title: NeuroView-RNN: It's About Time

abstract:
  Recurrent Neural Networks (RNNs) are important tools for processing
sequential data such as time-series or video. Interpretability is defined as
the ability to be understood by a person and is different from explainability,
which is the ability to be explained in a mathematical formulation. A key
interpretability issue with RNNs is that it is not clear how each hidden state
per time step contributes to the decision-making process in a quantitative
manner. We propose NeuroView-RNN as a family of new RNN architectures that
explains how all the time steps are used for the decision-making process. Each
member of the family is derived from a standard RNN architecture by
concatenation of the hidden steps into a global linear classifier. The global
linear classifier has all the hidden states as the input, so the weights of the
classifier have a linear mapping to the hidden states. Hence, from the weights,
NeuroView-RNN can quantify how important each time step is to a particular
decision. As a bonus, NeuroView-RNN also offers higher accuracy in many cases
compared to the RNNs and their variants. We showcase the benefits of
NeuroView-RNN by evaluating on a multitude of diverse time-series datasets.


title: Investigations of Performance and Bias in Human-AI Teamwork in Hiring

abstract:
  In AI-assisted decision-making, effective hybrid (human-AI) teamwork is not
solely dependent on AI performance alone, but also on its impact on human
decision-making. While prior work studies the effects of model accuracy on
humans, we endeavour here to investigate the complex dynamics of how both a
model's predictive performance and bias may transfer to humans in a
recommendation-aided decision task. We consider the domain of ML-assisted
hiring, where humans -- operating in a constrained selection setting -- can
choose whether they wish to utilize a trained model's inferences to help select
candidates from written biographies. We conduct a large-scale user study
leveraging a re-created dataset of real bios from prior work, where humans
predict the ground truth occupation of given candidates with and without the
help of three different NLP classifiers (random, bag-of-words, and deep neural
network). Our results demonstrate that while high-performance models
significantly improve human performance in a hybrid setting, some models
mitigate hybrid bias while others accentuate it. We examine these findings
through the lens of decision conformity and observe that our model architecture
choices have an impact on human-AI conformity and bias, motivating the explicit
need to assess these complex dynamics prior to deployment.


title:
AirGuard -- Protecting Android Users From Stalking Attacks By Apple Find
  My Devices

abstract:
  Finder networks in general, and Apple's Find My network in particular, can
pose a grave threat to users' privacy and even health if these networks are
abused for stalking. Apple's release of the AirTag, a very affordable tracker
covered by the nearly ubiquitous Find My network, amplified this issue. While
Apple provides a stalking detection feature within its ecosystem, billions of
Android users are still left in the dark. Apple recently released the Android
app "Tracker Detect," which does not deliver a convincing feature set for
stalking protection. We reverse engineer Apple's tracking protection in iOS and
discuss its features regarding stalking detection. We design "AirGuard" and
release it as an Android app to protect against abuse by Apple tracking
devices. We compare the performance of our solution with the Apple-provided one
in iOS and study the use of AirGuard in the wild over multiple weeks using data
contributed by tens of thousands of active users.


title:
Nonlinear Coupling of Electromagnetic and Spin-Electron-Acoustic Waves
  in Spin-polarized Degenerate Relativistic Astrophysical Plasma

abstract:
  Propagation of the finite amplitude electromagnetic wave through the
partially spin-polarized degenerate plasmas leads to the instability. The
instability happens at the interaction of the electromagnetic wave with the
small frequency longitudinal spin-electron-acoustic waves. Strongest
instability happens in the high density degenerate plasmas with the Fermi
momentum close to $m_{e}c$, where $m_{e}$ is the mass of electron, and $c$ is
the speed of light. The increase of the increment of instability with the
growth of the spin polarization of plasmas is found.


title: Polynomial effective equidistribution

abstract:
  We prove effective equidistribution theorems, with polynomial error rate, for
orbits of the unipotent subgroups of $\operatorname{SL}_2(\mathbb R)$ in
arithmetic quotients of $\operatorname{SL}_2(\mathbb C)$ and
$\operatorname{SL}_2(\mathbb R)\times\operatorname{SL}_2(\mathbb R)$. The proof
is based on the use of a Margulis function, tools from incidence geometry, and
the spectral gap of the ambient space.


title:
Nonlocal subpicosecond delay metrology using spectral quantum
  interference

abstract:
  Timing and positioning measurements are key requisites for essential quantum
network operations such as Bell state measurement. Conventional time-of-flight
measurements using single-photon detectors are often limited by detection
timing jitter. In this work, we demonstrate a nonlocal scheme to measure
changes in relative link latencies with subpicosecond resolution by using tight
timing correlation of broadband time-energy entangled photons. Our sensing
scheme relies on spectral interference achieved via phase modulation, followed
by filtering and biphoton coincidence measurements, and is resilient to
microsecond-scale mismatch between the optical link traversed by the biphotons.
Our experiments demonstrate a precision of +/-0.04 ps in measurements of
nonlocal delay changes and +/-0.7{\deg} in measurements of radio-frequency
phase changes. Furthermore, we complement our technique with time-tag
information from single-photon detectors in the same setup to present
unambiguous sensing of delay changes. The proposed technique can be implemented
using off-the-shelf telecom equipment thus rendering it adaptable to practical
quantum network infrastructure.


title: Benefit of Interpolation in Nearest Neighbor Algorithms

abstract:
  In some studies \citep[e.g.,][]{zhang2016understanding} of deep learning, it
is observed that over-parametrized deep neural networks achieve a small testing
error even when the training error is almost zero. Despite numerous works
towards understanding this so-called "double descent" phenomenon
\citep[e.g.,][]{belkin2018reconciling,belkin2019two}, in this paper, we turn
into another way to enforce zero training error (without over-parametrization)
through a data interpolation mechanism. Specifically, we consider a class of
interpolated weighting schemes in the nearest neighbors (NN) algorithms. By
carefully characterizing the multiplicative constant in the statistical risk,
we reveal a U-shaped performance curve for the level of data interpolation in
both classification and regression setups. This sharpens the existing result
\citep{belkin2018does} that zero training error does not necessarily jeopardize
predictive performances and claims a counter-intuitive result that a mild
degree of data interpolation actually {\em strictly} improve the prediction
performance and statistical stability over those of the (un-interpolated)
$k$-NN algorithm. In the end, the universality of our results, such as change
of distance measure and corrupted testing data, will also be discussed.


title: Consistent Dropout for Policy Gradient Reinforcement Learning

abstract:
  Dropout has long been a staple of supervised learning, but is rarely used in
reinforcement learning. We analyze why naive application of dropout is
problematic for policy-gradient learning algorithms and introduce consistent
dropout, a simple technique to address this instability. We demonstrate
consistent dropout enables stable training with A2C and PPO in both continuous
and discrete action environments across a wide range of dropout probabilities.
Finally, we show that consistent dropout enables the online training of complex
architectures such as GPT without needing to disable the model's native
dropout.


title: Improving Scalability with GPU-Aware Asynchronous Tasks

abstract:
  Asynchronous tasks, when created with over-decomposition, enable automatic
computation-communication overlap which can substantially improve performance
and scalability. This is not only applicable to traditional CPU-based systems,
but also to modern GPU-accelerated platforms. While the ability to hide
communication behind computation can be highly effective in weak scaling
scenarios, performance begins to suffer with smaller problem sizes or in strong
scaling due to fine-grained overheads and reduced room for overlap. In this
work, we integrate GPU-aware communication into asynchronous tasks in addition
to computation-communication overlap, with the goal of reducing time spent in
communication and further increasing GPU utilization. We demonstrate the
performance impact of our approach using a proxy application that performs the
Jacobi iterative method, Jacobi3D. In addition to optimizations to minimize
synchronizations between the host and GPU devices and increase the concurrency
of GPU operations, we explore techniques such as kernel fusion and CUDA Graphs
to mitigate fine-grained overheads at scale.


title:
Nowcasting the Financial Time Series with Streaming Data Analytics under
  Apache Spark

abstract:
  This paper proposes nowcasting of high-frequency financial datasets in
real-time with a 5-minute interval using the streaming analytics feature of
Apache Spark. The proposed 2 stage method consists of modelling chaos in the
first stage and then using a sliding window approach for training with machine
learning algorithms namely Lasso Regression, Ridge Regression, Generalised
Linear Model, Gradient Boosting Tree and Random Forest available in the MLLib
of Apache Spark in the second stage. For testing the effectiveness of the
proposed methodology, 3 different datasets, of which two are stock markets
namely National Stock Exchange & Bombay Stock Exchange, and finally One
Bitcoin-INR conversion dataset. For evaluating the proposed methodology, we
used metrics such as Symmetric Mean Absolute Percentage Error, Directional
Symmetry, and Theil U Coefficient. We tested the significance of each pair of
models using the Diebold Mariano (DM) test.


title:
Physics-informed neural networks for inverse problems in supersonic
  flows

abstract:
  Accurate solutions to inverse supersonic compressible flow problems are often
required for designing specialized aerospace vehicles. In particular, we
consider the problem where we have data available for density gradients from
Schlieren photography as well as data at the inflow and part of wall
boundaries. These inverse problems are notoriously difficult and traditional
methods may not be adequate to solve such ill-posed inverse problems. To this
end, we employ the physics-informed neural networks (PINNs) and its extended
version, extended PINNs (XPINNs), where domain decomposition allows deploying
locally powerful neural networks in each subdomain, which can provide
additional expressivity in subdomains, where a complex solution is expected.
Apart from the governing compressible Euler equations, we also enforce the
entropy conditions in order to obtain viscosity solutions. Moreover, we enforce
positivity conditions on density and pressure. We consider inverse problems
involving two-dimensional expansion waves, two-dimensional oblique and bow
shock waves. We compare solutions obtained by PINNs and XPINNs and invoke some
theoretical results that can be used to decide on the generalization errors of
the two methods.


title: Using natural language prompts for machine translation

abstract:
  We explore the use of natural language prompts for controlling various
aspects of the outputs generated by machine translation models. We demonstrate
that natural language prompts allow us to influence properties like formality
or specific dialect of the output. We show that using language names to control
the output language of multilingual translation models enables positive
transfer for unseen language pairs. This unlocks the ability to translate into
languages not seen during fine-tuning by using their English names. We
investigate how scale, number of pre-training steps, number of languages in
fine-tuning, and language similarity affect this phenomenon.


title: Differentially Private Speaker Anonymization

abstract:
  Sharing real-world speech utterances is key to the training and deployment of
voice-based services. However, it also raises privacy risks as speech contains
a wealth of personal data. Speaker anonymization aims to remove speaker
information from a speech utterance while leaving its linguistic and prosodic
attributes intact. State-of-the-art techniques operate by disentangling the
speaker information (represented via a speaker embedding) from these attributes
and re-synthesizing speech based on the speaker embedding of another speaker.
Prior research in the privacy community has shown that anonymization often
provides brittle privacy protection, even less so any provable guarantee. In
this work, we show that disentanglement is indeed not perfect: linguistic and
prosodic attributes still contain speaker information. We remove speaker
information from these attributes by introducing differentially private feature
extractors based on an autoencoder and an automatic speech recognizer,
respectively, trained using noise layers. We plug these extractors in the
state-of-the-art anonymization pipeline and generate, for the first time,
differentially private utterances with a provable upper bound on the speaker
information they contain. We evaluate empirically the privacy and utility
resulting from our differentially private speaker anonymization approach on the
LibriSpeech data set. Experimental results show that the generated utterances
retain very high utility for automatic speech recognition training and
inference, while being much better protected against strong adversaries who
leverage the full knowledge of the anonymization process to try to infer the
speaker identity.


title:
Using Deep Learning to Detect Digitally Encoded DNA Trigger for Trojan
  Malware in Bio-Cyber Attacks

abstract:
  This article uses Deep Learning technologies to safeguard DNA sequencing
against Bio-Cyber attacks. We consider a hybrid attack scenario where the
payload is encoded into a DNA sequence to activate a Trojan malware implanted
in a software tool used in the sequencing pipeline in order to allow the
perpetrators to gain control over the resources used in that pipeline during
sequence analysis. The scenario considered in the paper is based on
perpetrators submitting synthetically engineered DNA samples that contain
digitally encoded IP address and port number of the perpetrators machine in the
DNA. Genetic analysis of the samples DNA will decode the address that is used
by the software trojan malware to activate and trigger a remote connection.
This approach can open up to multiple perpetrators to create connections to
hijack the DNA sequencing pipeline. As a way of hiding the data, the
perpetrators can avoid detection by encoding the address to maximise similarity
with genuine DNAs, which we showed previously. However, in this paper we show
how Deep Learning can be used to successfully detect and identify the trigger
encoded data, in order to protect a DNA sequencing pipeline from trojan
attacks. The result shows nearly up to 100% accuracy in detection in such a
novel Trojan attack scenario even after applying fragmentation encryption and
steganography on the encoded trigger data. In addition, feasibility of
designing and synthesizing encoded DNA for such Trojan payloads is validated by
a wet lab experiment.


title: Extracting invariant of conjugacy from independence entropy

abstract:
  The concept of independence entropy for symbolic dynamical systems was
introduced in [LMP13]. This notion of entropy measures the extent to which one
can freely insert symbols in positions without violating the constraints
defined by the shift space. Independence entropy is not invariant under
topological conjugacy. We define an invariant version of the independence
entropy by setting sup h_{ind}(X) = sup{h_{ind}(Y )|Y ' X}. This invariant is
bounded above by the topological entropy. We prove that equality sup h_{ind}(X)
= h(X) holds for all sofic shift spaces over Z, then we give an example showing
that equality does not hold for general shift spaces.


title: The associative-commutative spectrum of a binary operation

abstract:
  We initiate the study of a quantitative measure for the failure of a binary
operation to be commutative and associative. We call this measure the
associative-commutative spectrum as it extends the so-called associative
spectrum (also known as the subassociativity type), which measures the
nonassociativity of a binary operation. In fact, the associative-commutative
spectrum (resp. associative spectrum) is the cardinality of the symmetric
(resp. nonsymmetric) operad obtained naturally from a groupoid (a set with a
binary operation). In this paper we provide some general results on the
associative-commutative spectrum, precisely determine this measure for certain
binary operations, and propose some problems for future study.


title: TARexp: A Python Framework for Technology-Assisted Review Experiments

abstract:
  Technology-assisted review (TAR) is an important industrial application of
information retrieval (IR) and machine learning (ML). While a small TAR
research community exists, the complexity of TAR software and workflows is a
major barrier to entry. Drawing on past open source TAR efforts, as well as
design patterns from the IR and ML open source software, we present an open
source Python framework for conducting experiments on TAR algorithms. Key
characteristics of this framework are declarative representations of workflows
and experiment plans, the ability for components to play variable numbers of
workflow roles, and state maintenance and restart capabilities. Users can draw
on reference implementations of standard TAR algorithms while incorporating
novel components to explore their research interests. The framework is
available at https://github.com/eugene-yang/tarexp.


title:
Novel Sensors for Particle Tracking: a Contribution to the Snowmass
  Community Planning Exercise of 2021

abstract:
  Five contemporary technologies are discussed in the context of their
potential roles in particle tracking for future high energy physics
applications. These include sensors of the 3D configuration, in both diamond
and silicon, submicron-dimension pixels, thin film detectors, and scintillating
quantum dots in gallium arsenide. Drivers of the technologies include radiation
hardness, excellent position, vertex, and timing resolution, simplified
integration, and optimized power, cost, and material.


title: Performance of planar Floquet codes with Majorana-based qubits

abstract:
  Quantum error correction is crucial for any quantum computing platform to
achieve truly scalable quantum computation. The surface code and its variants
have been considered the most promising quantum error correction scheme due to
their high threshold, low overhead, and relatively simple structure that can
naturally be implemented in many existing qubit architectures, such as
superconducting qubits. The recent development of Floquet codes offers another
promising approach. By going beyond the usual paradigm of stabilizer codes,
Floquet codes achieve similar performance while being constructed entirely from
two-qubit measurements. This makes them particularly suitable for platforms
where two-qubit measurements can be implemented directly, such as
measurement-only topological qubits based on Majorana zero modes (MZMs). Here,
we explain how two variants of Floquet codes can be implemented on MZM-based
architectures without any auxiliary qubits for syndrome measurement and with
shallow syndrome extraction sequences. We then numerically demonstrate their
favorable performance. In particular, we show that they improve the threshold
for scalable quantum computation in MZM-based systems by an order of magnitude,
and significantly reduce space and time overheads below threshold.


title:
Feedback Lock-in: A versatile multi-terminal measurement system for
  electrical transport devices

abstract:
  We present the design and implementation of a measurement system that enables
parallel drive and detection of small currents and voltages at numerous
electrical contacts to a multi-terminal electrical device. This system, which
we term a feedback lock-in, combines digital control-loop feedback with
software-defined lock-in measurements to dynamically source currents and
measure small, pre-amplified potentials. The effective input impedance of each
current/voltage probe can be set via software, permitting any given contact to
behave as an open-circuit voltage lead or as a virtually grounded current
source/sink. This enables programmatic switching of measurement configurations
and permits measurement of currents at multiple drain contacts without the use
of current preamplifiers. Our 32-channel implementation relies on commercially
available digital input/output boards, home-built voltage preamplifiers, and
custom open-source software. With our feedback lock-in, we demonstrate
differential measurement sensitivity comparable to a widely used commercially
available lock-in amplifier and perform efficient multi-terminal electrical
transport measurements on twisted bilayer graphene and $SrTiO_3$ quantum point
contacts. The feedback lock-in also enables a new style of current-biased
measurement which we demonstrate on a ballistic graphene device.


title: A modification of the conjugate direction method for motion estimation

abstract:
  A comparative study of different block matching alternatives for motion
estimation is presented. The study is focused on computational burden and
objective measures on the accuracy of prediction. Together with existing
algorithms several new variations have been tested. An interesting modification
of the conjugate direction method previously related in literature is reported.
This new algorithm shows a good trade-off between computational complexity and
accuracy of motion vector estimation. Computational complexity is evaluated
using a sequence of artificial images designed to incorporate a great variety
of motion vectors. The performance of block matching methods has been measured
in terms of the entropy in the error signal between the motion compensated and
the original frames.


title: Analysis of the IW And star ASAS J071404+7004.3

abstract:
  We made a time-resolved photometric campaign of the bright cataclysmic
variable ASAS J071404+7004.3 in 2020. Inight et al. (2022, arXiv/2109.14514)
recently published time-resolved optical spectroscopy, X-ray observations and
long- and short-term optical variations. Although their results were correct in
many parts, they classified ASAS J071404+7004.3 as a VY Scl-type novalike
object. By comparing the ASAS-SN data of this object and the IW And-type object
HO Pup, we showed that their type classification was incorrect. ASAS
J071404+7004.3 showed outbursts from a standstill followed by shallow dips,
which is the defining characteristic of an IW And star. This object
predominantly showed states with low-amplitude dwarf nova-type oscillations,
some of which could be identified as the "heartbeat"-type state as a variety of
the IW And-type phenomenon. The low state described by Inight et al. (2022) was
not a true low state of a VY Scl star, but a dwarf nova-type state with
increased outburst amplitudes. Both ground-based (our campaign) and TESS
observations detected orbital variations whose periods [0.136589(5) d by the
ground-based campaign and 0.1366476(3) d by the TESS data] are in very good
agreement with the one obtained by radial-velocity studies by Inight et al.
(2022). The standstill in 2019-2020 in ASAS J071404+7004.3 was not brighter
than its dwarf nova-type states. The brightest moment of this object occurred
when the amplitudes of dwarf nova-type variations were large, which challenges
the widely accepted interpretation that standstills in Z Cam stars occur when
the mass-transfer rates are high.


title: Near Perfect GAN Inversion

abstract:
  To edit a real photo using Generative Adversarial Networks (GANs), we need a
GAN inversion algorithm to identify the latent vector that perfectly reproduces
it. Unfortunately, whereas existing inversion algorithms can synthesize images
similar to real photos, they cannot generate the identical clones needed in
most applications. Here, we derive an algorithm that achieves near perfect
reconstructions of photos. Rather than relying on encoder- or
optimization-based methods to find an inverse mapping on a fixed generator
$G(\cdot)$, we derive an approach to locally adjust $G(\cdot)$ to more
optimally represent the photos we wish to synthesize. This is done by locally
tweaking the learned mapping $G(\cdot)$ s.t. $\| {\bf x} - G({\bf z})
\|<\epsilon$, with ${\bf x}$ the photo we wish to reproduce, ${\bf z}$ the
latent vector, $\|\cdot\|$ an appropriate metric, and $\epsilon > 0$ a small
scalar. We show that this approach can not only produce synthetic images that
are indistinguishable from the real photos we wish to replicate, but that these
images are readily editable. We demonstrate the effectiveness of the derived
algorithm on a variety of datasets including human faces, animals, and cars,
and discuss its importance for diversity and inclusion.


title:
Comparison of Combination Methods to Create Calibrated Ensemble
  Forecasts for Seasonal Influenza in the U.S

abstract:
  The characteristics of influenza seasons varies substantially from year to
year, posing challenges for public health preparation and response. Influenza
forecasting is used to inform seasonal outbreak response, which can in turn
potentially reduce the societal impact of an epidemic. The United States
Centers for Disease Control and Prevention, in collaboration with external
researchers, has run an annual prospective influenza forecasting exercise,
known as the FluSight challenge. A subset of participating teams has worked
together to produce a collaborative multi-model ensemble, the FluSight Network
ensemble. Uniting theoretical results from the forecasting literature with
domain-specific forecasts from influenza outbreaks, we applied parametric
forecast combination methods that simultaneously optimize individual model
weights and calibrate the ensemble via a beta transformation. We used the
beta-transformed linear pool and the finite beta mixture model to produce
ensemble forecasts retrospectively for the 2016/2017 to 2018/2019 influenza
seasons in the U.S. We compared their performance to methods currently used in
the FluSight challenge, namely the equally weighted linear pool and the linear
pool. Ensemble forecasts produced from methods with a beta transformation were
shown to outperform those from the equally weighted linear pool and the linear
pool for all week-ahead targets across in the test seasons based on average log
scores. We observed improvements in overall accuracy despite the
beta-transformed linear pool or beta mixture methods' modest under-prediction
across all targets and seasons. Combination techniques that explicitly adjust
for known calibration issues in linear pooling should be considered to improve
ensemble probabilistic scores in outbreak settings.


title: Values of zeta-one functions at positive even integers

abstract:
  Motivated by Euler-Goldbach and Shallit-Zikan theorems, we introduce zeta-one
functions with infinite sums of $n^{s}\pm1$ as an analogy of the Riemann zeta
function. Then we compute values of these functions at positive even integers
by the residue theorem.


title:
Sky Computing: Accelerating Geo-distributed Computing in Federated
  Learning

abstract:
  Federated learning is proposed by Google to safeguard data privacy through
training models locally on users' devices. However, with deep learning models
growing in size to achieve better results, it becomes increasingly difficult to
accommodate the whole model on one single device. Thus, model parallelism is
then used to divide the model weights among several devices. With this logic,
the approach currently used evenly allocates weights among devices. However, in
reality, a computation bottleneck may occur resulting from variant computing
power of different users' devices. To address this problem, load balancing is
needed to allocate the model weights based on the computational capability of
the device. In this paper, we proposed Sky Computing, a load-balanced model
parallelism framework to adaptively allocate the weights to devices. Sky
Computing outperforms the baseline method by 55% in training time when training
160-layer BERT with 64 nodes. The source code can be found at
https://github.com/hpcaitech/SkyComputing.


title: Computational 3D microscopy with optical coherence refraction tomography

abstract:
  Optical coherence tomography (OCT) has seen widespread success as an in vivo
clinical diagnostic 3D imaging modality, impacting areas including
ophthalmology, cardiology, and gastroenterology. Despite its many advantages,
such as high sensitivity, speed, and depth penetration, OCT suffers from
several shortcomings that ultimately limit its utility as a 3D microscopy tool,
such as its pervasive coherent speckle noise and poor lateral resolution
required to maintain millimeter-scale imaging depths. Here, we present 3D
optical coherence refraction tomography (OCRT), a computational extension of
OCT which synthesizes an incoherent contrast mechanism by combining multiple
OCT volumes, acquired across two rotation axes, to form a resolution-enhanced,
speckle-reduced, refraction-corrected 3D reconstruction. Our label-free
computational 3D microscope features a novel optical design incorporating a
parabolic mirror to enable the capture of 5D plenoptic datasets, consisting of
millimetric 3D fields of view over up to $\pm75^\circ$ without moving the
sample. We demonstrate that 3D OCRT reveals 3D features unobserved by
conventional OCT in fruit fly, zebrafish, and mouse samples.


title: Explanatory Paradigms in Neural Networks

abstract:
  In this article, we present a leap-forward expansion to the study of
explainability in neural networks by considering explanations as answers to
abstract reasoning-based questions. With $P$ as the prediction from a neural
network, these questions are `Why P?', `What if not P?', and `Why P, rather
than Q?' for a given contrast prediction $Q$. The answers to these questions
are observed correlations, observed counterfactuals, and observed contrastive
explanations respectively. Together, these explanations constitute the
abductive reasoning scheme. We term the three explanatory schemes as observed
explanatory paradigms. The term observed refers to the specific case of
post-hoc explainability, when an explanatory technique explains the decision
$P$ after a trained neural network has made the decision $P$. The primary
advantage of viewing explanations through the lens of abductive reasoning-based
questions is that explanations can be used as reasons while making decisions.
The post-hoc field of explainability, that previously only justified decisions,
becomes active by being involved in the decision making process and providing
limited, but relevant and contextual interventions. The contributions of this
article are: ($i$) realizing explanations as reasoning paradigms, ($ii$)
providing a probabilistic definition of observed explanations and their
completeness, ($iii$) creating a taxonomy for evaluation of explanations, and
($iv$) positioning gradient-based complete explanainability's replicability and
reproducibility across multiple applications and data modalities, ($v$) code
repositories, publicly available at
https://github.com/olivesgatech/Explanatory-Paradigms.


title:
Analysis method for 3D power spectrum of projected tensor field with
  fast estimator and window convolution modelling: an application to intrinsic
  alignments

abstract:
  Rank-2 tensor fields of large-scale structure, e.g. a tensor field inferred
from shapes of galaxies, open up a window to directly access 2 scalar, 2 vector
and 2 tensor modes, where the scalar fields can be measured independently from
the standard density field that is traced by distribution of galaxies. Here we
develop an estimator of the multipole moments of coordinate-independent power
spectra for the three-dimensional tensor field, taking into account the
projection of the tensor field onto plane perpendicular to the line-of-sight
direction. To do this, we find that a convenient representation of the power
spectrum multipoles can be obtained by the use of the associated Legendre
polynomials in the form which allows for the fast Fourier transform estimations
under the local plane-parallel (LPP) approximation. The formulation also allows
us to obtain the Hankel transforms to connect the two-point statistics in
Fourier and configuration space, which are needed to derive theoretical
templates of the power spectrum including convolution of a survey window. To
validate our estimators, we use the simulation data of the projected tidal
field assuming a survey window that mimics the BOSS-like survey footprint. We
show that the LPP estimators fairly well recover the multipole moments that are
inferred from the global plane-parallel approximation. We find that the survey
window causes a more significant change in the multipole moments of projected
tensor power spectrum at $k\lesssim 0.1\,h{\rm Mpc}^{-1}$ from the input power
spectrum, than in the density power spectrum. Nevertheless, the theoretical
predictions including the survey window effects match the multipole moments
measured from the simulations. The analysis method presented here paves the way
for a cosmological analysis using three-dimensional tensor-type tracers of
large-scale structure for current and future surveys.


title: Scalpel: The Python Static Analysis Framework

abstract:
  Despite being the most popular programming language, Python has not yet
received enough attention from the community. To the best of our knowledge,
there is no general static analysis framework proposed to facilitate the
implementation of dedicated Python static analyzers. To fill this gap, we
design and implement such a framework (named Scalpel) and make it publicly
available as an open-source project. The Scalpel framework has already
integrated a number of fundamental static analysis functions (e.g., call graph
constructions, control-flow graph constructions, alias analysis, etc.) that are
ready to be reused by developers to implement client applications focusing on
statically resolving dedicated Python problems such as detecting bugs or fixing
vulnerabilities.


title:
DC and SA: Robust and Efficient Hyperparameter Optimization of
  Multi-subnetwork Deep Learning Models

abstract:
  We present two novel hyperparameter optimization strategies for optimization
of deep learning models with a modular architecture constructed of multiple
subnetworks. As complex networks with multiple subnetworks become more
frequently applied in machine learning, hyperparameter optimization methods are
required to efficiently optimize their hyperparameters. Existing hyperparameter
searches are general, and can be used to optimize such networks, however, by
exploiting the multi-subnetwork architecture, these searches can be sped up
substantially. The proposed methods offer faster convergence to a
better-performing final model. To demonstrate this, we propose 2 independent
approaches to enhance these prior algorithms: 1) a divide-and-conquer approach,
in which the best subnetworks of top-performing models are combined, allowing
for more rapid sampling of the hyperparameter search space. 2) A subnetwork
adaptive approach that distributes computational resources based on the
importance of each subnetwork, allowing more intelligent resource allocation.
These approaches can be flexibily applied to many hyperparameter optimization
algorithms. To illustrate this, we combine our approaches with the
commonly-used Bayesian optimization method. Our approaches are then tested
against both synthetic examples and real-world examples and applied to multiple
network types including convolutional neural networks and dense feed forward
neural networks. Our approaches show an increased optimization efficiency of up
to 23.62x, and a final performance boost of up to 3.5% accuracy for
classification and 4.4 MSE for regression, when compared to comparable BO
approach.


title:
U-statistics of growing order and sub-Gaussian mean estimators with
  sharp constants

abstract:
  This paper addresses the following question: given a sample of i.i.d. random
variables with finite variance, can one construct an estimator of the unknown
mean that performs nearly as well as if the data were normally distributed? One
of the most popular examples achieving this goal is the median of means
estimator. However, it is inefficient in a sense that the constants in the
resulting bounds are suboptimal. We show that a permutation-invariant
modification of the median of means estimator admits deviation guarantees that
are sharp up to $1+o(1)$ factor if the underlying distribution possesses $3+p$
moments for some $p>0$ and is absolutely continuous with respect to the
Lebesgue measure. This result yields potential improvements for a variety of
algorithms that rely on the median of means estimator as a building block. At
the core of our argument is a new deviation inequality for the U-statistics of
order that is allowed to grow with the sample size, a result that could be of
independent interest. Finally, we demonstrate that a hybrid of the median of
means and Catoni's estimator is capable of achieving sub-Gaussian deviation
guarantees with nearly optimal constants assuming just the existence of the
second moment.


title: Metrical theorems for unconventional height functions

abstract:
  In this paper, we consider the simultaneous approximation of real points by
rational points with the error of approximation given by the functions of
`non-standard' heights. We prove analogues of Khintchine and
Jarn\'ik-Besicovitch theorems for this setting, thus answering some questions
raised by Fishman and Simmons (2017).


title: First is Better Than Last for Training Data Influence

abstract:
  The ability to identify influential training examples enables us to debug
training data and explain model behavior. Existing techniques are based on the
flow of influence through the model parameters. For large models in NLP
applications, it is often computationally infeasible to study this flow through
all model parameters, therefore techniques usually pick the last layer of
weights. Our first observation is that for classification problems, the last
layer is reductive and does not encode sufficient input level information.
Deleting influential examples, according to this measure, typically does not
change the model's behavior much. We propose a technique called TracIn-WE that
modifies a method called TracIn to operate on the word embedding layer instead
of the last layer. This could potentially have the opposite concern, that the
word embedding layer does not encode sufficient high level information.
However, we find that gradients (unlike embeddings) do not suffer from this,
possibly because they chain through higher layers. We show that TracIn-WE
significantly outperforms other data influence methods applied on the last
layer by 4-10 times on the case deletion evaluation on three language
classification tasks. In addition, TracIn-WE can produce scores not just at the
training data level, but at the word training data level, a further aid in
debugging.


title: Benchmarking the Planar Honeycomb Code

abstract:
  We improve the planar honeycomb code by describing boundaries that need no
additional physical connectivity, and by optimizing the shape of the qubit
patch. We then benchmark the code using Monte Carlo sampling to estimate
logical error rates and derive metrics including thresholds, lambdas, and
teraquop qubit counts. We determine that the planar honeycomb code can create a
logical qubit with one-in-a-trillion logical error rates using 7000 physical
qubits at a 0.1% gate-level error rate (or 900 physical qubits given native
two-qubit parity measurements). Our results cement the honeycomb code as a
promising candidate for two-dimensional qubit architectures with sparse
connectivity.


title: Parameter Estimation with Reluctant Quantum Walks

abstract:
  The parametric maximum likelihood estimation problem is addressed in the
context of quantum walk theory for quantum walks on the line, or on a finite
ring. Two different coin reshuffling actions are presented, with the real
parameter theta to be estimated being identified either with the angular
argument of an orthogonal reshuffling matrix, or the phase of a unitary
reshuffling matrix, acting in a 2 state coin space, respectively. We provide
analytic results for the probability distribution for a quantum walker to be
displaced by d units from its initial position after k steps. For k large, we
show that the likelihood is sharply peaked at a displacement determined by the
ratio d/k, which is correlated with the reshuffling parameter theta. We suggest
that this reluctant walker behaviour provides the framework for maximum
likelihood estimation analysis, allowing for robust parameter estimation of
theta via measurement of the walker reluctance index r = d/k.


title: CAISE: Conversational Agent for Image Search and Editing

abstract:
  Demand for image editing has been increasing as users' desire for expression
is also increasing. However, for most users, image editing tools are not easy
to use since the tools require certain expertise in photo effects and have
complex interfaces. Hence, users might need someone to help edit their images,
but having a personal dedicated human assistant for every user is impossible to
scale. For that reason, an automated assistant system for image editing is
desirable. Additionally, users want more image sources for diverse image
editing works, and integrating an image search functionality into the editing
tool is a potential remedy for this demand. Thus, we propose a dataset of an
automated Conversational Agent for Image Search and Editing (CAISE). To our
knowledge, this is the first dataset that provides conversational image search
and editing annotations, where the agent holds a grounded conversation with
users and helps them to search and edit images according to their requests. To
build such a system, we first collect image search and editing conversations
between pairs of annotators. The assistant-annotators are equipped with a
customized image search and editing tool to address the requests from the
user-annotators. The functions that the assistant-annotators conduct with the
tool are recorded as executable commands, allowing the trained system to be
useful for real-world application execution. We also introduce a
generator-extractor baseline model for this task, which can adaptively select
the source of the next token (i.e., from the vocabulary or from textual/visual
contexts) for the executable command. This serves as a strong starting point
while still leaving a large human-machine performance gap for useful future
work. Our code and dataset are publicly available at:
https://github.com/hyounghk/CAISE


title:
Selfsimilar free additive processes and freely selfdecomposable
  distributions

abstract:
  In the paper by Fan\cite{F06}, he introduced the marginal selfsimilarity of
non-commutative stochastic processes and proved the marginal distributions of
selfsimilar processes with freely independent increments are freely
selfdecomposable. In this paper, we, first, introduce a new definition of
selfsimilarity via linear combinations of non-commutative stochastic processes
and prove the converse of Fan's result, to complete the relationship between
selfsimilar free additive processes and freely selfdecomposable distributions.
Furthermore, we construct stochastic integrals with respect to free additive
processes for constructing the background driving free L{\'e}vy processes of
freely selfdecomposable distributions. A relation in terms of their free
cumulant transforms is also given and several examples are also discussed.


title:
A SUBLIME 3D Model for Cometary Coma Emission: the Hypervolatile-Rich
  Comet C/2016 R2 (PanSTARRS)

abstract:
  The coma of comet C/2016 R2 (PanSTARRS) is one of the most chemically
peculiar ever observed, in particular due to its extremely high CO/H2O and
N2+/H2O ratios}, and unusual trace volatile abundances. However, the complex
shape of its CO emission lines, as well as uncertainties in the coma structure
and excitation, has lead to ambiguities in the total CO production rate. We
performed high resolution, spatially, spectrally and temporally resolved CO
observations using the James Clerk Maxwell Telescope (JCMT) and Submillimeter
Array (SMA) to elucidate the outgassing behaviour of C/2016 R2. Results are
analyzed using a new, time-dependent, three dimensional radiative transfer code
(SUBLIME), incorporating for the first time, accurate state-to-state
collisional rate coefficients for the CO--CO system. The total CO production
rate was found to be in the range $(3.8-7.6)\times10^{28}$ s$^{-1}$ between
2018-01-13 and 2018-02-01, with a mean value of $(5.3\pm0.6)\times10^{28}$
s$^{-1}$ at r_H = 2.8-2.9 au. The emission is concentrated in a near-sunward
jet, with an outflow velocity $0.51\pm0.01$ km/s, compared to $0.25\pm0.01$
km/s in the ambient (and night-side) coma. Evidence was also found for an
extended source of CO emission, possibly due to icy grain sublimation around
$1.2\times10^5$ km from the nucleus. Based on the coma molecular abundances, we
propose that the nucleus ices of C/2016 R2 can be divided into a rapidly
sublimating apolar phase, rich in CO, CO2, N2 and CH3OH, and a predominantly
frozen (or less abundant), polar phase containing more H2O, CH4, H2CO and HCN.


title:
Robust Federated Learning with Connectivity Failures: A
  Semi-Decentralized Framework with Collaborative Relaying

abstract:
  Intermittent client connectivity is one of the major challenges in
centralized federated edge learning frameworks. Intermittently failing uplinks
to the central parameter server (PS) can induce a large generalization gap in
performance especially when the data distribution among the clients exhibits
heterogeneity. In this work, to mitigate communication blockages between
clients and the central PS, we introduce the concept of knowledge relaying
wherein the successfully participating clients collaborate in relaying their
neighbors' local updates to a central parameter server (PS) in order to boost
the participation of clients with intermittently failing connectivity. We
propose a collaborative relaying based semi-decentralized federated edge
learning framework where at every communication round each client first
computes a local consensus of the updates from its neighboring clients and
eventually transmits a weighted average of its own update and those of its
neighbors to the PS. We appropriately optimize these averaging weights to
reduce the variance of the global update at the PS while ensuring that the
global update is unbiased, consequently improving the convergence rate.
Finally, by conducting experiments on CIFAR-10 dataset we validate our
theoretical results and demonstrate that our proposed scheme is superior to
Federated averaging benchmark especially when data distribution among clients
is non-iid.


title:
Ultrasensitive, Ultrafast and Gate-Tunable Two-Dimensional
  Photodetectors in Ternary Rhombohedral ZnIn2S4 for Optical Neural Networks

abstract:
  The demand for high-performance semiconductors in electronics and
optoelectronics has prompted the expansion of low-dimensional materials
research to ternary compounds. However, photodetectors based on 2D ternary
materials usually suffer from large dark currents and slow response, which
means increased power consumption and reduced performance. Here we report a
systematic study of the optoelectronic properties of well-characterized
rhombohedral ZnIn2S4 (R-ZIS) nanosheets which exhibit an extremely low dark
current (7 pA at 5 V bias). The superior performance represented by a series of
parameters surpasses most 2D counterparts. The ultrahigh specific detectivity
(1.8 x 10^14 Jones), comparably short response time ({\tau}_rise = 222 {\mu}s,
{\tau}_decay = 158 {\mu}s) and compatibility with high-frequency operation
(1000 Hz) are particularly prominent. Moreover, a gate-tunable characteristic
is observed, which is attributed to photogating and improves the photoresponse
by two orders of magnitude. Gating technique can effectively modulate the
photocurrent-generation mechanism from photoconductive effect to dominant
photogating. The combination of ultrahigh sensitivity, ultrafast response and
high gate tunability makes the R-ZIS phototransistor an ideal device for
low-energy-consumption and high-frequency optoelectronic applications, which is
further demonstrated by its excellent performance in optical neural networks
and promising potential in optical deep learning and computing.


title: $H_0$ tension or $M$ overestimation?

abstract:
  There is a strong discrepancy between the value of the Hubble parameter
$H_0^P$ obtained from large scale observations such as the Planck mission, and
the small scale value $H_0^R$, obtained from low redshift supernovae (SNe). The
value of the absolute magnitude $M^{hom}$ used as prior in analyzing
observational data is obtained from low-redshift SNe, assuming a homogeneous
Universe, but the distance of the anchors used to calibrate the SNe to obtain
$M$ would be affected by a local inhomogeneity, making it inconsistent to test
the Copernican principle using $M^{hom}$, since $M$ estimation itself is
affected by local inhomogeneities.
  We perform an analysis of the luminosity distance of low redshift SNe, using
different values of $M$, $\{M^P,M^R\}$, corresponding to different values of
$H_0$, $\{H_0^P,H_0^R\}$, obtained from the model independent consistency
relation between $H_0$ and $M$ which can be derived from the definition of the
distance modulus. We find that the value of $M$ can strongly affect the
evidence of a local inhomogeneity. We analyze data from the Pantheon catalog,
finding no significant statistical evidence of a local inhomogeneity using the
parameters $\{M^R,H_0^R\}$, confirming previous studies, while with
$\{M^P,H_0^P\}$ we find evidence of a small local void, which cause an
overestimation of $M^R$ with respect to $M^P$.
  An inhomogeneous model with the parameters $\{M^P,H_0^P\}$ fits the data
better then a homogeneous model with $\{M^R,H_0^R\}$, resolving the apparent
$H_0$ tension. Using $\{M^P,H_0^P\}$, we obtain evidence of a local
inhomogeneity with a density contrast $-0.140 \pm 0.042 $, extending up to a
redshift of $z_v =0.056 \pm 0.0002$, in good agreement with recent results of
galaxy catalogs analysis.


title: Attainability and Optimality: The Equalized Odds Fairness Revisited

abstract:
  Fairness of machine learning algorithms has been of increasing interest. In
order to suppress or eliminate discrimination in prediction, various notions as
well as approaches have been proposed to impose fairness. Given a notion of
fairness, an essential problem is then whether or not it can always be
attained, even if with an unlimited amount of data. This issue is, however, not
well addressed yet. In this paper, focusing on the Equalized Odds notion of
fairness, we consider the attainability of this criterion and, furthermore, if
it is attainable, the optimality of the prediction performance under various
settings. In particular, for prediction performed by a deterministic function
of input features, we give conditions under which Equalized Odds can hold true;
if the stochastic prediction is acceptable, we show that under mild
assumptions, fair predictors can always be derived. For classification, we
further prove that compared to enforcing fairness by post-processing, one can
always benefit from exploiting all available features during training and get
potentially better prediction performance while remaining fair. Moreover, while
stochastic prediction can attain Equalized Odds with theoretical guarantees, we
also discuss its limitation and potential negative social impacts.


title:
Schatten classes and commutator in the two weight setting, I. Hilbert
  transform

abstract:
  We characterize the Hilbert--Schmidt class membership of commutator with the
Hilbert transform in the two weight setting. The characterization depends upon
the symbol of the commutator being in a new weighted Besov space. This follows
from a Schatten class $S_p$ result for dyadic paraproducts, where $1< p <
\infty $. We discuss the difficulties in extending the dyadic result to the
full range of Schatten classes for the Hilbert transform.


title: Learning Multi-Object Dynamics with Compositional Neural Radiance Fields

abstract:
  We present a method to learn compositional predictive models from image
observations based on implicit object encoders, Neural Radiance Fields (NeRFs),
and graph neural networks. A central question in learning dynamic models from
sensor observations is on which representations predictions should be
performed. NeRFs have become a popular choice for representing scenes due to
their strong 3D prior. However, most NeRF approaches are trained on a single
scene, representing the whole scene with a global model, making generalization
to novel scenes, containing different numbers of objects, challenging. Instead,
we present a compositional, object-centric auto-encoder framework that maps
multiple views of the scene to a \emph{set} of latent vectors representing each
object separately. The latent vectors parameterize individual NeRF models from
which the scene can be reconstructed and rendered from novel viewpoints. We
train a graph neural network dynamics model in the latent space to achieve
compositionality for dynamics prediction. A key feature of our approach is that
the learned 3D information of the scene through the NeRF model enables us to
incorporate structural priors in learning the dynamics models, making long-term
predictions more stable. The model can further be used to synthesize new scenes
from individual object observations. For planning, we utilize RRTs in the
learned latent space, where we can exploit our model and the implicit object
encoder to make sampling the latent space informative and more efficient. In
the experiments, we show that the model outperforms several baselines on a
pushing task containing many objects. Video:
https://dannydriess.github.io/compnerfdyn/


title: An analogy of Jacobi's formula and its applications

abstract:
  We give an analogy of Jacobi's formula, which relates the hypergeometric
function with parameters $(1/4,1/4,1)$ and theta constants. By using this
analogy and twice formulas of theta constants, we obtain a transformation
formula for this hypergeometric function. As its application, we express the
limit of a pair of sequences defined by a mean iteration by this hypergeometric
function.


title: Complexity Results on Untangling Planar Rectilinear Red-Blue Matchings

abstract:
  Given a rectilinear matching between n red points and n blue points in the
plane, we consider the problem of obtaining a crossing-free matching through
flip operations that replace two crossing segments by two non-crossing ones. We
first show that (i) it is NP-hard to alpha-approximate the shortest flip
sequence, for any constant alpha. Second, we show that when the red points are
colinear, (ii) given a matching, a flip sequence of length at most n(n-1)/2
always exists and (iii) the number of flips in any sequence never exceeds
(n(n-1)/2) (n+4)/6. Finally, we present (iv) a lower bounding flip sequence
with roughly 1.5 n(n-1)/2 flips, which disproves the conjecture that n(n-1)/2,
reached in the convex case, is the maximum. The last three results, based on
novel analyses, improve the constants of state-of-the-art bounds.


title:
Reduced bandwidth: a qualitative strengthening of twin-width in
  minor-closed classes (and beyond)

abstract:
  In a reduction sequence of a graph, vertices are successively identified
until the graph has one vertex. At each step, when identifying $u$ and $v$,
each edge incident to exactly one of $u$ and $v$ is coloured red. Bonnet, Kim,
Thomass\'e and Watrigant [J. ACM 2022] defined the twin-width of a graph $G$ to
be the minimum integer $k$ such that there is a reduction sequence of $G$ in
which every red graph has maximum degree at most $k$. For any graph parameter
$f$, we define the reduced $f$ of a graph $G$ to be the minimum integer $k$
such that there is a reduction sequence of $G$ in which every red graph has $f$
at most $k$. Our focus is on graph classes with bounded reduced bandwidth,
which implies and is stronger than bounded twin-width (reduced maximum degree).
We show that every proper minor-closed class has bounded reduced bandwidth,
which is qualitatively stronger than an analogous result of Bonnet et al.\ for
bounded twin-width. In many instances, we also make quantitative improvements.
For example, all previous upper bounds on the twin-width of planar graphs were
at least $2^{1000}$. We show that planar graphs have reduced bandwidth at most
$466$ and twin-width at most $583$. Our bounds for graphs of Euler genus
$\gamma$ are $O(\gamma)$. Lastly, we show that fixed powers of graphs in a
proper minor-closed class have bounded reduced bandwidth (irrespective of the
degree of the vertices). In particular, we show that map graphs of Euler genus
$\gamma$ have reduced bandwidth $O(\gamma^4)$. Lastly, we separate twin-width
and reduced bandwidth by showing that any infinite class of expanders excluding
a fixed complete bipartite subgraph has unbounded reduced bandwidth, while
there are bounded-degree expanders with twin-width at most 6.


title:
Nanodiamonds based optical-fiber quantum probe for magnetic field and
  biological sensing

abstract:
  Owing to the unique electronic spin properties, the nitrogen-vacancy (NV)
centers hosted in diamond have emerged as a powerful quantum sensor for various
physical parameters and biological species. In this work, a miniature
optical-fiber quantum probe, configured by chemically-modifying nanodiamonds NV
centers on the surface of a cone fiber tip, is developed. Based on
continue-wave optically detected magnetic resonance method and lock-in
amplifying technique, it is found that the sensing performance of the probe can
be engineered by varying the nanodiamonds dispersion concentration and
modification duration in the chemical modification process. Combined with a
pair of magnetic flux concentrators, the magnetic field detection sensitivity
of the probe is significantly enhanced to 0.57 nT/Hz1/2 @ 1Hz, a new record
among the fiber magnetometers based on nanodiamonds NV. Taking Gd3+ as the
demo, the capability of the probe in paramagnetic species detection is also
demonstrated experimentally. Our work provides a new approach to develop NV
center as quantum probe featuring high integration, miniature size,
multifunction, and high sensitivity, etc.


title:
Robust Transmission Design for RIS-assisted Secure Multiuser
  Communication Systems in the Presence of Hardware Impairments

abstract:
  This paper investigates reconfigurable intelligent surface (RIS)-assisted
secure multiuser communication systems subject to hardware impairments (HIs).
We jointly optimize the beamforming vectors at the base station (BS) and the
phase shifts of the reflecting elements at the RIS so as to maximize the
weighted minimum secrecy rate (WMSR), subject to both transmission power
constraints at the BS and unit-modulus constraints at the RIS. To address the
formulated optimization problem, we first decouple it into two tractable
subproblems and then use the block coordinate descent (BCD) method to
alternately optimize the subproblems. Two different methods are proposed to
solve the two obtained subproblems. The first method transforms each subproblem
into a second order cone programming (SOCP) problem, which can be directly
solved using CVX. The second method leverages the Minorization- Maximization
(MM) algorithm. Specifically, we first derive a concave approximation function,
which is a lower bound of the original objective function, and then the two
subproblems are transformed into two simple surrogate problems with closedform
solutions. Simulation results verify the performance gains of the proposed
robust transmission method over existing nonrobust designs. In addition, the MM
algorithm is shown to have much lower complexity than the SOCP-based algorithm.


title: Deploying Static Analysis

abstract:
  Static source code analysis is a powerful tool for finding and fixing bugs
when deployed properly; it is, however, all too easy to deploy it in a way that
looks good superficially, but which misses important defects, shows many false
positives, and brings the tool into disrepute. This article is a guide to the
process of deploying a static analysis tool in a large organization while
avoiding the worst organizational and technical pitfalls. My main point is the
importance of concentrating on the main goal of getting bugs fixed, against all
the competing lesser goals which will arise during the process.


title:
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose
  Your Model, Not Your Loss Function

abstract:
  In a world blessed with a great diversity of loss functions, we argue that
that choice between them is not a matter of taste or pragmatics, but of model.
Probabilistic depencency graphs (PDGs) are probabilistic models that come
equipped with a measure of "inconsistency". We prove that many standard loss
functions arise as the inconsistency of a natural PDG describing the
appropriate scenario, and use the same approach to justify a well-known
connection between regularizers and priors. We also show that the PDG
inconsistency captures a large class of statistical divergences, and detail
benefits of thinking of them in this way, including an intuitive visual
language for deriving inequalities between them. In variational inference, we
find that the ELBO, a somewhat opaque objective for latent variable models, and
variants of it arise for free out of uncontroversial modeling assumptions -- as
do simple graphical proofs of their corresponding bounds. Finally, we observe
that inconsistency becomes the log partition function (free energy) in the
setting where PDGs are factor graphs.


title: Thermodynamics for Nonlinearity under Hidden Structure Information

abstract:
  For substitutional alloys, typically refered to as classical discrete systems
under constant composition, we theoretically examine the role of hidden
structure information on evolution of nonlinearity (i.e., correspondence
between a set of potential energy surface and that of many-body interaction) in
canonical ensemble, in terms of the stochastic thermodynamics. When
thermodynamic properties for a given paritial system is controlled by those for
e.g., bulk as a hidden structure information, we derive that change in
nonlinearity on statistical manifold through any transition is identical to the
sum of negative bath entropy change, fluctuation of system entropy change and
fluctuation of stochastic mutual information change between the system
interested and hidden system: We successfully establish basic formulation of
how geometric aspect of nonlinearity evolves under feedback from hidden system
information, which especially provides deeper insight into the nonlinearity for
surface and interface alloys controlled under bulk thermodynamics.


title: Some Stylometric Remarks on Ovid's Heroides and the Epistula Sapphus

abstract:
  This article aims to contribute to two well-worn areas of debate in classical
Latin philology, relating to Ovid's Heroides. The first is the question of the
authenticity (and, to a lesser extent the correct position) of the letter
placed fifteenth by almost every editor -- the so-called Epistula Sapphus
(henceforth ES). The secondary question, although perhaps now less fervently
debated, is the authenticity of the 'Double Heroides', placed by those who
accept them as letters 16-21. I employ a variety of methods drawn from the
domain of computational stylometry to consider the poetics and the
lexico-grammatical features of these elegiac poems in the broader context of a
corpus of 'shorter' (from 20 to 546 lines) elegiac works from five authors (266
poems in all) comprising more or less all of the non-fragmentary classical
corpus. Based on a variety of techniques, every measure gives clear indication
that the poetic style of the Heroides is Ovidian, but distinctive; they can be
accurately isolated from Ovid more broadly. The Single and Double Heroides
split into two clear groups, with the ES grouped consistently with the single
letters. Furthermore, by comparing the style of the letters with the 'early'
(although there are complications in this label) works of the Amores and the
late works of the Ex Ponto, the evidence supports sequential composition --
meaning that the ES is correctly placed -- and, further, supports the growing
consensus that the double letters were composed significantly later, in exile.


title: Using calibrator to improve robustness in Machine Reading Comprehension

abstract:
  Machine Reading Comprehension(MRC) has achieved a remarkable result since
some powerful models, such as BERT, are proposed. However, these models are not
robust enough and vulnerable to adversarial input perturbation and
generalization examples. Some works tried to improve the performance on
specific types of data by adding some related examples into training data while
it leads to degradation on the original dataset, because the shift of data
distribution makes the answer ranking based on the softmax probability of model
unreliable. In this paper, we propose a method to improve the robustness by
using a calibrator as the post-hoc reranker, which is implemented based on
XGBoost model. The calibrator combines both manual features and representation
learning features to rerank candidate results. Experimental results on
adversarial datasets show that our model can achieve performance improvement by
more than 10\% and also make improvement on the original and generalization
datasets.


title:
Predicting binding motifs of complex adsorbates using machine learning
  with a physics-inspired graph representation

abstract:
  Computational screening in heterogeneous catalysis relies increasingly on
machine learning models for predicting key input parameters due to the high
cost of computing these directly using first-principles methods. This becomes
especially relevant when considering complex materials spaces, e.g. alloys, or
complex reaction mechanisms with adsorbates that may exhibit bi- or
higher-dentate adsorption motifs. Here we present a data-efficient approach to
the prediction of binding motifs and associated adsorption enthalpies of
complex adsorbates at transition metals (TMs) and their alloys based on a
customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian Process
Regression. The model shows good predictive performance, not only for the
elemental TMs on which it was trained, but also for an alloy based on these
TMs. Furthermore, incorporation of minimal new training data allows for
predicting an out-of-domain TM. We believe the model may be useful in active
learning approaches, for which we present an ensemble uncertainty estimation
approach.


title:
Joint Program Partitioning and Resource Allocation for Completion Time
  Minimization in Multi-MEC Systems

abstract:
  This paper considers a practical mobile edge computing (MEC) system, where
edge server does not pre-install the program required to perform user offloaded
computing tasks. A partial program offloading (PPO) scheme is proposed, which
can divide a user program into two parts, where the first part is executed by
the user itself and the second part is transferred to an edge server for remote
execution. However, the execution of the latter part requires the results of
the previous part (called intermediate result) as the input. We aim to minimize
the overall time consumption of a multi-server MEC system to complete all user
offloaded tasks. It is modeled as a mixed integer nonlinear programming (MINLP)
problem which considers user-and-server association, program partitioning, and
communication resource allocation in a joint manner. An effective algorithm is
developed to solve the problem by exploiting its structural features. First,
the task completion time of a single server is minimized given the computing
workload and available resource. Then, the working time of the edge servers are
balanced by updating user-and-server association and communication resource
allocation. Numerical results show that significant performance improvement can
be achieved by the proposed scheme.


title:
CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point
  Cloud

abstract:
  At present, the anchor-based or anchor-free models that use LiDAR point
clouds for 3D object detection use the center assigner strategy to infer the 3D
bounding boxes. However, in a real world scene, the LiDAR can only acquire a
limited object surface point clouds, but the center point of the object does
not exist. Obtaining the object by aggregating the incomplete surface point
clouds will bring a loss of accuracy in direction and dimension estimation. To
address this problem, we propose a corner-guided anchor-free single-stage 3D
object detection model (CG-SSD ).Firstly, 3D sparse convolution backbone
network composed of residual layers and sub-manifold sparse convolutional
layers are used to construct bird's eye view (BEV) features for further deeper
feature mining by a lite U-shaped network; Secondly, a novel corner-guided
auxiliary module (CGAM) is proposed to incorporate corner supervision signals
into the neural network. CGAM is explicitly designed and trained to detect
partially visible and invisible corners to obtains a more accurate object
feature representation, especially for small or partial occluded objects;
Finally, the deep features from both the backbone networks and CGAM module are
concatenated and fed into the head module to predict the classification and 3D
bounding boxes of the objects in the scene. The experiments demonstrate CG-SSD
achieves the state-of-art performance on the ONCE benchmark for supervised 3D
object detection using single frame point cloud data, with 62.77%mAP.
Additionally, the experiments on ONCE and Waymo Open Dataset show that CGAM can
be extended to most anchor-based models which use the BEV feature to detect
objects, as a plug-in and bring +1.17%-+14.27%AP improvement.


title:
From the asymmetric simple exclusion processes to the stationary
  measures of the KPZ fixed point on an interval

abstract:
  Barraquand and Le Doussal introduced a family of stationary measures for the
KPZ fixed point on an interval with Neumann boundary conditions, and predicted
that they arise as scaling limit of stationary measures of all models in the
KPZ class on an interval. In this paper we provide a rigorous analysis of an
example within the scope of this prediction: the stationary measures for the
KPZ fixed point on an interval arise as the scaling limits of the height
increment processes for the open asymmetric simple exclusion process in the
steady state, with parameters changing appropriately as the size of the system
tends to infinity.


title:
New procedure to estimate plasma parameters through the q-Weibull
  distribution by using a Langmuir probe in a cold plasma

abstract:
  We describe a procedure to obtain the plasma parameters from the I-V Langmuir
curve by using the Druyvesteyn equation. We propose to include two new
parameters, $q$ and $r$, to the usual plasma parameters: plasma potential
($V_p$), floating potential ($V_f$), electron density ($n$), and electron
temperature ($T$). These new parameters can be particularly useful to represent
non-Maxwellian distributions. The procedure is based on the fit of the I-V
Langmuir curve with the $q$-Weibull distribution function, and is motivated by
recent works which use the $q$-exponential distribution function derived from
Tsallis statistics. We obtain the usual plasma parameters employing three
techniques: the numerical differentiation using Savitzky Golay (SG) filters,
the $q$-exponential distribution function, and the $q$-Weibull distribution
function. We explain the limitations of the $q$-exponential function, where the
experimental data $V>V_p$ needs to be trimmed beforehand, and this results in a
lower accuracy compared to the numerical differentiation with SG. To overcome
this difficulty, the $q$-Weibull function is introduced as a natural
generalization to the $q$-exponential distribution, and it has greater
flexibility in order to represent the concavity change around $V_p$. We apply
this procedure to analyze the measurements corresponding to a nitrogen $N_2$
cold plasma obtained by using a single Langmuir probe located at different
heights from the cathode. We show that the $q$ parameter has a very stable
numerical value with the height. This work may contribute to clarify some
advantages and limitations of the use of non-extensive statistics in plasma
diagnostics, but the physical interpretation of the non-extensive parameters in
plasma physics remains not fully clarified, and requires further research.


title: No-Regret Learning in Games is Turing Complete

abstract:
  Games are natural models for multi-agent machine learning settings, such as
generative adversarial networks (GANs). The desirable outcomes from algorithmic
interactions in these games are encoded as game theoretic equilibrium concepts,
e.g. Nash and coarse correlated equilibria. As directly computing an
equilibrium is typically impractical, one often aims to design learning
algorithms that iteratively converge to equilibria. A growing body of negative
results casts doubt on this goal, from non-convergence to chaotic and even
arbitrary behaviour. In this paper we add a strong negative result to this
list: learning in games is Turing complete. Specifically, we prove Turing
completeness of the replicator dynamic on matrix games, one of the simplest
possible settings. Our results imply the undecicability of reachability
problems for learning algorithms in games, a special case of which is
determining equilibrium convergence.


title: Position and momentum operators for a moving particle in bulk

abstract:
  In this paper we explore how to describe a bulk moving particle in the dual
conformal field theories (CFTs). One aspect of this problem is to construct the
dual state of the moving particle. On the other hand one should find the
corresponding operators associated with the particle. The dynamics of the
particle, i.e., the geodesic equation, can be formulated as a Hamiltonian
system with canonical variables. The achievements of our paper are to construct
the dual CFT states and the operators corresponding to the canonical variables.
The expectation values of the operators give the expected solutions of the
geodesic line, and the quantum commutators reduce to the classical Poisson
brackets to leading order in the bulk gravitational coupling. Our work provides
a framework to understand the geodesic equation, that is gravitational
attraction, in the dual CFTs.


title: Real Non-Hermitian Energy Spectra Without Any Symmetry

abstract:
  Non-Hermitian models with real eigenenergies are highly desirable for their
stability. Yet, most of the currently known ones are constrained by symmetries
such as PT-symmetry, which is incompatible with realizing some of the most
exotic non-Hermitian phenomena. In this work, we investigate how the
non-Hermitian skin effect provides an alternative route towards enforcing real
spectra and system stability. We showcase, for different classes of energy
dispersions, various ansatz models that possess large parameter space regions
with real spectra, despite not having any obvious symmetry. These minimal local
models can be quickly implemented in non-reciprocal experimental setups such as
electrical circuits with operational amplifiers.


title:
Non-free Minimal rational curves and Wobbly type locus on Fano manifolds
  of Picard number one

abstract:
  Let $X$ be a Fano manifold of Picard number one. In this article, I will
introduce a notion of wobbly type points in $X$ and show that locus of wobbly
type points forms a divisor under certain hypothesis. In fact we will show that
wobbly type locus is completely invariant for any surjective endomorphism of
$X$. For the case of moduli space of stable vector bundles on curves, we will
show that the wobbly type locus and the usual wobbly locus coincide which gives
a proof of Drinfeld's conjecture which claims that wobbly locus is pure of
co-dimension one.
  Finally we will show that any endomorphism of the moduli space of stable
vector bundles is \'{e}tale outside the wobbly locus which gives an alternative
proof of Hwang-Ramanan theorem on endomorphisms of the mmoduli space.


title: Characterizing Spectral Properties of Bridge

abstract:
  Bridge graphs are special type of graphs which are constructed by connecting
identical connected graphs with path graphs. We discuss different type of
bridge graphs $B_{n\times l}^{m\times k}$ in this paper. In particular, we
discuss the following: complete-type bridge graphs, star-type bridge graphs,
and full binary tree bridge graphs. We also bound the second eigenvalues of the
graph Laplacian of these graphs using methods from Spectral Graph Theory. In
general, we prove that for general bridge graphs, $B_{n\times l}^2$, the second
eigenvalue of the graph Laplacian should between $0$ and $2$, inclusive. At the
end, we talk about the future work about infinite bridge graphs. We create
definitions and found the related theorems to support our future work about
infinite bridge graphs.


title:
Giant intrinsic rectification and nonlinear Hall effect under
  time-reversal symmetry in a trigonal superconductor

abstract:
  Nonreciprocal or nonlinear responses in symmetry-broken systems are powerful
probes of emergent properties in quantum materials, including superconductors,
magnets, and topological materials. Recently, vortex matter has been recognized
as a key ingredient of giant nonlinear responses in superconductors with broken
inversion symmetry. However, nonlinear effects have been probed as excess
voltage only under broken time-reversal symmetry. In this study, we report
intrinsic rectification and nonlinear anomalous Hall effect under time-reversal
symmetry in the noncentrosymmetric trigonal superconductor PbTaSe2. The
magnitude of anomalous nonlinear transport is two orders of magnitude larger
than those in the normal state, and the directional dependence of nonlinear
signals are fully consistent with crystal symmetry. The enhanced nonlinearity
is semiquantitatively explained by the asymmetric Hall effect of
vortex-antivortex string pairs in noncentrosymmetric systems. This study
enriches the literature on nonlinear phenomena by revealing a novel aspect of
quantum transport in noncentrosymmetric superconductors.


title:
A Unified Framework for Campaign Performance Forecasting in Online
  Display Advertising

abstract:
  Advertisers usually enjoy the flexibility to choose criteria like target
audience, geographic area and bid price when planning an campaign for online
display advertising, while they lack forecast information on campaign
performance to optimize delivery strategies in advance, resulting in a waste of
labour and budget for feedback adjustments. In this paper, we aim to forecast
key performance indicators for new campaigns given any certain criteria.
Interpretable and accurate results could enable advertisers to manage and
optimize their campaign criteria. There are several challenges for this very
task. First, platforms usually offer advertisers various criteria when they
plan an advertising campaign, it is difficult to estimate campaign performance
unifiedly because of the great difference among bidding types. Furthermore,
complex strategies applied in bidding system bring great fluctuation on
campaign performance, making estimation accuracy an extremely tough problem. To
address above challenges, we propose a novel Campaign Performance Forecasting
framework, which firstly reproduces campaign performance on historical logs
under various bidding types with a unified replay algorithm, in which essential
auction processes like match and rank are replayed, ensuring the
interpretability on forecast results. Then, we innovatively introduce a
multi-task learning method to calibrate the deviation of estimation brought by
hard-to-reproduce bidding strategies in replay. The method captures mixture
calibration patterns among related forecast indicators to map the estimated
results to the true ones, improving both accuracy and efficiency significantly.
Experiment results on a dataset from Taobao.com demonstrate that the proposed
framework significantly outperforms other baselines by a large margin, and an
online A/B test verifies its effectiveness in the real world.


title: New Benchmark for Household Garbage Image Recognition

abstract:
  Household garbage images are usually faced with complex backgrounds, variable
illuminations, diverse angles, and changeable shapes, which bring a great
difficulty in garbage image classification. Due to the ability to discover
problem-specific features, deep learning and especially convolutional neural
networks (CNNs) have been successfully and widely used for image representation
learning. However, available and stable household garbage datasets are
insufficient, which seriously limits the development of research and
application. Besides, the state of the art in the field of garbage image
classification is not entirely clear. To solve this problem, in this study, we
built a new open benchmark dataset for household garbage image classification
by simulating different lightings, backgrounds, angles, and shapes. This
dataset is named 30 Classes of Household Garbage Images (HGI-30), which
contains 18,000 images of 30 household garbage classes. The publicly available
HGI-30 dataset allows researchers to develop accurate and robust methods for
household garbage recognition. We also conducted experiments and performance
analysis of the state-of-the-art deep CNN methods on HGI-30, which serves as
baseline results on this benchmark.


title:
Positive Trigonometric Polynomials on the Stability of Spatially
  Interconnected Systems

abstract:
  This paper is devoted to the stability analysis of spatially interconnected
systems (SISs) via the sum-of-squares (SOS) decomposition of positive
trigonometric polynomials. For each spatial direction of SISs, three types of
interconnected structures are considered. Inspired by the idea of rational
parameterization and robust stabilizability function, necessary and sufficient
conditions are derived for establishing the stability of SISs with two
different combined topologies respectively. For these results, the primary
issue concerns the global or local positivity of trigonometric polynomials. SOS
decomposition and generalized trace parameterization of positive trigonometric
polynomials are utilized so that the addressed problems can be quantified by
two semidefinite programs (SDPs). The proposed methods are applicable to all
possible interconnected structures due to the assumption of spatial
reversibility. Numerical examples are given to illustrate the efficiency of the
derived theoretical results.


title:
On Nash-Stackelberg-Nash Games under Decision-Dependent Uncertainties:
  Model and Equilibrium

abstract:
  In this paper, we discuss a class of two-stage hierarchical games with
multiple leaders and followers, which is called Nash-Stackelberg-Nash (N-S-N)
games. Particularly, we consider N-S-N games under decision-dependent
uncertainties (DDUs). DDUs refer to the uncertainties that are affected by the
strategies of decision-makers and have been rarely addressed in game
equilibrium analysis. In this paper, we first formulate the N-S-N games with
DDUs of complete ignorance, where the interactions between the players and DDUs
are characterized by uncertainty sets that depend parametrically on the
players' strategies. Then, a rigorous definition for the equilibrium of the
game is established by consolidating generalized Nash equilibrium and
Pareto-Nash equilibrium. Afterward, we prove the existence of the equilibrium
of N-S-N games under DDUs by applying Kakutani's fixed-point theorem. Finally,
an illustrative example is provided to show the impact of DDUs on the
equilibrium of N-S-N games.


title: Modified-gravity theories with nondynamical background fields

abstract:
  We study the dynamics of a modified-gravity theory, which is supplemented by
an extended Gibbons-Hawking-York boundary term and incorporates diffeomorphism
violation through nondynamical background fields denoted as $u$ and
$s^{\mu\nu}$ in the literature. An ADM decomposition allows us to project the
modified Einstein equations into purely spacelike hypersurfaces, which implies
the field equations for the induced dynamical three-metric. We also obtain the
Hamilton-Jacobi equations of motion for the canonical variables of the theory
based on its Hamiltonian, which was derived in a previous work. The
computations show that the dynamical field equations obtained from the
Lagrangian and Hamiltonian approaches are consistent with each other.
Connections to Brans-Dicke theory and ghost-free massive gravity are
established.


title:
Precisely measuring the Hubble constant and dark energy using only
  gravitational-wave dark sirens

abstract:
  Using the measurements of tidal deformations in the binary neutron star (BNS)
coalescences can obtain the information of redshifts of gravitational wave (GW)
sources, and thus actually the cosmic expansion history can be investigated by
solely using such GW dark sirens. To do this, the key is to get large amounts
of accurate GW data, which can be achieved by employing the third-generation
(3G) GW detectors. In this paper, we wish to offer an answer to the question of
whether the Hubble constant and the equation of state (EoS) of dark energy can
be precisely measured by solely using GW dark sirens. We find that in the era
of 3G GW detectors ${\cal O}(10^5)$-${\cal O}(10^6)$ dark siren data (with the
tidal measurements) can be obtained in a several-year observation, and thus
using only dark sirens can actually achieve the precision cosmology. Based on a
network of 3G detectors, we obtain the constraint precisions of $0.1\%$ and
$0.6\%$ for the Hubble constant $H_0$ and the constant EoS of dark energy $w$,
respectively; for a two-parameter EoS parametrization of dark energy, the
precision of $w_0$ is $1.4\%$ and the error of $w_a$ is 0.086. We conclude that
3G GW detectors would lead to breakthroughs in solving the Hubble tension and
revealing the nature of dark energy.


title: A Note on Machine Learning Approach for Computational Imaging

abstract:
  Computational imaging has been playing a vital role in the development of
natural sciences. Advances in sensory, information, and computer technologies
have further extended the scope of influence of imaging, making digital images
an essential component of our daily lives. For the past three decades, we have
witnessed phenomenal developments of mathematical and machine learning methods
in computational imaging. In this note, we will review some of the recent
developments of the machine learning approach for computational imaging and
discuss its differences and relations to the mathematical approach. We will
demonstrate how we may combine the wisdom from both approaches, discuss the
merits and potentials of such a combination and present some of the new
computational and theoretical challenges it brings about.


title:
M2I: From Factored Marginal Trajectory Prediction to Interactive
  Prediction

abstract:
  Predicting future motions of road participants is an important task for
driving autonomously in urban scenes. Existing models excel at predicting
marginal trajectories for single agents, yet it remains an open question to
jointly predict scene compliant trajectories over multiple agents. The
challenge is due to exponentially increasing prediction space as a function of
the number of agents. In this work, we exploit the underlying relations between
interacting agents and decouple the joint prediction problem into marginal
prediction problems. Our proposed approach M2I first classifies interacting
agents as pairs of influencers and reactors, and then leverages a marginal
prediction model and a conditional prediction model to predict trajectories for
the influencers and reactors, respectively. The predictions from interacting
agents are combined and selected according to their joint likelihoods.
Experiments show that our simple but effective approach achieves
state-of-the-art performance on the Waymo Open Motion Dataset interactive
prediction benchmark.


title:
A Partition-and-Merge Algorithm for Solving the Steiner Tree Problem in
  Large Graphs

abstract:
  The Steiner tree problem, which asks for a minimum weighted tree spanning a
given set of terminal vertices in a given graph, is a classic problem arising
in numerous practical applications. Many algorithms about this problem emerged
in the past decade, especially presented in the 11th DIMACS Challenge in 2014
and the 3rd PACE Competition in 2018. In this paper, we present a novel
partition-and-merge algorithm for effectively solving this NP-hard problem in
large graphs. The algorithm first breaks the input graph into small fragments
and then gradually builds up the graph in a level-by-level manner. Intuitively,
the method aggregates information that is found by local search at each level
into the final graph. We assess the algorithm on a wide range of benchmark
instances, showing that the algorithm outperforms the winners of DIMACS and
PACE challenges on large instances and competes favorably with them on small or
middle-sized instances.


title:
Calibrated inference: statistical inference that accounts for both
  sampling uncertainty and distributional uncertainty

abstract:
  During data analysis, analysts often have to make seemingly arbitrary
decisions. For example during data pre-processing there are a variety of
options for dealing with outliers or inferring missing data. Similarly, many
specifications and methods can be reasonable to address a domain question. This
may be seen as a hindrance for reliable inference, since conclusions can change
depending on the analyst's choices. In this paper, we argue that this situation
is an opportunity to construct confidence intervals that account not only for
sampling uncertainty but also some type of distributional uncertainty. The
distributional uncertainty model is related to a variety of potential issues
with the data, ranging from dependence between observations to selection bias
and confounding. The final procedure can be seen as an extension of current
statistical practice. However, the underlying assumptions are quite different.
Standard statistical practice often relies on the i.i.d. assumption. We rely on
a strictly weaker symmetry assumption stating that the empirical distribution
and the target distribution differ by an isotropic distributional perturbation.


title: Weighted Erd\H{o}s-Burgess and Davenport constant in commutative rings

abstract:
  Let $R$ be a finite commutative unitary ring. An idempotent in $R$ is an
element $e\in R$ with $e^2=e$. Let $\Psi$ be a subgroup of the group ${\rm
Aut}(R)$ of all automorphisms of $R$. The $\Psi-$weighted Erd\H{o}s-Burgess
constant ${\rm I}_{\Psi}(R)$ is defined as the smallest positive integer $\ell$
such that every sequence over $R$ of length at least $\ell$ must contain a
nonempty subsequence $a_1,\ldots, a_{r}$ such that $\prod\limits_{i=1}^r
\psi_i(a_i)$ is one idempotent of $R$ where $\psi_1,\ldots,\psi_r\in \Psi$. In
this paper, for the finite quotient ring of a Dedekind domain $R$, a connection
is established between the $\Psi-$weighted-Erd\H{o}s-Burgess constant of $R$
and the $\Psi-$weighted Davenport constant of its group of units by all the
prime ideals of $R$.


title: Inverse Source Problem for Acoustically-Modulated Electromagnetic Waves

abstract:
  We propose a method to reconstruct the electrical current density from
acoustically-modulated boundary measurements of time-harmonic electromagnetic
fields. We show that the current can be uniquely reconstructed with Lipschitz
stability. We also report numerical simulations to illustrate the analytical
results.


title:
A spectral-spatial fusion anomaly detection method for hyperspectral
  imagery

abstract:
  In hyperspectral, high-quality spectral signals convey subtle spectral
differences to distinguish similar materials, thereby providing unique
advantage for anomaly detection. Hence fine spectra of anomalous pixels can be
effectively screened out from heterogeneous background pixels. Since the same
materials have similar characteristics in spatial and spectral dimension,
detection performance can be significantly enhanced by jointing spatial and
spectral information. In this paper, a spectralspatial fusion anomaly detection
(SSFAD) method is proposed for hyperspectral imagery. First, original spectral
signals are mapped to a local linear background space composed of median and
mean with high confidence, where saliency weight and feature enhancement
strategies are implemented to obtain an initial detection map in spectral
domain. Futhermore, to make full use of similarity information of local
background around testing pixel, a new detector is designed to extract the
local similarity spatial features of patch images in spatial domain. Finally,
anomalies are detected by adaptively combining the spectral and spatial
detection maps. The experimental results demonstrate that our proposed method
has superior detection performance than traditional methods.


title:
Multirate Partitioned Runge-Kutta Methods for Coupled Navier-Stokes
  Equations

abstract:
  Earth system models are complex integrated models of atmosphere, ocean, sea
ice, and land surface. Coupling the components can be a significant challenge
due to the difference in physics, temporal, and spatial scales. This study
explores new coupling strategies for the fluid-fluid interaction problem based
on multirate partitioned Runge-Kutta methods. We consider compressible
Navier-Stokes equations with gravity coupled through a rigid-lid interface. Our
large-scale numerical experiments reveal that multirate partitioned Runge-Kutta
coupling schemes (1) can conserve total mass; (2) have second-order accuracy in
time; and (3) provide favorable strong- and weak-scaling performance on modern
computing architectures. We also show that the speedup factors of multirate
partitioned Runge-Kutta methods match theoretical expectations over their base
(single-rate) method.

categories: math.NA cs.NA physics.comp-ph physics.flu-dyn


title:
HMD-EgoPose: Head-Mounted Display-Based Egocentric Marker-Less Tool and
  Hand Pose Estimation for Augmented Surgical Guidance

abstract:
  The success or failure of modern computer-assisted surgery procedures hinges
on the precise six-degree-of-freedom (6DoF) position and orientation (pose)
estimation of tracked instruments and tissue. In this paper, we present
HMD-EgoPose, a single-shot learning-based approach to hand and object pose
estimation and demonstrate state-of-the-art performance on a benchmark dataset
for monocular red-green-blue (RGB) 6DoF marker-less hand and surgical
instrument pose tracking. Further, we reveal the capacity of our HMD-EgoPose
framework for 6DoF near real-time pose estimation on a commercially available
optical see-through head-mounted display (OST-HMD) through a low-latency
streaming approach. Our framework utilized an efficient convolutional neural
network (CNN) backbone for multi-scale feature extraction and a set of
subnetworks to jointly learn the 6DoF pose representation of the rigid surgical
drill instrument and the grasping orientation of the hand of a user. To make
our approach accessible to a commercially available OST-HMD, the Microsoft
HoloLens 2, we created a pipeline for low-latency video and data communication
with a high-performance computing workstation capable of optimized network
inference. HMD-EgoPose outperformed current state-of-the-art approaches on a
benchmark dataset for surgical tool pose estimation, achieving an average tool
3D vertex error of 11.0 mm on real data and furthering the progress towards a
clinically viable marker-free tracking strategy. Through our low-latency
streaming approach, we achieved a round trip latency of 202.5 ms for pose
estimation and augmented visualization of the tracked model when integrated
with the OST-HMD. Our single-shot learned approach was robust to occlusion and
complex surfaces and improved on current state-of-the-art approaches to
marker-less tool and hand pose estimation.


title:
Nuclear medium effects in lepton-nucleus DIS in the region of $x \gtrsim
  1$

abstract:
  The nuclear medium effects in the nuclear structure functions and
differential cross sections in the deep inelastic scattering (DIS) of charged
lepton and neutrino from nuclear targets are studied in the region of large $x$
including $x\ge 1$. The nuclear medium effects due to the Fermi motion and the
binding energy of nucleons and the nucleon correlations are included using
nucleon spectral function calculated in a microscopic field theoretical model.
The numerical results for the nuclear structure functions and the cross
sections are obtained using the nucleon structure function evaluated at the
next-to-next-to-leading order (NNLO) with the Martin-Motylinski-Harland
Lang-Thorne (MMHT) parameterization of the nucleonic parton distribution
functions (PDFs) and are compared with the available experimental data on
electron scattering from the Jefferson Lab (JLab) and SLAC Nuclear Physics
Facility (NPAS). In the case of neutrino scattering the results are relevant
for understanding the DIS contributions to the recent inclusive cross sections
measured by the Main Injector Neutrino ExpeRiment to study v-A interactions
(MINERvA) as well as theoretical predictions are made for Deep Underground
Neutrino Experiment (DUNE). The importance of isoscalarity corrections in
heavier nuclear targets as well as the effect of the kinematic cut on the CM
energy $W$ in defining the DIS region have also been discussed.