3 million locations and more
than 1500 cities. We show that self-supervised methods can build a
generalizable representation from as few as 200 cities, with representations
achieving over 95\% accuracy in unseen cities with minimal additional training.
We also find that the performance discrepancy of such methods, when compared to
supervised methods, induced by the domain discrepancy between natural imagery
and abstract imagery is significant for remote sensing imagery. We compare all
analysis against existing supervised models from academic literature and
open-source our models for broader usage and further criticism.


title:
Tune your Place Recognition: Self-Supervised Domain Calibration via
  Robust SLAM

abstract:
  Visual place recognition techniques based on deep learning, which have
imposed themselves as the state-of-the-art in recent years, do not always
generalize well to environments that are visually different from the training
set. Thus, to achieve top performance, it is sometimes necessary to fine-tune
the networks to the target environment. To this end, we propose a completely
self-supervised domain calibration procedure based on robust pose graph
estimation from Simultaneous Localization and Mapping (SLAM) as the supervision
signal without requiring GPS or manual labeling. We first show that the
training samples produced by our technique are sufficient to train a visual
place recognition system from a pre-trained classification model. Then, we show
that our approach can improve the performance of a state-of-the-art technique
on a target environment dissimilar from the training set. We believe that this
approach will help practitioners to deploy more robust place recognition
solutions in real-world applications.


title:
Category-Theoretic Reconstruction of Schemes from Categories of Reduced
  Schemes

abstract:
  Let $S$ be a locally Noetherian normal scheme and $\blacklozenge/S$ a set of
properties of $S$-schemes. Then we shall write Sch$_{\blacklozenge/S}$ for the
full subcategory of the category of $S$-schemes Sch$_{/S}$ determined by the
objects $X\in {\rm Sch}_{\blacklozenge/S}$ that satisfy every property of
$\blacklozenge/S$. In the present paper, we shall mainly be concerned with the
properties "reduced", "quasi-compact over $S$", "quasi-separated over $S$", and
"separated over $S$". We give a functorial category-theoretic algorithm for
reconstructing $S$ from the intrinsic structure of the abstract category
Sch$_{\blacklozenge/S}$. This result is analogous to a result of Mochizuki
\cite{Mzk04} and may be regarded as a partial generalization of a result of de
Bruyn \cite{deBr19} in the case where $S$ is a locally Noetherian normal
scheme.


title:
TriggerZoo: A Dataset of Android Applications Automatically Infected
  with Logic Bombs

abstract:
  Many Android apps analyzers rely, among other techniques, on dynamic analysis
to monitor their runtime behavior and detect potential security threats.
However, malicious developers use subtle, though efficient, techniques to
bypass dynamic analyzers. Logic bombs are examples of popular techniques where
the malicious code is triggered only under specific circumstances, challenging
comprehensive dynamic analyses. The research community has proposed various
approaches and tools to detect logic bombs. Unfortunately, rigorous assessment
and fair comparison of state-of-the-art techniques are impossible due to the
lack of ground truth. In this paper, we present TriggerZoo, a new dataset of
406 Android apps containing logic bombs and benign trigger-based behavior that
we release only to the research community using authenticated API. These apps
are real-world apps from Google Play that have been automatically infected by
our tool AndroBomb. The injected pieces of code implementing the logic bombs
cover a large pallet of realistic logic bomb types that we have manually
characterized from a set of real logic bombs. Researchers can exploit this
dataset as ground truth to assess their approaches and provide comparisons
against other tools.


title:
Unsupervised learning of two-component nematicity from STM data on magic
  angle bilayer graphene

abstract:
  Moir\'e materials such as magic angle twisted bilayer graphene (MATBG)
exhibit remarkable phenomenology, but present significant challenges for
certain experimental methods, particularly scanning probes such as scanning
tunneling microscopy (STM). Typical STM studies that can image tens of
thousands of atomic unit cells can image roughly ten moir\'e cells, making data
analysis statistically fraught. Here, we propose a method to mitigate this
problem by aggregating STM conductance data from several bias voltages, and
then using the unsupervised machine learning method of gaussian mixture model
clustering to draw maximal insight from the resulting dataset. We apply this
method, using as input coarse-grained bond variables respecting the point group
symmetry, to investigate nematic ordering tendencies in MATBG for both charge
neutral and hole-doped samples. For the charge-neutral dataset, the clustering
reveals the surprising coexistence of multiple types of nematicity that are
unrelated by symmetry, and therefore generically nondegenerate. By contrast,
the clustering in the hole doped data is consistent with long range order of a
single type. Beyond its value in analyzing nematicity in MATBG, our method has
the potential to enhance understanding of symmetry breaking and its spatial
variation in a variety of moir\'e materials.


title:
CIDER: Exploiting Hyperspherical Embeddings for Out-of-Distribution
  Detection

abstract:
  Out-of-distribution (OOD) detection is a critical task for reliable machine
learning. Recent advances in representation learning give rise to developments
in distance-based OOD detection, where testing samples are detected as OOD if
they are relatively far away from the centroids or prototypes of
in-distribution (ID) classes. However, prior methods directly take
off-the-shelf loss functions that suffice for classifying ID samples, but are
not optimally designed for OOD detection. In this paper, we propose CIDER, a
simple and effective representation learning framework by exploiting
hyperspherical embeddings for OOD detection. CIDER jointly optimizes two losses
to promote strong ID-OOD separability: (1) a dispersion loss that promotes
large angular distances among different class prototypes, and (2) a compactness
loss that encourages samples to be close to their class prototypes. We show
that CIDER is effective under various settings and establishes state-of-the-art
performance. On a hard OOD detection task CIFAR-100 vs. CIFAR-10, our method
substantially improves the AUROC by 14.20% compared to the embeddings learned
by the cross-entropy loss.


title:
Transitions between peace and systemic war as bifurcations in a signed
  network dynamical system

abstract:
  We investigate structural features and processes associated with the onset of
systemic conflict using an approach which integrates complex systems theory
with network modeling and analysis. We present a signed network model of
cooperation and conflict dynamics in the context of international relations
between states. The model evolves ties between nodes under the influence of a
structural balance force and a dyad-specific force. Model simulations exhibit a
sharp bifurcation from peace to systemic war as structural balance pressures
increase, a bistable regime in which both peace and war stable equilibria
exist, and a hysteretic reverse bifurcation from war to peace. We show how the
analytical expression we derive for the peace-to-war bifurcation condition
implies that polarized network structure increases susceptibility to systemic
war. We develop a framework for identifying patterns of relationship
perturbations that are most destabilizing and apply it to the network of
European great powers before World War I. We also show that the model exhibits
critical slowing down, in which perturbations to the peace equilibrium take
longer to decay as the system draws closer to the bifurcation. We discuss how
our results relate to international relations theories on the causes and
catalysts of systemic war.


title:
Cooperative Trajectory Planning in Uncertain Environments with Monte
  Carlo Tree Search and Risk Metrics

abstract:
  Automated vehicles require the ability to cooperate with humans for smooth
integration into today's traffic. While the concept of cooperation is well
known, developing a robust and efficient cooperative trajectory planning method
is still a challenge. One aspect of this challenge is the uncertainty
surrounding the state of the environment due to limited sensor accuracy. This
uncertainty can be represented by a Partially Observable Markov Decision
Process. Our work addresses this problem by extending an existing cooperative
trajectory planning approach based on Monte Carlo Tree Search for continuous
action spaces. It does so by explicitly modeling uncertainties in the form of a
root belief state, from which start states for trees are sampled. After the
trees have been constructed with Monte Carlo Tree Search, their results are
aggregated into return distributions using kernel regression. We apply two risk
metrics for the final selection, namely a Lower Confidence Bound and a
Conditional Value at Risk. It can be demonstrated that the integration of risk
metrics in the final selection policy consistently outperforms a baseline in
uncertain environments, generating considerably safer trajectories.


title: Machine Learning in NextG Networks via Generative Adversarial Networks

abstract:
  Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms
that have the ability to address competitive resource allocation problems
together with detection and mitigation of anomalous behavior. In this paper, we
investigate their use in next-generation (NextG) communications within the
context of cognitive networks to address i) spectrum sharing, ii) detecting
anomalies, and iii) mitigating security attacks. GANs have the following
advantages. First, they can learn and synthesize field data, which can be
costly, time consuming, and nonrepeatable. Second, they enable pre-training
classifiers by using semi-supervised data. Third, they facilitate increased
resolution. Fourth, they enable the recovery of corrupted bits in the spectrum.
The paper provides the basics of GANs, a comparative discussion on different
kinds of GANs, performance measures for GANs in computer vision and image
processing as well as wireless applications, a number of datasets for wireless
applications, performance measures for general classifiers, a survey of the
literature on GANs for i)-iii) above, and future research directions. As a use
case of GAN for NextG communications, we show that a GAN can be effectively
applied for anomaly detection in signal classification (e.g., user
authentication) outperforming another state-of-the-art ML technique such as an
autoencoder.


title:
Statistical Depth for Point Process via the Isometric Log-Ratio
  Transformation

abstract:
  Statistical depth, a useful tool to measure the center-outward rank of
multivariate and functional data, is still under-explored in temporal point
processes. Recent studies on point process depth proposed a weighted product of
two terms - one indicates the depth of the cardinality of the process, and the
other characterizes the conditional depth of the temporal events given the
cardinality. The second term is of great challenge because of the apparent
nonlinear structure of event times, and so far only basic parametric
representations such as Gaussian and Dirichlet densities were adopted in the
definitions. However, these simplified forms ignore the underlying distribution
of the process events, which makes the methods difficult to interpret and to
apply to complicated patterns. To deal with these problems, we in this paper
propose a distribution-based approach to the conditional depth via the
well-known Isometric Log-Ratio (ILR) transformation on the inter-event times.
The new depth, called the ILR depth, is at first defined for homogeneous
Poisson process by using the density function on the transformed space. The
definition is then extended to any general point process via a time-rescaling
transformation. We illustrate the ILR depth using simulations of Poisson and
non-Poisson processes and demonstrate its superiority over previous methods. We
also thoroughly examine its mathematical properties and asymptotics in large
samples. Finally, we apply the ILR depth in a real dataset and the result
clearly shows the effectiveness of the new method.


title:
Pruning Graph Convolutional Networks to select meaningful graph
  frequencies for fMRI decoding

abstract:
  Graph Signal Processing is a promising framework to manipulate brain signals
as it allows to encompass the spatial dependencies between the activity in
regions of interest in the brain. In this work, we are interested in better
understanding what are the graph frequencies that are the most useful to decode
fMRI signals. To this end, we introduce a deep learning architecture and adapt
a pruning methodology to automatically identify such frequencies. We experiment
with various datasets, architectures and graphs, and show that low graph
frequencies are consistently identified as the most important for fMRI
decoding, with a stronger contribution for the functional graph over the
structural one. We believe that this work provides novel insights on how
graph-based methods can be deployed to increase fMRI decoding accuracy and
interpretability.


title:
Probabilistic Rotation Representation With an Efficiently Computable
  Bingham Loss Function and Its Application to Pose Estimation

abstract:
  In recent years, a deep learning framework has been widely used for object
pose estimation. While quaternion is a common choice for rotation
representation of 6D pose, it cannot represent an uncertainty of the
observation. In order to handle the uncertainty, Bingham distribution is one
promising solution because this has suitable features, such as a smooth
representation over SO(3), in addition to the ambiguity representation.
However, it requires the complex computation of the normalizing constants. This
is the bottleneck of loss computation in training neural networks based on
Bingham representation. As such, we propose a fast-computable and
easy-to-implement loss function for Bingham distribution. We also show not only
to examine the parametrization of Bingham distribution but also an application
based on our loss function.


title:
Converting tilings with multiple types of rhombuses to pentagonal
  tilings

abstract:
  The results involving rotationally symmetric tilings with multiple types of
rhombuses, discovered by Penrose, Ammann, Beenker, or Socolar, are converted to
tilings with multiple types of pentagons are presented. The pentagons can be
convex or concave, and can be degenerated into a trapezoid or parallelogram. If
the pentagons are convex, they belong to the Type 2 family.


title:
Toward discovering the excited $\Omega$ baryons through nonleptonic weak
  decays of $\Omega_c$

abstract:
  The nonleptonic weak decay processes $\Omega_c \to
\Omega\pi^+/\Omega(1P)\pi^+/\Omega(1D)\pi^+/\Omega(2S)\pi^+$ are studied using
the constituent quark model. The branching fraction of $\Omega_c \to
\Omega\pi^+$ is predicted to be $0.17\%$. Considering the newly observed
$\Omega(2012)$ resonance as a conventional $1P$-wave $\Omega$ excite state with
spin-parity $J^P=3/2^-$, the ratio of the branching fraction of $\Omega_c^0\to
\pi^+\Omega(2012)\to \pi^+(\Xi K)^-$ relative to $\Omega_c \to \Omega\pi^+$,
which is newly measured at Belle, can be well understood. Besides, the
production rates for the missing $1P-$wave state $\Omega (1^2P_{1/2^-})$, two
spin quartet $1D-$wave states $\Omega (1^4D_{1/2^+})$ and $\Omega
(1^4D_{3/2^+})$, and two $2S$-wave states $\Omega(2^2S_{1/2^+})$ and
$\Omega(2^4S_{3/2^+})$ are also investigated. It is expected that these missing
excited $\Omega$ baryons should have large potentials to be discovered through
the nonleptonic weak decays of $\Omega_c$ in forthcoming experiments by Belle
II and/or LHCb.


title: Contextual Networks and Unsupervised Ranking of Sentences

abstract:
  We construct a contextual network to represent a document with syntactic and
semantic relations between word-sentence pairs, based on which we devise an
unsupervised algorithm called CNATAR (Contextual Network And Text Analysis
Rank) to score sentences, and rank them through a bi-objective 0-1 knapsack
maximization problem over topic analysis and sentence scores. We show that
CNATAR outperforms the combined ranking of the three human judges provided on
the SummBank dataset under both ROUGE and BLEU metrics, which in term
significantly outperforms each individual judge's ranking. Moreover, CNATAR
produces so far the highest ROUGE scores over DUC-02, and outperforms previous
supervised algorithms on the CNN/DailyMail and NYT datasets. We also compare
the performance of CNATAR and the latest supervised neural-network
summarization models and compute oracle results.


title: Model for independent particle motion

abstract:
  Independent particle model in nuclear physics assumes that the nucleon in the
nucleus moves in the average (mean field) potential generated by all other
nucleons. This chapter gives a short overview of basic features of the
independent particle motion in atomic nuclei and its theoretical realization in
the framework of shell models for spherical, deformed and rotating nuclei as
well as in more sophisticated approaches such as microscopic+macroscopic model
and density functional theories. Independent particle motion of nucleons leads
to global and single-particle consequences. The global ones manifest themselves
in the shell structure and its consequences for global structure of nuclear
landscape, the existence of superheavy nuclei and the superdeformation at high
spin are briefly reviewed. The latter shows itself in the single-particle
properties such as energies, alignments and densities; their manifestations are
illustrated on specific examples.


title:
The fractal geometry of growth: fluctuation-dissipation theorem and
  hidden symmetry

abstract:
  Growth in crystals can be { usually } described by field equations such as
the Kardar-Parisi-Zhang (KPZ) equation. While the crystalline structure can be
characterized by Euclidean geometry with its peculiar symmetries, the growth
dynamics creates a fractal structure at the interface of a crystal and its
growth medium, which in turn determines the growth. Recent work (The KPZ
exponents for the 2+ 1 dimensions, MS Gomes-Filho, ALA Penna, FA Oliveira;
\textit{Results in Physics}, 104435 (2021)) associated the fractal dimension of
the interface with the growth exponents for KPZ, and provides explicit values
for them. In this work we discuss how the fluctuations and the responses to it
are associated with this fractal geometry and the new hidden symmetry
associated with the universality of the exponents.


title: Downstream Fairness Caveats with Synthetic Healthcare Data

abstract:
  This paper evaluates synthetically generated healthcare data for biases and
investigates the effect of fairness mitigation techniques on utility-fairness.
Privacy laws limit access to health data such as Electronic Medical Records
(EMRs) to preserve patient privacy. Albeit essential, these laws hinder
research reproducibility. Synthetic data is a viable solution that can enable
access to data similar to real healthcare data without privacy risks.
Healthcare datasets may have biases in which certain protected groups might
experience worse outcomes than others. With the real data having biases, the
fairness of synthetically generated health data comes into question. In this
paper, we evaluate the fairness of models generated on two healthcare datasets
for gender and race biases. We generate synthetic versions of the dataset using
a Generative Adversarial Network called HealthGAN, and compare the real and
synthetic model's balanced accuracy and fairness scores. We find that synthetic
data has different fairness properties compared to real data and fairness
mitigation techniques perform differently, highlighting that synthetic data is
not bias free.


title:
Autonomous Mosquito Habitat Detection Using Satellite Imagery and
  Convolutional Neural Networks for Disease Risk Mapping

abstract:
  Mosquitoes are known vectors for disease transmission that cause over one
million deaths globally each year. The majority of natural mosquito habitats
are areas containing standing water that are challenging to detect using
conventional ground-based technology on a macro scale. Contemporary approaches,
such as drones, UAVs, and other aerial imaging technology are costly when
implemented and are only most accurate on a finer spatial scale whereas the
proposed convolutional neural network(CNN) approach can be applied for disease
risk mapping and further guide preventative efforts on a more global scale. By
assessing the performance of autonomous mosquito habitat detection technology,
the transmission of mosquito-borne diseases can be prevented in a
cost-effective manner. This approach aims to identify the spatiotemporal
distribution of mosquito habitats in extensive areas that are difficult to
survey using ground-based technology by employing computer vision on satellite
imagery for proof of concept. The research presents an evaluation and the
results of 3 different CNN models to determine their accuracy of predicting
large-scale mosquito habitats. For this approach, a dataset was constructed
containing a variety of geographical features. Larger land cover variables such
as ponds/lakes, inlets, and rivers were utilized to classify mosquito habitats
while minute sites were omitted for higher accuracy on a larger scale. Using
the dataset, multiple CNN networks were trained and evaluated for accuracy of
habitat prediction. Utilizing a CNN-based approach on readily available
satellite imagery is cost-effective and scalable, unlike most aerial imaging
technology. Testing revealed that YOLOv4 obtained greater accuracy in mosquito
habitat detection for identifying large-scale mosquito habitats.


title:
On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question
  Generation

abstract:
  We study the task of predicting a set of salient questions from a given
paragraph without any prior knowledge of the precise answer. We make two main
contributions. First, we propose a new method to evaluate a set of predicted
questions against the set of references by using the Hungarian algorithm to
assign predicted questions to references before scoring the assigned pairs. We
show that our proposed evaluation strategy has better theoretical and practical
properties compared to prior methods because it can properly account for the
coverage of references. Second, we compare different strategies to utilize a
pre-trained seq2seq model to generate and select a set of questions related to
a given paragraph. The code is available.


title:
A cocyclic construction of $S^1$-equivariant homology and application to
  string topology

abstract:
  Given a space with a circle action, we study certain cocyclic chain complex
and prove a theorem relating cyclic homology to $S^1$-equivariant homology, in
the spirit of celebrated work of Jones. As an application, we describe a chain
level refinement of the gravity algebra structure on the (negative)
$S^1$-equivariant homology of the free loop space of a closed oriented smooth
manifold, based on work of Irie on chain level string topology and work of Ward
on an $S^1$-equivariant version of operadic Deligne's conjecture.


title:
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another
  in Neural Networks

abstract:
  Neural networks tend to achieve better accuracy with training if they are
larger -- even if the resulting models are overparameterized. Nevertheless,
carefully removing such excess parameters before, during, or after training may
also produce models with similar or even improved accuracy. In many cases, that
can be curiously achieved by heuristics as simple as removing a percentage of
the weights with the smallest absolute value -- even though magnitude is not a
perfect proxy for weight relevance. With the premise that obtaining
significantly better performance from pruning depends on accounting for the
combined effect of removing multiple weights, we revisit one of the classic
approaches for impact-based pruning: the Optimal Brain Surgeon(OBS). We propose
a tractable heuristic for solving the combinatorial extension of OBS, in which
we select weights for simultaneous removal, as well as a systematic update of
the remaining weights. Our selection method outperforms other methods under
high sparsity, and the weight update is advantageous even when combined with
the other methods.


title: Boilerplate Detection via Semantic Classification of TextBlocks

abstract:
  We present a hierarchical neural network model called SemText to detect HTML
boilerplate based on a novel semantic representation of HTML tags, class names,
and text blocks. We train SemText on three published datasets of news webpages
and fine-tune it using a small number of development data in CleanEval and
GoogleTrends-2017. We show that SemText achieves the state-of-the-art accuracy
on these datasets. We then demonstrate the robustness of SemText by showing
that it also detects boilerplate effectively on out-of-domain community-based
question-answer webpages.


title: Noisy Label Learning for Security Defects

abstract:
  Data-driven software engineering processes, such as vulnerability prediction
heavily rely on the quality of the data used. In this paper, we observe that it
is infeasible to obtain a noise-free security defect dataset in practice.
Despite the vulnerable class, the non-vulnerable modules are difficult to be
verified and determined as truly exploit free given the limited manual efforts
available. It results in uncertainty, introduces labeling noise in the datasets
and affects conclusion validity. To address this issue, we propose novel
learning methods that are robust to label impurities and can leverage the most
from limited label data; noisy label learning. We investigate various noisy
label learning methods applied to software vulnerability prediction.
Specifically, we propose a two-stage learning method based on noise cleaning to
identify and remediate the noisy samples, which improves AUC and recall of
baselines by up to 8.9% and 23.4%, respectively. Moreover, we discuss several
hurdles in terms of achieving a performance upper bound with semi-omniscient
knowledge of the label noise. Overall, the experimental results show that
learning from noisy labels can be effective for data-driven software and
security analytics.


title: The set of trace ideals of curve singularities

abstract:
  This paper mainly focuses on commutative local domains of dimension one. We
then obtain a criterion for a ring to have a finite number of trace ideals in
terms of integrally closed ideals. We also explore properties of such rings
related to birational extensions, reflexive ideals, and reflexive Ulrich
modules. Special attention is given in the case of numerical semigroup rings of
non-gap four. We then obtain a criterion for a ring to have a finite number of
reflexive ideals up to isomorphism. Non-domains arising from fiber products are
also explored.


title: General Null Lagrangians and Their Novel Role in Classical Dynamics

abstract:
  A method for constructing general null Lagrangians and their higher harmonics
is presented for dynamical systems with one degree of freedom. It is shown that
these Lagrangians can be used to obtain non-standard Lagrangians, which give
equations of motion for the law of inertia and some dissipative dynamical
systems. The necessary condition for deriving equations of motion by using null
Lagrangians is presented, and it is demonstrated that this condition plays the
same role for null Lagrangians as the Euler-Lagrange equation plays for
standard and non-standard Lagrangians. The obtained results and their
applications establish a novel role of null Lagrangians in classical dynamics.


title: Topological Shiba Metals

abstract:
  Shiba bands formed by overlapping Yu-Shiba-Rusinov subgap states in magnetic
impurities on a superconductor play an important role in topological
superconductors. Here, we theoretically demonstrate the existence of a new type
of Shiba bands (dubbed topological Shiba metal) on a magnetically doped
$s$-wave superconducting surface with Rashba spin-orbit coupling in the
presence of a weak in-plane magnetic field. Such topological gapless Shiba
bands develop from gapped Shiba bands through Lifshitz phase transitions
accompanied by second-order quantum phase transitions for the intrinsic thermal
Hall conductance. We also find a new mechanism in Shiba lattices that protects
the first-order quantum phase transitions for the intrinsic thermal Hall
conductance. Due to the long-range hopping in Shiba lattices, the topological
Shiba metal exhibits intrinsic thermal Hall conductance with large nonquantized
values. As a consequence, there emerge a large number of second-order quantum
phase transitions.


title:
BinMLM: Binary Authorship Verification with Flow-aware Mixture-of-Shared
  Language Model

abstract:
  Binary authorship analysis is a significant problem in many software
engineering applications. In this paper, we formulate a binary authorship
verification task to accurately reflect the real-world working process of
software forensic experts. It aims to determine whether an anonymous binary is
developed by a specific programmer with a small set of support samples, and the
actual developer may not belong to the known candidate set but from the wild.
We propose an effective binary authorship verification framework, BinMLM.
BinMLM trains the RNN language model on consecutive opcode traces extracted
from the control-flow-graph (CFG) to characterize the candidate developers'
programming styles. We build a mixture-of-shared architecture with multiple
shared encoders and author-specific gate layers, which can learn the
developers' combination preferences of universal programming patterns and
alleviate the problem of low training resources. Through an optimization
pipeline of external pre-training, joint training, and fine-tuning, our
framework can eliminate additional noise and accurately distill developers'
unique styles. Extensive experiments show that BinMLM achieves promising
results on Google Code Jam (GCJ) and Codeforces datasets with different numbers
of programmers and supporting samples. It significantly outperforms the
baselines built on the state-of-the-art feature set (4.73% to 19.46%
improvement) and remains robust in multi-author collaboration scenarios.
Furthermore, BinMLM can perform organization-level verification on a real-world
APT malware dataset, which can provide valuable auxiliary information for
exploring the group behind the APT attack.


title:
Revealing timescale-dependent oxygen vacancy distributions in solid
  oxide fuel cell electrodes using frequency-resolved X-ray absorption (FR-XAS)

abstract:
  Development of materials for electrochemical energy conversion requires a
deep understanding of the factors governing chemical and physical rates at
submicron length scales. Many workers have sought to develop chemically
sensitive in situ or operando imaging techniques targeting these length scales.
However, current methods focus on steady-state or stepwise response. To probe
electrode processes both spatially and temporally, we have developed a
frequency-resolved implementation of X-ray absorption imaging (FR-XAS) that can
measure local electrochemical response in operando during a global sinusoidal
impedance measurement. Frequency-resolved 1-D images of the oxygen vacancy
distribution in a thin film SOFC cathode material ($La_{1-x}Sr_xCoO_{3-
\delta}$) reveal, for the first time experimentally, the defect concentrations
associated with a Warburg and Gerischer impedance. Analysis of these images
allows direct extraction of diffusion and kinetic rate parameters, independent
of the global impedance.


title:
Optimal smoothing factor with coarsening by three for the MAC scheme for
  the Stokes equations

abstract:
  In this work, we propose a local Fourier analysis for multigrid methods with
coarsening by a factor of three for the staggered finite-difference method
applied to the Stokes equations. In [21], local Fourier analysis has been
applied to a mass-based Braess-Sarazin relaxation, a mass-based $\sigma$-Uzawa
relaxation, and a mass-based distributive relaxation, with standard coarsening
on staggered grids for the Stokes equations. Here, we consider multigrid
methods with coarsening by three for these relaxation schemes. We derive
theoretically optimal smoothing factors for this coarsening strategy. The
optimal smoothing factors of coarsening by three are nearly equal to those
obtained from standard coarsening. Thus, coarsening by three is superior
computationally. Moreover, coarsening by three generates a nested hierarchy of
grids, which simplifies and unifies the construction of grid-transfer
operators.


title:
Spectral stability of small-amplitude dispersive shocks in quantum
  hydrodynamics with viscosity

abstract:
  A compressible viscous-dispersive Euler system in one space dimension in the
context of quantum hydrodynamics is considered. The dispersive term is due to
quantum effects described through the Bohm potential and the viscosity term is
of linear type. It is shown that small-amplitude viscous-dispersive shock
profiles for the system under consideration are spectrally stable, proving in
this fashion a previous numerical observation by Lattanzio et al. (Phys. D 402,
2020, p. 132222; Appl. Math. Comput. 385, 2020, p. 125450). The proof is based
on spectral energy estimates which profit from the monotonicty of the profiles
in the small-amplitude regime.


title: Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

abstract:
  Action recognition from videos, i.e., classifying a video into one of the
pre-defined action types, has been a popular topic in the communities of
artificial intelligence, multimedia, and signal processing. However, existing
methods usually consider an input video as a whole and learn models, e.g.,
Convolutional Neural Networks (CNNs), with coarse video-level class labels.
These methods can only output an action class for the video, but cannot provide
fine-grained and explainable cues to answer why the video shows a specific
action. Therefore, researchers start to focus on a new task, Part-level Action
Parsing (PAP), which aims to not only predict the video-level action but also
recognize the frame-level fine-grained actions or interactions of body parts
for each person in the video. To this end, we propose a coarse-to-fine
framework for this challenging task. In particular, our framework first
predicts the video-level class of the input video, then localizes the body
parts and predicts the part-level action. Moreover, to balance the accuracy and
computation in part-level action parsing, we propose to recognize the
part-level actions by segment-level features. Furthermore, to overcome the
ambiguity of body parts, we propose a pose-guided positional embedding method
to accurately localize body parts. Through comprehensive experiments on a
large-scale dataset, i.e., Kinetics-TPS, our framework achieves
state-of-the-art performance and outperforms existing methods over a 31.10% ROC
score.


title:
On the late-time tails of massive perturbations in spherically symmetric
  black holes

abstract:
  It was first pointed out by Koyama and Tomimatsu that, under reasonable
assumptions, the asymptotic late-time tails of massive scalar perturbations in
the far zone of spherically symmetric black hole spacetimes decays universally
as $t^{-5/6}$. The late-time tail is furnished by the contribution from the
branch cut of the frequency-domain Green's function, which is constructed in
terms of two appropriate solutions of the corresponding homogeneous equation.
The present study is focused on some particular forms of the in-going wave that
were not explicitly considered in the original derivations but nonetheless have
been taken into account in the literature by other authors. In this regard, we
reassess the authors' arguments and provide a detailed complimentary analysis
that covers a few specific aspects. For some particular cases, the tail is
found to possess the form $t^{-1}$. We also discuss the possible implications
of the present findings.


title: 3SD: Self-Supervised Saliency Detection With No Labels

abstract:
  We present a conceptually simple self-supervised method for saliency
detection. Our method generates and uses pseudo-ground truth labels for
training. The generated pseudo-GT labels don't require any kind of human
annotations (e.g., pixel-wise labels or weak labels like scribbles). Recent
works show that features extracted from classification tasks provide important
saliency cues like structure and semantic information of salient objects in the
image. Our method, called 3SD, exploits this idea by adding a branch for a
self-supervised classification task in parallel with salient object detection,
to obtain class activation maps (CAM maps). These CAM maps along with the edges
of the input image are used to generate the pseudo-GT saliency maps to train
our 3SD network. Specifically, we propose a contrastive learning-based training
on multiple image patches for the classification task. We show the multi-patch
classification with contrastive loss improves the quality of the CAM maps
compared to naive classification on the entire image. Experiments on six
benchmark datasets demonstrate that without any labels, our 3SD method
outperforms all existing weakly supervised and unsupervised methods, and its
performance is on par with the fully-supervised methods. Code is available at
:https://github.com/rajeevyasarla/3SD


title:
Ballistic-to-hydrodynamic transition and collective modes for
  two-dimensional electron systems in magnetic field

abstract:
  The recent demonstrations of viscous hydrodynamic electron flow in
two-dimensional electron systems poses serious questions to the validity of
existing transport theories, including the ballistic model, the
collision-induced and collisionless hydrodynamics. While the theories of
transport at hydrodynamic-to-ballistic crossover for free 2d electrons are well
established, the same is not true for electrons in magnetic fields. In this
work, we develop an analytically solvable model describing the transition from
ballistic to hydrodynamic transport with changing the strength of
electron-electron collisions in magnetic fields. Within this model, we find an
expression for the high-frequency non-local conductivity tensor of 2d
electrons. It is valid at arbitrary relation between frequency of external
field $\omega$, the cyclotron frequency $\omega_c$, and the frequency of e-e
collisions $\tau^{-1}_{ee}$. We use the obtained expression to study the
transformation of 2d magnetoplasmon modes at hydrodynamic-to-ballistic
crossover. In the true hydrodynamic regime, $\omega\tau_{ee} \ll 1$, the 2DES
supports a single magnetoplasmon mode that is not split at cyclotron harmonics.
In the ballistic regime, $\omega\tau_{ee} \gg 1$, the plasmon dispersion
develops splittings at cyclotron harmonics, forming the Bernstein modes. A
formal long-wavelength expansion of kinetic equations ("collisionless
hydrodynamics") predicts the first splitting of plasmon dispersion at
$\omega\approx 2\omega_c$. Still, such expansion fails to predict the zero and
negative group velocity sections of true magnetoplasmon dispersion, for which
the full kinetic model is required.


title:
The impact of isospin dependence of pairing on fission barriers in the
  fission cycling regions

abstract:
  A systematic analysis of the ground state and fission properties of actinides
and superheavy nuclei important for the $r$ process modeling has been performed
within the framework of covariant density functional theory for the first time
in Ref. [1]. A brief review of the results related to the heights of primary
fission barriers and systematic uncertainties in their prediction is presented.
In addition, new results on the potential impact of the isospin dependence of
pairing on fission barriers in fission cycling regions is provided for the
first time.


title:
Stability for acoustic wave motion with random force on the locally
  reacting boundary

abstract:
  This paper concerns about the stability for the acoustic wave motion with
boundary frictions and random forces. We will show there exists a unique
invariant measure for the stochastic evolution equation associated with this
acoustic wave motion, and the invariant measure possesses the property of
strong mixing. This result is new with respect to the literature on two
accounts: (i) stochasticity is accounted for in the acoustic wave model; (ii)
the controllability of the dynamical system modeling the acoustic wave motion
implies the mixing.


title: Multi-Agent Policy Transfer via Task Relationship Modeling

abstract:
  Team adaptation to new cooperative tasks is a hallmark of human intelligence,
which has yet to be fully realized in learning agents. Previous work on
multi-agent transfer learning accommodate teams of different sizes, heavily
relying on the generalization ability of neural networks for adapting to unseen
tasks. We believe that the relationship among tasks provides the key
information for policy adaptation. In this paper, we try to discover and
exploit common structures among tasks for more efficient transfer, and propose
to learn effect-based task representations as a common space of tasks, using an
alternatively fixed training scheme. We demonstrate that the task
representation can capture the relationship among tasks, and can generalize to
unseen tasks. As a result, the proposed method can help transfer learned
cooperation knowledge to new tasks after training on a few source tasks. We
also find that fine-tuning the transferred policies help solve tasks that are
hard to learn from scratch.


title: Error-based Knockoffs Inference for Controlled Feature Selection

abstract:
  Recently, the scheme of model-X knockoffs was proposed as a promising
solution to address controlled feature selection under high-dimensional
finite-sample settings. However, the procedure of model-X knockoffs depends
heavily on the coefficient-based feature importance and only concerns the
control of false discovery rate (FDR). To further improve its adaptivity and
flexibility, in this paper, we propose an error-based knockoff inference method
by integrating the knockoff features, the error-based feature importance
statistics, and the stepdown procedure together. The proposed inference
procedure does not require specifying a regression model and can handle feature
selection with theoretical guarantees on controlling false discovery proportion
(FDP), FDR, or k-familywise error rate (k-FWER). Empirical evaluations
demonstrate the competitive performance of our approach on both simulated and
real data.


title: An Atomic Viewpoint of the TP Completion Problem

abstract:
  We present two complementary techniques called catalysis and inhibition which
allow one to determine if a given pattern is TP completable or TP
non-completable, respectively. Empirically, these techniques require
considering only one unspecified entry at a time in a vast majority of cases,
which makes these techniques ripe for automation and a powerful framework for
future work in the TP completion problem. With small modifications, these
techniques are also applicable to the TN completion problem.
  We provide a two major applications. First, we characterize all 4-by-4
patterns by completability. There are a total of 78 new obstructions of this
size, six times as many as the 3-by-$n$ case for all $n$ combined. Second, we
provide a characterization of the so-called 1-variable obstructions in the TN
case, which includes as a corollary a characterization of patterns with a
single unspecified entry. This also provides a novel partial result towards
proving the conjecture that all TN-completable patterns are TP-completable.


title: A composite generalization of Ville's martingale theorem

abstract:
  We provide a composite version of Ville's theorem that an event has zero
measure if and only if there exists a martingale which explodes to infinity
when that event occurs. This is a classic result connecting measure-theoretic
probability to the sequence-by-sequence game-theoretic probability, recently
developed by Shafer and Vovk. Our extension of Ville's result involves
appropriate composite generalizations of martingales and measure-zero events:
these are respectively provided by ``e-processes'', and a new inverse-capital
outer measure. We then develop a novel line-crossing inequality for sums of
random variables which are only required to have a finite first moment, which
we use to prove a composite version of the strong law of large numbers (SLLN).
This allows us to show that violation of the SLLN is an event of outer measure
zero in this setting and that our e-process explodes to infinity on every such
violating sequence, while this is provably not achievable with a martingale.


title:
Magnetotransport due to conductivity fluctuations in non-magnetic ZrTe2
  nanoplates

abstract:
  Transition metal dichalcogenides with nontrivial band structures exhibit
various fascinating physical properties and have sparked intensively research
interest. Here, we performed systematic magnetotransport measurements on
mechanical exfoliation prepared ZrTe2 nanoplates. We revealed that the negative
longitudinal magnetoresistivity observed at high field region in the presence
of parallel electric and magnetic fields could stem from the conductivity
fluctuations due to the excess Zr in the nanoplates. In addition, the
parametric plot, the planar Hall resistivity as function of the in-plane
anisotropic magnetoresistivity, has an ellipse-shaped pattern with shifted
orbital center, which further strengthen the evidence for the conductivity
fluctuations. Our work provides some useful insights into transport phenomena
in topological materials.


title:
Synthesis of Eu(HCOO)$_3$ and Eu(HCOO)$_{3}\cdot$(HCONH$_2$)$_2$
  crystals and observation of their $^5$D$_{0}\rightarrow ^{7}$F$_0$ transition
  for quantum information systems

abstract:
  Two stoichiometric metal-organic frameworks containing Eu$^{3+}$ cations are
probed as candidates for photon-based quantum information storage. Synthesis
procedures for growing 0.2 mm, rod-shaped Eu(HCOO)$_3$ and 1-3 mm, rhombohedral
Eu(HCOO)$_{3}\cdot$(HCONH$_2$)$_2$ single crystals are presented with visible
precipitation as soon as 1 h into heating for Eu(HCOO)$_3$ and 24 h for
Eu(HCOO)$_{3}\cdot$(HCONH$_2$)$_2$. Room temperature and 1.4 K
photoluminescence measurements of the $^5$D$_{0}\rightarrow {^7}$F$_J$
transitions of Eu$^{3+}$ are analyzed for both compounds. Comparisons of peak
width and intensity are discussed along with the notable first report for both
of the $^5$D$_{0}\rightarrow {^7}$F$_0$ transition, the hyperfine structure of
which has potential use in quantum memory applications. The air instability of
Eu(HCOO)$_{3}\cdot$(HCONH$_2$)$_2$ and the transformation of its
photoluminescence properties are discussed.


title:
Nonreciprocal thermal radiation in ultrathin magnetized
  epsilon-near-zero semiconductors

abstract:
  Spectral/angular emissivity $e$ and absorptivity ${\alpha}$ of an object are
widely believed to be identical by Kirchhoff's law of thermal radiation in
reciprocal systems, but this introduces an intrinsic and inevitable energy loss
for energy conversion and harvesting devices. So far, experimental evidences of
breaking this well-known balance are still absent, and previous theoretical
proposals are restricted to narrow single-band nonreciprocal radiation. Here we
observe for the first time, to our knowledge, the violation of Kirchhoff's law
using ultrathin ($<{\lambda}/40$, ${\lambda}$ is the working wavelength)
magnetized InAs semiconductor films at epsilon-near-zero (ENZ) frequencies.
Large difference of $|{\alpha}-e|>0.6$ has been experimentally demonstrated
under a moderate external magnetic field. Moreover, based on magnetized ENZ
building blocks supporting asymmetrically radiative Berreman and surface ENZ
modes, we show versatile shaping of nonreciprocal thermal radiation:
single-band, dual-band, and broadband nonreciprocal emission spectra at
different wavebands. Our findings of breaking Kirchhoff's law will advance the
conventional understanding of emission and absorption processes of natural
objects, and lay a solid foundation for more comprehensive studies in designing
various nonreciprocal thermal emitters. The reported recipe of diversely
shaping nonreciprocal emission will also breed new possibilities in renovating
next-generation nonreciprocal energy devices in the areas of solar cells,
thermophotovoltaic, radiative cooling, etc.


title:
Online Non-linear Centroidal MPC for Humanoid Robot Locomotion with Step
  Adjustment

abstract:
  This paper presents a Non-Linear Model Predictive Controller for humanoid
robot locomotion with online step adjustment capabilities. The proposed
controller considers the Centroidal Dynamics of the system to compute the
desired contact forces and torques and contact locations. Differently from
bipedal walking architectures based on simplified models, the presented
approach considers the reduced centroidal model, thus allowing the robot to
perform highly dynamic movements while keeping the control problem still
treatable online. We show that the proposed controller can automatically adjust
the contact location both in single and double support phases. The overall
approach is then tested with a simulation of one-leg and two-leg systems
performing jumping and running tasks, respectively. We finally validate the
proposed controller on the position-controlled Humanoid Robot iCub. Results
show that the proposed strategy prevents the robot from falling while walking
and pushed with external forces up to 40 Newton for 1 second applied at the
robot arm.


title:
Quantum corrections to the thermodynamics of rotating charged BTZ black
  hole in gravity's rainbow

abstract:
  In this manuscript, we investigate the thermal properties of the rotating
charged BTZ black hole under the GR formalism at first according to the usual
Heisenberg algebra. Then, we consider a deformed algebra that leads to a change
in the Heisenberg uncertainty principle and we compare the Hawking temperature,
entropy, thermodynamical volume, pressure, and heat capacity functions with the
previous results. Thus, we understand and interpret the quantum effects on the
BTZ black hole.


title:
Towards Large-Scale and Spatio-temporally Resolved Diagnosis of
  Electronic Density of States

abstract:
  Modern laboratory techniques like ultrafast laser excitation and shock
compression can bring matter into highly nonequilibrium states with complex
structural transformation, metallization and dissociation dynamics. To
understand and model the dramatic change of both electronic structures and ion
dynamics during such dynamic processes, the traditional method faces
difficulties. Here, we demonstrate the ability of deep neural network (DNN) to
capture the atomic local-environment dependence of electronic density of states
(DOS) for both multicomponent system under exoplanet thermodynamic condition
and nonequilibrium system during super-heated melting process. Large scale and
time-resolved diagnosis of DOS can be efficiently achieved within the accuracy
of ab initio method. Moreover, the atomic contribution to DOS given by DNN
model accurately reveals the information of local neighborhood for selected
atom, thus can serve as robust order parameters to identify different phases
and intermediate local structures, strongly highlights the efficacy of this DNN
model in studying dynamic processes.

categories: physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci physics.atm-clus


title: Bounds for sets with few distances distinct modulo a prime ideal

abstract:
  Let $K$ be an algebraic number field, and $\mathcal{O}_K$ the ring of
integers of $K$. Let $X$ be a subset of the Euclidean space $\mathbb{R}^d$, and
$D(X)$ be the set of the squared distances of two distinct points in $X$. In
this paper, we prove that if $D(X)\subset \mathcal{O}_K$ and there exist $s$
values $a_1,\ldots, a_s \in \mathcal{O}_K$ distinct modulo a prime ideal
$\mathfrak{p}$ of $\mathcal{O}_K$ such that each $a_i$ is not zero modulo
$\mathfrak{p}$ and each element of $D(X)$ is congruent to some $a_i$, then $|X|
\leq \binom{d+s}{s}+\binom{d+s-1}{s-1}$.


title: Intelligent Feedback Overhead Reduction (iFOR) in Wi-Fi 7 and Beyond

abstract:
  The IEEE 802.11 standard based wireless local area networks (WLANs) or Wi-Fi
networks are critical to provide internet access in today's world. The
increasing demand for high data rate in Wi-Fi networks has led to several
advancements in the 802.11 standard. Supporting MIMO transmissions with higher
number of transmit antennas operating on wider bandwidths is one of the key
capabilities for reaching higher throughput. However, the increase in sounding
feedback overhead due to higher number of transmit antennas may significantly
curb the throughput gain. In this paper, we develop an unsupervised
learning-based method to reduce the sounding duration in a Wi-Fi MIMO link.
Simulation results show that our method uses approximately only 8% of the
number of bits required by the existing feedback mechanism and it can boost the
system throughput by up to 52%.


title:
The Hot Neptune WASP-166 b with ESPRESSO II: Confirmation of atmospheric
  sodium

abstract:
  The hot Neptune desert, a distinct lack of highly irradiated planets in the
size range of Neptune, remains one of the most intriguing results of exoplanet
population studies. A deeper understanding of the atmosphere of exoplanets
sitting at the edge or even within the Neptune desert will allow us to better
understand if planetary formation or evolution processes are at the origin of
the desert. A detection of sodium in WASP-166b was presented previously with
tentative line broadening at the 3.4 sigma with the HARPS spectrograph. We
update this result with two transits observed with the ESPRESSO spectrograph,
confirming the detection in each night and the broadened character of the line.
This result marks the first confirmed resolved sodium detection within the
Neptune desert. In this work, we additionally highlight the importance of
treating low-SNR spectral regions, particularly where absorption lines of
stellar sodium and planetary sodium overlap at mid-transit - an important
caveat for future observations of the system.


title:
Threshold odd solutions to the nonlinear Schr\"{o}dinger equation in one
  dimension

abstract:
  We consider odd solutions to the Schr\"{o}dinger equation with the
$L^2$-supercritical power type nonlinearity in one dimensional Euclidean space.
It is known that the odd solution scatters or blows up if its action is less
than twice as that of the ground state. In the present paper, we show that the
odd solutions with the action as twice as that of the ground state scatter or
blow up.


title:
Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge
  Monitoring

abstract:
  Millimeter-scale embedded sensing systems have unique advantages over larger
devices as they are able to capture, analyze, store, and transmit data at the
source while being unobtrusive and covert. However, area-constrained systems
pose several challenges, including a tight energy budget and peak power,
limited data storage, costly wireless communication, and physical integration
at a miniature scale. This paper proposes a novel 6.7$\times$7$\times$5mm
imaging system with deep-learning and image processing capabilities for
intelligent edge applications, and is demonstrated in a home-surveillance
scenario. The system is implemented by vertically stacking custom
ultra-low-power (ULP) ICs and uses techniques such as dynamic behavior-specific
power management, hierarchical event detection, and a combination of data
compression methods. It demonstrates a new image-correcting neural network that
compensates for non-idealities caused by a mm-scale lens and ULP front-end. The
system can store 74 frames or offload data wirelessly, consuming 49.6$\mu$W on
average for an expected battery lifetime of 7 days.


title: The Exact WKB analysis for asymmetric scalar preheating

abstract:
  Using the exact WKB analysis of the higher-order differential equation, we
analyze the mechanism of asymmetric preheating of a complex scalar field. The
solution requires the Stokes phenomena of the fourth-order differential
equation. We have identified the Stokes phenomena, which are crucial for the
matter-antimatter asymmetry. The new Stokes lines and the virtual turning
points, which are developed to analyze the Stokes phenomena of the higher-order
differential equations, are crucial for global consistency but do not introduce
a new effect in this model.


title: Estimating Phase from Observed Trajectories Using the Temporal 1-Form

abstract:
  Oscillators are ubiquitous in nature, and usually associated with the
existence of an asymptotic phase that governs the long-term dynamics of the
oscillator. % We show that asymptotic phase can be estimated using a carefully
chosen series expansion which directly computes the phase response curve and
provide an algorithm for estimating the co-efficients of this series. Unlike
all previously available data driven phase estimation methods, our algorithm
can: (i) use observations that are much shorter than a cycle; (ii) recover
phase within any forward invariant region for which sufficient data are
available; (iii) recover the phase response curves (PRC-s) that govern weak
oscillator coupling; (iv) show isochron curvature, and recover nonlinear
features of isochron geometry. Our method may find application wherever models
of oscillator dynamics need to be constructed from measured or simulated
time-series.


title: Growth Rates of Hydrogen Microbubbles in Reacting Femtoliter Droplets

abstract:
  Chemical reactions in small droplets are extensively explored to accelerate
the discovery of new materials, increase efficiency and specificity in
catalytic biphasic conversion and in high throughput analytics. In this work,
we investigate the local rate of gas-evolution reaction within femtoliter
droplets immobilized on a solid surface. The growth rate of hydrogen
microbubbles (> 500 nm in radius) produced from the reaction was measured
online by high-resolution confocal microscopic images. The growth rate of
bubbles was faster in smaller droplets, and of bubbles near the three-phase
boundary in the same droplet. The results were consistent for both pure and
binary reacting droplets and on substrates of different wettability. Our
theoretical analysis based on diffusion, chemical reaction, and bubble growth
in a steady state predicted that the concentration of the reactant diffusing
from the surrounding depended on the droplet size and the bubble location
inside the droplet, in good agreement with experimental results. Our results
reveal that the reaction rate may be spatially non-uniform in the reacting
microdroplets. The findings may have implications for formulating chemical
properties and uses of these droplets.


title: Image Steganography based on Style Transfer

abstract:
  Image steganography is the art and science of using images as cover for
covert communications. With the development of neural networks, traditional
image steganography is more likely to be detected by deep learning-based
steganalysis. To improve upon this, we propose image steganography network
based on style transfer, and the embedding of secret messages can be disguised
as image stylization. We embed secret information while transforming the
content image style. In latent space, the secret information is integrated into
the latent representation of the cover image to generate the stego images,
which are indistinguishable from normal stylized images. It is an end-to-end
unsupervised model without pre-training. Extensive experiments on the benchmark
dataset demonstrate the reliability, quality and security of stego images
generated by our steganographic network.


title: The Exact WKB for Decaying Sonic Black Hole

abstract:
  Using the Exact WKB analysis, we calculate distortion of the radiation when a
sonic black hole is "unstable".


title:
Learning Invariant Stabilizing Controllers for Frequency Regulation
  under Variable Inertia

abstract:
  Declines in cost and concerns about the environmental impact of traditional
generation have boosted the penetration of renewables and non-conventional
distributed energy resources into the power grid. The intermittent availability
of these resources causes the inertia of the power system to vary over time. As
a result, there is a need to go beyond traditional controllers designed to
regulate frequency under the assumption of invariant dynamics. This paper
presents a learning-based framework for the design of stable controllers based
on imitating datasets obtained from linear-quadratic regulator (LQR)
formulations for different switching sequences of inertia modes. The proposed
controller is linear and invariant, thereby interpretable, does not require the
knowledge of the current operating mode, and is guaranteed to stabilize the
switching power dynamics. We also show that it is always possible to stabilize
the switched system using a communication-free local controller, whose
implementation only requires each node to use its own state. Simulations on a
12-bus 3-region network illustrate our results.


title:
An Energy Sharing Mechanism Considering Network Constraints and Market
  Power Limitation

abstract:
  As the number of prosumers with distributed energy resources (DERs) grows,
the conventional centralized operation scheme may suffer from conflicting
interests, privacy concerns, and incentive inadequacies. In this paper, we
propose an energy sharing mechanism to address the above challenges. It takes
into account network constraints and fairness among prosumers. In the proposed
energy sharing market, all prosumers play a generalized Nash game. The market
equilibrium is proven to have nice features in a large market or when it is a
variational equilibrium. To deal with the possible market failure,
inefficiency, or instability in general cases, we introduce a price regulation
policy to avoid market power exploitation. The improved energy sharing
mechanism with price regulation can guarantee existence and uniqueness of a
socially near-optimal market equilibrium. Some advantageous properties are
proven, such as individual rationality of prosumers, an elegant structure of
sharing prices, and the tendency towards social optimum with an increasing
number of prosumers. For implementation, a practical bidding process is
developed with convergence condition. Experimental results validate the
theoretical outcomes and show the practicability of our model and method.


title:
Ground and excited states of coupled exciton liquids in electron-hole
  quadrilayers

abstract:
  Interlayer excitons are bound states of electrons and holes confined in
separate two-dimensional layers. Due to their repulsive dipolar interaction,
interlayer excitons can form a correlated liquid. If another electron-hole
bilayer is present, excitons from different bilayers can exhibit mutual
attraction. We study such a quadrilayer system by a hypernetted chain
formalism. We compute ground state energies, pair correlation functions, and
collective mode velocities as functions of the exciton densities. We estimate
the critical density for the transition to a paired biexciton phase. For a
strongly unbalanced (unequal density) system, the excitons in the more dilute
bilayer behave as polarons. We compute energies and effective masses of such
exciton-polarons.


title:
Ferromagnetism in layered metallic Fe1/4TaS2 in the presence of
  conventional and Dirac carriers

abstract:
  In this paper we present the microscopic origin of the ferromagnetism of
Fe0.25TaS2 and its finite-temperature magnetic properties. We first obtain the
band structures of Fe0.25TaS2 by the first-principles calculations and find
that both conventional and Dirac carriers coexist in metallic Fe0.25TaS2.
Accordingly, considering the spin-orbit coupling of Fe 3d ion, we derive an
effective RKKY-type Hamiltonian between Fe spins in the presence of both the
conventional parabolic-dispersion and the Dirac linear-dispersion carriers,
which contains a Heisenberg-like, an Ising-like and an XY-like term. In
addition, we obtain the ferromagnetic Curie temperature Tc by using the cluster
self-consistent field method. Our results could address not only the high
ferromagnetic Curie temperature, but also the large magnetic anisotropy in
FexTaS2.


title: The Directed Probabilistic Powerspace

abstract:
  Probabilistic powerdomain in domain theory plays an important role in
modeling the semantics of nondeterministic functional programming languages
with probabilistic choice. In this paper, we extend the notion of powerdomain
to directed spaces, which is equivalent to the notion of the T0
monotone-determined space [4]. We construct the probabilistic powerspace of the
directed space, which is defined as a free directed space-cone. In addition,
the relationships between our construction and classical probabilistic
powerdomain are studied.