title:
Stochastic Approximation Based Confidence Regions for Stochastic
  Variational Inequalities

abstract:
  The sample average approximation (SAA) and the stochastic approximation (SA)
are two popular schemes for solving the stochastic variational inequalities
problem (SVIP). In the past decades, theories on the consistency of the SAA
solutions and SA solutions have been well studied. More recently, the
asymptotic confidence regions of the true solution to SVIP have been
constructed when the SAA scheme is implemented. It is of fundamental interest
to develop confidence regions of the true solution to the SVIP when the SA
scheme is employed. In this paper, we discuss the framework of constructing
asymptotic confidence regions for the true solution of SVIP with a focus on
stochastic dual average method. We first establish the asymptotic normality of
the SA solutions both in ergodic sense and non-ergodic sense. Then the online
methods of estimating the covariance matrices in the normal distributions are
studied. Finally, practical procedures of building the asymptotic confidence
regions of solutions to SVIP with numerical simulations are presented.


title:
Coupling approaches for classical linear elasticity and bond-based
  peridynamic models

abstract:
  Local-nonlocal coupling approaches provide a means to combine the
computational efficiency of local models and the accuracy of nonlocal models.
This paper studies the continuous and discrete formulations of three existing
approaches for the coupling of classical linear elasticity and bond-based
peridynamic models, namely 1) a method that enforces matching displacements in
an overlap region, 2) a variant that enforces a constraint on the stresses
instead, and 3) a method that considers a variable horizon in the vicinity of
the interfaces. The performance of the three coupling approaches is compared on
a series of one-dimensional numerical examples that involve cubic and quartic
manufactured solutions. Accuracy of the proposed methods is measured in terms
of the difference between the solution to the coupling approach and the
solution to the classical linear elasticity model, which can be viewed as a
modeling error. The objective of the paper is to assess the quality and
performance of the discrete formulation for this class of force-based coupling
methods.


title:
Optimizing a Superconducting Radiofrequency Gun Using Deep Reinforcement
  Learning

abstract:
  Superconducting photoelectron injectors are a promising technique for
generating high brilliant pulsed electron beams with high repetition rates and
low emittances. Experiments such as ultra-fast electron diffraction,
experiments at the Terahertz scale, and energy recovery linac applications
require such properties. However, optimization of the beam properties is
challenging due to the high amount of possible machine parameter combinations.
In this article, we show the successful automated optimization of beam
properties utilizing an already existing simulation model. To reduce the amount
of required computation time, we replace the costly simulation by a faster
approximation with a neural network. For optimization, we propose a
reinforcement learning approach leveraging the simple computation of the
derivative of the approximation. We prove that our approach outperforms common
optimization methods for the required function evaluations given a defined
minimum accuracy.


title: Fake News Detection Using Majority Voting Technique

abstract:
  Due to the evolution of the Web and social network platforms it becomes very
easy to disseminate the information. Peoples are creating and sharing more
information than ever before, which may be misleading, misinformation or fake
information. Fake news detection is a crucial and challenging task due to the
unstructured nature of the available information. In the recent years,
researchers have provided significant solutions to tackle with the problem of
fake news detection, but due to its nature there are still many open issues. In
this paper, we have proposed majority voting approach to detect fake news
articles. We have used different textual properties of fake and real news. We
have used publicly available fake news dataset, comprising of 20,800 news
articles among which 10,387 are real and 10,413 are fake news labeled as binary
0 and 1. For the evaluation of our approach, we have used commonly used machine
learning classifiers like, Decision Tree, Logistic Regression, XGBoost, Random
Forest, Extra Trees, AdaBoost, SVM, SGD and Naive Bayes. Using the
aforementioned classifiers, we built a multi-model fake news detection system
using Majority Voting technique to achieve the more accurate results. The
experimental results show that, our proposed approach achieved accuracy of
96.38%, precision of 96%, recall of 96% and F1-measure of 96%. The evaluation
confirms that, Majority Voting technique achieved more acceptable results as
compare to individual learning technique.


title:
On the sensitivity of pose estimation neural networks: rotation
  parameterizations, Lipschitz constants, and provable bounds

abstract:
  In this paper, we approach the task of determining sensitivity bounds for
pose estimation neural networks. This task is particularly challenging as it
requires characterizing the sensitivity of 3D rotations. We develop a
sensitivity measure that describes the maximum rotational change in a network's
output with respect to a Euclidean change in its input. We show that this
measure is a type of Lipschitz constant, and that it is bounded by the product
of a network's Euclidean Lipschitz constant and an intrinsic property of a
rotation parameterization which we call the "distance ratio constant". We
derive the distance ratio constant for several rotation parameterizations, and
then discuss why the structure of most of these parameterizations makes it
difficult to construct a pose estimation network with provable sensitivity
bounds. However, we show that sensitivity bounds can be computed for networks
which parameterize rotation using unconstrained exponential coordinates. We
then construct and train such a network and compute sensitivity bounds for it.


title:
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware
  Detection

abstract:
  In this research, we compare malware detection techniques based on static,
dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs
) on both static and dynamic feature sets and compare the resulting detection
rates over a substantial number of malware families. We also consider hybrid
cases, where dynamic analysis is used in the training phase, with static
techniques used in the detection phase, and vice versa. In our experiments, a
fully dynamic approach generally yields the best detection rates. We discuss
the implications of this research for malware detection based on hybrid
techniques.


title:
Novel Low Workfunction Semiconductors for Calorimetry and Detection:
  High Energy, Dark Matter and Neutrino Phenomena

abstract:
  This White Paper seeks to extend the applications of two weakly bound
semiconductor materials, Cs3Sb and Ag-O-Cs first developed decades ago for
vacuum photodetectors S1, S-11 photocathodes, respectively, proven to have low
electron-hole pair energies. Rather than thin film photocathodes, we propose
fabrication of these semiconductors in shapes and volumes which could be used
as diode or drifted ion detectors for low energy depositions via atom or
few-atomic layers. Ag-O-Cs has an e-hole pair threshold pair low as 0.5-0.7 eV
deposited energy, practical when cooled below 4 degrees K; Cs3Sb, with pair
energy as low as 1.6-2 eV - less than Si (3.6 eV), but with thermal noise at
room temperature similar to Si at -30 deg C. Exposure of photomultipliers to 10
MRad doses has shown that thin alkali photocathodes do not degrade more than 2%
of initial quantum efficiency.


title: Defending Variational Autoencoders from Adversarial Attacks with MCMC

abstract:
  Variational autoencoders (VAEs) are deep generative models used in various
domains. VAEs can generate complex objects and provide meaningful latent
representations, which can be further used in downstream tasks such as
classification. As previous work has shown, one can easily fool VAEs to produce
unexpected latent representations and reconstructions for a visually slightly
modified input. Here, we examine several objective functions for adversarial
attacks construction, suggest metrics assess the model robustness, and propose
a solution to alleviate the effect of an attack. Our method utilizes the Markov
Chain Monte Carlo (MCMC) technique in the inference step and is motivated by
our theoretical analysis. Thus, we do not incorporate any additional costs
during training or we do not decrease the performance on non-attacked inputs.
We validate our approach on a variety of datasets (MNIST, Fashion MNIST, Color
MNIST, CelebA) and VAE configurations ($\beta$-VAE, NVAE, TC-VAE) and show that
it consistently improves the model robustness to adversarial attacks.


title:
Photomultipliers as High Rate Radiation-Resistant In-Situ Sensors in
  Future Experiments

abstract:
  In the Energy Frontier we suggest developing high rate (100 MHz) finely
segmented forward calorimetry preradiators with time resolution <50 ps which
will survive the first 1-2 Lint of incident high radiation doses, protecting
forward calorimeters 3<y<6; less than 5 degrees to the beam behind them from
radiation damage, with high granularity, high rate capability and 30ps time
resolution (4D calorimetry) providing lepton and photon ID and measurement. In
the Intensity Frontier beam particle selection, such as tagged neutrino and
kaon beams, and lepton violation experiments with muons require very high
rates. Cosmic Frontiers requiring low power, non-cooled calorimetry or optical
detection that can keep track of particles or photons arriving at 100 MHz, and
survivable for years in space radiation may also benefit. The basic research is
to use compact channelized PMTs with quartz or other radiation resistant
windows with metal envelopes as an in-situ sensor, directly coupled to Cerenkov
(or radiation-resistant scintillator) tiles, utilizing the dynode signals as a
potentially compensating 2nd signal, and with no active electronics. If
successful, directions include proposals for high SE yield mesh dynode
activator materials such as GaP or B doped diamond films with 25 SEe at 300 eV
electron energies, and possibly for compact low cost tile SE sensors with no
photocathode, far easier to fabricate than PMTs with all metal final assembly
in air, brazed seals; bakeout 900 C; pump out with tipoff - vacuum 100x higher
than PMTs. Such sensors have many applications beyond HEP, in research,
medicine, industry and defense.


title: High Rate and High Precision Timing and Calorimeter Detectors

abstract:
  High precision timing, high rate calorimeters, and radiation resistance are
becoming an important issue in particle physics especially in Energy and
Intensity Frontiers. We discuss doped Zinc Oxide (ZnO:Ga or GZO; ZnO:X where X
is Al, Cu or others) as a very fast scintillator and wavelength shifter (WLS),
Total internal reflection films, and PMT capable of counting at 300 MHz with 10
ps timing precision, with superior radiation resistance.


title: Training a Tokenizer for Free with Private Federated Learning

abstract:
  Federated learning with differential privacy, i.e. private federated learning
(PFL), makes it possible to train models on private data distributed across
users' devices without harming privacy. PFL is efficient for models, such as
neural networks, that have a fixed number of parameters, and thus a
fixed-dimensional gradient vector. Such models include neural-net language
models, but not tokenizers, the topic of this work. Training a tokenizer
requires frequencies of words from an unlimited vocabulary, and existing
methods for finding an unlimited vocabulary need a separate privacy budget.
  A workaround is to train the tokenizer on publicly available data. However,
in this paper we first show that a tokenizer trained on mismatched data results
in worse model performance compared to a privacy-violating "oracle" tokenizer
that accesses user data, with perplexity increasing by 20%. We also show that
sub-word tokenizers are better suited to the federated context than word-level
ones, since they can encode new words, though with more tokens per word.
  Second, we propose a novel method to obtain a tokenizer without using any
additional privacy budget. During private federated learning of the language
model, we sample from the model, train a new tokenizer on the sampled
sequences, and update the model embeddings. We then continue private federated
learning, and obtain performance within 1% of the "oracle" tokenizer. Since
this process trains the tokenizer only indirectly on private data, we can use
the "postprocessing guarantee" of differential privacy and thus use no
additional privacy budget.


title: Test Beam and Irradiation Facilities

abstract:
  Progress in particle physics depends on a multitude of unique facilities and
capabilities that enable to advance detector technologies. Among others, key
facilities involve test beams and irradiation facilities, which allow users to
test the performance and lifetime of their detectors under realistic
conditions. Test beam facilities are particularly important for collider and
neutrino detector applications, while irradiation facilities are crucial for
collider as well as some space-based astro particle detectors. This contributed
Snowmass paper aims to summarize existing test beam and irradiation facilities
as well as develop the need and proposals for future facilities.


title: Portability: A Necessary Approach for Future Scientific Software

abstract:
  Today's world of scientific software for High Energy Physics (HEP) is powered
by x86 code, while the future will be much more reliant on accelerators like
GPUs and FPGAs. The portable parallelization strategies (PPS) project of the
High Energy Physics Center for Computational Excellence (HEP/CCE) is
investigating solutions for portability techniques that will allow the coding
of an algorithm once, and the ability to execute it on a variety of hardware
products from many vendors, especially including accelerators. We think without
these solutions, the scientific success of our experiments and endeavors is in
danger, as software development could be expert driven and costly to be able to
run on available hardware infrastructure. We think the best solution for the
community would be an extension to the C++ standard with a very low entry bar
for users, supporting all hardware forms and vendors. We are very far from that
ideal though. We argue that in the future, as a community, we need to request
and work on portability solutions and strive to reach this ideal.

categories: physics.comp-ph hep-ex physics.data-an physics.ins-det


title:
Prompt-based Generative Approach towards Multi-Hierarchical Medical
  Dialogue State Tracking

abstract:
  The medical dialogue system is a promising application that can provide great
convenience for patients. The dialogue state tracking (DST) module in the
medical dialogue system which interprets utterances into the machine-readable
structure for downstream tasks is particularly challenging. Firstly, the states
need to be able to represent compound entities such as symptoms with their body
part or diseases with degrees of severity to provide enough information for
decision support. Secondly, these named entities in the utterance might be
discontinuous and scattered across sentences and speakers. These also make it
difficult to annotate a large corpus which is essential for most methods.
Therefore, we first define a multi-hierarchical state structure. We annotate
and publish a medical dialogue dataset in Chinese. To the best of our
knowledge, there are no publicly available ones before. Then we propose a
Prompt-based Generative Approach which can generate slot values with
multi-hierarchies incrementally using a top-down approach. A dialogue style
prompt is also supplemented to utilize the large unlabeled dialogue corpus to
alleviate the data scarcity problem. The experiments show that our approach
outperforms other DST methods and is rather effective in the scenario with
little data.


title:
Convergence properties of an Objective-Function-Free Optimization
  regularization algorithm, including an $\mathcal{O}(\epsilon^{-3/2})$
  complexity bound

abstract:
  An adaptive regularization algorithm for unconstrained nonconvex optimization
is presented in which the objective function is never evaluated, but only
derivatives are used. This algorithm belongs to the class of adaptive
regularization methods, for which optimal worst-case complexity results are
known for the standard framework where the objective function is evaluated. It
is shown in this paper that these excellent complexity bounds are also valid
for the new algorithm, despite the fact that significantly less information is
used. In particular, it is shown that, if derivatives of degree one to $p$ are
used, the algorithm will find a $\epsilon_1$-approximate first-order minimizer
in at most $O(\epsilon_1^{-(p+1)/p})$ iterations, and an
$(\epsilon_1,\epsilon_2)$-approximate second-order minimizer in at most
$O(\max[\epsilon^{-(p+1)/p},\epsilon_2^{-(p+1)/(p-1)}])$ iterations. As a
special case, the new algorithm using first and second derivatives, when
applied to functions with Lipschitz continuous Hessian, will find an iterate
$x_k$ at which the gradient's norm is less than $\epsilon_1$ in at most
$O(\epsilon_1^{-3/2})$ iterations.


title:
Neural Enhanced Belief Propagation for Data Association in Multiobject
  Tracking

abstract:
  Situation-aware technologies enabled by multiobject tracking (MOT) methods
will create new services and applications in fields such as autonomous
navigation and applied ocean sciences. Belief propagation (BP) is a
state-of-the-art method for Bayesian MOT but fully relies on a statistical
model and preprocessed sensor measurements. In this paper, we establish a
hybrid method for model-based and data-driven MOT. The proposed neural enhanced
belief propagation (NEBP) approach complements BP by information learned from
raw sensor data with the goal to improve data association and to reject false
alarm measurements. We evaluate the performance of our NEBP approach for MOT on
the nuScenes autonomous driving dataset and demonstrate that it can outperform
state-of-the-art reference methods.


title:
Designing a thermodynamically stable and intrinsically ductile
  refractory alloy

abstract:
  The stringent environmental norms are pushing the aviation and
power-generation industries to increase the operating temperatures of their
jet-engines and gas-turbines which will improve the efficiencies of these
energy conversion systems. Aviation and power generation industries are
researching the alternatives for the currently used Ni-based superalloys, which
is the most widely used material in the high-temperature region of these
engines. Refractory alloys are being actively developed as an alternative to
the Ni-based superalloys due to their high melting-points and superior strength
retention capabilities at higher temperatures. Enhancing the intrinsic
ductility of these refractory alloys has remained a challenge. Reducing the
valence electron concentration (VEC) below 4.5 have been shown to improve the
intrinsic ductility of few alloys, which means addition of Ti, Zr, and Hf can
improve the ductility of refractory alloys. However, Re has been widely used to
ductilize the W which goes contrary to the VEC criteria suggested in
literature. The reason this seems to work is may be due to the reduced USFE of
the alloy due to alloying with low USFE elements. Here we use density function
theory (DFT) simulations to understand the factors affecting the ductility of
refractory alloys. A DFT-based workflow is developed which reliably calculates
the ductility parameter of refractory alloys. The developed workflow ensures
that the error in estimating USFE is less than 50mJ/m$^2$. The enthalpy,
lattice distortion, and ductility parameter are calculated using DFT for Group
IV, V, VI elements & their 25 equiatomic binary alloys. The deformability of
refractory BCC alloys is correlated with their enthalpy.


title: Peaceable Self-Stabilizing Byzantine Pulse Synchronization

abstract:
  For reaching fast and efficient self-stabilizing Byzantine pulse
synchronization (SSBPS) upon the bounded-delay message-passing networks, we
consider the peaceable SSBPS problem where the resource occupation in the
stabilized system is required to be as sparse as possible and the stabilization
of the system is required to be as fast as possible. To solve it, the decoupled
absorption process and emergency process are investigated under a general
framework. The constant-time two-stage absorption process and more than one
kind of emergency process are provided in integrating the merits of temporal
trails, approximate agreements, deterministic and randomized Byzantine
agreements, and self-stabilizing protocols. With this, not only SSBPS but
self-stabilizing Byzantine clock synchronization can be fast established and
efficiently maintained. In the optimal-resilient basic solutions, the
deterministic linear stabilization time and the stabilized resource occupation
are all optimized to those of the underlying primitives, which is optimal in
the presence of Byzantine faults under the classical static adversary. In the
hybrid solutions, faster stabilization can be expected with a worst-case
deterministic stabilization time. In all solutions, the stabilized resource
occupation is at most at the order of approximate agreement, which is a good
property in considering real-world ultra-high-reliable hard-real-time
applications where pulse synchronization is expected to be established before
all upper-layer functions and to be efficiently maintained when the upper-layer
functions are in service.


title:
Experimental and theoretical study of field-dependent spin splitting at
  ferromagnetic-insulator-superconductor interfaces

abstract:
  We present a combined experimental and theoretical work that investigates the
magnetic proximity effect at a ferromagnetic-insulator - superconductor (FI-S)
interface. The simulation is based on the boundary condition for diffusive
quasiclassical Greens functions, that accounts for arbitrarily strong
spin-dependent effects and spin-mixing angles. The experimentally found
differential conductance of an EuS-Al heterostructure is compared with a
theoretical calculation. With the assumption of a uniform spin-mixing angle
that depends on the externally applied field, we find good agreement between
theory and experiment. The theory depends only on very few parameters, mostly
specified by the experimental setup. We determine the effective spin of the
interface moments as $ J\approx 0.74\hbar $.


title:
Conquering Ghosts: Relation Learning for Information Reliability
  Representation and End-to-End Robust Navigation

abstract:
  Environmental disturbances, such as sensor data noises, various lighting
conditions, challenging weathers and external adversarial perturbations, are
inevitable in real self-driving applications. Existing researches and testings
have shown that they can severely influence the vehicles perception ability and
performance, one of the main issue is the false positive detection, i.e., the
ghost object which is not real existed or occurs in the wrong position (such as
a non-existent vehicle). Traditional navigation methods tend to avoid every
detected objects for safety, however, avoiding a ghost object may lead the
vehicle into a even more dangerous situation, such as a sudden break on the
highway. Considering the various disturbance types, it is difficult to address
this issue at the perceptual aspect. A potential solution is to detect the
ghost through relation learning among the whole scenario and develop an
integrated end-to-end navigation system. Our underlying logic is that the
behavior of all vehicles in the scene is influenced by their neighbors, and
normal vehicles behave in a logical way, while ghost vehicles do not. By
learning the spatio-temporal relation among surrounding vehicles, an
information reliability representation is learned for each detected vehicle and
then a robot navigation network is developed. In contrast to existing works, we
encourage the network to learn how to represent the reliability and how to
aggregate all the information with uncertainties by itself, thus increasing the
efficiency and generalizability. To the best of the authors knowledge, this
paper provides the first work on using graph relation learning to achieve
end-to-end robust navigation in the presence of ghost vehicles. Simulation
results in the CARLA platform demonstrate the feasibility and effectiveness of
the proposed method in various scenarios.


title: Chaos in the three-site Bose-Hubbard model -- classical vs quantum

abstract:
  We consider a quantum many-body system - the Bose-Hubbard system on three
sites - which has a classical limit, and which is neither strongly chaotic nor
integrable but rather shows a mixture of the two types of behavior. We compare
quantum measures of chaos (eigenvalue statistics and eigenvector structure) in
the quantum system, with classical measures of chaos (Lyapunov exponents) in
the corresponding classical system. As a function of energy and interaction
strength, we demonstrate a strong overall correspondence between the two cases.
In contrast to both strongly chaotic and integrable systems, the largest
Lyapunov exponent is shown to be a multi-valued function of energy.


title:
Learning to Optimize Resource Assignment for Task Offloading in Mobile
  Edge Computing

abstract:
  In this paper, we consider a multiuser mobile edge computing (MEC) system,
where a mixed-integer offloading strategy is used to assist the resource
assignment for task offloading. Although the conventional branch and bound
(BnB) approach can be applied to solve this problem, a huge burden of
computational complexity arises which limits the application of BnB. To address
this issue, we propose an intelligent BnB (IBnB) approach which applies deep
learning (DL) to learn the pruning strategy of the BnB approach. By using this
learning scheme, the structure of the BnB approach ensures near-optimal
performance and meanwhile DL-based pruning strategy significantly reduces the
complexity. Numerical results verify that the proposed IBnB approach achieves
optimal performance with complexity reduced by over 80%.


title: Direct Modulation of Electrically Pumped Coupled Microring Lasers

abstract:
  We demonstrate how the presence of gain-loss contrast between two coupled
identical resonators can be used as a new degree of freedom to enhance the
modulation frequency response of laser diodes. An electrically pumped microring
laser system with a bending radius of 50 {\mu}m is fabricated on an
InAlGaAs/InP MQW p-i-n structure. The room temperature continuous wave (CW)
laser threshold current of the device is 27 mA. By adjusting the ratio between
the injection current levels in the two coupled microrings, our experimental
results clearly show a bandwidth improvement by up to 1.63 times the
fundamental resonant frequency of the individual device. This matches well with
our rate equation simulation model.


title:
Plasmonic dichroism and all-optical magnetization switching in
  nanophotonic structures with GdFeCo

abstract:
  We report on a phenomenon of plasmonic dichroism observed in magnetic
materials with the transverse magnetization under the excitation of the surface
plasmon polariton waves. The effect originates from the interplay of the two
magnetization-dependent contributions to the material absorption, both of which
are enhanced under plasmon excitation. Similar to the recently discovered
effect of a all-optical helicity-dependent magnetization switching, this effect
provides a possibility to perform a deterministic magnetization switching to
the desired state. We show by electromagnetic modeling that laser pulses
exciting counter-propagating plasmons can be used to write +M or -M state in a
deterministic way independent on the initial magnetization state. The presented
approach applies to various ferrimagnetic materials exhibiting the phenomenon
of all-optical switching of thermal nature and broadens the horizons of their
applications in data storage devices.


title:
Enhancement of Novel View Synthesis Using Omnidirectional Image
  Completion

abstract:
  We present a method for synthesizing novel views from a single 360-degree
image based on the neural radiance field (NeRF) . Prior studies rely on the
neighborhood interpolation capability of multi-layer perceptrons to complete
missing regions caused by occlusion and zooming, and this leads to artifacts.
In the proposed method, the input image is reprojected to 360-degree images at
other camera positions, the missing regions of the reprojected images are
completed by a self-supervised trained generative model, and the completed
images are utilized to train the NeRF. Because multiple completed images
contain inconsistencies in 3D, we introduce a method to train NeRF while
dynamically selecting a sparse set of completed images, to reduce the
discrimination error of the synthesized views with real images. Experiments
indicate that the proposed method can synthesize plausible novel views while
preserving the features of the scene for both artificial and real-world data.


title:
Thermal Comptonization in a changing corona in the changing-look active
  galaxy NGC 1566

abstract:
  We present broadband UV/X-ray spectral variability of the changing-look
active galactic nucleus NGC 1566 based on simultaneous near-ultraviolet (NUV)
and X-ray observations performed by XMM-Newton, Swift, and NuSTAR satellites at
five different epochs during the declining phase of the 2018 outburst. We found
that the accretion disk, soft X-ray excess, and the X-ray power-law components
were extremely variable. Additionally, the X-ray power-law flux was correlated
with both the soft excess plus disk and the pure disk fluxes. Our finding shows
that at high flux levels the soft X-ray excess and the disk emission both
provided the seed photons for thermal Comptonization in the hot corona, whereas
at low flux levels where the soft excess was absent, the pure disk emission
alone provided the seed photons. The X-ray power-law photon-index was only
weakly variable ($\Delta{\Gamma_{hot}}\leq0.06$) and it was not well correlated
with the X-ray flux over the declining timescale. On the other hand, we found
that the electron temperature of the corona increased from $\sim22$ to
$\sim200$ keV with decreasing number of seed photons from June 2018 to August
2019. At the same time, the optical depth of the corona decreased from
$\tau_{hot}\sim4$ to $\sim0.7$, and the scattering fraction increased from
$\sim1\%$ to $\sim10\%$. These changes suggest structural changes in the hot
corona such that it grew in size and became hotter with decreasing accretion
rate during the declining phase. The AGN is most likely evolving with
decreasing accretion rate towards a state similar to the low/hard state of
black hole X-ray binaries.


title:
How Do Programmers Express High-Level Concepts using Primitive Data
  Types?

abstract:
  We investigated how programmers express high-level concepts such as path
names and coordinates using primitive data types. While relying too much on
primitive data types is sometimes criticized as a bad smell, it is still a
common practice among programmers. We propose a novel way to accurately
identify expressions for certain predefined concepts by examining API calls. We
defined twelve conceptual types used in the Java Standard API. We then obtained
expressions for each conceptual type from 26 open source projects. Based on the
expressions obtained, we trained a decision tree-based classifier. It achieved
83% F-score for correctly predicting the conceptual type for a given
expression. Our result indicates that it is possible to infer a conceptual type
from a source code reasonably well once enough examples are given. The obtained
classifier can be used for potential bug detection, test case generation and
documentation.


title:
Improving Semantic Consistency of Variable Names with Use-Flow Graph
  Analysis

abstract:
  Consistency is one of the keys to maintainable source code and hence a
successful software project. We propose a novel method of extracting the intent
of programmers from source code of a large project (~300kLOC) and checking the
semantic consistency of its variable names. Our system learns a
project-specific naming convention for variables based on its role solely from
source code, and suggest alternatives when it violates its internal
consistency. The system can also show the reasoning why a certain variable
should be named in a specific way. The system does not rely on any external
knowledge. We applied our method to 12 open-source projects and evaluated its
results with human reviewers. Our system proposed alternative variable names
for 416 out of 1080 (39%) instances that are considered better than ones
originally used by the developers. Based on the results, we created patches to
correct the inconsistent names and sent them to its developers. Three
open-source projects adopted it.


title: Personalized Filled-pause Generation with Group-wise Prediction Models

abstract:
  In this paper, we propose a method to generate personalized filled pauses
(FPs) with group-wise prediction models. Compared with fluent text generation,
disfluent text generation has not been widely explored. To generate more
human-like texts, we addressed disfluent text generation. The usage of
disfluency, such as FPs, rephrases, and word fragments, differs from speaker to
speaker, and thus, the generation of personalized FPs is required. However, it
is difficult to predict them because of the sparsity of position and the
frequency difference between more and less frequently used FPs. Moreover, it is
sometimes difficult to adapt FP prediction models to each speaker because of
the large variation of the tendency within each speaker. To address these
issues, we propose a method to build group-dependent prediction models by
grouping speakers on the basis of their tendency to use FPs. This method does
not require a large amount of data and time to train each speaker model. We
further introduce a loss function and a word embedding model suitable for FP
prediction. Our experimental results demonstrate that group-dependent models
can predict FPs with higher scores than a non-personalized one and the
introduced loss function and word embedding model improve the prediction
performance.


title:
SS-SAM : Stochastic Scheduled Sharpness-Aware Minimization for
  Efficiently Training Deep Neural Networks

abstract:
  By driving optimizers to converge to flat minima, sharpness-aware
minimization (SAM) has shown the power to improve the model generalization.
However, SAM requires to perform two forward-backward propagations for one
parameter update, which largely burdens the practical computation. In this
paper, we propose a novel and efficient training scheme, called Stochastic
Scheduled SAM (SS-SAM). Specifically, in SS-SAM, the optimizer is arranged by a
predefined scheduling function to perform a random trial at each update step,
which would randomly select to perform the SGD optimization or the SAM
optimization. In this way, the overall count of propagation pair could be
largely reduced. Then, we empirically investigate four typical types of
scheduling functions, and demonstrates the computational efficiency and their
impact on model performance respectively. We show that with proper scheduling
functions, models could be trained to achieve comparable or even better
performance with much lower computation cost compared to models trained with
only SAM training scheme.


title: Towards Lithuanian grammatical error correction

abstract:
  Everyone wants to write beautiful and correct text, yet the lack of language
skills, experience, or hasty typing can result in errors. By employing the
recent advances in transformer architectures, we construct a grammatical error
correction model for Lithuanian, the language rich in archaic features. We
compare subword and byte-level approaches and share our best trained model,
achieving F$_{0.5}$=0.92, and accompanying code, in an online open-source
repository.


title:
A relaxed localized trust-region reduced basis approach for optimization
  of multiscale problems

abstract:
  In this contribution, we introduce and analyze a new relaxed and localized
version of the trust-region method for PDE-constrained parameter optimization
in the context of multiscale problems. As an underlying efficient
discretization framework, we rely on the Petrov-Galerkin localized orthogonal
decomposition method and its recently introduced two-scale reduced basis
approximation. We derive efficient localizable a posteriori error estimates for
the primal and dual equations of the optimality system, as well as for the
two-scale reduced objective functional. While the relaxation of the outer
trust-region optimization loop still allows for a rigorous convergence result,
the resulting method converges much faster due to larger step sizes in the
initial phase of the iterative algorithms. The resulting algorithm is
parallelized in order to take advantage of the localization. Numerical
experiments are given for a multiscale thermal block benchmark problem. The
experiments demonstrate the efficiency of the approach, particularly for large
scale problems, where methods based on traditional finite element approximation
schemes are prohibitive or fail entirely.


title: Hierarchies of resources for measurement-based quantum computation

abstract:
  For certain restricted computational tasks, quantum mechanics provides a
provable advantage over any possible classical implementation. Several of these
results have been proven using the framework of measurement-based quantum
computation (MBQC), where non-locality and more generally contextuality have
been identified as necessary resources for certain quantum computations. Here,
we consider the computational power of MBQC in more detail by refining its
resource requirements, both on the allowed operations and the number of
accessible qubits. More precisely, we identify which Boolean functions can be
computed in non-adaptive MBQC, with local operations contained within a finite
level in the Clifford hierarchy. Moreover, for non-adaptive MBQC restricted to
certain subtheories such as stabiliser MBQC, we compute the minimal number of
qubits required to compute a given Boolean function. Our results point towards
hierarchies of resources that more sharply characterise the power of MBQC
beyond the binary of contextuality vs non-contextuality.


title: Multifold Hopf semimetals

abstract:
  Three-dimensional topological semimetals exhibit linear energy band crossing
points that act as monopole sources of Berry curvature. Here, an alternative
class of semimetals is introduced, featuring linear $N$-fold crossing points
each of which acts as a source of a \emph{Berry dipole}. Continuum and lattice
models for such \emph{multifold Hopf semimetals (MHSMs)} are proposed for $N =
3, 4, 5$. Weak field magnetotransport properties of MHSMs are revealed: a
single Berry dipole generates not only an anomalous Hall effect, but also
dissipative current contributions to linear order in $\mathbf{B}$, similar to
the axial anomaly, chiral magnetic, and magnetochiral effects familiar from a
pair of coupled Weyl nodes. For strong $\mathbf{B}$, the Landau level spectrum
highlights the importance of quantum geometry: intraband corrections to the
Onsager quantization condition crucially depend on the angle between
$\mathbf{B}$ and the Berry dipole, and interband corrections reflect the strong
coupling between degenerate orbits. Finally, it is shown that MHSMs mediate
topological phase transitions between $N$-band Hopf insulators, suggesting them
as an ideal playground to explore the physics of delicate topological
semimetals and insulators.


title: Saturation, seminormalization and homeomorphisms of algebraic varieties

abstract:
  We address the question under which conditions a bijective morphism between
algebraic varieties over an algebraically closed field of characteristic zero
is an isomorphism. Our two answers involve a study of seminormalization and
saturation for morphisms between algebraic varieties, together with an
interpretation in terms of continuous rational functions on the closed points
of an algebraic variety. The continuity refers here to the usual Euclidean
continuity in the complex case, and comes from the theory of real closed fields
otherwise.


title: Quantum Black hole--White hole entangled states

abstract:
  We investigate the quantum deformation of the Wheeler--DeWitt equation of a
Schwarzchild black hole. Specifically, the quantum deformed black hole is a
quantized model constructed from the quantum Heisenberg--Weyl $U_q(h_4)$ group.
We show that the event horizon area and the mass are quantized, degenerate, and
bounded. The degeneracy of states indicates entangled quantum black hole/white
hole states. Accordingly, quantum deformation provides a new framework to
examine Einstein--Rosen wormhole solutions. Besides, we obtain the mass, the
temperature, and the entropy of the q-deformed quantum Schwarzschild black
hole. We find an upper bound on the mass of a black hole/white hole pair. Also,
at the quantum deformation level, the entropy of the black hole contains three
parts: the usual Bekenstein--Hawking entropy, the logarithmic term, and a Cube
of usual black hole entropy.


title:
Intro-Stabilizing Byzantine Clock Synchronization in Heterogeneous IoT
  Networks

abstract:
  For reaching dependable high-precision clock synchronization (CS) upon IoT
networks, the distributed CS paradigm adopted in ultra-high reliable systems
and the master-slave CS paradigm adopted in high-performance but unreliable
systems are integrated. Meanwhile, traditional internal clock synchronization
is also integrated with external time references to achieve efficient
stabilization. Low network connectivity, low complexity, high precision, and
high reliability are all considered. To tolerate permanent failures, the
Byzantine CS is integrated with the common CS protocols. To tolerate transient
failures, the self-stabilizing Byzantine CS is also extended upon open-world
IoT networks. With these, the proposed intro-stabilizing Byzantine CS solution
can establish and maintain synchronization with arbitrary initial states in the
presence of permanent Byzantine faults. With the formal analysis and numerical
simulations, it is shown that the best of the CS solutions provided for the
ultra-high reliable systems and the high-performance unreliable systems can be
well integrated upon IoT networks to derive dependable high-precision CS even
across the traditional closed safety-boundary.


title: Spherical model with Dzyaloshinskii-Moriya interactions

abstract:
  We analyze the thermodynamic behavior of a ferromagnetic mean-spherical model
with three distinct spin components and the addition of Dzyaloshinkii-Moriya
interactions. Exact calculations are performed for classical and quantum
versions of this lattice model system. We show the onset of space modulated
structures at low temperatures.


title: Truthful Aggregation of Budget Proposals with Proportionality Guarantees

abstract:
  We study a participatory budgeting problem, where a set of strategic agents
wish to split a divisible budget among different projects, by aggregating their
proposals on a single division. Unfortunately, the straight-forward rule that
divides the budget proportionally is susceptible to manipulation. In a recent
work, Freeman et al. [arXiv:1905.00457] proposed a class of truthful
mechanisms, called moving phantom mechanisms. Among others, they propose a
proportional mechanism, in the sense that in the extreme case where all agents
prefer a single project to receive the whole amount, the budget is assigned
proportionally. While proportionality is a naturally desired property, it is
defined over a limited type of preference profiles. To address this, we expand
the notion of proportionality, by proposing a quantitative framework which
evaluates a budget aggregation mechanism according to its worst-case distance
from the proportional allocation. Crucially, this is defined for every
preference profile. We study this measure on the class of moving phantom
mechanisms, and we provide approximation guarantees. For two projects, we show
that the Uniform Phantom mechanism is the optimal among all truthful
mechanisms. For three projects, we propose a new, proportional mechanism which
is virtually optimal among all moving phantom mechanisms. Finally, we provide
impossibility results regarding the approximability of moving phantom
mechanisms.


title: Cournot duopoly games with isoelastic demands and diseconomies of scale

abstract:
  In this discussion draft, we investigate five different models of duopoly
games, where the market is assumed to have an isoelastic demand function.
Moreover, quadratic cost functions reflecting decreasing returns to scale are
considered. The games in this draft are formulated with systems of two
nonlinear difference equations. Existing equilibria and their local stability
are analyzed by symbolic computations. In the model where a gradiently
adjusting player and a rational (or a boundedly rational) player compete with
each other, diseconomies of scale are proved to have an effect of stability
enhancement, which is consistent with the similar results found by Fisher for
homogeneous oligopolies with linear demand functions.


title: On regularized Shannon sampling formulas with localized sampling

abstract:
  In this paper we present new regularized Shannon sampling formulas which use
localized sampling with special window functions, namely Gaussian, B-spline,
and sinh-type window functions. In contrast to the classical Shannon sampling
series, the regularized Shannon sampling formulas possess an exponential decay
and are numerically robust in the presence of noise. Several numerical
experiments illustrate the theoretical results.


title: SynthStrip: Skull-Stripping for Any Brain Image

abstract:
  The removal of non-brain signal from magnetic resonance imaging (MRI) data,
known as skull-stripping, is an integral component of many neuroimage analysis
streams. Despite their abundance, popular classical skull-stripping methods are
usually tailored to images with specific acquisition properties, namely
near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are
prevalent in research settings. As a result, existing tools tend to adapt
poorly to other image types, such as stacks of thick slices acquired with fast
spin-echo (FSE) MRI that are common in the clinic. While learning-based
approaches for brain extraction have gained traction in recent years, these
methods face a similar burden, as they are only effective for image types seen
during the training procedure. To achieve robust skull-stripping across a
landscape of protocols, we introduce SynthStrip, a rapid, learning-based
brain-extraction tool. By leveraging anatomical segmentations to generate an
entirely synthetic training dataset with anatomies, intensity distributions,
and artifacts that far exceed the realistic range of medical images, SynthStrip
learns to successfully generalize to a variety of real acquired brain images,
removing the need for training data with target contrasts. We demonstrate the
efficacy of SynthStrip for a diverse set of image acquisitions and resolutions
across subject populations, ranging from newborn to adult. We show substantial
improvements in accuracy over popular skull-stripping baselines - all with a
single trained model. Our method and labeled evaluation data are available at
https://w3id.org/synthstrip.


title: BIOS: An Algorithmically Generated Biomedical Knowledge Graph

abstract:
  Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for
biomedical and healthcare big data and artificial intelligence (AI),
facilitating natural language processing, model development, and data exchange.
For decades, these knowledge graphs have been developed via expert curation;
however, this method can no longer keep up with today's AI development, and a
transition to algorithmically generated BioMedKGs is necessary. In this work,
we introduce the Biomedical Informatics Ontology System (BIOS), the first
large-scale publicly available BioMedKG generated completely by machine
learning algorithms. BIOS currently contains 4.1 million concepts, 7.4 million
terms in two languages, and 7.3 million relation triplets. We present the
methodology for developing BIOS, including the curation of raw biomedical
terms, computational identification of synonymous terms and aggregation of
these terms to create concept nodes, semantic type classification of the
concepts, relation identification, and biomedical machine translation. We
provide statistics on the current BIOS content and perform preliminary
assessments of term quality, synonym grouping, and relation extraction. The
results suggest that machine learning-based BioMedKG development is a viable
alternative to traditional expert curation.


title: Probing thermal effects on static spacetimes with Unruh-DeWitt detectors

abstract:
  In the lack of a full-fledged theory of quantum gravity, I consider free,
scalar, quantum fields on curved spacetimes to gain insight into the
interaction between quantum and gravitational phenomena. I employ the
Unruh-DeWitt detector approach to probe thermal, quantum effects on static,
non-globally hyperbolic spacetimes. In this context, all physical observables
depend on the choice of a boundary condition that cannot be singled-out, in
general, without resorting to an experiment. Notwithstanding, the framework
applied admits a large family of (Robin) boundary conditions and grants us
physically-sensible dynamics and two-point functions of local Hadamard form. I
discover that the anti-Unruh/Hawking effects are not manifest for thermal
states on the BTZ black hole, nor on massless topological black holes of four
dimensions. Whilst the physical significance of these statistical effects
remains puzzling, my work corroborates their non-trivial relation with the KMS
condition and reveals the pivotal influence of the spacetime dimension in their
manifestation. On global monopoles, I find that for massless minimally coupled
fields the transition rate, the thermal fluctuations and the energy density
remain finite at the singularity only for Dirichlet boundary condition. For
conformally coupled fields, although the energy density diverges for all
boundary conditions, the transition rate and the thermal fluctuations vanish at
the monopole; indicating that even if there is infinite energy, no spontaneous
emission occur if the quantum field is not fluctuating. Moreover, I explicitly
construct two-point functions for ground and thermal states on Lifshitz
topological black holes, setting the ground for future explorations in this
Lorentz breaking context.


title: Non-Abelian Toda lattice and analogs of Painlev\'e III equation

abstract:
  In integrable models, stationary equations for higher symmetries serve as one
of the main sources of reductions consistent with dynamics. We apply this
method to the non-Abelian two-dimensional Toda lattice. It is shown that
already the stationary equation of the simplest higher flow gives a non-trivial
non-autonomous constraint that reduces the Toda lattice to a non-Abelian analog
of the pumped Maxwell--Bloch equations. The Toda lattice itself is interpreted
as an auto-B\"acklund transformation acting on the solutions of this system.
Further self-similar reduction leads to non-Abelian analogs of the Painlev\'e
III equation.


title:
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
  Tasks

abstract:
  Machine learning models often fail to generalize well under distributional
shifts. Understanding and overcoming these failures have led to a research
field of Out-of-Distribution (OOD) generalization. Despite being extensively
studied for static computer vision tasks, OOD generalization has been
underexplored for time series tasks. To shine light on this gap, we present
WOODS: eight challenging open-source time series benchmarks covering a diverse
range of data modalities, such as videos, brain recordings, and sensor signals.
We revise the existing OOD generalization algorithms for time series tasks and
evaluate them using our systematic framework. Our experiments show a large room
for improvement for empirical risk minimization and OOD generalization
algorithms on our datasets, thus underscoring the new challenges posed by time
series tasks. Code and documentation are available at
https://woods-benchmarks.github.io .


title: Groupes de Coxeter finis : centralisateurs d'involutions

abstract:
  We give a case-by-case description of the centralizers of involutions in
finite Coxeter groups.


title:
X-ray Nano-imaging of Defects in Thin Film Catalysts via Cluster
  Analysis

abstract:
  Functional properties of transition-metal oxides strongly depend on
crystallographic defects. In transition-metal-oxide electrocatalysts such as
SrIrO3 (SIO), crystallographic lattice deviations can affect ionic diffusion
and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging
of local structural distortions across an extended spatial region of thin
samples. Line defects remain challenging to detect and localize using
nanodiffraction, due to their weak diffuse scattering. Here we apply an
unsupervised machine learning clustering algorithm to isolate the low-intensity
diffuse scattering in as-grown and alkaline-treated thin epitaxially strained
SIO films. We pinpoint the defect locations, find additional strain variation
in the morphology of electrochemically cycled SIO, and interpret the defect
type by analyzing the diffraction profile through clustering. Our findings
demonstrate the use of a machine learning clustering algorithm for identifying
and characterizing hard-to-find crystallographic defects in thin films of
electrocatalysts and highlight the potential to study electrochemical reactions
at defect sites in operando experiments.


title: Image Storage on Synthetic DNA Using Autoencoders

abstract:
  Over the past years, the ever-growing trend on data storage demand, more
specifically for "cold" data (rarely accessed data), has motivated research for
alternative systems of data storage. Because of its biochemical
characteristics, synthetic DNA molecules are now considered as serious
candidates for this new kind of storage. This paper presents some results on
lossy image compression methods based on convolutional autoencoders adapted to
DNA data storage.
  The model architectures presented here have been designed to efficiently
compress images, encode them into a quaternary code, and finally store them
into synthetic DNA molecules. This work also aims at making the compression
models better fit the problematics that we encounter when storing data into
DNA, namely the fact that the DNA writing, storing and reading methods are
error prone processes. The main take away of this kind of compressive
autoencoder is our quantization and the robustness to substitution errors
thanks to the noise model that we use during training.


title:
CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented
  Cross-lingual Natural Language Understanding

abstract:
  Task-oriented personal assistants enable people to interact with a host of
devices and services using natural language. One of the challenges of making
neural dialogue systems available to more users is the lack of training data
for all but a few languages. Zero-shot methods try to solve this issue by
acquiring task knowledge in a high-resource language such as English with the
aim of transferring it to the low-resource language(s). To this end, we
introduce CrossAligner, the principal method of a variety of effective
approaches for zero-shot cross-lingual transfer based on learning alignment
from unlabelled parallel data. We present a quantitative analysis of individual
methods as well as their weighted combinations, several of which exceed
state-of-the-art (SOTA) scores as evaluated across nine languages, fifteen test
sets and three benchmark multilingual datasets. A detailed qualitative error
analysis of the best methods shows that our fine-tuned language models can
zero-shot transfer the task knowledge better than anticipated.


title:
Kramers-Kronig Relations for Nonlinear Rheology: 2. Validation of Medium
  Amplitude Oscillatory Shear (MAOS) Measurements

abstract:
  The frequency dependence of third-harmonic medium amplitude oscillatory shear
(MAOS) modulus $G_{33}^{*}(\omega)$ provides insight into material behavior and
microstructure in the asymptotically nonlinear regime. Motivated by the
difficulty in the measurement of MAOS moduli, we propose a test for data
validation based on nonlinear Kramers-Kronig relations. We extend the approach
used to assess the consistency of linear viscoelastic data by expressing the
real and imaginary parts of $G_{33}^{*}(\omega)$ as a linear combination of
Maxwell elements: the functional form for the MAOS kernels is inspired by
time-strain separability (TSS). We propose a statistical fitting technique
called the SMEL test, which works well on a broad range of materials and models
including those that do not obey TSS. It successfully copes with experimental
data that are noisy, or confined to a limited frequency range. When Maxwell
modes obtained from the SMEL test are used to predict the first-harmonic MAOS
modulus $G_{31}^{*}$, it is possible to identify the range of timescales over
which a material exhibits TSS.


title:
Kramers-Kronig Relations for Nonlinear Rheology: 1. General Expression
  and Implications

abstract:
  The principle of causality leads to linear Kramers-Kronig relations (KKR)
that relate the real and imaginary parts of the complex modulus $G^{*}$ through
integral transforms. Using the multiple integral generalization of the
Boltzmann superposition principle for nonlinear rheology, and the principle of
causality, we derived nonlinear KKR, which relate the real and imaginary parts
of the $n^\text{th}$ order complex modulus $G_{n}^{*}$. For $n$=3, we obtained
nonlinear KKR for medium amplitude parallel superposition (MAPS) rheology. A
special case of MAPS is medium amplitude oscillatory shear (MAOS); we obtained
MAOS KKR for the third-harmonic MAOS modulus $G_{33}^{*}$; however, no such KKR
exists for the first harmonic MAOS modulus $G_{31}^{*}$. We verified MAPS and
MAOS KKR for the single mode Giesekus model. We also probed the sensitivity of
MAOS KKR when the domain of integration is truncated to a finite frequency
window. We found that that (i) inferring $G_{33}^{\prime\prime}$ from
$G_{33}^{\prime}$ is more reliable than vice-versa, (ii) predictions over a
particular frequency range require approximately an excess of one decade of
data beyond the frequency range of prediction, and (iii) $G_{33}^{\prime}$ is
particularly susceptible to errors at large frequencies.


title:
On the Precision of Full-Spectrum Fitting of Simple Stellar Populations.
  IV. A Systematic Comparison with Results from Colour-Magnitude Diagrams

abstract:
  In this fourth paper of a series on the precision of ages of stellar
populations obtained through the full-spectrum fitting technique, we present a
first systematic analysis that compare the age, metallicity and reddening of
star clusters obtained from resolved and unresolved data (namely
colour-magnitude diagrams (CMDs) and integrated-light spectroscopy) using the
same sets of isochrones. We investigate the results obtained with both Padova
isochrones and MIST isochrones. We find that there generally is a good
agreement between the ages derived from CMDs and integrated spectra. However,
for metallicity and reddening, the agreement between results from analyses of
CMD and integrated spectra is significantly worse. Our results also show that
the ages derived with Padova isochrones match those derived using MIST
isochrones, both with the full spectrum fitting technique and the CMD fitting
method. However, the metallicity derived using Padova isochrones does not match
that derived using MIST isochrones using the CMD method. We examine the ability
of the full-spectrum fitting technique in detecting age spreads in clusters
that feature the extended Main Sequence Turn Off (eMSTO) phenomenon using
two-population fits. We find that 3 out of 5 eMSTO clusters in our sample are
best fit with one single age, suggesting that eMSTOs do not necessarily
translate to detectable age spreads in integrated-light studies.


title:
GiNGR: Generalized Iterative Non-Rigid Point Cloud and Surface
  Registration Using Gaussian Process Regression

abstract:
  In this paper, we unify popular non-rigid registration methods for point sets
and surfaces under our general framework, GiNGR. GiNGR builds upon Gaussian
Process Morphable Models (GPMM) and hence separates modeling the deformation
prior from model adaptation for registration. In addition, it provides
explainable hyperparameters, multi-resolution registration, trivial inclusion
of expert annotation, and the ability to use and combine analytical and
statistical deformation priors. But more importantly, the reformulation allows
for a direct comparison of registration methods. Instead of using a general
solver in the optimization step, we show how Gaussian process regression (GPR)
iteratively can warp a reference onto a target, leading to smooth deformations
following the prior for any dense, sparse, or partial estimated correspondences
in a principled way. We show how the popular CPD and ICP algorithms can be
directly explained with GiNGR. Furthermore, we show how existing algorithms in
the GiNGR framework can perform probabilistic registration to obtain a
distribution of different registrations instead of a single best registration.
This can be used to analyze the uncertainty e.g. when registering partial
observations. GiNGR is publicly available and fully modular to allow for
domain-specific prior construction.


title:
Uniaxial Pressure Induced Stripe Order Rotation in
  La$_{1.88}$Sr$_{0.12}$CuO$_4$

abstract:
  Static stripe order is detrimental to superconductivity. Yet, it has been
proposed that transverse stripe fluctuations may enhance the inter-stripe
Josephson coupling and thus promote superconductivity. Direct experimental
studies of stripe dynamics, however, remain difficult. From a strong-coupling
perspective, transverse stripe fluctuations are realized in the form of dynamic
"kinks" -- sideways shifting stripe sections. Here, we show how modest uniaxial
pressure tuning reorganizes directional kink alignment. Our starting point is
La$_{1.88}$Sr$_{0.12}$CuO$_4$, where transverse kink ordering results in a
rotation of stripe order away from the crystal axis. Application of mild
uniaxial pressure changes the ordering pattern and pins the stripe order to the
crystal axis. This reordering occurs at a much weaker pressure than that to
detwin the stripe domains and suggests a rather weak transverse stripe
stiffness. Weak spatial stiffness and transverse quantum fluctuations are
likely key prerequisites for stripes to coexist with superconductivity.


title:
A constrained Shannon-Fano entropy coder for image storage in synthetic
  DNA

abstract:
  The exponentially increasing demand for data storage has been facing more and
more challenges during the past years. The energy costs that it represents are
also increasing, and the availability of the storage hardware is not able to
follow the storage demand's trend. The short lifespan of conventional storage
media -- 10 to 20 years - forces the duplication of the hardware and worsens
the situation. The majority of this storage demand concerns "cold" data, data
very rarely accessed but that has to be kept for long periods of time. The
coding abilities of synthetic DNA, and its long durability (several hundred
years), make it a serious candidate as an alternative storage media for "cold"
data. In this paper, we propose a variable-length coding algorithm adapted to
DNA data storage with improved performance. The proposed algorithm is based on
a modified Shannon-Fano code that respects some biochemichal constraints
imposed by the synthesis chemistry. We have inserted this code in a JPEG
compression algorithm adapted to DNA image storage and we highlighted an
improvement of the compression ratio ranging from 0.5 up to 2 bits per
nucleotide compared to the state-of-the-art solution, without affecting the
reconstruction quality.


title: Verification of colorable hypergraph states with stabilizer test

abstract:
  Many-body quantum states, as a matter of fact, are extremely essential to
solve certain mathematical problems or simulate quantum systems in
measurement-based quantum computation. However, how to verify large scale
quantum states, such as hypergraph states, is an exceedingly hard task for
multi-body quantum systems. Here, we propose a novel fault-tolerant solution
for verification of colorable hypergraph states by using stabilizer test.
Compared with the adaptive stabilizer test, our protocol is dramatically
facilitating by making only Pauli-X and Pauli-Z measurement. As to appliance,
it will be also applied to blind quantum computing.


title:
Identifying structure-absorption relationships and predicting absorption
  strength of non-fullerene acceptors for organic photovoltaics

abstract:
  Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin
of such high optical extinction is not well understood. In this work, we
investigate the absorption strength of NFAs by building a database of
time-dependent density functional theory (TDDFT) calculations of ~500
pi-conjugated molecules. The calculations are first validated by comparison
with experimental measurements on liquid and solid state using common fullerene
and non-fullerene acceptors. We find that the molar extinction coefficient
({\epsilon}_(d,max)) shows reasonable agreement between calculation in vacuum
and experiment for molecules in solution, highlighting the effectiveness of
TDDFT for predicting optical properties of organic pi-conjugated molecules. We
then perform a statistical analysis based on molecular descriptors to identify
which features are important in defining the absorption strength. This allows
us to identify structural features that are correlated with high absorption
strength in NFAs and could be used to guide molecular design: highly absorbing
NFAs should possess a planar, linear, and fully conjugated molecular backbone
with highly polarisable heteroatoms. We then exploit a random decision forest
to draw predictions for {\epsilon}_(d,max) using a computational framework
based on extended tight-binding Hamiltonians, which shows reasonable predicting
accuracy with lower computational cost than TDDFT. This work provides a general
understanding of the relationship between molecular structure and absorption
strength in pi-conjugated organic molecules, including NFAs, while introducing
predictive machine-learning models of low computational cost.


title: Optical and Near-infrared View of Planet-forming Disks and Protoplanets

abstract:
  In this chapter of the Protostars and Planets VII, we review the breakthrough
progress that has been made in the field of high-resolution, high-contrast
optical and near-infrared imaging of planet-forming disks. These advancements
include the direct detection of protoplanets embedded in some disks, and
derived limits on planetary masses in others. Morphological substructures,
including: rings, spirals, arcs, and shadows, are seen in all imaged
infrared-bright disks to date, and are ubiquitous across spectral types. These
substructures are believed to be the result of disk evolution processes, and in
particular disk-planet interactions. Since small dust grains that scatter light
are tightly bound to the disk's gas, these observations closely trace disk
structures predicted by hydrodynamical models and serve as observational tests
of the predictions of planet formation theories. We argue that the results of
current and next-generation high-contrast imaging surveys will, when combined
with complementary data from ALMA, lead to a much deeper understanding of the
co-evolution of disks and planets, and the mechanisms by which planets form.


title:
Diffusion and Volume Maximization-Based Clustering of Highly Mixed
  Hyperspectral Images

abstract:
  Hyperspectral images of a scene or object are a rich data source, often
encoding a hundred or more spectral bands of reflectance at each pixel. Despite
being very high-dimensional, these images typically encode latent
low-dimensional structure that can be exploited for material discrimination.
However, due to an inherent trade-off between spectral and spatial resolution,
many hyperspectral images are generated at a coarse spatial scale, and single
pixels may correspond to spatial regions containing multiple materials. This
article introduces the Diffusion and Volume maximization-based Image Clustering
(D-VIC) algorithm for unsupervised material discrimination. D-VIC locates
cluster modes - high-density, high-purity pixels in the hyperspectral image
that are far in diffusion distance (a data-dependent distance metric) from
other high-density, high-purity pixels - and assigns these pixels unique
labels, as these points are meant to exemplify underlying material structure.
Non-modal pixels are labeled according to their diffusion distance nearest
neighbor of higher density and purity that is already labeled. By directly
incorporating pixel purity into its modal and non-modal labeling, D-VIC
upweights pixels that correspond to a spatial region containing just a single
material, yielding more interpretable clusterings. D-VIC is shown to outperform
baseline and comparable state-of-the-art methods in extensive numerical
experiments on a range of hyperspectral images, implying that it is
well-equipped for material discrimination and clustering of these data.


title:
WebRobot: Web Robotic Process Automation using Interactive
  Programming-by-Demonstration

abstract:
  It is imperative to democratize robotic process automation (RPA), as RPA has
become a main driver of the digital transformation but is still technically
very demanding to construct, especially for non-experts. In this paper, we
study how to automate an important class of RPA tasks, dubbed web RPA, which
are concerned with constructing software bots that automate interactions across
data and a web browser. Our main contributions are twofold. First, we develop a
formal foundation which allows semantically reasoning about web RPA programs
and formulate its synthesis problem in a principled manner. Second, we propose
a web RPA program synthesis algorithm based on a new idea called speculative
rewriting. This leads to a novel speculate-and-validate methodology in the
context of rewrite-based program synthesis, which has also shown to be both
theoretically simple and practically efficient for synthesizing programs from
demonstrations. We have built these ideas in a new interactive synthesizer
called WebRobot and evaluate it on 76 web RPA benchmarks. Our results show that
WebRobot automated a majority of them effectively. Furthermore, we show that
WebRobot compares favorably with a conventional rewrite-based synthesis
baseline implemented using egg. Finally, we conduct a small user study
demonstrating WebRobot is also usable.


title: Graph-Text Multi-Modal Pre-training for Medical Representation Learning

abstract:
  As the volume of Electronic Health Records (EHR) sharply grows, there has
been emerging interest in learning the representation of EHR for healthcare
applications. Representation learning of EHR requires appropriate modeling of
the two dominant modalities in EHR: structured data and unstructured text. In
this paper, we present MedGTX, a pre-trained model for multi-modal
representation learning of the structured and textual EHR data. MedGTX uses a
novel graph encoder to exploit the graphical nature of structured EHR data, and
a text encoder to handle unstructured text, and a cross-modal encoder to learn
a joint representation space. We pre-train our model through four proxy tasks
on MIMIC-III, an open-source EHR data, and evaluate our model on two clinical
benchmarks and three novel downstream tasks which tackle real-world problems in
EHR data. The results consistently show the effectiveness of pre-training the
model for joint representation of both structured and unstructured information
from EHR. Given the promising performance of MedGTX, we believe this work opens
a new door to jointly understanding the two fundamental modalities of EHR data.


title: Elastica Models for Color Image Regularization

abstract:
  One classical approach to regularize color is to tream them as two
dimensional surfaces embedded in a five dimensional spatial-chromatic space. In
this case, a natural regularization term arises as the image surface area.
Choosing the chromatic coordinates as dominating over the spatial ones, the
image spatial coordinates could be thought of as a paramterization of the image
surface manifold in a three dimensional color space. Minimizing the area of the
image manifold leads to the Beltrami flow or mean curvature flow of the image
surface in the 3D color space, while minimizing the elastica of the image
surface yields an additional interesting regularization. Recently, the authors
proposed a color elastica model, which minimizes both the surface area and
elastica of the image manifold. In this paper, we propose to modify the color
elastica and introduce two new models for color image regularization. The
revised measures are motivated by the relations between the color elastica
model, Euler's elastica model and the total variation model for gray level
images. Compared to our previous color elastica model, the new models are
direct extensions of Euler's elastica model to color images. The proposed
models are nonlinear and challenging to minimize. To overcome this difficulty,
two operator-splitting methods are suggested. Specifically, nonlinearities are
decoupled by introducing new vector- and matrix-valued variables. Then, the
minimization problems are converted to solving initial value problems which are
time-discretized by operator splitting. Each subproblem, after splitting
either, has a closed-form solution or can be solved efficiently. The
effectiveness and advantages of the proposed models are demonstrated by
comprehensive experiments. The benefits of incorporating the elastica of the
image surface as regularization terms compared to common alternatives are
empirically validated.


title:
Effects of two-body currents in the one-particle one-hole
  electromagnetic responses within a relativistic mean-field model

abstract:
  The longitudinal and transverse experimental responses from inclusive
electron scattering are simultaneously reproduced using an independent-particle
relativistic mean-field model to describe the initial and final states, and
one- and two-body current operators as the interaction mechanism that leads to
the one particle-one hole responses. We find that the two-body operator has
little effect on the longitudinal response but it increases the transverse one
by up to 35%, depending on the energy and momentum transfer. Going beyond the
plane-wave approach, i.e., incorporating the distortion of the nucleons, and
making the initial and final states orthogonal, is essential to reproduce both
the shape and magnitude of the responses. Our calculation is fully relativistic
and considers in a coherent quantum mechanical description the initial and
final state of the nucleon involved in the vertex, incorporating realistic
dynamics. The good agreement with the experimental data reinforces the idea of
using this approach to describe neutrino-induced scattering reaction.


title:
Semileptonic $B_{s}\to K_0^*(1430)$ transitions with the light-cone sum
  rules

abstract:
  In the $\rm SU(3)_F$ symmetry limit, using two- and three-particle
distribution amplitudes of $B^{\pm}$-meson for $B_s$, the transition form
factors of semileptonic $B_{s}\to K_{0}^*(1430)$ decays are calculated in the
framework of the light-cone sum rules. The two-particle distribution
amplitudes, $\varphi_{_{+}}(\omega)$ and $\varphi_{_{-}}(\omega)$ have the most
important contribution in estimation of the form factors $f_{+}(q^2)$,
$f_{-}(q^2)$ and $f_{T}(q^2)$. The knowledge of the behavior of
$\varphi_{_+}(\omega)$ is still rather limited. Therefore, we consider three
different parametrizations for the shapes of $\varphi_{_+}(\omega)$ that are
derived from the phenomenological models. Using the form factors $f_{+}$,
$f_{-}$ and $f_{T}$, the semileptonic $B_s \to K_0^*(1430) l \bar{\nu_{l}}$ and
$B_s \to K_0^*(1430) l \bar{l}/\nu \bar{\nu}$, $l=e, \mu, \tau$ decays are
analyzed. The branching fractions for the aforementioned decays, in addition
the longitudinal lepton polarization asymmetries are calculated. A comparison
between our results with predictions of other approaches is provided.


title:
Casimir-Polder interactions of Rydberg atoms with graphene-based van der
  Waals heterostructures

abstract:
  We investigate the thermal Casimir-Polder (CP) potential of
\textsuperscript{87}Rb atoms in Rydberg $n$S-states near single- and
double-layer graphene. The dependence of the CP potential on parameters such as
atom-surface distance, temperature, principal quantum number $n$ and graphene
Fermi energy are explored. Through large scale numerical simulations, we show
that, in the non-retarded regime, the CP potential is dominated by the
non-resonant and evanescent-wave terms which are monotonic, and that, in the
retarded regime, the CP potential exhibits spatial oscillations. We identify
that the most important contributions to the resonant component of the CP
potential come from the $n$S-$n$P and $n$S-$(n-1)$P transitions. Scaling of the
CP potential as a function of the principal quantum number and temperature is
obtained. A heterostructure comprising hexagonal boron nitride layers
sandwiched between two graphene layers is also studied. When the boron nitride
layer is sufficiently thin, the CP potential can be weakened by changing the
Fermi energy of the top graphene layer. Our study provides insights for
understanding and controlling CP potentials experienced by Rydberg atoms near
single and multi-layer graphene-based van der Waals heterostructures.


title: Lens free holographic imaging for urinary tract infection screening

abstract:
  Urinary tract infections (UTIs) are a common condition that can lead to
serious complications including kidney injury, altered mental status, sepsis,
and death. Laboratory tests such as urinalysis and urine culture are the
mainstays of UTI diagnosis, whereby a urine specimen is collected and processed
to reveal its cellular and chemical composition. This process requires precise
specimen collection, handling infectious human waste, controlled urine storage,
and timely transportation to modern laboratory equipment for analysis.
Holographic lens free imaging (LFI) can measure large volumes of urine via a
simple and compact optical setup, potentially enabling automatic urine analysis
at the patient bedside. We introduce an LFI system capable of resolving
important urine clinical biomarkers such as red blood cells, white blood cells,
crystals, casts, and E. Coli in urine phantoms. This approach is sensitive to
the particulate concentrations relevant for detecting several clinical urine
abnormalities such as hematuria, pyuria, and bacteriuria. We show bacteria
concentrations across eight orders of magnitude can be estimated by analyzing
LFI measurements. LFI measurements of blood cell concentrations are relatively
insensitive to changes in bacteria concentrations of over seven orders of
magnitude. Lastly, LFI reveals clear differences between UTI-positive and
UTI-negative urine from human patients. Together, these results show promise
for LFI as a tool for urine screening, potentially offering early,
point-of-care detection of UTI and other pathological processes.


title:
Highly Adaptive and Automated Tetrahedral Mesh Generator for
  Multi-Compartment Human Head Model with Deep Brain Structures in EEG

abstract:
  This paper introduces a highly adaptive and automated approach for generating
Finite Element (FE) discretization for a given realistic multi-compartment
human head model obtained through magnetic resonance imaging (MRI) dataset. We
aim at obtaining accurate tetrahedral FE meshes for electroencephalographic
source localization. We present recursive solid angle labeling for the surface
segmentation of the model and then adapt it with a set of smoothing, inflation,
and optimization routines to further enhance the quality of the FE mesh. The
results show that our methodology can produce FE mesh with an accuracy greater
than 1 millimeter, significant with respect to both their 3D structure
discretization outcome and electroencephalographic source localization
estimates. FE meshes can be achieved for the human head including complex deep
brain structures. Our algorithm has been implemented using the open
Matlab-based Zeffiro Interface toolbox with it effective time-effective
parallel computing system.}


title: FORCE: A Framework of Rule-Based Conversational Recommender System

abstract:
  The conversational recommender systems (CRSs) have received extensive
attention in recent years. However, most of the existing works focus on various
deep learning models, which are largely limited by the requirement of
large-scale human-annotated datasets. Such methods are not able to deal with
the cold-start scenarios in industrial products. To alleviate the problem, we
propose FORCE, a Framework Of Rule-based Conversational Recommender system that
helps developers to quickly build CRS bots by simple configuration. We conduct
experiments on two datasets in different languages and domains to verify its
effectiveness and usability.


title:
A Comparison of Different Methods to Adjust Survival Curves for
  Confounders

abstract:
  Treatment specific survival curves are an important tool to illustrate the
treatment effect in studies with time-to-event outcomes. In non-randomized
studies, unadjusted estimates can lead to biased depictions due to confounding.
Multiple methods to adjust survival curves for confounders exist. However, it
is currently unclear which method is the most appropriate in which situation.
Our goal is to compare these methods in different scenarios with a focus on
their bias and goodness-of-fit. We provide a short review of all methods and
illustrate their usage by contrasting the survival of smokers and non-smokers,
using data from the German Epidemiological Trial on Ankle Brachial Index.
Subsequently, we compare the methods using a Monte-Carlo simulation. We
consider scenarios in which correctly or incorrectly specified covariate sets
for describing the treatment assignment and the time-to-event outcome are used
with varying sample sizes. The bias and goodness-of-fit is determined by
summary statistics which take into account the entire survival curve. When used
properly, all methods showed no systematic bias in medium to large samples.
Cox-Regression based methods, however, showed systematic bias in small samples.
The goodness-of-fit varied greatly between different methods and scenarios.
Methods utilizing an outcome model were more efficient than other techniques,
while augmented estimators using an additional treatment assignment model were
unbiased when either model was correct with a goodness-of-fit comparable to
other methods. These "doubly-robust" methods have important advantages in every
considered scenario. Pseudo-Value based methods, coupled with isotonic
regression to correct for non-monotonicity, are viable alternatives to
traditional methods.


title: On flag spheres with few equators

abstract:
  In this note we construct a flag simplicial $3$-sphere $\Delta$ with the
following properties: - $\Delta$ is not a suspension; - $\Delta$ has no edge
that can be contracted to obtain another flag sphere; - The only equators
(induced subcomplexes which are spheres of codimension $1$) of $\Delta$ are
vertex links. Our construction has $12$ vertices, the minimum number of
vertices such a simplicial complex can have. This answers a question posed by
Chudnovsky and Nevo.


title: Observation of the Wannier-Stark ladder in plasmonic waveguide arrays

abstract:
  Evanescently coupled waveguides are a powerful platform to study and
visualize the wave dynamics in tight-binding systems. Here, we investigate the
propagation of surface plasmon polaritons in arrays of dielectric loaded
surface plasmon polariton waveguides with a propagation constant gradient
acting as an effective external potential. Using leakage radiation microscopy,
we observe in real-space for single site excitation a periodic breathing of the
wavepacket and an oscillatory motion in the case of Gaussian excitation of
multiple waveguides. The corresponding momentum resolved spectra are composed
of sets of equally spaced modes. We interpret these observation as the
plasmonic analogues of Bloch oscillations and the Wannier-Stark ladder,
respectively.


title:
Application of Top-hat Transformation for Enhanced Blood Vessel
  Extraction

abstract:
  In the medical domain, different computer-aided diagnosis systems have been
proposed to extract blood vessels from retinal fundus images for the clinical
treatment of vascular diseases. Accurate extraction of blood vessels from the
fundus images using a computer-generated method can help the clinician to
produce timely and accurate reports for the patient suffering from these
diseases. In this article, we integrate top-hat based preprocessing approach
with fine-tuned B-COSFIRE filter to achieve more accurate segregation of blood
vessel pixels from the background. The use of top-hat transformation in the
preprocessing stage enhances the efficacy of the algorithm to extract blood
vessels in presence of structures like fovea, exudates, haemorrhages, etc.
Furthermore, to reduce the false positives, small clusters of blood vessel
pixels are removed in the postprocessing stage. Further, we find that the
proposed algorithm is more efficient as compared to various modern algorithms
reported in the literature.


title:
Ultra-low Latency Spiking Neural Networks with Spatio-Temporal
  Compression and Synaptic Convolutional Block

abstract:
  Spiking neural networks (SNNs), as one of the brain-inspired models, has
spatio-temporal information processing capability, low power feature, and high
biological plausibility. The effective spatio-temporal feature makes it
suitable for event streams classification. However, neuromorphic datasets, such
as N-MNIST, CIFAR10-DVS, DVS128-gesture, need to aggregate individual events
into frames with a new higher temporal resolution for event stream
classification, which causes high training and inference latency. In this work,
we proposed a spatio-temporal compression method to aggregate individual events
into a few time steps of synaptic current to reduce the training and inference
latency. To keep the accuracy of SNNs under high compression ratios, we also
proposed a synaptic convolutional block to balance the dramatic change between
adjacent time steps. And multi-threshold Leaky Integrate-and-Fire (LIF) with
learnable membrane time constant is introduced to increase its information
processing capability. We evaluate the proposed method for event streams
classification tasks on neuromorphic N-MNIST, CIFAR10-DVS, DVS128 gesture
datasets. The experiment results show that our proposed method outperforms the
state-of-the-art accuracy on nearly all datasets, using fewer time steps.


title: OB Associations

abstract:
  OB associations are low-density groups of young stars that are dispersing
from their birth environment into the Galactic field. They are important for
understanding the star formation process, early stellar evolution, the
properties and distribution of young stars and the processes by which young
stellar groups disperse. Recent observations, particularly from Gaia, have
shown that associations are highly complex, with a high degree of spatial,
kinematic and temporal substructure. The kinematics of associations have shown
them to be globally unbound and expanding, with the majority of recent studies
revealing evidence for clear expansion patterns in the association subgroups,
suggesting the subgroups were more compact in the past. This expansion is often
non-isotropic, arguing against a simple explosive expansion, as predicted by
some models of residual gas expulsion. The star formation histories of
associations are often complex, exhibit moderate age spreads and temporal
substructure, but so far have failed to reveal simple patterns of star
formation propagation (e.g., triggering). These results have challenged the
historical paradigm of the origin of associations as the expanded remnants of
dense star clusters and suggests instead that they originate as highly
substructured systems without a linear star formation history, but with
multiple clumps of stars that have since expanded and begun to overlap,
producing the complex systems we observe today. This has wide-ranging
consequences for the early formation environments of most stars and planetary
systems, including our own Solar System.


title:
Picosecond Hyperspectral Fringe Pattern Projection for 3D Surface
  Measurement

abstract:
  Active stereovision systems for the 3D measurement of surfaces rely on the
sequential projection of different fringe patterns onto the scene to robustly
and accurately generate 3D surface data. This limits the temporal resolution to
the time by which a sufficiently high number of patterns can be projected and
recorded. By encoding patterns spectrally and recording them with a
hyperspectral imager, it is possible to record several patterns in a single
image, limiting the temporal resolution to only the duration of the
illumination. A picosecond 3D surface measurement was demonstrated using a high
pulse energy femtosecond Ti:Sa laser, spectrally broadened in a hollow core
fiber, and two hyperspectral cameras recording the patterns generated by
diffraction at an Echelle grating.


title: Analyzing EEG Data with Machine and Deep Learning: A Benchmark

abstract:
  Nowadays, machine and deep learning techniques are widely used in different
areas, ranging from economics to biology. In general, these techniques can be
used in two ways: trying to adapt well-known models and architectures to the
available data, or designing custom architectures. In both cases, to speed up
the research process, it is useful to know which type of models work best for a
specific problem and/or data type. By focusing on EEG signal analysis, and for
the first time in literature, in this paper a benchmark of machine and deep
learning for EEG signal classification is proposed. For our experiments we used
the four most widespread models, i.e., multilayer perceptron, convolutional
neural network, long short-term memory, and gated recurrent unit, highlighting
which one can be a good starting point for developing EEG classification
models.


title: CaMEL: Case Marker Extraction without Labels

abstract:
  We introduce CaMEL (Case Marker Extraction without Labels), a novel and
challenging task in computational morphology that is especially relevant for
low-resource languages. We propose a first model for CaMEL that uses a
massively multilingual corpus to extract case markers in 83 languages based
only on a noun phrase chunker and an alignment system. To evaluate CaMEL, we
automatically construct a silver standard from UniMorph. The case markers
extracted by our model can be used to detect and visualise similarities and
differences between the case systems of different languages as well as to
annotate fine-grained deep cases in languages in which they are not overtly
marked.


title: Rate-optimal sparse approximation of compact break-of-scale embeddings

abstract:
  The paper is concerned with the sparse approximation of functions having
hybrid regularity borrowed from the theory of solutions to electronic
Schr\"odinger equations due to Yserentant [43]. We use hyperbolic wavelets to
introduce corresponding new spaces of Besov- and Triebel-Lizorkin-type to
particularly cover the energy norm approximation of functions with dominating
mixed smoothness. Explicit (non-)adaptive algorithms are derived that yield
sharp dimension-independent rates of convergence.


title:
Report from the NSF Future Directions Workshop on Automatic Evaluation
  of Dialog: Research Directions and Challenges

abstract:
  This is a report on the NSF Future Directions Workshop on Automatic
Evaluation of Dialog. The workshop explored the current state of the art along
with its limitations and suggested promising directions for future work in this
important and very rapidly changing area of research.


title:
PYROBOCOP: Python-based Robotic Control & Optimization Package for
  Manipulation

abstract:
  PYROBOCOP is a Python-based package for control, optimization and estimation
of robotic systems described by nonlinear Differential Algebraic Equations
(DAEs). In particular, the package can handle systems with contacts that are
described by complementarity constraints and provides a general framework for
specifying obstacle avoidance constraints. The package performs direct
transcription of the DAEs into a set of nonlinear equations by performing
orthogonal collocation on finite elements. PYROBOCOP provides automatic
reformulation of the complementarity constraints that are tractable to NLP
solvers to perform optimization of robotic systems. The package is interfaced
with ADOL-C[1] for obtaining sparse derivatives by automatic differentiation
and IPOPT[2] for performing optimization. We evaluate PYROBOCOP on several
manipulation problems for control and estimation.


title:
Parametric Scaling of Preprocessing assisted U-net Architecture for
  Improvised Retinal Vessel Segmentation

abstract:
  Extracting blood vessels from retinal fundus images plays a decisive role in
diagnosing the progression in pertinent diseases. In medical image analysis,
vessel extraction is a semantic binary segmentation problem, where blood
vasculature needs to be extracted from the background. Here, we present an
image enhancement technique based on the morphological preprocessing coupled
with a scaled U-net architecture. Despite a relatively less number of trainable
network parameters, the scaled version of U-net architecture provides better
performance compare to other methods in the domain. We validated the proposed
method on retinal fundus images from the DRIVE database. A significant
improvement as compared to the other algorithms in the domain, in terms of the
area under ROC curve (>0.9762) and classification accuracy (>95.47%) are
evident from the results. Furthermore, the proposed method is resistant to the
central vessel reflex while sensitive to detect blood vessels in the presence
of background items viz. exudates, optic disc, and fovea.


title:
Second-order optimality conditions for general nonconvex optimization
  problems and variational analysis of disjunctive systems

abstract:
  In this paper, we propose second-order sufficient optimality conditions for a
very general nonconvex constrained optimization problem, which covers many
prominent mathematical programs.Unlike the existing results in the literature,
our conditions prove to be sufficient, for an essential local minimizer of
second order, under merely basic smoothness and closedness assumptions on the
data defining the problem.In the second part, we propose a comprehensive first-
and second-order variational analysis of disjunctive systems and demonstrate
how the second-order objects appearing in the optimality conditions can be
effectively computed in this case.


title: ESS: Learning Event-based Semantic Segmentation from Still Images

abstract:
  Retrieving accurate semantic information in challenging high dynamic range
(HDR) and high-speed conditions remains an open challenge for image-based
algorithms due to severe image degradations. Event cameras promise to address
these challenges since they feature a much higher dynamic range and are
resilient to motion blur. Nonetheless, semantic segmentation with event cameras
is still in its infancy which is chiefly due to the novelty of the sensor, and
the lack of high-quality, labeled datasets. In this work, we introduce ESS,
which tackles this problem by directly transferring the semantic segmentation
task from existing labeled image datasets to unlabeled events via unsupervised
domain adaptation (UDA). Compared to existing UDA methods, our approach aligns
recurrent, motion-invariant event embeddings with image embeddings. For this
reason, our method neither requires video data nor per-pixel alignment between
images and events and, crucially, does not need to hallucinate motion from
still images. Additionally, to spur further research in event-based semantic
segmentation, we introduce DSEC-Semantic, the first large-scale event-based
dataset with fine-grained labels. We show that using image labels alone, ESS
outperforms existing UDA approaches, and when combined with event labels, it
even outperforms state-of-the-art supervised approaches on both DDD17 and
DSEC-Semantic. Finally, ESS is general-purpose, which unlocks the vast amount
of existing labeled image datasets and paves the way for new and exciting
research directions in new fields previously inaccessible for event cameras.


title: Quantum Algorithms for Testing Hamiltonian Symmetry

abstract:
  Symmetries in a Hamiltonian play an important role in quantum physics because
they correspond directly with conserved quantities of the related system. In
this paper, we propose quantum algorithms capable of testing whether a
Hamiltonian exhibits symmetry with respect to a group. We demonstrate that
familiar expressions of Hamiltonian symmetry in quantum mechanics correspond
directly with the acceptance probabilities of our algorithms. We execute one of
our symmetry-testing algorithms on existing quantum computers for simple
examples of both symmetric and asymmetric cases.