title: Transport, refraction and interface arcs in junctions of Weyl semimetals

abstract:
  We study the low-energy single-electron transport across a junction of two
magnetic Weyl semimetals, in which the anisotropy axes are tilted one respect
to the other. Using a two-band model with a potential step, we compute the
transmission factor for normal and Klein tunneling and the refraction
properties of the interface as a function of the tilt angle. We show that the
interface acts as a beam splitter, separating electrons with different
chiralities. We also characterize interface states, only appearing for finite
tilt angle, which connect the projection of the Fermi surfaces on the two sides
of the junction, and we discuss transport effects due to their interplay with
Fermi arcs.


title:
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural
  Networks for Efficient Human Activity Recognition

abstract:
  In many machine learning tasks, input features with varying degrees of
predictive capability are acquired at varying costs. In order to optimize the
performance-cost trade-off, one would select features to observe a priori.
However, given the changing context with previous observations, the subset of
predictive features to select may change dynamically. Therefore, we face the
challenging new problem of foresight dynamic selection (FDS): finding a dynamic
and light-weight policy to decide which features to observe next, before
actually observing them, for overall performance-cost trade-offs. To tackle
FDS, this paper proposes a Bayesian learning framework of Variational Foresight
Dynamic Selection (VFDS). VFDS learns a policy that selects the next feature
subset to observe, by optimizing a variational Bayesian objective that
characterizes the trade-off between model performance and feature cost. At its
core is an implicit variational distribution on binary gates that are dependent
on previous observations, which will select the next subset of features to
observe. We apply VFDS on the Human Activity Recognition (HAR) task where the
performance-cost trade-off is critical in its practice. Extensive results
demonstrate that VFDS selects different features under changing contexts,
notably saving sensory costs while maintaining or improving the HAR accuracy.
Moreover, the features that VFDS dynamically select are shown to be
interpretable and associated with the different activity types. We will release
the code.


title:
Relativistic frequency shifts in Cr, Ti, Fe, Ni, Ca, Na, and V to search
  for variation in the fine structure constant

abstract:
  Sensitivity of the frequencies of twenty two atomic transitions to the
variation of the fine structure constant $\alpha$ is calculated. The findings
are to be used in search for possible variation of $\alpha$ across our Galaxy
using high quality spectroscopic data for stars similar to our Sun.


title:
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using
  Domain Adaptation

abstract:
  This work aims to address the challenges in domain adaptation of 3D object
detection using infrastructure LiDARs. We design a model DASE-ProPillars that
can detect vehicles in infrastructure-based LiDARs in real-time. Our model uses
PointPillars as the baseline model with additional modules to improve the 3D
detection performance. To prove the effectiveness of our proposed modules in
DASE-ProPillars, we train and evaluate the model on two datasets, the open
source A9-Dataset and a semi-synthetic infrastructure dataset created within
the Regensburg Next project. We do several sets of experiments for each module
in the DASE-ProPillars detector that show that our model outperforms the
SE-ProPillars baseline on the real A9 test set and a semi-synthetic A9 test
set, while maintaining an inference speed of 45 Hz (22 ms). We apply domain
adaptation from the semi-synthetic A9-Dataset to the semi-synthetic dataset
from the Regensburg Next project by applying transfer learning and achieve a 3D
mAP@0.25 of 93.49% on the Car class of the target test set using 40 recall
positions.


title:
Anomalous Hall Effect and Perpendicular Magnetic Anisotropy in Ultrathin
  Ferrimagnetic NiCo$_2$O$_4$ Films

abstract:
  The inverse spinel ferrimagnetic NiCo$_2$O$_4$ possesses high magnetic Curie
temperature $T_C$, high spin polarization, and strain-tunable magnetic
anisotropy. Understanding the thickness scaling limit of these intriguing
magnetic properties in NiCo$_2$O$_4$ thin films is critical for their
implementation in nanoscale spintronic applications. In this work, we report
the unconventional magnetotransport properties of epitaxial (001) NiCo$_2$O$_4$
films on MgAl$_2$O$_4$ substrates in the ultrathin limit. Anomalous Hall effect
measurements reveal strong perpendicular magnetic anisotropy for films down to
1.5 unit cell (1.2 nm), while $T_C$ for 3 unit cell and thicker films remains
above 300 K. The sign change in the anomalous Hall conductivity ($\sigma_{xy}$)
and its scaling relation with the longitudinal conductivity ($\sigma_{xx}$) can
be attributed to the competing effects between impurity scattering and band
intrinsic Berry curvature, with the latter vanishing upon the thickness driven
metal-insulator transition. Our study reveals the critical role of film
thickness in tuning the relative strength of charge correlation, Berry phase
effect, spin orbit interaction, and impurity scattering, providing important
material information for designing scalable epitaxial magnetic tunnel junctions
and sensing devices using NiCo$_2$O$_4$.


title: Model Predictive Control for Fluid Human-to-Robot Handovers

abstract:
  Human-robot handover is a fundamental yet challenging task in human-robot
interaction and collaboration. Recently, remarkable progressions have been made
in human-to-robot handovers of unknown objects by using learning-based grasp
generators. However, how to responsively generate smooth motions to take an
object from a human is still an open question. Specifically, planning motions
that take human comfort into account is not a part of the human-robot handover
process in most prior works. In this paper, we propose to generate smooth
motions via an efficient model-predictive control (MPC) framework that
integrates perception and complex domain-specific constraints into the
optimization problem. We introduce a learning-based grasp reachability model to
select candidate grasps which maximize the robot's manipulability, giving it
more freedom to satisfy these constraints. Finally, we integrate a neural net
force/torque classifier that detects contact events from noisy data. We
conducted human-to-robot handover experiments on a diverse set of objects with
several users (N=4) and performed a systematic evaluation of each module. The
study shows that the users preferred our MPC approach over the baseline system
by a large margin. More results and videos are available at
https://sites.google.com/nvidia.com/mpc-for-handover.


title:
Equivariant formality of corank-one isotropy actions and products of
  rational spheres

abstract:
  We completely characterize the pairs of connected Lie groups $G > K$ such
that $\mathrm{rank}(G) - \mathrm{rank}(K) = 1$ and the left action of $K$ on
$G/K$ is equivariantly formal. The analysis requires us to correct and extend
an existing partial classification of homogeneous quotients $G/K$ with the
rational homotopy type of a product of an odd- and an even-dimensional sphere.


title:
CAsimulations: Modeling of topological dynamics in a disease using
  cellular automata

abstract:
  The prediction of the behavior of the disease, the level of affectation in a
population and the ways to control it are the most important aspects studied by
epidemiology using tools such as historical data and mathematical models. So,
our objective is (1) to provide a tool capable of analyzing epidemiological
phenomena starting from the most common social interactions within a group of
individuals. (2) To provide a methodology to build epidemiological models from
patterns and logical rules. (3) Determine the impact of social interactions on
the spread of a disease. This paper describes the logical construction of two
epidemiological models in cellular automata together with two of their
variations based on topological and dynamical principles.


title:
Thermodynamic approach to proton number fluctuations in baryon-rich
  heavy-ion matter created at moderate collision energies

abstract:
  We point out how proton number cumulants measured in heavy-ion collisions
within a momentum space acceptance can be related to the grand-canonical
cumulants of baryon number, assuming that particles are emitted from a fireball
with uniform distribution of temperature and baryochemical potential,
superimposed on a hydrodynamic flow velocity profile. Analysis of recent
experimental measurements of the HADES Collaboration shows that the rapidity
acceptance dependence of proton cumulants is consistent with thermal emission
of nucleons from a grand-canonical heat bath. The extracted baryon number
susceptibilities exhibit an hierarchy $\chi_4^B \gg -\chi_3^B \gg \chi_2^B \gg
\chi_1^B$, indicating large non-Gaussian fluctuations that might point to the
presence of the QCD critical point close to the chemical freeze-out of
$\sqrt{s_{\rm NN}} = 2.4$ GeV Au-Au collisions at $T \sim 70$ MeV and $\mu_B
\sim 850-900$ MeV. However, the description of the experimental data at large
rapidity acceptances becomes challenging once the effect of exact baryon number
conservation is incorporated, suggesting that more theoretical and experimental
studies are needed to reach a firm conclusion.


title:
Distributionally Robust Decision Making Leveraging Conditional
  Distributions

abstract:
  Distributionally robust optimization (DRO) is a powerful tool for decision
making under uncertainty. It is particularly appealing because of its ability
to leverage existing data. However, many practical problems call for
decision-making with some auxiliary information, and DRO in the context of
conditional distribution is not straightforward. We propose a conditional
kernel distributionally robust optimization (CKDRO) method that enables robust
decision making under conditional distributions through kernel DRO and the
conditional mean operator in the reproducing kernel Hilbert space (RKHS). In
particular, we consider problems where there is a correlation between the
unknown variable y and an auxiliary observable variable x. Given past data of
the two variables and a queried auxiliary variable, CKDRO represents the
conditional distribution P(y|x) as the conditional mean operator in the RKHS
space and quantifies the ambiguity set in the RKHS as well, which depends on
the size of the dataset as well as the query point. To justify the use of RKHS,
we demonstrate that the ambiguity set defined in RKHS can be viewed as a ball
under a metric that is similar to the Wasserstein metric. The DRO is then
dualized and solved via a finite dimensional convex program. The proposed CKDRO
approach is applied to a generation scheduling problem and shows that the
result of CKDRO is superior to common benchmarks in terms of quality and
robustness.


title:
Integration of Deep Learning and Nonlinear Model Predictive Control for
  Emission Reduction of Compression Ignition Combustion Engines: A Simulation
  Study

abstract:
  Machine learning (ML) and nonlinear model predictive control (NMPC) are used
in this paper to minimize the emissions and fuel consumption of a compression
ignition engine. In this work machine learning is used for two applications. In
the first application, ML is to identify a model for implementation in model
predictive control optimization problems. In the second application, ML is used
as a replacement of a model predictive controller where the ML controller
learns the optimal control action by imitating or mimicking the behavior of the
model predictive controller. In this study, a deep recurrent neural network
including long-short term memory (LSTM) layers are used to model the emission
and performance of a 4.5 liter 4-cylinder Cummins compression ignition engine.
This model is then used for model predictive controller implementation. Then, a
deep learning scheme is deployed to clone the behavior of the developed
controller. In the LSTM integration, a novel scheme is used by augmenting
hidden and cell states of the network in an NMPC optimization problem. The
developed LSTM-NMPC and the imitative NMPC are compared with the calibrated
Cummins Engine Control Unit (ECU) model in an experimentally validated engine
simulation platform. Results show a significant reduction in Nitrogen Oxides
(NOx) emissions and a slight decrease in the injected fuel quantity while
maintaining the same load. In addition, the imitative NMPC has a similar
performance as the NMPC but with two orders of magnitude less computation time.


title: Unbinned Likelihood Analysis for X-ray Polarization

abstract:
  We present a systematic study of the unbinned likelihood technique, which can
be used as an alternative method to analyse phase-dependent, X-ray
spectro-polarimetric observations obtained with IXPE and other photo-electric
polarimeters. We apply the unbinned technique to models of the luminous X-ray
pulsar Hercules X-1, for which we produce simulated observations using
IXPEObsSim package. We consider minimal knowledge about the actual physical
process responsible for the polarized emission from the accreting pulsar and
assume that the observed phase-dependent polarization angle can be described by
the rotating vector model. Using the unbinned technique, the detector's
modulation factor, and the polarization information alone, we found that both
the rotating vector model and the underlying spectro-polarimetry model can
reconstruct equally well the geometric configuration angles of the accreting
pulsar. However, the measured polarization fraction becomes biased with respect
to underlying model unless the energy redistribution and effective area of the
detector are also taken into account. For the different analyses, we obtain
posterior distributions from multiple IXPEObsSim realizations and show that the
unbinned technique yields $\sim 10\%$ smaller error bars than the binned
technique. We also discuss alternative sources, such as magnetars, in which the
unbinned technique and the rotating vector model might be applied.


title:
MOF: A Modular Framework for Rapid Application of Optimization
  Methodologies to General Engineering Design Problems

abstract:
  A variety of optimization algorithms have been developed to solve engineering
design problems in which the solution space is too large to manually determine
the optimal solution. The Modular Optimization Framework (MOF) was developed to
facilitate the development and application of these optimization algorithms.
MOF is written in Python 3, and it used object-oriented programming to create a
modular design that allows users to easily incorporate new optimization
algorithms, methods, or engineering design problems into the framework.
Additionally, a common input file allows users to easily specify design
problems, update the optimization parameters, and perform comparisons between
various optimization methods and algorithms. In the current MOF version,
genetic algorithm (GA) and simulated annealing (SA) approaches are implemented.
Applications in different nuclear engineering optimization problems are
included as examples. The effectiveness of the GA and SA optimization
algorithms within MOF are demonstrated through an unconstrained nuclear fuel
assembly pin lattice optimization, a first cycle fuel loading constrained
optimization of a three-loop pressurized water reactor (PWR), and a third cycle
constrained optimization of a four-loop PWR. In all cases, the algorithms
efficiently searched the solution spaces and found optimized solutions to the
given problems that satisfied the imposed constraints. These results
demonstrate the capabilities of the existing optimization tools within MOF, and
they also provide a set of benchmark cases that can be used to evaluate the
progress of future optimization methodologies with MOF.


title:
Machine Learning Integrated with Model Predictive Control for Imitative
  Optimal Control of Compression Ignition Engines

abstract:
  The high thermal efficiency and reliability of the compression-ignition
engine makes it the first choice for many applications. For this to continue, a
reduction of the pollutant emissions is needed. One solution is the use of
machine learning (ML) and model predictive control (MPC) to minimize emissions
and fuel consumption, without adding substantial computational cost to the
engine controller. ML is developed in this paper for both modeling engine
performance and emissions and for imitating the behaviour of an Linear
Parameter Varying (LPV) MPC. Using a support vector machine-based linear
parameter varying model of the engine performance and emissions, a model
predictive controller is implemented for a 4.5 Cummins diesel engine. This
online optimized MPC solution offers advantages in minimizing the
\nox~emissions and fuel consumption compared to the baseline feedforward
production controller. To reduce the computational cost of this MPC, a deep
learning scheme is designed to mimic the behavior of the developed controller.
The performance in reducing NOx emissions at a constant load by the imitative
controller is similar to that of the online optimized MPC compared to the
Cummins production controller. In addition, the imitative controller requires
50 times less computation time compared to that of the online MPC optimization.


title: Bell's nonlocality and gravity

abstract:
  The experimental results that test Bell's inequality have found strong
evidence suggesting that there are nonlocal aspects in nature. Evidently, these
nonlocal effects, which concern spacelike separated regions, create an enormous
tension between general relativity and quantum mechanics. In addition, by
avoiding the coincidence limit, semiclassical gravity can also accommodate
nonlocal aspects. Motivated by these results, we study if it is possible to
construct geometrical theories of gravitation that are nonlocal in the sense of
Bell. We propose three constructions of such theories, which could constitute
an important step towards our understanding of the interplay between quantum
mechanics and gravitation.


title:
Effect of Balancing Data Using Synthetic Data on the Performance of
  Machine Learning Classifiers for Intrusion Detection in Computer Networks

abstract:
  Attacks on computer networks have increased significantly in recent days, due
in part to the availability of sophisticated tools for launching such attacks
as well as thriving underground cyber-crime economy to support it. Over the
past several years, researchers in academia and industry used machine learning
(ML) techniques to design and implement Intrusion Detection Systems (IDSes) for
computer networks. Many of these researchers used datasets collected by various
organizations to train ML models for predicting intrusions. In many of the
datasets used in such systems, data are imbalanced (i.e., not all classes have
equal amount of samples). With unbalanced data, the predictive models developed
using ML algorithms may produce unsatisfactory classifiers which would affect
accuracy in predicting intrusions. Traditionally, researchers used
over-sampling and under-sampling for balancing data in datasets to overcome
this problem. In this work, in addition to over-sampling, we also use a
synthetic data generation method, called Conditional Generative Adversarial
Network (CTGAN), to balance data and study their effect on various ML
classifiers. To the best of our knowledge, no one else has used CTGAN to
generate synthetic samples to balance intrusion detection datasets. Based on
extensive experiments using a widely used dataset NSL-KDD, we found that
training ML models on dataset balanced with synthetic samples generated by
CTGAN increased prediction accuracy by up to $8\%$, compared to training the
same ML models over unbalanced data. Our experiments also show that the
accuracy of some ML models trained over data balanced with random over-sampling
decline compared to the same ML models trained over unbalanced data.


title:
MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on
  a Smartwatch with Speech

abstract:
  Current activity tracking technologies are largely trained on younger adults'
data, which can lead to solutions that are not well-suited for older adults. To
build activity trackers for older adults, it is crucial to collect training
data with them. To this end, we examine the feasibility and challenges with
older adults in collecting activity labels by leveraging speech. Specifically,
we built MyMove, a speech-based smartwatch app to facilitate the in-situ
labeling with a low capture burden. We conducted a 7-day deployment study,
where 13 older adults collected their activity labels and smartwatch sensor
data, while wearing a thigh-worn activity monitor. Participants were highly
engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities
with corresponding effort level and timespan, and examined the usefulness of
these reports as activity labels. We discuss the implications of our approach
and the collected dataset in supporting older adults through personalized
activity tracking technologies.


title: Some loose ends on eventual domination and positivity

abstract:
  We consider two $C_0$-semigroups $(e^{tA})_{t \ge 0}$ and $(e^{tB})_{t \ge
0}$ on function spaces (or, more generally, on Banach lattices) and analyse
eventual domination between them in the sense that $\lvert e^{tA}f \rvert \le
e^{tB} \lvert f \rvert$ for all sufficiently large times $t$. We characterise
this behaviour and prove a number of theoretical results which complement
earlier results given by Mugnolo and the second author in the special case
where both semigroups are positive for large times.
  Moreover, we study the analogous question of whether the resolvent of $B$
eventually dominates the resolvent of $A$ close to the spectral bound of $B$.
This is closely related to the so-called maximum and anti-maximum principles.
In order to demonstrate how our results can be used, we include several
applications to concrete differential operators.
  At the end of the paper, we demonstrate that the eventual positivity of the
resolvent of an operator is closely related to the eventual positivity of the
Ces\`aro means of the generated semigroup.


title:
Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth
  Boxes

abstract:
  Semi- and weakly-supervised learning have recently attracted considerable
attention in the object detection literature since they can alleviate the cost
of annotation needed to successfully train deep learning models. State-of-art
approaches for semi-supervised learning rely on student-teacher models trained
using a multi-stage process, and considerable data augmentation. Custom
networks have been developed for the weakly-supervised setting, making it
difficult to adapt to different detectors. In this paper, a weakly
semi-supervised training method is introduced that reduces these training
challenges, yet achieves state-of-the-art performance by leveraging only a
small fraction of fully-labeled images with information in weakly-labeled
images. In particular, our generic sampling-based learning strategy produces
pseudo-ground-truth (GT) bounding box annotations in an online fashion,
eliminating the need for multi-stage training, and student-teacher network
configurations. These pseudo GT boxes are sampled from weakly-labeled images
based on the categorical score of object proposals accumulated via a score
propagation process. Empirical results on the Pascal VOC dataset, indicate that
the proposed approach improves performance by 5.0% when using VOC 2007 as
fully-labeled, and VOC 2012 as weak-labeled data. Also, with 5-10% fully
annotated images, we observed an improvement of more than 10% in mAP, showing
that a modest investment in image-level annotation, can substantially improve
detection performance.


title:
Robust remote estimation over the collision channel in the presence of
  an intelligent jammer

abstract:
  We consider a sensor-receiver pair communicating over a wireless channel in
the presence of a jammer who may launch a denial-of-service attack. We
formulate a zero-sum game between a coordinator that jointly designs the
transmission and estimation policies, and the jammer. We consider two cases
depending on whether the jammer can sense the channel or not. We characterize a
saddle-point equilibrium for the class of symmetric and unimodal probability
density functions when the jammer cannot sense the channel. If the jammer can
sense if the channel is being used, we provide an efficient algorithm that
alternates between iterations of Projected Gradient Ascent and the
Convex-Concave Procedure to find approximate First-order Nash-Equilibria. Our
numerical results show that in certain cases the jammer may decide to launch a
denial-of-service attack with the goal of deceiving the receiver even when the
sensor decides not to transmit.


title: Electronics for Fast Timing

abstract:
  Picosecond-level timing will be an important component of the next generation
of particle physics detectors. The ability to add a 4$^{th}$ dimension to our
measurements will help address the increasing complexity of events at hadron
colliders and provide new tools for precise tracking and calorimetry for all
experiments. Detectors are described in detail on other whitepapers. In this
note, we address challenges in electronics design for the new generations of
fast timing detectors


title:
DBCal: Density Based Calibration of classifier predictions for
  uncertainty quantification

abstract:
  Measurement of uncertainty of predictions from machine learning methods is
important across scientific domains and applications. We present, to our
knowledge, the first such technique that quantifies the uncertainty of
predictions from a classifier and accounts for both the classifier's belief and
performance. We prove that our method provides an accurate estimate of the
probability that the outputs of two neural networks are correct by showing an
expected calibration error of less than 0.2% on a binary classifier, and less
than 3% on a semantic segmentation network with extreme class imbalance. We
empirically show that the uncertainty returned by our method is an accurate
measurement of the probability that the classifier's prediction is correct and,
therefore has broad utility in uncertainty propagation.


title:
Experimental control of Tc in AlB2-type compounds using an applied
  voltage

abstract:
  We utilize the van de Pauw technique combined with Density Functional Theory
to show that an external voltage applied to superconducting AlB2-type
compounds, such as MgB2 and Mg(B1.9C0.1), effects substantive changes in
transition temperature due to modification of electronic band structure. An
applied voltage results in a symmetric split of degenerate sigma bands in MgB2
similar to that calculated for atom displacement along the B-B bond aligned
with E2g phonon mode directions. For AlB2, similar splitting of sigma bands
occurs albeit with higher voltage requirement compared to MgB2. Experimental
data show that Tc values for MgB2 and Mg(B1.9C0.1) reduce consistently with
increased increments of applied voltage. Zero resistance is limited by an upper
applied voltage depending on the compound.


title:
Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model
  Predictive Control and Control Lyapunov Functions

abstract:
  Modern control systems must operate in increasingly complex environments
subject to safety constraints and input limits, and are often implemented in a
hierarchical fashion with different controllers running at multiple time
scales. Yet traditional constructive methods for nonlinear controller synthesis
typically "flatten" this hierarchy, focusing on a single time scale, and
thereby limited the ability to make rigorous guarantees on constraint
satisfaction that hold for the entire system. In this work we seek to address
the stabilization of constrained nonlinear systems through a
\textit{multi-rate} control architecture. This is accomplished by iteratively
planning continuous reference trajectories for a nonlinear system using a
linearized model and Model Predictive Control (MPC), and tracking said
trajectories using the full-order nonlinear model and Control Lyapunov
Functions (CLFs). Connecting these two levels of control design in a way that
ensures constraint satisfaction is achieved through the use of
\textit{B\'{e}zier curves}, which enable planning continuous trajectories
respecting constraints by planning a sequence of discrete points. Our framework
is encoded via convex optimization problems which may be efficiently solved, as
demonstrated in simulation.


title: An Ising machine based on networks of subharmonic electrical resonators

abstract:
  We explore a case example of networks of classical electronic oscillators
evolving towards the solution of complex optimization problems. We show that
when driven into subharmonic response, a network of such nonlinear electrical
resonators can minimize the Ising Hamiltonian on non-trivial graphs such as
antiferromagnetically coupled rewired-M{\"o}bius ladders. In this context, the
spin-up and spin-down states of the Ising machine are represented by the
oscillators' response at the even or odd driving cycles. Our experimental
setting of driven nonlinear oscillators coupled via a programmable switch
matrix leads to a unique energy minimizer when one such exists, and probes
frustration where appropriate. Theoretical modeling of the electronic
oscillators and their couplings allows us to accurately reproduce the
qualitative features of the experimental results. This suggests the promise of
this setup as a prototypical one for exploring the capabilities and limitations
of such an unconventional computing platform.


title:
An End-to-end Supervised Domain Adaptation Framework for Cross-Domain
  Change Detection

abstract:
  Existing deep learning-based change detection methods try to elaborately
design complicated neural networks with powerful feature representations, but
ignore the universal domain shift induced by time-varying land cover changes,
including luminance fluctuations and season changes between pre-event and
post-event images, thereby producing sub-optimal results. In this paper, we
propose an end-to-end Supervised Domain Adaptation framework for cross-domain
Change Detection, namely SDACD, to effectively alleviate the domain shift
between bi-temporal images for better change predictions. Specifically, our
SDACD presents collaborative adaptations from both image and feature
perspectives with supervised learning. Image adaptation exploits generative
adversarial learning with cycle-consistency constraints to perform cross-domain
style transformation, effectively narrowing the domain gap in a two-side
generation fashion. As to feature adaptation, we extract domain-invariant
features to align different feature distributions in the feature space, which
could further reduce the domain gap of cross-domain images. To further improve
the performance, we combine three types of bi-temporal images for the final
change prediction, including the initial input bi-temporal images and two
generated bi-temporal images from the pre-event and post-event domains.
Extensive experiments and analyses on two benchmarks demonstrate the
effectiveness and universality of our proposed framework. Notably, our
framework pushes several representative baseline models up to new
State-Of-The-Art records, achieving 97.34% and 92.36% on the CDD and WHU
building datasets, respectively. The source code and models are publicly
available at https://github.com/Perfect-You/SDACD.


title: How heated is it? Understanding GitHub locked issues

abstract:
  Although issues of open source software are created to discuss and solve
technical problems, conversations can become heated, with discussants getting
angry and/or agitated for a variety of reasons, such as poor suggestions or
violation of community conventions. To prevent and mitigate discussions from
getting heated, tools like GitHub have introduced the ability to lock issue
discussions that violate the code of conduct or other community guidelines.
Despite some early research on locked issues, there is a lack of understanding
of how communities use this feature and of potential threats to validity for
researchers relying on a dataset of locked issues as an oracle for heated
discussions. To address this gap, we (i) quantitatively analyzed 79 GitHub
projects that have at least one issue locked as too heated, and (ii)
qualitatively analyzed all issues locked as too heated of the 79 projects, a
total of 205 issues comprising 5,511 comments. We found that projects have
different behaviors when locking issues: while 54 locked less than 10% of their
closed issues, 14 projects locked more than 90% of their closed issues.
Additionally, locked issues tend to have a similar number of comments,
participants, and emoji reactions to non-locked issues. For the 205 issues
locked as too heated, we found that one-third do not contain any uncivil
discourse, and only 8.82% of the analyzed comments are actually uncivil.
Finally, we found that the locking justifications provided by maintainers do
not always match the label used to lock the issue. Based on our results, we
identified three pitfalls to avoid when using the GitHub locked issues data and
we provide recommendations for researchers and practitioners.


title:
Stereo Unstructured Magnification: Multiple Homography Image for View
  Synthesis

abstract:
  This paper studies the problem of view synthesis with certain amount of
rotations from a pair of images, what we called stereo unstructured
magnification. While the multi-plane image representation is well suited for
view synthesis with depth invariant, how to generalize it to unstructured views
remains a significant challenge. This is primarily due to the depth-dependency
caused by camera frontal parallel representation. Here we propose a novel
multiple homography image (MHI) representation, comprising of a set of scene
planes with fixed normals and distances. A two-stage network is developed for
novel view synthesis. Stage-1 is an MHI reconstruction module that predicts the
MHIs and composites layered multi-normal images along the normal direction.
Stage-2 is a normal-blending module to find blending weights. We also derive an
angle-based cost to guide the blending of multi-normal images by exploiting
per-normal geometry. Compared with the state-of-the-art methods, our method
achieves superior performance for view synthesis qualitatively and
quantitatively, especially for cases when the cameras undergo rotations.


title: LASER: LAtent SpacE Rendering for 2D Visual Localization

abstract:
  We present LASER, an image-based Monte Carlo Localization (MCL) framework for
2D floor maps. LASER introduces the concept of latent space rendering, where 2D
pose hypotheses on the floor map are directly rendered into a
geometrically-structured latent space by aggregating viewing ray features.
Through a tightly coupled rendering codebook scheme, the viewing ray features
are dynamically determined at rendering-time based on their geometries (i.e.
length, incident-angle), endowing our representation with view-dependent
fine-grain variability. Our codebook scheme effectively disentangles feature
encoding from rendering, allowing the latent space rendering to run at speeds
above 10KHz. Moreover, through metric learning, our geometrically-structured
latent space is common to both pose hypotheses and query images with arbitrary
field of views. As a result, LASER achieves state-of-the-art performance on
large-scale indoor localization datasets (i.e. ZInD and Structured3D) for both
panorama and perspective image queries, while significantly outperforming
existing learning-based methods in speed.


title: Some 2-adic conjectures concerning polyomino tilings of Aztec diamonds

abstract:
  For various sets of tiles, we count the ways to tile an Aztec diamond of
order $n$ using tiles from that set. The resulting function $f(n)$ often has
interesting behavior when one looks at $n$ and $f(n)$ modulo powers of 2.


title:
Secure and Ultra-Reliable Provenance Recovery in Sparse Networks:
  Strategies and Performance Bounds

abstract:
  Provenance embedding algorithms are well known for tracking the footprints of
information flow in wireless networks. Recently, low-latency provenance
embedding algorithms have received traction in vehicular networks owing to
strict deadlines on the delivery of packets. While existing low-latency
provenance embedding methods focus on reducing the packet delay, they assume a
complete graph on the underlying topology due to the mobility of the
participating nodes. We identify that the complete graph assumption leads to
sub-optimal performance in provenance recovery, especially when the vehicular
network is sparse, which is usually observed outside peak-hour traffic
conditions. As a result, we propose a two-part approach to design provenance
embedding algorithms for sparse vehicular networks. In the first part, we
propose secure and practical topology-learning strategies, whereas in the
second part, we design provenance embedding algorithms that guarantee
ultra-reliability by incorporating the topology knowledge at the destination
during the provenance recovery process. Besides the novel idea of using
topology knowledge for provenance recovery, a distinguishing feature for
achieving ultra-reliability is the use of hash-chains in the packet, which
trade communication-overhead of the packet with the complexity-overhead at the
destination. We derive tight upper bounds on the performance of our strategies,
and show that the derived bounds, when optimized with appropriate constraints,
deliver design parameters that outperform existing methods. Finally, we also
implement our ideas on OMNeT++ based simulation environment to show that their
latency benefits indeed make them suitable for vehicular network applications.


title:
Deformations and cohomology theory of weighted Rota-Baxter $3$-Lie
  algebras

abstract:
  A cohomology theory of weighted Rota-Baxter $3$-Lie algebras is introduced.
Formal deformations, abelian extensions, skeletal weighted Rota-Baxter $3$-Lie
2-algebras and crossed modules of weighted Rota-Baxter 3-Lie algebras are
interpreted by using lower degree cohomology groups.


title: Mutual Scene Synthesis for Mixed Reality Telepresence

abstract:
  Remote telepresence via next-generation mixed reality platforms can provide
higher levels of immersion for computer-mediated communications, allowing
participants to engage in a wide spectrum of activities, previously not
possible in 2D screen-based communication methods. However, as mixed reality
experiences are limited to the local physical surrounding of each user, finding
a common virtual ground where users can freely move and interact with each
other is challenging. In this paper, we propose a novel mutual scene synthesis
method that takes the participants' spaces as input, and generates a virtual
synthetic scene that corresponds to the functional features of all
participants' local spaces. Our method combines a mutual function optimization
module with a deep-learning conditional scene augmentation process to generate
a scene mutually and physically accessible to all participants of a mixed
reality telepresence scenario. The synthesized scene can hold mutual walkable,
sittable and workable functions, all corresponding to physical objects in the
users' real environments. We perform experiments using the MatterPort3D dataset
and conduct comparative user studies to evaluate the effectiveness of our
system. Our results show that our proposed approach can be a promising research
direction for facilitating contextualized telepresence systems for
next-generation spatial computing platforms.


title: Tutte polynomials for oriented matroids

abstract:
  The Tutte polynomial is a fundamental invariant of graphs and matroids. In
this article, we define a generalization of the Tutte polynomial to oriented
graphs and regular oriented matroids. To any regular oriented matroid $N$, we
associate a polynomial invariant $A_N(q,y,z)$, which we call the A-polynomial.
The A-polynomial has the following interesting properties among many others:
  1. a specialization of $A_N$ gives the Tutte polynomial of the unoriented
matroid underlying $N$,
  2. when the oriented matroid $N$ corresponds to an unoriented matroid (that
is, when the elements of the ground set come in pairs with opposite
orientations), the $A$-polynomial is equivalent to the Tutte polynomial of this
unoriented matroid (up to a change of variables),
  3. the A-polynomial $A_N$ detects, among other things, whether $N$ is acyclic
and whether $N$ is totally cyclic.
  We explore various properties and specializations of the A-polynomial. We
show that some of the known properties or the Tutte polynomial of matroids can
be extended to the A-polynomial of regular oriented matroids. For instance, we
show that a specialization of $A_N$ counts all the acyclic orientations
obtained by reorienting some elements of $N$, according to the number of
reoriented elements.


title:
Predicting the black hole mass and correlations in X-ray reverberating
  AGN using neural networks

abstract:
  We develop neural network models to predict the black hole mass using 22
reverberating AGN samples in the XMM-Newton archive. The model features include
the fractional excess variance ($F_{\rm var}$) in 2-10 keV band, Fe-K lag
amplitude, 2-10 keV photon counts and redshift. We find that the prediction
accuracy of the neural network model is significantly higher than what is
obtained from the traditional linear regression method. Our predicted mass can
be confined within $\pm (2$-5) per cent of the true value, suggesting that the
neural network technique is a promising and independent way to constrain the
black hole mass. We also apply the model to 21 non-reverberating AGN to rule
out their possibility to exhibit the lags (some have too small mass and $F_{\rm
var}$, while some have too large mass and $F_{\rm var}$ that contradict the
$F_{\rm var}$-lag-mass relation in reverberating AGN). We also simulate 3200
reverberating AGN samples using the multi-feature parameter space from the
neural network model to investigate the global relations if the number of
reverberating AGN increases. We find that the $F_{\rm var}$-mass
anti-correlation is likely stronger with increasing number of newly-discovered
reverberating AGN. Contrarily, to maintain the lag-mass scaling relation, the
tight anti-correlation between the lag and $F_{\rm var}$ must preserve. In an
extreme case, the lag-mass correlation coefficient can significantly decrease
and, if observed, may suggest the extended corona framework where their
observed lags are more driven by the coronal property rather than geometry.


title: Filter-based Discriminative Autoencoders for Children Speech Recognition

abstract:
  Children speech recognition is indispensable but challenging due to the
diversity of children's speech. In this paper, we propose a filter-based
discriminative autoencoder for acoustic modeling. To filter out the influence
of various speaker types and pitches, auxiliary information of the speaker and
pitch features is input into the encoder together with the acoustic features to
generate phonetic embeddings. In the training phase, the decoder uses the
auxiliary information and the phonetic embedding extracted by the encoder to
reconstruct the input acoustic features. The autoencoder is trained by
simultaneously minimizing the ASR loss and feature reconstruction error. The
framework can make the phonetic embedding purer, resulting in more accurate
senone (triphone-state) scores. Evaluated on the test set of the CMU Kids
corpus, our system achieves a 7.8% relative WER reduction compared to the
baseline system. In the domain adaptation experiment, our system also
outperforms the baseline system on the British-accent PF-STAR task.


title: Descents on nonnesting multipermutations

abstract:
  Motivated by recent results on quasi-Stirling permutations, which are
permutations of the multiset $\{1,1,2,2,\dots,n,n\}$ that avoid the "crossing"
patterns 1212 and 2121, we consider nonnesting permutations, defined as those
that avoid the patterns 1221 and 2112 instead. We show that the polynomial
giving the distribution of the number of descents on nonnesting permutations is
a product of an Eulerian polynomial and a Narayana polynomial. It follows that,
rather unexpectedly, this polynomial is palindromic. We provide bijective
proofs of these facts by composing various transformations on Dyck paths,
including the Lalanne--Kreweras involution.


title:
Making Pre-trained Language Models End-to-end Few-shot Learners with
  Contrastive Prompt Tuning

abstract:
  Pre-trained Language Models (PLMs) have achieved remarkable performance for
various language understanding tasks in IR systems, which require the
fine-tuning process based on labeled training data. For low-resource scenarios,
prompt-based learning for PLMs exploits prompts as task guidance and turns
downstream tasks into masked language problems for effective few-shot
fine-tuning. In most existing approaches, the high performance of prompt-based
learning heavily relies on handcrafted prompts and verbalizers, which may limit
the application of such approaches in real-world scenarios. To solve this
issue, we present CP-Tuning, the first end-to-end Contrastive Prompt Tuning
framework for fine-tuning PLMs without any manual engineering of task-specific
prompts and verbalizers. It is integrated with the task-invariant continuous
prompt encoding technique with fully trainable prompt parameters. We further
propose the pair-wise cost-sensitive contrastive learning procedure to optimize
the model in order to achieve verbalizer-free class mapping and enhance the
task-invariance of prompts. It explicitly learns to distinguish different
classes and makes the decision boundary smoother by assigning different costs
to easy and hard cases. Experiments over a variety of language understanding
tasks used in IR systems and different PLMs show that CP-Tuning outperforms
state-of-the-art methods.


title: Asynchronous Diffusion of Data on Networked Lattices

abstract:
  We study time-dependent dynamics on a network of algebraic lattices, where
structure-preserving lattice maps are used to fuse lattice-valued data over
vertices and edges. The principal contribution is a novel asynchronous
Laplacian, generalizing the usual graph Laplacian, adapted to a network of
heterogeneous lattices. This resulting asynchronous diffusion dynamics are
shown to converge asymptotically to stable "harmonic" distributions of lattice
data. This general theorem is applicable to several general problems, including
lattice-valued consensus, Kripke semantics, and threat detection, all using
asynchronous local update rules.


title:
Enhancing Spin Coherence in Optically Addressable Molecular Qubits
  through Host-Matrix Control

abstract:
  Optically addressable spins are a promising platform for quantum information
science due to their combination of a long-lived qubit with a spin-optical
interface for external qubit control and read out. The ability to chemically
synthesize such systems - to generate optically addressable molecular spins -
offers a modular qubit architecture which can be transported across different
environments, and atomistically tailored for targeted applications through
bottom-up design and synthesis. Here we demonstrate how the spin coherence in
such optically addressable molecular qubits can be controlled through
engineering their host environment. By inserting chromium (IV)-based molecular
qubits into a non-isostructural host matrix, we generate noise-insensitive
clock transitions, through a transverse zero-field splitting, that are not
present when using an isostructural host. This host-matrix engineering leads to
spin-coherence times of more than 10 microseconds for optically addressable
molecular spin qubits in a nuclear and electron-spin rich environment. We model
the dependence of spin coherence on transverse zero-field splitting from first
principles and experimentally verify the theoretical predictions with four
distinct molecular systems. Finally, we explore how to further enhance
optical-spin interfaces in molecular qubits by investigating the key parameters
of optical linewidth and spin-lattice relaxation time. Our results demonstrate
the ability to test qubit structure-function relationships through a tunable
molecular platform and highlight opportunities for using molecular qubits for
nanoscale quantum sensing in noisy environments.


title:
Existence of blowup solutions to the semilinear heat equation with
  double power nonlinearity

abstract:
  We consider the semilinear heat equation $u_t=\Delta u+|u|^{p-1}u-|u|^{q-1}u$
in $\mathbb{R}^n\times(0,T)$, where $n=5$, $p=\frac{n+2}{n-2}$ and $q\in(0,1)$.
By the presence of $-|u|^{q-1}u$, this equation has a finite time extinction
property. We show the existence of a new type of blowup solutions by using this
property. In fact, we obtain such blowup solutions by connecting a specific
blowup solution of $u_t=\Delta u+|u|^{p-1}u$ and a specific solution of
$u_t=\Delta u-|u|^{q-1}u$, and by adding correction terms.


title:
Universal Adaptor: Converting Mel-Spectrograms Between Different
  Configurations for Speech Synthesis

abstract:
  Most recent TTS systems are composed of a synthesizer and a vocoder. However,
the existing synthesizers and vocoders can only be matched to acoustic features
extracted with a specific configuration. Hence, we can't combine arbitrary
synthesizers and vocoders together to form a complete TTS system, not to
mention applying to a newly developed model. In this paper, we proposed a
universal adaptor, which takes a Mel-spectogram parametrized by the source
configuration and converts it into a Mel-spectrogram parametrized by the target
configuration, as long as we feed in the source and the target configurations.
Experiments show that the quality of speeches synthesized from our output of
the universal adaptor is comparable to those synthesized from ground truth
Mel-spectrogram. Moreover, our universal adaptor can be applied in the recent
TTS systems and in multi-speaker speech synthesis without dropping quality.


title: Convergence analysis of discrete high-index saddle dynamics

abstract:
  Saddle dynamics is a time continuous dynamics to efficiently compute the
any-index saddle points and construct the solution landscape. In practice, the
saddle dynamics needs to be discretized for numerical computations, while the
corresponding numerical analysis are rarely studied in the literature,
especially for the high-index cases. In this paper we propose the convergence
analysis of discrete high-index saddle dynamics. To be specific, we prove the
local linear convergence rates of numerical schemes of high-index saddle
dynamics, which indicates that the local curvature in the neighborhood of the
saddle point and the accuracy of computing the eigenfunctions are main factors
that affect the convergence of discrete saddle dynamics. The proved results
serve as compensations for the convergence analysis of high-index saddle
dynamics and are substantiated by numerical experiments.


title: A Unified Framework for Domain Adaptive Pose Estimation

abstract:
  While pose estimation is an important computer vision task, it requires
expensive annotation and suffers from domain shift. In this paper, we
investigate the problem of domain adaptive 2D pose estimation that transfers
knowledge learned on a synthetic source domain to a target domain without
supervision. While several domain adaptive pose estimation models have been
proposed recently, they are not generic but only focus on either human pose or
animal pose estimation, and thus their effectiveness is somewhat limited to
specific scenarios. In this work, we propose a unified framework that
generalizes well on various domain adaptive pose estimation problems. We
propose to align representations using both input-level and output-level cues
(pixels and pose labels, respectively), which facilitates the knowledge
transfer from the source domain to the unlabeled target domain. Our experiments
show that our method achieves state-of-the-art performance under various domain
shifts. Our method outperforms existing baselines on human pose estimation by
up to 4.5 percent points (pp), hand pose estimation by up to 7.4 pp, and animal
pose estimation by up to 4.8 pp for dogs and 3.3 pp for sheep. These results
suggest that our method is able to mitigate domain shift on diverse tasks and
even unseen domains and objects (e.g., trained on horse and tested on dog).


title:
Pose correction scheme for camera-scanning Fourier ptychography based on
  camera calibration and homography transform

abstract:
  Fourier ptychography (FP), as a computational imaging method, is a powerful
tool to improve imaging resolution. Camera-scanning Fourier ptychography
extends the application of FP from micro to macro creatively. Due to the
non-ideal scanning of the camera driven by the mechanical translation stage,
the pose error of the camera occurs, greatly degrading the reconstruction
quality, while a precise translation stage is expensive and not suitable for
wide-range imaging. Here, to improve the imaging performance of camera-scanning
Fourier ptychography, we propose a pose correction scheme based on camera
calibration and homography transform approaches. The scheme realizes the
accurate alignment of data set and location error correction in the frequency
domain. Simulation and experimental results demonstrate this method can
optimize the reconstruction results and realize high-quality imaging
effectively. Combined with the feature recognition algorithm, the scheme
provides the possibility for applying FP in remote sensing imaging and space
imaging.


title: InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR

abstract:
  This paper proposes InterAug: a novel training method for CTC-based ASR using
augmented intermediate representations for conditioning. The proposed method
exploits the conditioning framework of self-conditioned CTC to train robust
models by conditioning with "noisy" intermediate predictions. During the
training, intermediate predictions are changed to incorrect intermediate
predictions, and fed into the next layer for conditioning. The subsequent
layers are trained to correct the incorrect intermediate predictions with the
intermediate losses. By repeating the augmentation and the correction,
iterative refinements, which generally require a special decoder, can be
realized only with the audio encoder. To produce noisy intermediate
predictions, we also introduce new augmentation: intermediate feature space
augmentation and intermediate token space augmentation that are designed to
simulate typical errors. The combination of the proposed InterAug framework
with new augmentation allows explicit training of the robust audio encoders. In
experiments using augmentations simulating deletion, insertion, and
substitution error, we confirmed that the trained model acquires robustness to
each error, boosting the speech recognition performance of the strong
self-conditioned CTC baseline.


title: Multi-sequence Intermediate Conditioning for CTC-based ASR

abstract:
  End-to-end automatic speech recognition (ASR) directly maps input speech to a
character sequence without using pronunciation lexica. However, in languages
with thousands of characters, such as Japanese and Mandarin, modeling all these
characters is problematic due to data scarcity. To alleviate the problem, we
propose a multi-task learning model with explicit interaction between
characters and syllables by utilizing Self-conditioned connectionist temporal
classification (CTC) technique. While the original Self-conditioned CTC
estimates character-level intermediate predictions by applying auxiliary CTC
losses to a set of intermediate layers, the proposed method additionally
estimates syllable-level intermediate predictions in another set of
intermediate layers. The character-level and syllable-level predictions are
alternately used as conditioning features to deal with mutual dependency
between characters and syllables. Experimental results on Japanese and Mandarin
datasets show that the proposed multi-sequence intermediate conditioning
outperformed the conventional multi-task-based and Self-conditioned CTC-based
methods.


title: Better Intermediates Improve CTC Inference

abstract:
  This paper proposes a method for improved CTC inference with searched
intermediates and multi-pass conditioning. The paper first formulates
self-conditioned CTC as a probabilistic model with an intermediate prediction
as a latent representation and provides a tractable conditioning framework. We
then propose two new conditioning methods based on the new formulation: (1)
Searched intermediate conditioning that refines intermediate predictions with
beam-search, (2) Multi-pass conditioning that uses predictions of previous
inference for conditioning the next inference. These new approaches enable
better conditioning than the original self-conditioned CTC during inference and
improve the final performance. Experiments with the LibriSpeech dataset show
relative 3%/12% performance improvement at the maximum in test clean/other sets
compared to the original self-conditioned CTC.


title:
Na$_5$Co$_{15.5}$Te$_6$O$_{36}$: an $S$ = 1/2 stacked Ising kagome
  antiferromagnet with a partially disordered ground state

abstract:
  $S$ = 1/2 Ising kagome antiferromagnets (KAFs) are rare in reality. In this
work, we report the magnetic ground state and field-induced transitions of an
$S$ = 1/2 Ising antiferromagnet with a stacked kagome geometry,
Na$_5$Co$_{15.5}$Te$_6$O$_{36}$. Upon cooling, magnetic susceptibility
measurement reveals three successive magnetic transitions at $T_1$ = 45.2 K,
$T_2$ = 35.4 K, and $T_3 \sim$ 20 K. When the magnetic field is applied along
the Ising $c$ axis, a strikingly anomalous initial magnetization lying outside
the hysteresis loop is observed below $T_3$. Through the detailed
characterizations, the magnetic field $vs$ temperature phase diagram is
established. A partially disordered antiferromagnetic state is proposed below
$T_1$, which becomes frozen below $T_3$. Na$_5$Co$_{15.5}$Te$_6$O$_{36}$ is
therefore a promising candidate for $S$ = 1/2 Ising KAFs, and this unique
composite structure may shed light on the exploratory path for $S$ = 1/2 Ising
KAFs.


title: VNF and Container Placement: Recent Advances and Future Trends

abstract:
  With the growing demand for openness, scalability, and granularity, mobile
network function virtualization (NFV) has emerged as a key enabler for most
mobile network operators. NFV decouples network functions from hardware
devices. This decoupling allows network services, referred to as Virtualized
Network Functions (VNFs), to be hosted on commodity hardware which simplifies
and enhances service deployment and management for providers, improves
flexibility, and leads to efficient and scalable resource usage, and lower
costs. The proper placement of VNFs in the hosting infrastructures is one of
the main technical challenges. This placement significantly influences the
network's performance, reliability, and operating costs. The VNF placement is
NP-Hard. Hence, there is a need for placement methods that can scale with the
issue's complexity and find appropriate solutions in a reasonable duration. The
primary purpose of this study is to provide a taxonomy of optimization
techniques used to tackle the VNF placement problems. We classify the studied
papers based on performance metrics, methods, algorithms, and environment.
Virtualization is not limited to simply replacing physical machines with
virtual machines or VNFs, but may also include micro-services, containers, and
cloud-native systems. In this context, the second part of our article focuses
on the placement of containers in edge/fog computing. Many issues have been
considered as traffic congestion, resource utilization, energy consumption,
performance degradation, security, etc. For each matter, various solutions are
proposed through different surveys and research papers in which each one
addresses the placement problem in a specific manner by suggesting single
objective or multi-objective methods based on different types of algorithms
such as heuristic, meta-heuristic, and machine learning algorithms.


title:
GraftNet: Towards Domain Generalized Stereo Matching with a
  Broad-Spectrum and Task-Oriented Feature

abstract:
  Although supervised deep stereo matching networks have made impressive
achievements, the poor generalization ability caused by the domain gap prevents
them from being applied to real-life scenarios. In this paper, we propose to
leverage the feature of a model trained on large-scale datasets to deal with
the domain shift since it has seen various styles of images. With the cosine
similarity based cost volume as a bridge, the feature will be grafted to an
ordinary cost aggregation module. Despite the broad-spectrum representation,
such a low-level feature contains much general information which is not aimed
at stereo matching. To recover more task-specific information, the grafted
feature is further input into a shallow network to be transformed before
calculating the cost. Extensive experiments show that the model generalization
ability can be improved significantly with this broad-spectrum and
task-oriented feature. Specifically, based on two well-known architectures
PSMNet and GANet, our methods are superior to other robust algorithms when
transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is
available at https://github.com/SpadeLiu/Graft-PSMNet.


title:
Measuring Diagnostic Test Performance Using Imperfect Reference Tests: A
  Partial Identification Approach

abstract:
  Diagnostic tests are almost never perfect. Studies quantifying their
performance use knowledge of the true health status, measured with a reference
diagnostic test. Researchers commonly assume that the reference test is
perfect, which is not the case in practice. When the assumption fails,
conventional studies identify only the "apparent" performance or performance
with respect to the reference that is not of interest. This paper provides the
smallest possible bounds on the measures of true performance - sensitivity and
specificity, in standard settings. Existing methods for inference based on
moment inequalities are used to construct uniformly consistent confidence sets
in level over a relevant family of data distributions. Analysis of published
studies for the ubiquitous Abbott BinaxNOW COVID-19 Ag2 CARD antigen test
demonstrates that the bound estimates can be very informative. Under plausible
assumptions, estimates based on the Emergency Use Authorization (EUA) study
data reveal that the false negative rates can be up to 3.89 times higher than
the frequently cited "apparent" false negative rate. Independent study data
show the estimated false negative rates in asymptomatic patients are up to 5.4
times higher than the cited "apparent" figure, warranting further attention.


title:
Spectral extremal results on the $\alpha$-index of graphs without minors
  and star forests

abstract:
  Let $G$ be a graph of order $n$, and let $A(G)$ and $D(G)$ be the adjacency
matrix and the degree matrix of $G$ respectively. Define the convex linear
combinations
  $A_\alpha (G)$ of $A (G)$ and $D (G) $ by $$A_\alpha (G)=\alpha
D(G)+(1-\alpha)A(G)$$ for any real number $0\leq\alpha\leq1$. The
\emph{$\alpha$-index} of $G$ is the largest eigenvalue of $A_\alpha(G)$. In
this paper, we determine the maximum $\alpha$-index and characterize all
extremal graphs for $K_r$ minor-free graphs, $K_{s,t}$ minor-free graphs, and
star-forest-free graphs for any $0<\alpha<1$ by unified eigenvector approach,
respectively.


title:
The mass-degenerate SM-like Higgs and anomaly of $(g-2)_\mu$ in
  $\mu$-term extended NMSSM

abstract:
  We chose the $\mu$-term extended next-to-minimal supersymmetric standard
model ($\mu$NMSSM) for this work, and the phenomenological research is based on
the assumption of double Higgs resonance state as the Standard Model (SM)-like
Higgs considering the recent $(g-2)_\mu$ result. The study also take into
account a variety of experimental results, including direct detection of dark
matter (DM) and searching results for sparticles at the Large Hadron Collider
(LHC). We study the characteristic of DM confronted with limitations of direct
detection experiments. Following that, we concentrate on the properties of the
mass-degenerate SM-like Higgs bosons and explaining the anomaly of $(g-2)_\mu$.
We conclude that the anomaly of $(g-2)_\mu$ can be explained in the scenario
with two mass-degenerate SM-like Higgs, and there are samples that meet all
current constraints and outperform SM in fitting Higgs data.


title: Dynamic Supervisor for Cross-dataset Object Detection

abstract:
  The application of cross-dataset training in object detection tasks is
complicated because the inconsistency in the category range across datasets
transforms fully supervised learning into semi-supervised learning. To address
this problem, recent studies focus on the generation of high-quality missing
annotations. In this study, we first point out that it is not enough to
generate high-quality annotations using a single model, which only looks once
for annotations. Through detailed experimental analyses, we further conclude
that hard-label training is conducive to generating high-recall annotations,
while soft-label training tends to obtain high-precision annotations. Inspired
by the aspects mentioned above, we propose a dynamic supervisor framework that
updates the annotations multiple times through multiple-updated submodels
trained using hard and soft labels. In the final generated annotations, both
recall and precision improve significantly through the integration of
hard-label training with soft-label training. Extensive experiments conducted
on various dataset combination settings support our analyses and demonstrate
the superior performance of the proposed dynamic supervisor.


title: Nondeterminism subject to output commitment in combinatorial filters

abstract:
  We study a class of filters -- discrete finite-state transition systems
employed as incremental stream transducers -- that have application to
robotics: e.g., to model combinatorial estimators and also as concise encodings
of feedback plans/policies. The present paper examines their minimization
problem under some new assumptions. Compared to strictly deterministic filters,
allowing nondeterminism supplies opportunities for compression via re-use of
states. But this paper suggests that the classic automata-theoretic concept of
nondeterminism, though it affords said opportunities for reduction in state
complexity, is problematic in many robotics settings. Instead, we argue for a
new constrained type of nondeterminism that preserves input-output behavior for
circumstances when, as for robots, causation forbids 'rewinding' of the world.
We identify problem instances where compression under this constrained form of
nondeterminism results in improvements over all deterministic filters. In this
new setting, we examine computational complexity questions for the problem of
reducing the state complexity of some given input filter. A hardness result for
general deterministic input filters is presented, as well as for checking
specific, narrower requirements, and some special cases. These results show
that this class of nondeterminism gives problems of the same complexity class
as classical nondeterminism, and the narrower questions help give a more
nuanced understanding of the source of this complexity.


title:
Distill-VQ: Learning Retrieval Oriented Vector Quantization By
  Distilling Knowledge from Dense Embeddings

abstract:
  Vector quantization (VQ) based ANN indexes, such as Inverted File System
(IVF) and Product Quantization (PQ), have been widely applied to embedding
based document retrieval thanks to the competitive time and memory efficiency.
Originally, VQ is learned to minimize the reconstruction loss, i.e., the
distortions between the original dense embeddings and the reconstructed
embeddings after quantization. Unfortunately, such an objective is inconsistent
with the goal of selecting ground-truth documents for the input query, which
may cause severe loss of retrieval quality. Recent works identify such a
defect, and propose to minimize the retrieval loss through contrastive
learning. However, these methods intensively rely on queries with ground-truth
documents, whose performance is limited by the insufficiency of labeled data.
  In this paper, we propose Distill-VQ, which unifies the learning of IVF and
PQ within a knowledge distillation framework. In Distill-VQ, the dense
embeddings are leveraged as "teachers", which predict the query's relevance to
the sampled documents. The VQ modules are treated as the "students", which are
learned to reproduce the predicted relevance, such that the reconstructed
embeddings may fully preserve the retrieval result of the dense embeddings. By
doing so, Distill-VQ is able to derive substantial training signals from the
massive unlabeled data, which significantly contributes to the retrieval
quality. We perform comprehensive explorations for the optimal conduct of
knowledge distillation, which may provide useful insights for the learning of
VQ based ANN index. We also experimentally show that the labeled data is no
longer a necessity for high-quality vector quantization, which indicates
Distill-VQ's strong applicability in practice.


title:
Computational stability analysis of PDEs with integral terms using the
  PIE framework

abstract:
  The Partial Integral Equation (PIE) framework was developed to
computationally analyze linear Partial Differential Equations (PDEs) where the
PDE is first converted to a PIE and then the analysis problem is solved by
solving operator-valued optimization problems. Previous works on the PIE
framework focused on the analysis of PDEs with spatial derivatives up to
$2^{nd}$-order. In this paper, we extend the class of PDEs by including
integral terms and performing stability analysis using the PIE framework. More
specifically, we show that PDEs with the integral terms where the integration
is with respect to the spatial variable and the kernel of the integral operator
is matrix-valued polynomials can be converted to PIEs if the boundary
conditions satisfy certain criteria. The conversion is performed by using a
change of variable where every PDE state is substituted in terms of its highest
derivative and boundary values to obtain a new equation (a PIE) in a variable
that does not have any continuity requirements. Later, we show that this change
of variable can be represented using explicit maps from the parameters of the
PDE to the parameters of the PIE and the stability test can be posed as an
optimization problem involving these parameters. Lastly, we present numerical
examples to demonstrate the simplicity and application of this method.


title:
Comparative Analysis of Interval Reachability for Robust Implicit and
  Feedforward Neural Networks

abstract:
  We use interval reachability analysis to obtain robustness guarantees for
implicit neural networks (INNs). INNs are a class of implicit learning models
that use implicit equations as layers and have been shown to exhibit several
notable benefits over traditional deep neural networks. We first establish that
tight inclusion functions of neural networks, which provide the tightest
rectangular over-approximation of an input-output map, lead to sharper
robustness guarantees than the well-studied robustness measures of local
Lipschitz constants. Like Lipschitz constants, tight inclusions functions are
computationally challenging to obtain, and we thus propose using mixed
monotonicity and contraction theory to obtain computationally efficient
estimates of tight inclusion functions for INNs. We show that our approach
performs at least as well as, and generally better than, applying
state-of-the-art interval bound propagation methods to INNs. We design a novel
optimization problem for training robust INNs and we provide empirical evidence
that suitably-trained INNs can be more robust than comparably-trained
feedforward networks.


title: Novelty Driven Evolutionary Neural Architecture Search

abstract:
  Evolutionary algorithms (EA) based neural architecture search (NAS) involves
evaluating each architecture by training it from scratch, which is extremely
time-consuming. This can be reduced by using a supernet for estimating the
fitness of an architecture due to weight sharing among all architectures in the
search space. However, the estimated fitness is very noisy due to the
co-adaptation of the operations in the supernet which results in NAS methods
getting trapped in local optimum. In this paper, we propose a method called
NEvoNAS wherein the NAS problem is posed as a multi-objective problem with 2
objectives: (i) maximize architecture novelty, (ii) maximize architecture
fitness/accuracy. The novelty search is used for maintaining a diverse set of
solutions at each generation which helps avoiding local optimum traps while the
architecture fitness is calculated using supernet. NSGA-II is used for finding
the \textit{pareto optimal front} for the NAS problem and the best architecture
in the pareto front is returned as the searched architecture. Exerimentally,
NEvoNAS gives better results on 2 different search spaces while using
significantly less computational resources as compared to previous EA-based
methods. The code for our paper can be found in
https://github.com/nightstorm0909/NEvoNAS.


title:
The spillover effect of neighboring port on regional industrial
  diversification and regional economic resilience

abstract:
  We examine the spillover effect of neighboring ports on regional industrial
diversification and their economic resilience using the export data of South
Korea from 2006 to 2020. First, we build two distinct product spaces of ports
and port regions, and provide direct estimates of the role of neighboring ports
as spillover channels spatially linked. This is in contrast to the previous
literature that mainly regarded ports as transport infrastructure per se.
Second, we confirm that the knowledge spillover effect from neighboring ports
had a non-negligible role in sustaining regional economies during the recovery
after the economic crisis but its power has weakened recently due to a loosened
global value chain.


title:
Spikes, Roots, and Modulations: Phase Retrieval for Finitely-Supported
  Complex Measures

abstract:
  We study the recovery of a finitely supported distribution, a complex linear
combination of Dirac measures, from intensity measurements. The distribution
$\mu=\sum_{j=1}^{s}c_{j}\delta_{t_{j}}$ is given by a coefficient vector
$c\in\mathbb{C}^s$ and its support $\{t_1, t_2, \dots, t_s\}$ is contained in $
[0,\Lambda]$ for some $\Lambda>0$. The intensity measurements evaluate
(squared) magnitudes of a set of linear functionals applied to $\mu$, obtained
by sampling $\hat \mu$, the Fourier transform of $\mu$, or by evaluating
differences between modulated samples. Following a strategy by Alexeev et al.,
the structure of the linear functionals, and hence of the non-linear magnitude
measurement, is encoded with a graph, where the vertices represent point
evaluations of $\hat \mu$ at $\{v_1, v_2, \dots, v_n\} \subset
[-\Omega,\Omega]$ and each edge represents a (modulated) difference between
vertices incident with it. We show that a Ramanujan graph with degree $d \ge 3$
and $n>\frac{6(1 + 6 /\ln(s/\Lambda\Omega)) s}{1-2\sqrt{d-1}/d}$ vertices
provides $M=(d+1)n$ magnitudes that are sufficient for identifying the complex
measure up to an overall unimodular multiplicative constant. At the cost of
including an additional oversampling step and with an additional requirement
that $n-1$ is prime, we construct an explicit recovery algorithm that is based
on the Prony method.


title:
Wasserstein Two-Sided Chance Constraints with An Application to Optimal
  Power Flow

abstract:
  As a natural approach to modeling system safety conditions, chance constraint
(CC) seeks to satisfy a set of uncertain inequalities individually or jointly
with high probability. Although a joint CC offers stronger reliability
certificate, it is oftentimes much more challenging to compute than individual
CCs. Motivated by the application of optimal power flow, we study a special
joint CC, named two-sided CC. We model the uncertain parameters through a
Wasserstein ball centered at a Gaussian distribution and derive a hierarchy of
conservative approximations based on second-order conic constraints, which can
be efficiently computed by off-the-shelf commercial solvers. In addition, we
show the asymptotic consistency of these approximations and derive their
approximation guarantee when only a finite hierarchy is adopted. We demonstrate
the out-of-sample performance and scalability of the proposed model and
approximations in a case study based on the IEEE 118-bus and 3120-bus systems.


title: Redefining entanglement entropy -- can it solve the information paradox?

abstract:
  Qubit Models which aim to resolve the information paradox must not redefine
entanglement entropy as a redefinition does not address the correct issue of
the information paradox. The purpose of this letter is to understand why any
redefinition of entanglement entropy is not relevant to the paradox. Also,
conditions required for a bit model to resolve the information paradox are
listed, motivating the community to look for such conditions as a consistency
check in their qubit models.


title:
Epipolar Focus Spectrum: A Novel Light Field Representation and
  Application in Dense-view Reconstruction

abstract:
  Existing light field representations, such as epipolar plane image (EPI) and
sub-aperture images, do not consider the structural characteristics across the
views, so they usually require additional disparity and spatial structure cues
for follow-up tasks. Besides, they have difficulties dealing with occlusions or
larger disparity scenes. To this end, this paper proposes a novel Epipolar
Focus Spectrum (EFS) representation by rearranging the EPI spectrum. Different
from the classical EPI representation where an EPI line corresponds to a
specific depth, there is a one-to-one mapping from the EFS line to the view.
Accordingly, compared to a sparsely-sampled light field, a densely-sampled one
with the same field of view (FoV) leads to a more compact distribution of such
linear structures in the double-cone-shaped region with the identical opening
angle in its corresponding EFS. Hence the EFS representation is invariant to
the scene depth. To demonstrate its effectiveness, we develop a trainable
EFS-based pipeline for light field reconstruction, where a dense light field
can be reconstructed by compensating the "missing EFS lines" given a sparse
light field, yielding promising results with cross-view consistency, especially
in the presence of severe occlusion and large disparity. Experimental results
on both synthetic and real-world datasets demonstrate the validity and
superiority of the proposed method over SOTA methods.


title:
Direct synthesis of single-crystal bilayer graphene on various
  dielectric substrates

abstract:
  In this work, a novel method to grow high-quality and large bilayer graphene
(BLG) directly on various dielectric substrates was demonstrated. Large area
single-crystal monolayer graphene was applied as a seeding layer to facilitate
the homo-epitaxial synthesis of single crystal BLG directly on insulating
substrates. The Cu nano-powders (Cu NP) with nanostructure and high
surface-area were used as the remote catalysis to provide long-lasting
catalytic activity during the graphene growth. The TEM results confirmed the
single-crystalline nature of the BLG domains, which validates the superiority
of the homo-epitaxial growth technique. The as-grown BLG show comparable
quality with the CVD-grown BLG on metal surface. Field-effect transistors
directly fabricated on the as-grown BLG/SiO$ _2 $/Si showed a room temperature
carrier mobility as high as 2297 cm $ ^2 $ V$ ^{-1}$ s $^{-1} $.


title: Visualizing Energy Transfer Between Redox-Active Colloids

abstract:
  Redox-based electrical conduction in nonconjugated polymers has been explored
less than a decade, yet is already showing promise as a new concept for
electrical energy transport. Here using monolayers and sub-monolayers of
touching micron-sized redox active colloids (RAC) containing high densities of
ethyl-viologen (EV) side groups, intercolloid redox-based electron transport
was directly observed via fluorescence microscopy. This observation was enabled
by the discovery that these RAC exhibit a highly non-linear
electrofluorochromism which can be quantitatively coupled to the colloid redox
state. By evaluating the quasi-Fickian nature of the charge transfer (CT)
kinetics, the apparent CT diffusion coefficient DCT was extracted. Along with
addressing more fundamental questions regarding energy transport in colloidal
materials, this first real-time real-space imaging of energy transport within
monolayers of redox-active colloids may provide insights into energy transfer
in flow batteries, and enable design of new forms of conductive polymers for
applications including organic electronics.


title: Newly detected open clusters in the Galactic disk using Gaia EDR3

abstract:
  The astrometric satellite Gaia recently released part of its third data set,
which provides a good opportunity to hunt for more open clusters in the Milky
Way. In this work, we conduct a blind search for open clusters in the Galactic
disk using a sample-based clustering search method with high spatial
resolution, which is especially suited to finding hidden targets. In addition
to confirming 1930 previously known open clusters and 82 known globular
clusters, 704 new stellar clusters are proposed as potential open clusters at
Galactic latitudes of $|$b$|$ < 20 {deg}. For each of these new open clusters,
we present the coordinates, detailed astrometric parameters, and ages, as well
as the radial velocity, if available. Our blind search greatly increases the
number of Galactic open clusters as objects of study and shows the
incompleteness of the open cluster census across our Galaxy


title:
Measuring Cognitive Load of Software Developers Based on Nasal Skin
  Temperature

abstract:
  It has recently become increasingly important to measure the cognitive load
of developers. This is because continuing with a development under a high
cognitive load may cause human errors. Therefore, to automatically measure the
cognitive load, existing studies have used biometric measures such as brain
waves and the heart rate. However, developers are often required to equip
certain devices when measuring them, and can therefore be physically burdened.
In this study, we evaluated the feasibility of non-invasive biometric measures
based on the nasal skin temperature. The nasal skin temperature has been widely
used in other fields to measure mental status. In the present experiment, the
subjects created small Java programs, and we estimated their cognitive load
using the proposed biometric measures based on the nasal skin temperature. As a
result, the proposed biometric measures were shown to be more effective than
non-biometric measures. Hence, biometric measures based on nasal skin
temperature are promising for estimating the cognitive load of developers.


title:
Development of current estimated household data and agent-based
  simulation of the future population distribution of households in Japan

abstract:
  In response to the declining population and aging infrastructure in Japan,
local governments are implementing compact city policies such as the location
normalization plan. To optimize the reorganization of urban public
infrastructure, it is important to provide detailed and accurate forecasts of
the distribution of urban populations and households. However, many local
governments do not have the necessary data and forecasting capability.
Moreover, current forecasts of gender- and age-based population data only exist
at the municipal level, and household data are only available by family type at
the prefecture level. Meanwhile, the accuracy is limited with an assumption of
same change rate of population in all municipalities and within each city.
Therefore, the aim of this study was to develop an agent-based microsimulation
household transition model, with the household as the unit and agent, and
household data was estimated for all cities in Japan from 2015. Estimated
household data comprised the family type, house type, and address, age, and
gender of household members, obtained from the national census, and building
data. The resulting household transition model was used to forecast the
attributes of each household every five years. Simulations in Toyama and
Shizuoka Prefectures, Japan from 1980 to 2010 provided highly accurate
estimates of municipal-level population by age and household volume by family
type. The proposed model was also applied to predict the future distribution of
disappearing villages and vacant houses in Japan.


title: Reaching a Consensus with Limited Information

abstract:
  In its simplest form the well known consensus problem for a networked family
of autonomous agents is to devise a set of protocols or update rules, one for
each agent, which can enable all of the agents to adjust or tune their
"agreement variable" to the same value by utilizing real-time information
obtained from their "neighbors" within the network. The aim of this paper is to
study the problem of achieving a consensus in the face of limited information
transfer between agents. By this it is meant that instead of each agent
receiving an agreement variable or real-valued state vector from each of its
neighbors, it receives a linear function of each state instead. The specific
problem of interest is formulated and provably correct algorithms are developed
for a number of special cases of the problem.


title:
Nonperturbative Determination of Collins-Soper Kernel from Quasi
  Transverse-Momentum Dependent Wave Functions

abstract:
  In the framework of large-momentum effective theory at one-loop matching
accuracy, we perform a lattice calculation of the Collins-Soper kernel which
governs the rapidity evolution of transverse-momentum-dependent (TMD)
distributions. We first obtain the quasi TMD wave functions at three different
meson momenta on a lattice with valence clover quarks on a dynamical HISQ sea
and lattice spacing $a=0.12$~fm from MILC, and renormalize the pertinent linear
divergences using Wilson loops. Through one-loop matching to the light-cone
wave functions, we determine the Collins-Soper kernel with transverse
separation up to 0.6~fm. We study the systematic uncertainties from operator
mixing and scale dependence, as well as the impact from higher power
corrections. Our results potentially allow for a determination of the soft
function and other transverse-momentum dependent quantities at one-loop
accuracy.


title: Burstiness of human physical activities and their characterization

abstract:
  Human behavior is known to be heterogeneous and fluctuates temporally. Many
studies have focused on the fluctuation of Inter-Event Times (IETs), the times
between one action and the next in various types of human behavior. The
existence of this fluctuation cannot be explained by a stationary random
Poisson process and is called burstiness. In this study, we collected human
physical activity data while specifying the age of the subjects and their
situations (e.g., children's play and adults' housework) and analyzed their
event time series. We confirmed that burstiness both in children and adults.
Especially, burstiness in the physical activity of 2-3 year children is, for
the first time as far as we know, observed. We also confirmed that each
activity situation has its own specific IETs distribution characteristics. Our
results may provide important clues to identifying the mechanisms of burstiness
in human physical activities.


title:
Magnetotransport properties of the Quantum Spin Hall and Quantum Hall
  states in an inverted HgTe/CdTe and InAs/GaSb quantum wells

abstract:
  The quantum spin Hall (QSH) states discovered in an inverted band of
InAs/GaSb and HgTe/CdTe quantum wells categorize them among the very superior
candidates for topological insulators. In the presence of a magnetic field,
these QSH states persist up to a magnetic field equal to the critical field,
beyond which the edge states would consist of normal quantum Hall (QH) states.
We provide the expression of this critical field which is found consistent with
some previous literature. The critical field partitioned the spectrum into two
types of quantum states, \textit{viz}. , the Quantum spin Hall (QSH) and
Quantum Hall (QH) states. We present a theoretical study of the
magnetotransport properties based on the Bernevig-Hughes-Zhang Hamiltonian that
describes these QSH states. Our results of the Hall conductivity show the
different responses at these two different topological regions. Around the low
Fermi energy level, the system has a high Hall conductivity in the QH region,
while the same is less dominant in the QSH region. Our results of the Hall
conductivity thus help differentiate the type topological phase of the given
quantum well.


title: Graph Enhanced Contrastive Learning for Radiology Findings Summarization

abstract:
  The impression section of a radiology report summarizes the most prominent
observation from the findings section and is the most important section for
radiologists to communicate to physicians. Summarizing findings is
time-consuming and can be prone to error for inexperienced radiologists, and
thus automatic impression generation has attracted substantial attention. With
the encoder-decoder framework, most previous studies explore incorporating
extra knowledge (e.g., static pre-defined clinical ontologies or extra
background information). Yet, they encode such knowledge by a separate encoder
to treat it as an extra input to their models, which is limited in leveraging
their relations with the original findings. To address the limitation, we
propose a unified framework for exploiting both extra knowledge and the
original findings in an integrated way so that the critical information (i.e.,
key words and their relations) can be extracted in an appropriate way to
facilitate impression generation. In detail, for each input findings, it is
encoded by a text encoder, and a graph is constructed through its entities and
dependency tree. Then, a graph encoder (e.g., graph neural networks (GNNs)) is
adopted to model relation information in the constructed graph. Finally, to
emphasize the key words in the findings, contrastive learning is introduced to
map positive samples (constructed by masking non-key words) closer and push
apart negative ones (constructed by masking key words). The experimental
results on OpenI and MIMIC-CXR confirm the effectiveness of our proposed
method.


title:
LoCoV: low dimension covariance voting algorithm for portfolio
  optimization

abstract:
  Minimum-variance portfolio optimizations rely on accurate covariance
estimator to obtain optimal portfolios. However, it usually suffers from large
error from sample covariance matrix when the sample size $n$ is not
significantly larger than the number of assets $p$. We analyze the random
matrix aspects of portfolio optimization and identify the order of errors in
sample optimal portfolio weight and show portfolio risk are underestimated when
using samples. We also provide LoCoV (low dimension covariance voting)
algorithm to reduce error inherited from random samples. From various
experiments, LoCoV is shown to outperform the classical method by a large
margin.


title:
A Physics-Guided Neural Operator Learning Approach to Model Biological
  Tissues from Digital Image Correlation Measurements

abstract:
  We present a data-driven workflow to biological tissue modeling, which aims
to predict the displacement field based on digital image correlation (DIC)
measurements under unseen loading scenarios, without postulating a specific
constitutive model form nor possessing knowledges on the material
microstructure. To this end, a material database is constructed from the DIC
displacement tracking measurements of multiple biaxial stretching protocols on
a porcine tricuspid valve anterior leaflet, with which we build a neural
operator learning model. The material response is modeled as a solution
operator from the loading to the resultant displacement field, with the
material microstructure properties learned implicitly from the data and
naturally embedded in the network parameters. Using various combinations of
loading protocols, we compare the predictivity of this framework with finite
element analysis based on the phenomenological Fung-type model. From
in-distribution tests, the predictivity of our approach presents good
generalizability to different loading conditions and outperforms the
conventional constitutive modeling at approximately one order of magnitude.
When tested on out-of-distribution loading ratios, the neural operator learning
approach becomes less effective. To improve the generalizability of our
framework, we propose a physics-guided neural operator learning model via
imposing partial physics knowledge. This method is shown to improve the model's
extrapolative performance in the small-deformation regime. Our results
demonstrate that with sufficient data coverage and/or guidance from partial
physics constraints, the data-driven approach can be a more effective method
for modeling biological materials than the traditional constitutive modeling.


title: Isometric group actions with vanishing rate of escape on CAT(0) spaces

abstract:
  Let $\Gamma$ be a finitely generated group equipped with a symmetric and
nondegenerate probability measure $\mu$ with finite second moment, and $Y$ a
CAT(0) space which is either proper or of finite telescopic dimension. We show
that if an isometric action of $\Gamma$ on $Y$ has vanishing rate of escape
with respect to $\mu$ and does not fix a point in the boundary at infinity of
$Y$, then there exists a flat subspace in $Y$ which is left invariant under the
action of $\Gamma$. In the proof of this result, an equivariant $\mu$-harmonic
map from $\Gamma$ into $Y$ plays an important role.


title: PogoDrone: Design, Model, and Control of a Jumping Quadrotor

abstract:
  We present a design, model, and control for a novel jumping-flying robot that
is called PogoDrone. The robot is composed of a quadrotor with a passive
mechanism for jumping. The robot can continuously jump in place or fly like a
normal quadrotor. Jumping in place allows the robot to quickly move and operate
very close to the ground. For instance, in agricultural applications, the
jumping mechanism allows the robot to take samples of soil. We propose a hybrid
controller that switches from attitude to position control to allow the robot
to fall horizontally and recover to the original position. We compare the
jumping mode with the hovering mode to analyze the energy consumption. In
simulations, we evaluate the effect of different factors on energy consumption.
In real experiments, we show that our robot can repeatedly impact the ground,
jump, and fly in a physical environment.


title: Predictive Control Barrier Functions for Online Safety Critical Control

abstract:
  This paper presents a methodology for constructing Control Barrier Functions
(CBFs) that proactively consider the future safety of a system along a nominal
trajectory, and effect corrective action before the trajectory leaves a
designated safe set. Specifically, this paper presents a systematic approach
for propagating a nominal trajectory on a receding horizon, and then encoding
the future safety of this trajectory into a CBF. If the trajectory is unsafe,
then a controller satisfying the CBF condition will modify the nominal
trajectory before the trajectory becomes unsafe. Compared to existing CBF
techniques, this strategy is proactive rather than reactive and thus
potentially results in smaller modifications to the nominal trajectory. The
proposed strategy is shown to be provably safe, and then is demonstrated in
simulated scenarios where it would otherwise be difficult to construct a
traditional CBF. In simulation, the predictive CBF results in less modification
to the nominal trajectory and smaller control inputs than a traditional CBF,
and faster computations than a nonlinear model predictive control approach.


title:
Green Routing Game: Strategic Logistical Planning using Mixed Fleets of
  ICEVs and EVs

abstract:
  This paper introduces a "green" routing game between multiple logistic
operators (players), each owning a mixed fleet of internal combustion engine
vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of
delayed delivery (due to charging requirements of EVs) and a pollution cost
levied on the ICEVs. This cost structure models: 1) limited battery capacity of
EVs and their charging requirement; 2) shared nature of charging facilities; 3)
pollution cost levied by regulatory agency on the use of ICEVs. We characterize
Nash equilibria of this game and derive a condition for its uniqueness. We also
use the gradient projection method to compute this equilibrium in a distributed
manner. Our equilibrium analysis is useful to analyze the trade-off faced by
players in incurring higher delay due to congestion at charging locations when
the share of EVs increases versus a higher pollution cost when the share of
ICEVs increases. A numerical example suggests that to increase marginal
pollution cost can dramatically reduce inefficiency of equilibria.