title:
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with
  Symbolic Reward Machines

abstract:
  A misspecified reward can degrade sample efficiency and induce undesired
behaviors in reinforcement learning (RL) problems. We propose symbolic reward
machines for incorporating high-level task knowledge when specifying the reward
signals. Symbolic reward machines augment existing reward machine formalism by
allowing transitions to carry predicates and symbolic reward outputs. This
formalism lends itself well to inverse reinforcement learning, whereby the key
challenge is determining appropriate assignments to the symbolic values from a
few expert demonstrations. We propose a hierarchical Bayesian approach for
inferring the most likely assignments such that the concretized reward machine
can discriminate expert demonstrated trajectories from other trajectories with
high accuracy. Experimental results show that learned reward machines can
significantly improve training efficiency for complex RL tasks and generalize
well across different task environment configurations.


title: Time-based Self-supervised Learning for Wireless Capsule Endoscopy

abstract:
  State-of-the-art machine learning models, and especially deep learning ones,
are significantly data-hungry; they require vast amounts of manually labeled
samples to function correctly. However, in most medical imaging fields,
obtaining said data can be challenging. Not only the volume of data is a
problem, but also the imbalances within its classes; it is common to have many
more images of healthy patients than of those with pathology. Computer-aided
diagnostic systems suffer from these issues, usually over-designing their
models to perform accurately. This work proposes using self-supervised learning
for wireless endoscopy videos by introducing a custom-tailored method that does
not initially need labels or appropriate balance. We prove that using the
inferred inherent structure learned by our method, extracted from the temporal
axis, improves the detection rate on several domain-specific applications even
under severe imbalance.


title: Attention in Reasoning: Dataset, Analysis, and Modeling

abstract:
  While attention has been an increasingly popular component in deep neural
networks to both interpret and boost the performance of models, little work has
examined how attention progresses to accomplish a task and whether it is
reasonable. In this work, we propose an Attention with Reasoning capability
(AiR) framework that uses attention to understand and improve the process
leading to task outcomes. We first define an evaluation metric based on a
sequence of atomic reasoning operations, enabling a quantitative measurement of
attention that considers the reasoning process. We then collect human
eye-tracking and answer correctness data, and analyze various machine and human
attention mechanisms on their reasoning capability and how they impact task
performance. To improve the attention and reasoning ability of visual question
answering models, we propose to supervise the learning of attention
progressively along the reasoning process and to differentiate the correct and
incorrect attention patterns. We demonstrate the effectiveness of the proposed
framework in analyzing and modeling attention with better reasoning capability
and task performance. The code and data are available at
https://github.com/szzexpoi/AiR


title: T-structures on a local 4-Calabi-Yau variety

abstract:
  Let $X$ denote the total space of cotangent bundle of projective plane. This
is a non-compact Calabi-Yau $4$-fold (also called local Calabi-Yau variety in
physics literature). The aim of this paper is to use tilting objects to
characterize a large class of t-structures in the relevant Calabi-Yau category
and try to calculate the combinatorics of them.


title:
Ferrimagnet GdFeCo characterization for spin-orbitronics: large
  field-like and damping-like torques

abstract:
  Spintronics is showing promising results in the search for new materials and
effects to reduce energy consumption in information technology. Among these
materials, ferrimagnets are of special interest, since they can produce large
spin currents that trigger the magnetization dynamics of adjacent layers or
even their own magnetization. Here, we present a study of the generation of
spin current by GdFeCo in a GdFeCo/Cu/NiFe trilayer where the FeCo sublattice
magnetization is dominant at room temperature. Magnetic properties such as the
saturation magnetization are deduced from magnetometry measurements while
damping constant is estimated from spin-torque ferromagnetic resonance
(ST-FMR). We show that the overall damping-like (DL) and field-like (FL)
effective fields as well as the associated spin Hall angles can be reliably
obtained by performing the dependence of ST-FMR by an added dc current. The sum
of the spin Hall angles for both the spin Hall effect (SHE) and the spin
anomalous Hall effect (SAHE) symmetries are: $\theta_{DL}^{SAHE} +
\theta_{DL}^{SHE}=-0.15 \pm 0.05$ and $\theta_{FL}^{SAHE} +
\theta_{FL}^{SHE}=0.026 \pm 0.005$. From the symmetry of ST-FMR signals we find
that $\theta_{DL}^{SHE}$ is positive and dominated by the negative
$\theta_{DL}^{SAHE}$. The present study paves the way for tuning the different
symmetries in spin conversion in highly efficient ferrimagnetic systems.


title: Multi-Focus Image Fusion based on Gradient Transform

abstract:
  Multi-focus image fusion is a challenging field of study that aims to provide
a completely focused image by integrating focused and un-focused pixels. Most
existing methods suffer from shift variance, misregistered images, and
data-dependent. In this study, we introduce a novel gradient information-based
multi-focus image fusion method that is robust for the aforementioned problems.
The proposed method first generates gradient images from original images by
using Halftoning-Inverse Halftoning (H-IH) transform. Then, Energy of Gradient
(EOG) and Standard Deviation functions are used as the focus measurement on the
gradient images to form a fused image. Finally, in order to enhance the fused
image a decision fusion approach is applied with the majority voting method.
The proposed method is compared with 17 different novel and conventional
techniques both visually and objectively. For objective evaluation, 6 different
quantitative metrics are used. It is observed that the proposed method is
promising according to visual evaluation and 83.3% success is achieved by being
first in five out of six metrics according to objective evaluation.


title: Horocycle flow on flat projective bundles: from topology to measures

abstract:
  In this paper we study some topological and measurable aspects of the
dynamics of the foliated horocycle flow on flat projective bundles over
hyperbolic surfaces. If $\rho : \Gamma \to {\rm PSL}(n+1,\mathbb{R})$ is a
representation of a non-elementary Fuchsian group $\Gamma$, the unit tangent
bundle $Y$ associated to the flat projective bundle defined by $\rho$ admits a
natural action of the affine group $B$ obtained by combining the foliated
geodesic and horocycle flows. If the image $\rho(\Gamma)$ satisfies
Conze-Guivarc'h condition, namely strong irreducibility and proximality, the
dynamics of the $B$-action is captured by the proximal dynamics of
$\rho(\Gamma)$ on $\mathbb{R}{\rm P}^n$ (Theorem A). In fact, the dynamics of
the foliated horocycle flow on the unique $B$-minimal subset of $Y$ can be
described in terms of dynamics of the horocycle flow on its non-wandering set
in the unit tangent bundle $X$ of the base surface $S= \Gamma \backslash
\mathbb{H}$ (Theorem B). Assuming the existence of a continuous limit map
induced by $\rho$, an one-to-one correspondence between conservative ergodic
invariant measures can be stated for the foliated horocycle flow on the
proximal part of the non-wandering set in $Y$ and the horocycle flow on the
non-wandering set in $X$ (Theorem C).


title:
Multi-Scale Features and Parallel Transformers Based Image Quality
  Assessment

abstract:
  With the increase in multimedia content, the type of distortions associated
with multimedia is also increasing. This problem of image quality assessment is
expanded well in the PIPAL dataset, which is still an open problem to solve for
researchers. Although, recently proposed transformers networks have already
been used in the literature for image quality assessment. At the same time, we
notice that multi-scale feature extraction has proven to be a promising
approach for image quality assessment. However, the way transformer networks
are used for image quality assessment until now lacks these properties of
multi-scale feature extraction. We utilized this fact in our approach and
proposed a new architecture by integrating these two promising quality
assessment techniques of images. Our experimentation on various datasets,
including the PIPAL dataset, demonstrates that the proposed integration
technique outperforms existing algorithms. The source code of the proposed
algorithm is available online: https://github.com/KomalPal9610/IQA


title: Can Voters Detect Errors on Their Printed Ballots? Absolutely

abstract:
  There is still debate on whether voters can detect malicious changes in their
printed ballot after making their selections on a Ballot Marking Device (BMD).
In this study, we altered votes on a voter's ballot after they had made their
selections on a BMD. We then required them to examine their ballots for any
changes from the slate they used to vote. Overall accuracy was exceptionally
high. Participants saw 1440 total contests, and of those 1440, there were a
total of 4 errors, so total accuracy was 99.8%. Participants were able to
perform with near-perfect accuracy regardless of ballot length, ballot type,
number of altered races, and location of altered races. Detection performance
was extremely robust. We conclude that with proper direction and resources,
voters can be near-perfect detectors of ballot changes on printed paper ballots
after voting with a BMD. This finding has significant implications for the
voting community as BMD use continues to grow. Research should now focus on
identifying administrative and behavioral methods that will prompt and
encourage voters to check their BMD-generated ballots before they drop them in
the ballot box.


title:
Multi-label classification for biomedical literature: an overview of the
  BioCreative VII LitCovid Track for COVID-19 literature topic annotations

abstract:
  The COVID-19 pandemic has been severely impacting global society since
December 2019. Massive research has been undertaken to understand the
characteristics of the virus and design vaccines and drugs. The related
findings have been reported in biomedical literature at a rate of about 10,000
articles on COVID-19 per month. Such rapid growth significantly challenges
manual curation and interpretation. For instance, LitCovid is a literature
database of COVID-19-related articles in PubMed, which has accumulated more
than 200,000 articles with millions of accesses each month by users worldwide.
One primary curation task is to assign up to eight topics (e.g., Diagnosis and
Treatment) to the articles in LitCovid. Despite the continuing advances in
biomedical text mining methods, few have been dedicated to topic annotations in
COVID-19 literature. To close the gap, we organized the BioCreative LitCovid
track to call for a community effort to tackle automated topic annotation for
COVID-19 literature. The BioCreative LitCovid dataset, consisting of over
30,000 articles with manually reviewed topics, was created for training and
testing. It is one of the largest multilabel classification datasets in
biomedical scientific literature. 19 teams worldwide participated and made 80
submissions in total. Most teams used hybrid systems based on transformers. The
highest performing submissions achieved 0.8875, 0.9181, and 0.9394 for macro
F1-score, micro F1-score, and instance-based F1-score, respectively. The level
of participation and results demonstrate a successful track and help close the
gap between dataset curation and method development. The dataset is publicly
available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for
benchmarking and further development.


title:
MultiPathGAN: Structure Preserving Stain Normalization using
  Unsupervised Multi-domain Adversarial Network with Perception Loss

abstract:
  Histopathology relies on the analysis of microscopic tissue images to
diagnose disease. A crucial part of tissue preparation is staining whereby a
dye is used to make the salient tissue components more distinguishable.
However, differences in laboratory protocols and scanning devices result in
significant confounding appearance variation in the corresponding images. This
variation increases both human error and the inter-rater variability, as well
as hinders the performance of automatic or semi-automatic methods. In the
present paper we introduce an unsupervised adversarial network to translate
(and hence normalize) whole slide images across multiple data acquisition
domains. Our key contributions are: (i) an adversarial architecture which
learns across multiple domains with a single generator-discriminator network
using an information flow branch which optimizes for perceptual loss, and (ii)
the inclusion of an additional feature extraction network during training which
guides the transformation network to keep all the structural features in the
tissue image intact. We: (i) demonstrate the effectiveness of the proposed
method firstly on H\&E slides of 120 cases of kidney cancer, as well as (ii)
show the benefits of the approach on more general problems, such as flexible
illumination based natural image enhancement and light source adaptation.


title:
TopoEmbedding, a web tool for the interactive analysis of persistent
  homology

abstract:
  Software libraries for Topological Data Analysis (TDA) offer limited support
for interactive visualization. Most libraries only allow to visualize
topological descriptors (e.g., persistence diagrams), and lose the connection
with the original domain of data. This makes it challenging for users to
interpret the results of a TDA pipeline in an exploratory context. In this
paper, we present TopoEmbedding, a web-based tool that simplifies the
interactive visualization and analysis of persistence-based descriptors.
TopoEmbedding allows non-experts in TDA to explore similarities and differences
found by TDA descriptors with simple yet effective visualization techniques.


title: A Schreier type property for modules

abstract:
  We extend to torsion-free modules over integral domains the theory of
(pre)-Schreier domains initiated by Cohn and Zafrullah.


title:
Estimating optimal individualized treatment rules with multistate
  processes

abstract:
  Multistate process data are common in studies of chronic diseases such as
cancer. These data are ideal for precision medicine purposes as they can be
leveraged to improve more refined health outcomes, compared to standard
survival outcomes, as well as incorporate patient preferences regarding
quantity versus quality of life. However, there are currently no methods for
the estimation of optimal individualized treatment rules with such data. In
this article, we propose a nonparametric outcome weighted learning approach for
this problem in randomized clinical trial settings. The theoretical properties
of the proposed methods, including Fisher consistency and asymptotic normality
of the estimated expected outcome under the estimated optimal individualized
treatment rule, are rigorously established. A consistent closed-form variance
estimator is provided and methodology for the calculation of simultaneous
confidence intervals is proposed. Simulation studies show that the proposed
methodology and inference procedures work well even with small sample sizes and
high rates of right censoring. The methodology is illustrated using data from a
randomized clinical trial on the treatment of metastatic squamous-cell
carcinoma of the head and neck.


title:
Evaluation of Robust Point Set Registration Applied to Automotive
  Doppler Radar

abstract:
  Point set registration is the process of finding the best alignment between
two point sets, and it is a common task in different domains, especially in the
automotive and mobile robotics domains. Lots of approaches are proposed in the
literature, where the iterative closest point ICP is a well-known approach in
this vein, which builds an explicit correspondence between both point sets to
achieve the registration task. However, this work is interested in achieving
the registration without building any explicit correspondence between both
point sets, following a probabilistic framework.
  The most critical task in point set registration is how to elaborate the cost
function, which measures the distance between both point sets. The
probabilistic framework includes two possible ways to build the cost function:
The summing and the likelihood. The main focus of this work is to analyze and
compare the behavior of both approaches. Therefore, a 1D synthetic scenario is
used to build the cost function step by step, besides the estimation error.
Finally, this work uses two data sets for evaluation: A 2D synthetic data set
and a real data set. The evaluation process compares and analyzes the
estimation error and estimated uncertainty. Thus, two different methods are
used in the evaluation process: The normalized estimation error squared NEES
and noncredibility index NCI. A 77 GHz automotive Doppler radar provides the
real data set, and in the real evaluation, we evaluate the ego-motion
estimation of a robot as an application for the registration.


title:
Sample-Efficient Reinforcement Learning for POMDPs with Linear Function
  Approximations

abstract:
  Despite the success of reinforcement learning (RL) for Markov decision
processes (MDPs) with function approximation, most RL algorithms easily fail if
the agent only has partial observations of the state. Such a setting is often
modeled as a partially observable Markov decision process (POMDP). Existing
sample-efficient algorithms for POMDPs are restricted to the tabular setting
where the state and observation spaces are finite. In this paper, we make the
first attempt at tackling the tension between function approximation and
partial observability. In specific, we focus on a class of undercomplete POMDPs
with linear function approximations, which allows the state and observation
spaces to be infinite. For such POMDPs, we show that the optimal policy and
value function can be characterized by a sequence of finite-memory Bellman
operators. We propose an RL algorithm that constructs optimistic estimators of
these operators via reproducing kernel Hilbert space (RKHS) embedding.
Moreover, we theoretically prove that the proposed algorithm finds an
$\varepsilon$-optimal policy with $\tilde O (1/\varepsilon^2)$ episodes of
exploration. Also, this sample complexity only depends on the intrinsic
dimension of the POMDP polynomially and is independent of the size of the state
and observation spaces. To our best knowledge, we develop the first provably
sample-efficient algorithm for POMDPs with function approximation.


title:
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather,
  Daytime and Viewpoints

abstract:
  Accurate scene understanding from multiple sensors mounted on cars is a key
requirement for autonomous driving systems. Nowadays, this task is mainly
performed through data-hungry deep learning techniques that need very large
amounts of data to be trained. Due to the high cost of performing segmentation
labeling, many synthetic datasets have been proposed. However, most of them
miss the multi-sensor nature of the data, and do not capture the significant
changes introduced by the variation of daytime and weather conditions. To fill
these gaps, we introduce SELMA, a novel synthetic dataset for semantic
segmentation that contains more than 30K unique waypoints acquired from 24
different sensors including RGB, depth, semantic cameras and LiDARs, in 27
different atmospheric and daytime conditions, for a total of more than 20M
samples. SELMA is based on CARLA, an open-source simulator for generating
synthetic data in autonomous driving scenarios, that we modified to increase
the variability and the diversity in the scenes and class sets, and to align it
with other benchmark datasets. As shown by the experimental evaluation, SELMA
allows the efficient training of standard and multi-modal deep learning
architectures, and achieves remarkable results on real-world data. SELMA is
free and publicly available, thus supporting open science and research.


title: Off-shell Partition Functions in 3d Gravity

abstract:
  We explore three-dimensional gravity with negative cosmological constant via
canonical quantization. We focus on chiral gravity which is related to a single
copy of $\mathrm{PSL}(2,\mathbb{R})$ Chern-Simons theory and is simpler to
treat in canonical quantization. Its phase space for an initial value surface
$\Sigma$ is given by the appropriate moduli space of Riemann surfaces. We use
geometric quantization to compute partition functions of chiral gravity on
three-manifolds of the form $\Sigma \times \mathrm{S}^1$, where $\Sigma$ can
have asymptotic boundaries. Most of these topologies do not admit a classical
solution and are thus not amenable to a direct semiclassical path integral
computation. We use an index theorem that expresses the partition function as
an integral of characteristic classes over phase space. In the presence of $n$
asymptotic boundaries, we use techniques from equivariant cohomology to
localize the integral to a finite-dimensional integral over
$\overline{\mathcal{M}}_{g,n}$, which we evaluate in low genus cases. Higher
genus partition functions quickly become complicated since they depend in an
oscillatory way on Newton's constant. There is a precise sense in which one can
isolate the non-oscillatory part which we call the fake partition function. We
establish that there is a topological recursion that computes the fake
partition functions for arbitrary Riemann surfaces $\Sigma$. There is a scaling
limit in which the model reduces to JT gravity and our methods give a novel way
to compute JT partition functions via equivariant localization.


title: Wrapped Distributions on homogeneous Riemannian manifolds

abstract:
  We provide a general framework for constructing probability distributions on
Riemannian manifolds, taking advantage of area-preserving maps and isometries.
Control over distributions' properties, such as parameters, symmetry and
modality yield a family of flexible distributions that are straightforward to
sample from, suitable for use within Monte Carlo algorithms and latent variable
models, such as autoencoders. As an illustration, we empirically validate our
approach by utilizing our proposed distributions within a variational
autoencoder and a latent space network model. Finally, we take advantage of the
generalized description of this framework to posit questions for future work.


title:
Robust Phase Retrieval via Reverse Kullback-Leibler Divergence and
  Wirtinger Flow

abstract:
  Robustness to noise and outliers is a desirable trait in phase retrieval
algorithms for many applications in imaging and signal processing. In this
paper, we develop a novel robust phase retrieval algorithm based on the
minimization of reverse Kullback-Leibler divergence (RKLD) within the Wirtinger
Flow (WF) framework. We use RKLD over intensity-only measurements in two
distinct ways: i) to design a novel initial estimate based on minimum
distortion design of spectral estimates, and ii) as a loss function for
iterative refinement based on WF. The RKLD-based loss function offers implicit
regularization by processing data at the logarithmic scale and provides the
following benefits: suppressing the influence of large magnitude errors and
promoting projections orthogonal to noise subspace. We present three algorithms
based on RKLD minimization, including two with truncation schemes to enhance
the robustness to significant contamination. Our numerical study demonstrates
the advantages of our algorithms in terms of sample efficiency, convergence
speed, and robustness to outliers over the state-of-the-art techniques using
both synthetic and real optical imaging data.


title:
Assessing Machine Learning Algorithms for Near-Real Time Bus Ridership
  Prediction During Extreme Weather

abstract:
  Given an increasingly volatile climate, the relationship between weather and
transit ridership has drawn increasing interest. However, challenges stemming
from spatio-temporal dependency and non-stationarity have not been fully
addressed in modelling and predicting transit ridership under the influence of
weather conditions especially with the traditional statistical approaches.
Drawing on three-month smart card data in Brisbane, Australia, this research
adopts and assesses a suite of machine-learning algorithms, i.e., random
forest, eXtreme Gradient Boosting (XGBoost) and Tweedie XGBoost, to model and
predict near real-time bus ridership in relation to sudden change of weather
conditions. The study confirms that there indeed exists a significant level of
spatio-temporal variability of weather-ridership relationship, which produces
equally dynamic patterns of prediction errors. Further comparison of model
performance suggests that Tweedie XGBoost outperforms the other two
machine-learning algorithms in generating overall more accurate prediction
outcomes in space and time. Future research may advance the current study by
drawing on larger data sets and applying more advanced machine and
deep-learning approaches to provide more enhanced evidence for real-time
operation of transit systems.


title:
Clustering of football players based on performance data and aggregated
  clustering validity indexes

abstract:
  We analyse football (soccer) player performance data with mixed type
variables from the 2014-15 season of eight European major leagues. We cluster
these data based on a tailor-made dissimilarity measure.
  In order to decide between the many available clustering methods and to
choose an appropriate number of clusters, we use the approach by Akhanli and
Hennig (2020). This is based on several validation criteria that refer to
different desirable characteristics of a clustering. These characteristics are
chosen based on the aim of clustering, and this allows to define a suitable
validation index as weighted average of calibrated individual indexes measuring
the desirable features.
  We derive two different clusterings. The first one is a partition of the data
set into major groups of essentially different players, which can be used for
the analysis of a team's composition. The second one divides the data set into
many small clusters (with 10 players on average), which can be used for finding
players with a very similar profile to a given player. It is discussed in depth
what characteristics are desirable for these clusterings. Weighting the
criteria for the second clustering is informed by a survey of football experts.


title: On the Generation of Topological Vector Solitons from Bessel-Like Beams

abstract:
  Coupled solitary waves in optics literature, are coined vector solitons to
reflect their particle-like nature that remains intact even after mutual
collisions. They are born from a nonlinear change in the refractive index of an
optical material induced by the light intensity. We've discovered that the
second harmonic intensity profile generated by Bessel-like beams, is composed
of solitons of various geometries surrounded by concentric rings; one of which
is two central solitons of similar radius knotted by ellipsoidal concentric
rings. We observe that their geometry and intensity distribution is dependent
on the topological charge of the fundamental Bessel beams incident on the
nonlinear medium. We show that their spatial profile is invariant against
propagation. We observe that their behavior is similar to that of screw
dislocations in wave trains: they collide and rebound at a 90$^\circ$ angle
from the beam propagation direction. In this way, we have generated linked
frequency doubled Bessel-type vector solitons with different topologies, that
are knotted as they oscillate along the optical axis, when propagating in the
laboratory environment.


title:
SciTS: A Benchmark for Time-Series Database in Scientific Experiments
  and Industrial Internet of Things

abstract:
  Time-series data has an increasingly growing usage in Industrial Internet of
Things (IIoT) and large-scale scientific experiments. Managing time-series data
needs a storage engine that can keep up with their constantly growing volumes
while providing an acceptable query latency. While traditional ACID databases
favor consistency over performance, many time-series databases with novel
storage engines have been developed to provide better ingestion performance and
lower query latency. To understand how the unique design of a time-series
database affects its performance, we design SciTS, a highly extensible and
parameterizable benchmark for time-series data. The benchmark studies the data
ingestion capabilities of time-series databases especially as they grow larger
in size. It also studies the latencies of 5 practical queries from the
scientific experiments use case. We use SciTS to evaluate the performance of 4
databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and
PostgreSQL.


title:
Distributed Runtime Verification of Metric Temporal Properties for
  Cross-Chain Protocols

abstract:
  Transactions involving multiple blockchains are implemented by cross-chain
protocols. These protocols are based on smart contracts, programs that run on
blockchains, executed by a network of computers. Because smart contracts can
automatically transfer ownership of cryptocurrencies, electronic securities,
and other valuable assets among untrusting parties, verifying the runtime
correctness of smart contracts is a problem of compelling practical interest.
Such verification is challenging since smart contract execution is
time-sensitive, and the clocks on different blockchains may not be perfectly
synchronized. This paper describes a method for runtime monitoring of
blockchain executions. First, we propose a generalized runtime verification
technique for verifying partially synchronous distributed computations for the
metric temporal logic (MTL) by exploiting bounded-skew clock synchronization.
Second, we introduce a progression-based formula rewriting scheme for
monitoring \MTL specifications which employ SMT solving techniques and report
experimental results.


title:
Multiply-and-Fire (MNF): An Event-driven Sparse Neural Network
  Accelerator

abstract:
  Machine learning, particularly deep neural network inference, has become a
vital workload for many computing systems, from data centers and HPC systems to
edge-based computing. As advances in sparsity have helped improve the
efficiency of AI acceleration, there is a continued need for improved system
efficiency for both high-performance and system-level acceleration.
  This work takes a unique look at sparsity with an event (or
activation-driven) approach to ANN acceleration that aims to minimize useless
work, improve utilization, and increase performance and energy efficiency. Our
analytical and experimental results show that this event-driven solution
presents a new direction to enable highly efficient AI inference for both CNN
and MLP workloads.
  This work demonstrates state-of-the-art energy efficiency and performance
centring on activation-based sparsity and a highly-parallel dataflow method
that improves the overall functional unit utilization (at 30 fps). This work
enhances energy efficiency over a state-of-the-art solution by 1.46$\times$.
Taken together, this methodology presents a novel, new direction to achieve
high-efficiency, high-performance designs for next-generation AI acceleration
platforms.


title: Mobility unevenness in rock-paper-scissors models

abstract:
  We investigate a tritrophic system whose cyclic dominance is modelled by the
rock-paper-scissors game. We consider that organisms of one or two species are
affected by movement limitations, which unbalances the cyclic spatial game.
Performing stochastic simulations, we show that mobility unevenness controls
the population dynamics. In the case of one slow species, the predominant
species depends on the level of mobility restriction, with the slow species
being preponderant if the mobility limitations are substantial. If two species
face mobility limitations, our outcomes show that being higher dispersive does
not constitute an advantage in terms of population growth. On the contrary, if
organisms move with higher mobility, they expose themselves to enemies more
frequently, being more vulnerable to being eliminated. Finally, our findings
show that biodiversity benefits in regions where species are slowed.
Biodiversity loss for high mobility organisms, common to cyclic systems, may be
avoided with coexistence probability being higher for robust mobility
limitations. Our results may help biologists understand the dynamics of
unbalanced spatial systems where organisms' dispersal is fundamental to
biodiversity conservation.

categories: q-bio.PE cond-mat.stat-mech nlin.AO nlin.PS physics.bio-ph


title:
Uniform regularity in the low Mach number and inviscid limits for the
  full Navier-Stokes system in domains with boundaries

abstract:
  Motivated by the studies on the low Mach number limit problem, this
manuscript establishes uniform regularity estimates with respect to the Mach
number for the non-isentropic compressible Navier-Stokes system in smooth
domains with Navier-slip boundary conditions, in the general case of
ill-prepared initial data. The thermal conduction is taken into account and the
large variation of temperature is allowed. Moreover, the obtained regularity
estimates are also uniform in the Reynolds number $\text{Re}\in[1,+\infty),$
P\'eclet number $\text{Pe}\in [1,+\infty),$ provided
$$\big|\frac{1}{\text{Re}}-\frac{\iota_0}{\text{Pe}}\big|\lesssim
\frac{1}{\text{Pe}^{\frac{1}{2}}}\frac{1}{\text{Re}},$$ where $\iota_0$ is a
fixed constant independent of Mach number, Reynolds number and P\'eclet number.
The convergence to the limit system when the Mach number tends to zero is then
justified for an exterior domain outside a smooth compact set in $\mathbb{R}^3$
in the spirit of \cite{MR2106119}.


title:
Features of magnetic field switchbacks in relation to the local-field
  geometry of large-amplitude Alfv\'enic oscillations: \emph{Wind} and
  \emph{PSP} observations

abstract:
  In this letter we report observations of magnetic switchback (SB) features
near 1 au using data from the \emph{Wind} spacecraft. These features appear to
be strikingly similar to the ones observed by the Parker Solar Probe mission
(PSP) closer to the Sun: namely, one-sided spikes (or enhancements) in the
solar-wind bulk speed $V$ that correlate/anti-correlate with the spikes seen in
the radial-field component $B_R$. In the solar-wind streams that we analyzed,
these specific SB features near 1 au are associated with large-amplitude
Alfv\'enic oscillations that propagate outward from the sun along a local
background (prevalent) magnetic field $\bf{B}_0$ that is nearly radial. We also
show that, when $\bf{B}_0$ is nearly perpendicular to the radial direction, the
large amplitude Alfv\'enic oscillations display variations in $V$ that are
two-sided (i.e., $V$ alternately increases and decreases depending on the
vector $\Delta\bf{B}=\bf{B} - \bf{B}_0$). As a consequence, SBs may not appear
always as one-sided spikes in $V$, especially at larger heliocentric distances
where the local background field statistically departs from the radial
direction. We suggest that SBs can be well described by large-amplitude
Alfv\'enic fluctuations if the field rotation is computed with respect to a
well-determined local background field that, in some cases, may deviate from
the large-scale Parker field.


title:
Exact Formulas for Finite-Time Estimation Errors of Decentralized
  Temporal Difference Learning with Linear Function Approximation

abstract:
  In this paper, we consider the policy evaluation problem in multi-agent
reinforcement learning (MARL) and derive exact closed-form formulas for the
finite-time mean-squared estimation errors of decentralized temporal difference
(TD) learning with linear function approximation. Our analysis hinges upon the
fact that the decentralized TD learning method can be viewed as a Markov jump
linear system (MJLS). Then standard MJLS theory can be applied to quantify the
mean and covariance matrix of the estimation error of the decentralized TD
method at every time step. Various implications of our exact formulas on the
algorithm performance are also discussed. An interesting finding is that under
a necessary and sufficient stability condition, the mean-squared TD estimation
error will converge to an exact limit at a specific exponential rate.


title: On state transfer in Cayley graphs for abelian groups

abstract:
  In this paper, we characterize perfect state transfer in Cayley graphs for
abelian groups that have a cyclic Sylow-2-subgroup. This generalizes a result
of Ba\v{s}i\'c from 2013 where he provides a similar characterization for
Cayley graphs of cyclic groups.


title: GUARD: Graph Universal Adversarial Defense

abstract:
  Recently, graph convolutional networks (GCNs) have shown to be vulnerable to
small adversarial perturbations, which becomes a severe threat and largely
limits their applications in security-critical scenarios. To mitigate such a
threat, considerable research efforts have been devoted to increasing the
robustness of GCNs against adversarial attacks. However, current approaches for
defense are typically designed for the whole graph and consider the global
performance, posing challenges in protecting important local nodes from
stronger adversarial targeted attacks. In this work, we present a simple yet
effective method, named \textbf{\underline{G}}raph
\textbf{\underline{U}}niversal
\textbf{\underline{A}}dve\textbf{\underline{R}}sarial
\textbf{\underline{D}}efense (GUARD). Unlike previous works, GUARD protects
each individual node from attacks with a universal defensive patch, which is
generated once and can be applied to any node (node-agnostic) in a graph.
Extensive experiments on four benchmark datasets demonstrate that our method
significantly improves robustness for several established GCNs against multiple
adversarial attacks and outperforms existing adversarial defense methods by
large margins. Our code is publicly available at
https://github.com/EdisonLeeeee/GUARD.


title:
Multimodal Gaussian Mixture Model for Realtime Roadside LiDAR Object
  Detection

abstract:
  Background modeling is widely used for intelligent surveillance systems to
detect the moving targets by subtracting the static background components. Most
roadside LiDAR object detection methods filter out foreground points by
comparing new points to pre-trained background references based on descriptive
statistics over many frames (e.g., voxel density, slopes, maximum distance).
These solutions are not efficient under heavy traffic, and parameter values are
hard to transfer from one scenario to another. In early studies, the
video-based background modeling methods were considered not suitable for
roadside LiDAR surveillance systems due to the sparse and unstructured point
clouds data. In this paper, the raw LiDAR data were transformed into a
multi-dimensional tensor structure based on the elevation and azimuth value of
each LiDAR point. With this high-order data representation, we break the
barrier to allow the efficient Gaussian Mixture Model (GMM) method for roadside
LiDAR background modeling. The probabilistic GMM is built with superior agility
and real-time capability. The proposed Method was compared against two
state-of-the-art roadside LiDAR background models and evaluated based on point
level, object level, and path level, demonstrating better robustness under
heavy traffic and challenging weather. This multimodal GMM method is capable of
handling dynamic backgrounds with noisy measurements and substantially enhances
the infrastructure-based LiDAR object detection, whereby various 3D modeling
for smart city applications could be created


title: fairDMS: Rapid Model Training by Data and Model Reuse

abstract:
  Extracting actionable information from data sources such as the Linac
Coherent Light Source (LCLS-II) and Advanced Photon Source Upgrade (APS-U) is
becoming more challenging due to the fast-growing data generation rate. The
rapid analysis possible with ML methods can enable fast feedback loops that can
be used to adjust experimental setups in real-time, for example when errors
occur or interesting events are detected. However, to avoid degradation in ML
performance over time due to changes in an instrument or sample, we need a way
to update ML models rapidly while an experiment is running. We present here a
data service and model service to accelerate deep neural network training with
a focus on ML-based scientific applications. Our proposed data service achieves
100x speedup in terms of data labeling compare to the current state-of-the-art.
Further, our model service achieves up to 200x improvement in training speed.
Overall, fairDMS achieves up to 92x speedup in terms of end-to-end model
updating time.


title:
Multi-polariton control in attosecond transient absorption of
  autoionizing states

abstract:
  Tunable attosecond transient absorption spectroscopy is an ideal tool for
studying and manipulating autoionization dynamics in the continuum. We
investigate near-resonant two-photon couplings between the bright 3s^-1 4p and
dark 3s^-1 4f autoionizing states of argon that lead to Autler-Townes like
interactions, forming entangled light-matter states, or polaritons. We observe
that one-photon couplings with intermediate dark states play an important role
in this interaction, leading to the formation of multiple polaritonic branches
whose energies exhibit avoided crossings as a function of the dressing-laser
frequency. Our experimental measurements and theoretical essential-state
simulations show good agreement and reveal how the delay, frequency, and
intensity of the dressing pulse govern the properties of autoionizing polariton
multiplets. These results demonstrate new pathways for quantum control of
autoionizing states with optical fields.


title:
Quantum Analog of Vibration Isolation: From Room Temperature
  Superfluorescence to High Temperature Superconductivity

abstract:
  The development and the use of quantum technologies are hindered by a
fundamental challenge: Quantum materials exhibit macroscopic quantum properties
at extremely low temperatures due to the loss of quantum coherence at elevated
temperatures. Here, based on our recent discovery of room temperature
superfluorescence in perovskites, we present the Quantum Analog of Vibration
Isolation, 'QAVI', model and explain how it protects the quantum phase against
dephasing at high temperatures. We then postulate the requirements for
observation of macroscopic quantum phenomena at practical temperatures and
propose a unified model for all macroscopic quantum phase transitions. We
further present the general features of the temperature and density phase
diagram of macroscopic quantum phase transitions that are mediated by the QAVI
process and identify the similarities observed in the phase diagram of high Tc
superconductors. Understanding this fundamental quantum coherence protection
mechanism is imperative to accelerate the discovery of high temperature
macroscopic quantum phenomena, and offers significant potential for developing
quantum technologies functioning under practical conditions.


title:
Strategies to reduce the thermoelastic loss of multimaterial coated
  finite substrates

abstract:
  Thermoelastic loss is one of the main energy dissipation mechanisms in
resonant systems. A careful analysis of the thermoelastic loss is critical to
design low-noise resonators for high-precision applications, such as
gravitational-wave detectors. This paper presents an analytical solution to the
thermoelastic loss in multimaterial coated finite substrates with realistic
assumptions on the model structure and the elastic fields. The mechanism
responsible for thermoelastic loss is taken as a function of material
properties, operating temperature and frequency, and other design parameters.
We calculate the thermoelastic loss for specific applications over a wide range
of frequencies (1 Hz to 10 GHz) and temperatures (1 K to 300 K), and for a
variety of substrate and coating materials. The result is relevant for
gravitational-wave detectors and for experiments sensitive to mechanical
dissipation.


title:
Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1
  Medium Deep Survey: A Case Study for Science with Machine Learning-Based
  Classification

abstract:
  With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time
(LSST), it is expected that only $\sim 0.1\%$ of all transients will be
classified spectroscopically. To conduct studies of rare transients, such as
Type I superluminous supernovae (SLSNe), we must instead rely on photometric
classification. In this vein, here we carry out a pilot study of SLSNe from the
Pan-STARRS1 Medium-Deep Survey (PS1-MDS) classified photometrically with our
SuperRAENN and Superphot algorithms. We first construct a sub-sample of the
photometric sample using a list of simple selection metrics designed to
minimize contamination and ensure sufficient data quality for modeling. We then
fit the multi-band light curves with a magnetar spin-down model using the
Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar
engine and ejecta parameter distributions of the photometric sample to those of
the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample,
we find that these samples are overall consistent, but that the photometric
sample extends to slower spins and lower ejecta masses, which correspond to
lower luminosity events, as expected for photometric selection. While our
PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic
sample, our methodology paves the way to an orders-of-magnitude increase in the
SLSN sample in the LSST era through photometric selection and study.


title:
Deep transfer learning for partial differential equations under
  conditional shift with DeepONet

abstract:
  Traditional machine learning algorithms are designed to learn in isolation,
i.e. address single tasks. The core idea of transfer learning (TL) is that
knowledge gained in learning to perform one task (source) can be leveraged to
improve learning performance in a related, but different, task (target). TL
leverages and transfers previously acquired knowledge to address the expense of
data acquisition and labeling, potential computational power limitations, and
the dataset distribution mismatches. Although significant progress has been
made in the fields of image processing, speech recognition, and natural
language processing (for classification and regression) for TL, little work has
been done in the field of scientific machine learning for functional regression
and uncertainty quantification in partial differential equations. In this work,
we propose a novel TL framework for task-specific learning under conditional
shift with a deep operator network (DeepONet). Inspired by the conditional
embedding operator theory, we measure the statistical distance between the
source domain and the target feature domain by embedding conditional
distributions onto a reproducing kernel Hilbert space. Task-specific operator
learning is accomplished by fine-tuning task-specific layers of the target
DeepONet using a hybrid loss function that allows for the matching of
individual target samples while also preserving the global properties of the
conditional distribution of target data. We demonstrate the advantages of our
approach for various TL scenarios involving nonlinear PDEs under conditional
shift. Our results include geometry domain adaptation and show that the
proposed TL framework enables fast and efficient multi-task operator learning,
despite significant differences between the source and target domains.


title:
Acoustic-phonon-mediated superconductivity in moir\'eless graphene
  multilayers

abstract:
  We investigate the competition between acoustic-phonon-mediated
superconductivity and the long-range Coulomb interaction in moir\'eless
graphene multilayers, specifically, Bernal bilayer graphene, rhombohedral
trilayer graphene, and ABCA-stacked tetralayer graphene. In these graphene
multilayers, the acoustic phonons can realize, through electron-phonon
coupling, both spin-singlet and spin-triplet pairings, and the intra-sublattice
pairings ($s$-wave spin-singlet and $f$-wave spin-triplet) are the dominant
channels. Our theory naturally explains the distinct recent experimental
findings in Bernal bilayer graphene and rhombohedral trilayer graphene, and we
further predict existence of superconductivity in ABCA tetralayer graphene
arising from electron-phonon interactions. In particular, we demonstrate that
the acoustic-phonon-mediated superconductivity prevails over a wide range of
doping in rhombohedral trilayer graphene and ABCA tetralayer graphene while
superconductivity exists only in a narrow range of doping near the Van Hove
singularity in Bernal bilayer graphene. Key features of our theory are the
inclusion of realistic band structures with the appropriate Van Hove
singularities and Coulomb repulsion effects (the so-called "$\mu^*$ effect")
opposing the phonon-induced superconducting pairing. We also discuss how
intervalley scatterings can suppress the spin-triplet spin-polarized
superconductivity. Our work provides detailed prediction based on
electron-acoustic-phonon-interaction-induced graphene superconductivity, which
should be investigated in future experiments.


title:
Comment on "On the recurrence times of neutron star X-ray binary
  transients and the nature of the Galactic Center quiescent X-ray binaries"

abstract:
  In 2018, we reported our discovery of a dozen quiescent X-ray binaries in the
central parsec (pc) of the Galaxy (Hailey et al. 2018). In a recent follow-up
paper (Mori et al. 2021), we published an extended analysis of these sources
and other X-ray binaries (XRBs) in the central pc and beyond, showing that most
if not all of the 12 non-thermal sources are likely black hole low-mass X-ray
binary (BH-LMXB) candidates. In response, Maccarone et al. 2022 (TM22
hereafter) argued, primarily on the claim that neutron star low-mass X-ray
binaries (NS-LMXBs) often do not have short outburst recurrence times (<~ 10
yr), that they cannot be excluded as a designation for the 12 quiescent X-ray
binary sources. TM22 cites three main factors in their study: (1) X-ray
outburst data of NS transients detected by RXTE and MAXI, (2) the Galactic
population of NS-LMXBs, and (3) (persistently) quiescent NS-LMXBs in globular
clusters. We address these arguments of TM22 and correct their
misunderstandings of our work and the literature, even though most of these
points have already been thoroughly addressed by Mori et al. 2021. We also
correct TM22's assertion that our arguments are based solely on NS transients'
recurrence times.


title: MashUp: Scaling TCAM-based IP Lookup to Larger Databases by Tiling Trees

abstract:
  Ternary content addressable memories (TCAMs) are commonly used to implement
IP lookup, but suffer from high power and area costs. Thus TCAM included in
modern chips is limited and can support moderately large datasets in data
centers and enterprises, but fails to scale to backbone WAN databases of
millions of prefixes. IPv6 deployment also makes it harder to deploy TCAMs
because of the larger prefixes used in the 128-bit address space. While the
combination of algorithmic techniques and TCAM has been proposed before for
reducing power consumption or update costs(e.g., CoolCAM [32] and TreeCAM
[28]), we focus on reducing TCAM bits using a scheme we call MashUp that can
easily be implemented in modern reconfigurable pipeline chips such as Tofino-3.
MashUp uses a new technique, tiling trees, which takes into account TCAM grain
(tile) sizes. When applied to a publicly available IPv6 dataset using Tofino-3
TCAM grain sizes (44 by 512), there was a 2X reduction in TCAM required.
Further, if we mix TCAM and SRAM using a new technique we call node
hybridization, MashUp decreases TCAM bits by 4.5X for IPv6, and by 7.5X for
IPv4, allowing wide area databases of 900,000 prefixes to be supported by
Tofino-3 and similar chips


title: The Dwork-Frobenius operator on hypergeometric series

abstract:
  We describe the action of the Dwork-Frobenius operator on certain
$A$-hypergeometric series. As a consequence, we obtain an integrality result
for the coefficients of those series. This implies an integrality result for
classical hypergeometric series.


title: Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)

abstract:
  In this paper, we consider the restoration and reconstruction of piecewise
constant objects in two and three dimensions using PaLEnTIR, a significantly
enhanced Parametric level set (PaLS) model relative to the current
state-of-the-art. The primary contribution of this paper is a new PaLS
formulation which requires only a single level set function to recover a scene
with piecewise constant objects possessing multiple unknown contrasts. Our
model offers distinct advantages over current approaches to the multi-contrast,
multi-object problem, all of which require multiple level sets and explicit
estimation of the contrast magnitudes. Given upper and lower bounds on the
contrast, our approach is able to recover objects with any distribution of
contrasts and eliminates the need to know either the number of contrasts in a
given scene or their values. We provide an iterative process for finding these
space-varying contrast limits. Relative to most PaLS methods which employ
radial basis functions (RBFs), our model makes use of non-isotropic basis
functions, thereby expanding the class of shapes that a PaLS model of a given
complexity can approximate. Finally, PaLEnTIR improves the conditioning of the
Jacobian matrix required as part of the parameter identification process and
consequently accelerates the optimization methods by controlling the magnitude
of the PaLS expansion coefficients, fixing the centers of the basis functions,
and the uniqueness of parametric to image mappings provided by the new
parameterization. We demonstrate the performance of the new approach using both
2D and 3D variants of X-ray computed tomography, diffuse optical tomography
(DOT), denoising, deconvolution problems. Application to experimental sparse CT
data and simulated data with different types of noise are performed to further
validate the proposed method.


title: A strengthening of Freiman's 3k-4 theorem

abstract:
  In its usual form, Freiman's 3k-4 theorem states that if A and B are subsets
of the integers of size k with small sumset (of size close to 2k) then they are
very close to arithmetic progressions. Our aim in this paper is to strengthen
this by allowing only a bounded number of possible summands from one of the
sets. We show that if A and B are subsets of the integers of size k such that
for any four-element subset X of B the sumset A+X has size not much more than
2k then already this implies that A and B are very close to arithmetic
progressions.


title:
Making the Most of Text Semantics to Improve Biomedical Vision--Language
  Processing

abstract:
  Multi-modal data abounds in biomedicine, such as radiology images and
reports. Interpreting this data at scale is essential for improving clinical
care and accelerating clinical research. Biomedical text with its complex
semantics poses additional challenges in vision-language modelling compared to
the general domain, and previous work has used insufficiently adapted models
that lack domain-specific language understanding. In this paper, we show that
principled textual semantic modelling can substantially improve contrastive
learning in self-supervised vision--language processing. We release a language
model that achieves state-of-the-art results in radiology natural language
inference through its improved vocabulary and novel language pretraining
objective leveraging semantics and discourse characteristics in radiology
reports. Further, we propose a self-supervised joint vision--language approach
with a focus on better text modelling. It establishes new state of the art
results on a wide range of publicly available benchmarks, in part by leveraging
our new domain-specific language model. We release a new dataset with
locally-aligned phrase grounding annotations by radiologists to facilitate the
study of complex semantic modelling in biomedical vision--language processing.
A broad evaluation, including on this new dataset, shows that our contrastive
learning approach, aided by textual-semantic modelling, outperforms prior
methods in segmentation tasks, despite only using a global-alignment objective.


title:
Variance estimation in pseudo-expected estimating equations for missing
  data

abstract:
  Missing data is a common challenge in biomedical research. This fact, along
with growing dataset volumes of the modern era, make the issue of
computationally-efficient analysis with missing data of crucial practical
importance. A general computationally-efficient estimation framework for
dealing with missing data is the pseudo-expected estimating equations (PEEE)
approach. The method is applicable with any parametric model for which
estimation involves the solution of a set of estimating equations, such as
likelihood score equations. A key limitation of the PEEE approach is that there
is currently no closed-form variance estimator, and variance estimation
requires the computationally burdensome bootstrap method. In this work, we
address the gap and provide a closed-form variance estimator whose computation
can be significantly faster than a bootstrap approach. Our variance estimator
is shown to be consistent even with auxiliary variables and under misspecified
models for the incomplete variables. Simulation studies show that our variance
estimator performs well and that its computation can be over 50 times faster
than the bootstrap. The computational efficiency gain from our proposed
variance estimator is crucial with large datasets or when the main analysis
method is computationally intensive. Finally, the PEEE approach along with our
variance estimator are used to analyze incomplete electronic health record data
of patients with traumatic brain injury.


title:
Extensible Database Simulator for Fast Prototyping In-Database
  Algorithms

abstract:
  With the rapid increasing of data scale, in-database analytics and learning
has become one of the most studied topics in data science community, because of
its significance on reducing the gap between the management and the analytics
of data. By extending the capability of database on analytics and learning,
data scientists can save much time on exchanging data between databases and
external analytic tools. For this goal, researchers are attempting to integrate
more data science algorithms into database. However, implementing the
algorithms in mainstream databases is super time-consuming, especially when it
is necessary to have a deep dive into the database kernels. Thus there are
demands for an easy-to-extend database simulator to help fast prototype and
verify the in-database algorithms before implementing them in real databases.
In this demo, we present such an extensible relational database simulator,
DBSim, to help data scientists prototype their in-database analytics and
learning algorithms and verify the effectiveness of their ideas with minimal
cost. DBSim simulates a real relational database by integrating all the major
components in mainstream RDBMS, including SQL parser, relational operators,
query optimizer, etc. In addition, DBSim provides various interfaces for users
to flexibly plug their custom extension modules into any of the major
components, without modifying the kernel. By those interfaces, DBSim supports
easy extensions on SQL syntax, relational operators, query optimizer rules and
cost models, and physical plan execution. Furthermore, DBSim provides utilities
to facilitate users' developing and debugging, like query plan visualizer and
interactive analyzer on optimization rules. We develop DBSim using pure Python
to support seamless implementation of most data science algorithms into it,
since many of them are written in Python.


title: Machine Learning for Optical Scanning Probe Nanoscopy

abstract:
  The ability to perform nanometer-scale optical imaging and spectroscopy is
key to deciphering the low-energy effects in quantum materials, as well as
vibrational fingerprints in planetary and extraterrestrial particles, catalytic
substances, and aqueous biological samples. The scattering-type scanning
near-field optical microscopy (s-SNOM) technique has recently spread to many
research fields and enabled notable discoveries. In this brief perspective, we
show that the s-SNOM, together with scanning probe research in general, can
benefit in many ways from artificial intelligence (AI) and machine learning
(ML) algorithms. We show that, with the help of AI- and ML-enhanced data
acquisition and analysis, scanning probe optical nanoscopy is poised to become
more efficient, accurate, and intelligent.


title:
Understanding AR Activism: An Interview Study with Creators of Augmented
  Reality Experiences for Social Change

abstract:
  The rise of consumer augmented reality (AR) technology has opened up new
possibilities for interventions intended to disrupt and subvert cultural
conventions. From defacing corporate logos to erecting geofenced digital
monuments, more and more people are creating AR experiences for social causes.
We sought to understand this new form of activism, including why people use AR
for these purposes, opportunities and challenges in using it, and how well it
can support activist goals. We conducted semi-structured interviews with twenty
people involved in projects that used AR for a social cause across six
different countries. We found that AR can overcome physical world limitations
of activism to convey immersive, multilayered narratives that aim to reveal
invisible histories and perspectives. At the same time, people experienced
challenges in creating, maintaining, and distributing their AR experiences to
audiences. We discuss open questions and opportunities for creating AR tools
and experiences for social change.


title: Nonlocality of electrically-induced spin accumulation in chiral metals

abstract:
  Spin accumulation induced by an electric field in chiral electron system is
investigated based on a linear response theory. It is shown that the spin
response function has a spatially uniform component due to the chiral angular
momentum generation effect, resulting in a nonlocal spin generation. The result
suggests a scenario for nonlocal long-range spin transport in chiral metals
reported experimentally recently.


title:
Magnetic on-surface assemblies predicted from a pious computational
  method

abstract:
  Molecular self-assembly will not become a routine method for building
nanomaterials unless our ability to predict the outcome of this process is
dramatically improved. Even then, reliable strategies for realizing molecular
assemblies with novel properties are required for building nanomaterials for
specific device applications. In this paper, I simulate the self-assembly of
metal phthalocyanine derivatives adsorbed to gold(111) surfaces using a
detailed statistical mechanical model and a new computational method based upon
genetic algorithms and Markov chain Monte Carlo (MCMC). This method yields
predictions that are not only are superior to those of ordinary MCMC but also
show good agreement with experimental results. Crucially, it is predicted that
molecular assemblies displaying locally disordered magnetic moments - and
having potential applications as non-Gaussian noise sources for device
applications - can be realized by the simple strategy of introducing asymmetry
into the phthalocyanine ligands.


title: Equivariant moduli of sheaves on $K3$ surfaces

abstract:
  We study the Hirzebruch-Riemann-Roch (HRR) theorem for proper smooth quotient
Deligne-Mumford stacks, explore its relation with representation theory of
finite groups, and derive a new orbifold HRR formula via an orbifold Mukai
pairing. As a first application, we use this formula to compute the dimensions
of $ G $-equivariant moduli spaces of stable sheaves on a $ K3 $ surface $ X $
under the action of a finite subgroup $ G $ of its symplectic automorphism
group. We then apply the orbifold HRR formula to reproduce the number of fixed
points on $ X $ when $ G $ is cyclic without using the Lefschetz fixed point
formula. We prove that under some mild conditions, equivariant moduli spaces of
stable sheaves on $ X $ are irreducible symplectic manifolds deformation
equivalent to Hilbert schemes of points on $ X $ via a connection between
Gieseker and Bridgeland moduli spaces, as well as the derived McKay
correspondence. As a corollary, these moduli spaces are also deformation
equivalent to $ G $-equivaraint Hilbert schemes of points on $ X. $


title:
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection
  Algorithms

abstract:
  Anomaly detection has many applications ranging from bank-fraud detection and
cyber-threat detection to equipment maintenance and health monitoring. However,
choosing a suitable algorithm for a given application remains a challenging
design decision, often informed by the literature on anomaly detection
algorithms. We extensively reviewed twelve of the most popular unsupervised
anomaly detection methods. We observed that, so far, they have been compared
using inconsistent protocols - the choice of the class of interest or the
positive class, the split of training and test data, and the choice of
hyperparameters - leading to ambiguous evaluations. This observation led us to
define a coherent evaluation protocol which we then used to produce an updated
and more precise picture of the relative performance of the twelve methods on
five widely used tabular datasets. While our evaluation cannot pinpoint a
method that outperforms all the others on all datasets, it identifies those
that stand out and revise misconceived knowledge about their relative
performances.


title:
SimMC: Simple Masked Contrastive Learning of Skeleton Representations
  for Unsupervised Person Re-Identification

abstract:
  Recent advances in skeleton-based person re-identification (re-ID) obtain
impressive performance via either hand-crafted skeleton descriptors or skeleton
representation learning with deep learning paradigms. However, they typically
require skeletal pre-modeling and label information for training, which leads
to limited applicability of these methods. In this paper, we focus on
unsupervised skeleton-based person re-ID, and present a generic Simple Masked
Contrastive learning (SimMC) framework to learn effective representations from
unlabeled 3D skeletons for person re-ID. Specifically, to fully exploit
skeleton features within each skeleton sequence, we first devise a masked
prototype contrastive learning (MPC) scheme to cluster the most typical
skeleton features (skeleton prototypes) from different subsequences randomly
masked from raw sequences, and contrast the inherent similarity between
skeleton features and different prototypes to learn discriminative skeleton
representations without using any label. Then, considering that different
subsequences within the same sequence usually enjoy strong correlations due to
the nature of motion continuity, we propose the masked intra-sequence
contrastive learning (MIC) to capture intra-sequence pattern consistency
between subsequences, so as to encourage learning more effective skeleton
representations for person re-ID. Extensive experiments validate that the
proposed SimMC outperforms most state-of-the-art skeleton-based methods. We
further show its scalability and efficiency in enhancing the performance of
existing models. Our codes are available at https://github.com/Kali-Hac/SimMC.


title:
Data-Driven Design of a New Organic Semiconductor via an Electronic
  Structure Chart

abstract:
  Data-driven methodologies for designing new materials are developing apace,
yet advances for organic crystals have been infrequent. For organic crystals,
the need to predict solid-state electronic properties from molecular structure
alone is an exceedingly difficult task for typical, regression-based design
strategies. In this paper, we present a new strategy for designing organic
crystals which circumvents the need to regress solid-state physical properties
directly. At the core of this strategy is an electronic structure chart, a
two-dimensional projection of an organic crystal database in which each
material is positioned according to its solid-state electronic properties. We
illustrate this strategy by identifying a new molecule which is predicted to
show a targeted band gap and better-than-average band curvatures in the
crystalline state. This strategy is the first data-driven method which can
design new molecules on the basis of genuine solid-state electronic properties,
and has potential to accelerate breakthroughs in the field of organic
electronics and beyond.


title:
Relevance-guided Unsupervised Discovery of Abilities with
  Quality-Diversity Algorithms

abstract:
  Quality-Diversity algorithms provide efficient mechanisms to generate large
collections of diverse and high-performing solutions, which have shown to be
instrumental for solving downstream tasks. However, most of those algorithms
rely on a behavioural descriptor to characterise the diversity that is
hand-coded, hence requiring prior knowledge about the considered tasks. In this
work, we introduce Relevance-guided Unsupervised Discovery of Abilities; a
Quality-Diversity algorithm that autonomously finds a behavioural
characterisation tailored to the task at hand. In particular, our method
introduces a custom diversity metric that leads to higher densities of
solutions near the areas of interest in the learnt behavioural descriptor
space. We evaluate our approach on a simulated robotic environment, where the
robot has to autonomously discover its abilities based on its full sensory
data. We evaluated the algorithms on three tasks: navigation to random targets,
moving forward with a high velocity, and performing half-rolls. The
experimental results show that our method manages to discover collections of
solutions that are not only diverse, but also well-adapted to the considered
downstream task.


title:
Block Hunter: Federated Learning for Cyber Threat Hunting in
  Blockchain-based IIoT Networks

abstract:
  Nowadays, blockchain-based technologies are being developed in various
industries to improve data security. In the context of the Industrial Internet
of Things (IIoT), a chain-based network is one of the most notable applications
of blockchain technology. IIoT devices have become increasingly prevalent in
our digital world, especially in support of developing smart factories.
Although blockchain is a powerful tool, it is vulnerable to cyber attacks.
Detecting anomalies in blockchain-based IIoT networks in smart factories is
crucial in protecting networks and systems from unexpected attacks. In this
paper, we use Federated Learning (FL) to build a threat hunting framework
called Block Hunter to automatically hunt for attacks in blockchain-based IIoT
networks. Block Hunter utilizes a cluster-based architecture for anomaly
detection combined with several machine learning models in a federated
environment. To the best of our knowledge, Block Hunter is the first federated
threat hunting model in IIoT networks that identifies anomalous behavior while
preserving privacy. Our results prove the efficiency of the Block Hunter in
detecting anomalous activities with high accuracy and minimum required
bandwidth.


title: Quantum Walk Inspired Dynamic Adiabatic Local Search

abstract:
  We investigate the irreconcilability issue that raises from translating the
search algorithm from the Continuous-Time Quantum Walk (CTQW) framework to the
Adiabatic Quantum Computing (AQC) framework. One major issue is the constant
energy gap in the translated Hamiltonian throughout the AQC schedule. To
resolve the issue in the initial investigation, we choose only Z operator as
the catalyst Hamiltonian and show that this modification keeps the running time
optimal. Inspired by this irreconcilability issue and our solution, we further
investigate to find the proper timing for releasing the chosen catalyst
Hamiltonian and the suitable coefficient function of the catalyst Hamiltonian
in the AQC schedule to improve the Adiabatic local search.


title:
Accurate Molecular-Orbital-Based Machine Learning Energies via
  Unsupervised Clustering of Chemical Space

abstract:
  We introduce an unsupervised clustering algorithm to improve training
efficiency and accuracy in predicting energies using molecular-orbital-based
machine learning (MOB-ML). This work determines clusters via the Gaussian
mixture model (GMM) in an entirely automatic manner and simplifies an earlier
supervised clustering approach [J. Chem. Theory Comput., 15, 6668 (2019)] by
eliminating both the necessity for user-specified parameters and the training
of an additional classifier. Unsupervised clustering results from GMM have the
advantage of accurately reproducing chemically intuitive groupings of frontier
molecular orbitals and having improved performance with an increasing number of
training examples. The resulting clusters from supervised or unsupervised
clustering is further combined with scalable Gaussian process regression (GPR)
or linear regression (LR) to learn molecular energies accurately by generating
a local regression model in each cluster. Among all four combinations of
regressors and clustering methods, GMM combined with scalable exact Gaussian
process regression (GMM/GPR) is the most efficient training protocol for
MOB-ML. The numerical tests of molecular energy learning on thermalized
datasets of drug-like molecules demonstrate the improved accuracy,
transferability, and learning efficiency of GMM/GPR over not only other
training protocols for MOB-ML, i.e., supervised regression-clustering combined
with GPR(RC/GPR) and GPR without clustering. GMM/GPR also provide the best
molecular energy predictions compared with the ones from literature on the same
benchmark datasets. With a lower scaling, GMM/GPR has a 10.4-fold speedup in
wall-clock training time compared with scalable exact GPR with a training size
of 6500 QM7b-T molecules.


title:
An Information-theoretical Secured Byzantine-fault Tolerance Consensus
  in Quantum Key Distribution Network

abstract:
  Quantum key distribution (QKD) networks is expected to provide
information-theoretical secured (ITS) communication over long distances. QKD
networks based trusted relay architecture are now the most widely used scheme
in practice. However, it is an unrealistic assumption that all relays are fully
trustable in complex networks. In the past, only a few studies have
theoretically analyzed the case of passive eavesdropping attack by dishonest
relays and corresponding defense method. However, we have found that active
attacks by dishonest relays can be more threatening. With the consideration of
passive and active attacks, we treat dishonest relays as Byzantine nodes and
analyzes the upper limit of Byzantine nodes that the QKD network can
accommodate. In this paper, we propose an ITS Byzantine-fault tolerance (BFT)
QKD network scheme to achieve end-to-end key distribution based on
point-to-point QKD links. To ensure consistency and provide BFT ability in the
QKD network, we design an ITSBFT-consensus protocol for this network scheme. To
ensure the information-theoretic security of consensus, we design a temporary
signature scheme based on point-to-point QKD link keys. To prevent Byzantine
nodes from disrupting the execution process of key distribution, we design an
end-to-end key distribution scheme combined with consensus. We theoretically
analyze proposed ITSBFT-QKD network scheme from four aspects: QKD key
distribution security, temporary signature security, consensus security, and
leader election fairness. The simulation result proved the feasibility and
demonstrate the performance.


title:
Sample-Based Bounds for Coherent Risk Measures: Applications to Policy
  Synthesis and Verification

abstract:
  The dramatic increase of autonomous systems subject to variable environments
has given rise to the pressing need to consider risk in both the synthesis and
verification of policies for these systems. This paper aims to address a few
problems regarding risk-aware verification and policy synthesis, by first
developing a sample-based method to bound the risk measure evaluation of a
random variable whose distribution is unknown. These bounds permit us to
generate high-confidence verification statements for a large class of robotic
systems. Second, we develop a sample-based method to determine solutions to
non-convex optimization problems that outperform a large fraction of the
decision space of possible solutions. Both sample-based approaches then permit
us to rapidly synthesize risk-aware policies that are guaranteed to achieve a
minimum level of system performance. To showcase our approach in simulation, we
verify a cooperative multi-agent system and develop a risk-aware controller
that outperforms the system's baseline controller. We also mention how our
approach can be extended to account for any $g$-entropic risk measure - the
subset of coherent risk measures on which we focus.


title:
Coronagraph Experiment on Dark-hole Control by Speckle Area Nulling
  Method

abstract:
  In high-contrast imaging optical systems for direct observation of planets
outside our solar system, adaptive optics with an accuracy of lambda/10,000
root mean square is required to reduce the speckle noise down to 1e-10 level in
addition to the nulling coronagraph which eliminate the diffracted light. We
developed the speckle area nulling (SAN) method as a new dark-hole control
algorithm which is capable of controlling speckle electric field in a wide area
quickly, in spite of an extension of speckle nulling, and is robust not relying
upon an optical model. We conducted a validation experiment for the SAN method
with a monochromatic light and succeeded in reducing the intensity of areal
speckles by 4.4e-2.


title:
High-Performance Optimal Incentive-Seeking in Transactive Control for
  Traffic Congestion

abstract:
  Traffic congestion has dire economic and social impacts in modern
metropolitan areas. To address this problem, in this paper we introduce a novel
type of model-free transactive controllers to manage vehicle traffic in highway
networks for which precise mathematical models are not available. Specifically,
we consider a highway system with managed lanes on which dynamic tolling
mechanisms can be implemented in real-time using measurements from the roads.
We present three incentive-seeking feedback controllers able to find in
real-time the optimal economic incentives (e.g., tolls) that persuade highway
users to follow a suitable driving behavior that minimizes a predefined
performance index. The controllers are agnostic with respect to the exact model
of the highway, and they are also able to guarantee fast convergence to the
optimal tolls by leveraging non-smooth and hybrid dynamic mechanisms that
combine continuous-time dynamics and discrete-time dynamics. We provide
numerical examples to illustrate the advantages of the different presented
techniques.


title: On nonlinear Miyadera-Voigt perturbations

abstract:
  Let $A,C,P:D(A)\subset X\to X$ be linear operators on a Banach space $X$ such
that $-A$ generates a strongly continuous semigroup on $X$, and $F:X\to X$ be a
globally Lipschitz function. We study the well-posedness of semilinear
equations of the form $\dot{u}(t)=G(u(t))$, where $G:D(A)\to X$ is a nonlinear
map defined by $G=-A+C+F\circ P$. In fact, using the concept of maximal
$L^p$-regularity and a fixed point theorem, we establish the existence and
uniqueness of a strong solution for the above-mentioned semilinear equation. We
illustrate our results by applications to nonlinear heat equations with respect
to Dirichlet and Neumann boundary conditions, and a nonlocal unbounded
nonlinear perturbation.


title: Memory Bounds for the Experts Problem

abstract:
  Online learning with expert advice is a fundamental problem of sequential
prediction. In this problem, the algorithm has access to a set of $n$ "experts"
who make predictions on each day. The goal on each day is to process these
predictions, and make a prediction with the minimum cost. After making a
prediction, the algorithm sees the actual outcome on that day, updates its
state, and then moves on to the next day. An algorithm is judged by how well it
does compared to the best expert in the set.
  The classical algorithm for this problem is the multiplicative weights
algorithm. However, every application, to our knowledge, relies on storing
weights for every expert, and uses $\Omega(n)$ memory. There is little work on
understanding the memory required to solve the online learning with expert
advice problem, or run standard sequential prediction algorithms, in natural
streaming models, which is especially important when the number of experts, as
well as the number of days on which the experts make predictions, is large.
  We initiate the study of the learning with expert advice problem in the
streaming setting, and show lower and upper bounds. Our lower bound for i.i.d.,
random order, and adversarial order streams uses a reduction to a custom-built
problem using a novel masking technique, to show a smooth trade-off for regret
versus memory. Our upper bounds show novel ways to run standard sequential
prediction algorithms in rounds on small "pools" of experts, thus reducing the
necessary memory. For random-order streams, we show that our upper bound is
tight up to low order terms. We hope that these results and techniques will
have broad applications in online learning, and can inspire algorithms based on
standard sequential prediction techniques, like multiplicative weights, for a
wide range of other problems in the memory-constrained setting.


title: Fast AdvProp

abstract:
  Adversarial Propagation (AdvProp) is an effective way to improve recognition
models, leveraging adversarial examples. Nonetheless, AdvProp suffers from the
extremely slow training speed, mainly because: a) extra forward and backward
passes are required for generating adversarial examples; b) both original
samples and their adversarial counterparts are used for training (i.e.,
2$\times$ data). In this paper, we introduce Fast AdvProp, which aggressively
revamps AdvProp's costly training components, rendering the method nearly as
cheap as the vanilla training. Specifically, our modifications in Fast AdvProp
are guided by the hypothesis that disentangled learning with adversarial
examples is the key for performance improvements, while other training recipes
(e.g., paired clean and adversarial training samples, multi-step adversarial
attackers) could be largely simplified.
  Our empirical results show that, compared to the vanilla training baseline,
Fast AdvProp is able to further model performance on a spectrum of visual
benchmarks, without incurring extra training cost. Additionally, our ablations
find Fast AdvProp scales better if larger models are used, is compatible with
existing data augmentation methods (i.e., Mixup and CutMix), and can be easily
adapted to other recognition tasks like object detection. The code is available
here: https://github.com/meijieru/fast_advprop.


title:
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial
  Nets with Reinforcement Learning

abstract:
  Global IPv6 scanning has always been a challenge for researchers because of
the limited network speed and computational power. Target generation algorithms
are recently proposed to overcome the problem for Internet assessments by
predicting a candidate set to scan. However, IPv6 custom address configuration
emerges diverse addressing patterns discouraging algorithmic inference.
Widespread IPv6 alias could also mislead the algorithm to discover aliased
regions rather than valid host targets. In this paper, we introduce 6GAN, a
novel architecture built with Generative Adversarial Net (GAN) and
reinforcement learning for multi-pattern target generation. 6GAN forces
multiple generators to train with a multi-class discriminator and an alias
detector to generate non-aliased active targets with different addressing
pattern types. The rewards from the discriminator and the alias detector help
supervise the address sequence decision-making process. After adversarial
training, 6GAN's generators could keep a strong imitating ability for each
pattern and 6GAN's discriminator obtains outstanding pattern discrimination
ability with a 0.966 accuracy. Experiments indicate that our work outperformed
the state-of-the-art target generation algorithms by reaching a higher-quality
candidate set.


title: Multi-Tier Platform for Cognizing Massive Electroencephalogram

abstract:
  An end-to-end platform assembling multiple tiers is built for precisely
cognizing brain activities. Being fed massive electroencephalogram (EEG) data,
the time-frequency spectrograms are conventionally projected into the
episode-wise feature matrices (seen as tier-1). A spiking neural network (SNN)
based tier is designed to distill the principle information in terms of
spike-streams from the rare features, which maintains the temporal implication
in the nature of EEGs. The proposed tier-3 transposes time- and space-domain of
spike patterns from the SNN; and feeds the transposed pattern-matrices into an
artificial neural network (ANN, Transformer specifically) known as tier-4,
where a special spanning topology is proposed to match the two-dimensional
input form. In this manner, cognition such as classification is conducted with
high accuracy. For proof-of-concept, the sleep stage scoring problem is
demonstrated by introducing multiple EEG datasets with the largest comprising
42,560 hours recorded from 5,793 subjects. From experiment results, our
platform achieves the general cognition overall accuracy of 87% by leveraging
sole EEG, which is 2% superior to the state-of-the-art. Moreover, our developed
multi-tier methodology offers visible and graphical interpretations of the
temporal characteristics of EEG by identifying the critical episodes, which is
demanded in neurodynamics but hardly appears in conventional cognition
scenarios.


title: Multiscale Analysis for Improving Texture Classification

abstract:
  Information from an image occurs over multiple and distinct spatial scales.
Image pyramid multiresolution representations are a useful data structure for
image analysis and manipulation over a spectrum of spatial scales. This paper
employs the Gaussian-Laplacian pyramid to treat different spatial frequency
bands of a texture separately. First, we generate three images corresponding to
three levels of the Gaussian-Laplacian pyramid for an input image to capture
intrinsic details. Then we aggregate features extracted from gray and color
texture images using bio-inspired texture descriptors, information-theoretic
measures, gray-level co-occurrence matrix features, and Haralick statistical
features into a single feature vector. Such an aggregation aims at producing
features that characterize textures to their maximum extent, unlike employing
each descriptor separately, which may lose some relevant textural information
and reduce the classification performance. The experimental results on texture
and histopathologic image datasets have shown the advantages of the proposed
method compared to state-of-the-art approaches. Such findings emphasize the
importance of multiscale image analysis and corroborate that the descriptors
mentioned above are complementary.


title: Some sufficient conditions for path-factor uniform graphs

abstract:
  For a set $\mathcal{H}$ of connected graphs, a spanning subgraph $H$ of $G$
is called an $\mathcal{H}$-factor of $G$ if each component of $H$ is isomorphic
to an element of $\mathcal{H}$. A graph $G$ is called an $\mathcal{H}$-factor
uniform graph if for any two edges $e_1$ and $e_2$ of $G$, $G$ has an
$\mathcal{H}$-factor covering $e_1$ and excluding $e_2$. Let each component in
$\mathcal{H}$ be a path with at least $d$ vertices, where $d\geq2$ is an
integer. Then an $\mathcal{H}$-factor and an $\mathcal{H}$-factor uniform graph
are called a $P_{\geq d}$-factor and a $P_{\geq d}$-factor uniform graph,
respectively. In this article, we verify that (\romannumeral1) a
2-edge-connected graph $G$ is a $P_{\geq3}$-factor uniform graph if
$\delta(G)>\frac{\alpha(G)+4}{2}$; (\romannumeral2) a $(k+2)$-connected graph
$G$ of order $n$ with $n\geq5k+3-\frac{3}{5\gamma-1}$ is a $P_{\geq3}$-factor
uniform graph if $|N_G(A)|>\gamma(n-3k-2)+k+2$ for any independent set $A$ of
$G$ with $|A|=\lfloor\gamma(2k+1)\rfloor$, where $k$ is a positive integer and
$\gamma$ is a real number with $\frac{1}{3}\leq\gamma\leq1$.


title: Generalized Langevin equation with fluctuating diffusivity

abstract:
  A generalized Langevin equation with fluctuating diffusivity (GLEFD) is
proposed, and it is shown that the GLEFD satisfies a generalized
fluctuation-dissipation relation. If the memory kernel is a power law, the
GLEFD exhibits anomalous subdiffusion, non-Gaussianity, and
stretched-exponential relaxation. The case in which the memory kernel is given
by a single exponential function is also investigated as an analytically
tractable example. In particular, the mean-square displacement and the
self-intermediate-scattering function of this system show plateau structures. A
numerical scheme to integrate the GLEFD is also presented.


title:
On the admissibility of observation operators in the context of maximal
  regularity

abstract:
  We study admissible observation operators for perturbed evolution equations
using the concept of maximal regularity. We first show the invariance of the
maximal $L^p$-regularity under non-autonomous Miyadera-Voigt perturbations.
Second, we establish the invariance of admissibility of observation operators
under such a class of perturbations. Finally, we illustrate our result with two
examples, one on a non-autonomous parabolic system, and the other on an
evolution equation with mixed boundary conditions and a non-local perturbation.


title:
On the Kawaguchi--Silverman Conjecture for birational automorphisms of
  irregular varieties

abstract:
  We study the main open parts of the Kawaguchi--Silverman Conjecture,
asserting that for a birational self-map $f$ of a smooth projective variety $X$
defined over $\overline{\mathbb Q}$, the arithmetic degree $\alpha_f(x)$ exists
and coincides with the first dynamical degree $\delta_f$ for any
$\overline{\mathbb Q}$-point $x$ of $X$ with a Zariski dense orbit. Among other
results, we show that this holds when $X$ has Kodaira dimension zero and
irregularity $q(X) \ge \dim X -1$ or $X$ is an irregular threefold (modulo one
possible exception). We also study the existence of Zariski dense orbits, with
explicit examples.


title:
Tailoring of neuromorphic switching by the CuNx mediated orbital
  currents

abstract:
  Current-induced spin-orbit torque (SOT) is regarded as a promising mechanism
for driving neuromorphic behavior in spin-orbitronic devices. In principle, the
strong SOT in heavy metal-based magnetic heterostructure is attributed to the
spin-orbit coupling (SOC)-induced spin Hall effect (SHE) and/or the spin
Rashba-Edelstein effect (SREE). Recently, SOC-free mechanisms such as the
orbital angular momentum (OAM)-induced orbital Hall effect (OHE) and/or the
orbital Rashba-Edelstein effect (OREE) have been proposed to generate sizable
torques comparable to those from the conventional spin Hall mechanism. In this
work, we show that the orbital current can be effectively generated by the
nitrided light metal Cu. The overall damping-like SOT efficiency, which
consists of both the spin and the orbital current contributions, can be
tailored from ~ 0.06 to 0.4 in a Pt/Co/CuNx magnetic heterostructure by tuning
the nitrogen doping concentration. Current-induced magnetization switching
further verifies the efficacy of such orbital current with a critical switching
current density as low as Jc ~ 5 x 10^10 A/m2. Most importantly, the
orbital-current-mediated memristive switching behavior can be observed in such
heterostructures, which reveals that the gigantic SOT and efficient
magnetization switching are the tradeoffs for the applicable window of
memristive switching. Our work provides insights into the role of orbital
current might play in SOT neuromorphic devices and paves a new route for making
energy-efficient spin-orbitronic devices.


title:
Unseen Object Instance Segmentation with Fully Test-time RGB-D
  Embeddings Adaptation

abstract:
  Segmenting unseen objects is a crucial ability for the robot since it may
encounter new environments during the operation. Recently, a popular solution
is leveraging RGB-D features of large-scale synthetic data and directly
applying the model to unseen real-world scenarios. However, even though depth
data have fair generalization ability, the domain shift due to the Sim2Real gap
is inevitable, which presents a key challenge to the unseen object instance
segmentation (UOIS) model. To tackle this problem, we re-emphasize the
adaptation process across Sim2Real domains in this paper. Specifically, we
propose a framework to conduct the Fully Test-time RGB-D Embeddings Adaptation
(FTEA) based on parameters of the BatchNorm layer. To construct the learning
objective for test-time back-propagation, we propose a novel non-parametric
entropy objective that can be implemented without explicit classification
layers. Moreover, we design a cross-modality knowledge distillation module to
encourage the information transfer during test time. The proposed method can be
efficiently conducted with test-time images, without requiring annotations or
revisiting the large-scale synthetic training data. Besides significant time
savings, the proposed method consistently improves segmentation results on both
overlap and boundary metrics, achieving state-of-the-art performances on two
real-world RGB-D image datasets. We hope our work could draw attention to the
test-time adaptation and reveal a promising direction for robot perception in
unseen environments.


title: Weakly Aligned Feature Fusion for Multimodal Object Detection

abstract:
  To achieve accurate and robust object detection in the real-world scenario,
various forms of images are incorporated, such as color, thermal, and depth.
However, multimodal data often suffer from the position shift problem, i.e.,
the image pair is not strictly aligned, making one object has different
positions in different modalities. For the deep learning method, this problem
makes it difficult to fuse multimodal features and puzzles the convolutional
neural network (CNN) training. In this article, we propose a general multimodal
detector named aligned region CNN (AR-CNN) to tackle the position shift
problem. First, a region feature (RF) alignment module with adjacent similarity
constraint is designed to consistently predict the position shift between two
modalities and adaptively align the cross-modal RFs. Second, we propose a novel
region of interest (RoI) jitter strategy to improve the robustness to
unexpected shift patterns. Third, we present a new multimodal feature fusion
method that selects the more reliable feature and suppresses the less useful
one via feature reweighting. In addition, by locating bounding boxes in both
modalities and building their relationships, we provide novel multimodal
labeling named KAIST-Paired. Extensive experiments on 2-D and 3-D object
detection, RGB-T, and RGB-D datasets demonstrate the effectiveness and
robustness of our method.


title: Standard conjecture D for local stacky matrix factorizations

abstract:
  We establish the non-commutative analogue of Grothendieck's standard
conjecture D for the differential graded category of $G$-equivariant matrix
factorizations associated to an isolated hypersurface singularity where $G$ is
a finite group.


title:
FedCL: Federated Contrastive Learning for Privacy-Preserving
  Recommendation

abstract:
  Contrastive learning is widely used for recommendation model learning, where
selecting representative and informative negative samples is critical. Existing
methods usually focus on centralized data, where abundant and high-quality
negative samples are easy to obtain. However, centralized user data storage and
exploitation may lead to privacy risks and concerns, while decentralized user
data on a single client can be too sparse and biased for accurate contrastive
learning. In this paper, we propose a federated contrastive learning method
named FedCL for privacy-preserving recommendation, which can exploit
high-quality negative samples for effective model training with privacy well
protected. We first infer user embeddings from local user data through the
local model on each client, and then perturb them with local differential
privacy (LDP) before sending them to a central server for hard negative
sampling. Since individual user embedding contains heavy noise due to LDP, we
propose to cluster user embeddings on the server to mitigate the influence of
noise, and the cluster centroids are used to retrieve hard negative samples
from the item pool. These hard negative samples are delivered to user clients
and mixed with the observed negative samples from local data as well as
in-batch negatives constructed from positive samples for federated model
training. Extensive experiments on four benchmark datasets show FedCL can
empower various recommendation methods in a privacy-preserving way.