title:
The Resistive Nature of Decomposing Interfaces of Solid Electrolytes
  with Alkali Metal Electrodes

abstract:
  A crucial ingredient in lithium (Li) and sodium (Na)-ion batteries (LIBs and
NIBs) is the electrolytes. The use of Li-metal (Na-metal) as anode in liquid
electrolyte LIBs (NIBs) is constrained by several issues including thermal
runway and flammability, electrolyte leakage, and limited chemical stability.
Considerable effort has been devoted toward the development of solid
electrolytes (SEs) and all-solid-state batteries, which are presumed to
mitigate some of the issues of Li-metal(Na-metal) in contact with flammable
liquid electrolytes. However, most SEs, such as Li$_3$PS$_4$, Li$_6$PS$_5$Cl
and Na$_3$PS$_4$ readily decompose against the highly reducing Li-metal and
Na-metal anodes. Using first-principles calculations we elucidate the stability
of more than 20 solid$||$solid interfaces formed between the decomposition
products of Li$_3$PS$_4$, Li$_6$PS$_5$Cl (and Na$_3$PS$_4$) against the
Li-metal (Na-metal) electrode. We suggest that the work of adhesion needed to
form a hetereogenous interfaces is an important descriptor to quantify the
stability of interfaces. Subsequently, we clarify the atomistic origins of the
resistance to Li-ion transport at interfaces of the Li-metal anode and selected
decomposition products (Li$_3$P, Li$_2$S} and LiCl) of SEs, via a high-fidelity
machine learned potential (MLP). Utilising an MLP enables nano-second-long
molecular dynamics simulations on `large' interface models (here with 8320
atoms), but with similar accuracy to first-principles approaches. Our
simulations demonstrate that the interfaces formed between Li-metal and
argyrodite (e.g., Li$_6$PS$_5$Cl) decomposition products are resistive to
Li-ion transport. The implications of this study are important since binary
compounds are commonly found in the vicinity of Li(Na)-metal upon chemical
and/or electrochemical decomposition of ternary and quaternary SEs.


title:
From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based
  Visual Programming Tasks

abstract:
  Block-based visual programming environments are increasingly used to
introduce computing concepts to beginners. Given that programming tasks are
open-ended and conceptual, novice students often struggle when learning in
these environments. AI-driven programming tutors hold great promise in
automatically assisting struggling students, and need several components to
realize this potential. We investigate the crucial component of student
modeling, in particular, the ability to automatically infer students'
misconceptions for predicting (synthesizing) their behavior. We introduce a
novel benchmark, StudentSyn, centered around the following challenge: For a
given student, synthesize the student's attempt on a new target task after
observing the student's attempt on a fixed reference task. This challenge is
akin to that of program synthesis; however, instead of synthesizing a
{solution} (i.e., program an expert would write), the goal here is to
synthesize a {student attempt} (i.e., program that a given student would
write). We first show that human experts (TutorSS) can achieve high performance
on the benchmark, whereas simple baselines perform poorly. Then, we develop two
neuro/symbolic techniques (NeurSS and SymSS) in a quest to close this gap with
TutorSS. We will publicly release the benchmark to facilitate future research
in this area.


title:
The weak order on the hyperoctahedral group and the monomial basis for
  the Hopf algebra of signed permutations

abstract:
  We give a combinatorial description for the weak order on the hyperoctahedral
group. This characterization is then used to analyze the order-theoretic
properties of the shifted products of hyperoctahedral groups. It is shown that
each shifted product is a disjoint union of some intervals, which can be convex
embedded into a hyperoctahedral group. As an application, we investigate the
monomial basis for the Hopf algebra $\mathfrak{H}Sym$ of signed permutations,
related to the fundamental basis via M\"obius inversion on the weak order on
hyperoctahedral groups. It turns out that the image of a monomial basis element
under the descent map from $\mathfrak{H}Sym$ to the algebra of type $B$
quasi-symmetric functions is either zero or a monomial quasi-symmetric function
of type $B$.


title:
PropEM-L: Radio Propagation Environment Modeling and Learning for
  Communication-Aware Multi-Robot Exploration

abstract:
  Multi-robot exploration of complex, unknown environments benefits from the
collaboration and cooperation offered by inter-robot communication. Accurate
radio signal strength prediction enables communication-aware exploration.
Models which ignore the effect of the environment on signal propagation or rely
on a priori maps suffer in unknown, communication-restricted (e.g.
subterranean) environments. In this work, we present Propagation Environment
Modeling and Learning (PropEM-L), a framework which leverages real-time
sensor-derived 3D geometric representations of an environment to extract
information about line of sight between radios and attenuating walls/obstacles
in order to accurately predict received signal strength (RSS). Our data-driven
approach combines the strengths of well-known models of signal propagation
phenomena (e.g. shadowing, reflection, diffraction) and machine learning, and
can adapt online to new environments. We demonstrate the performance of
PropEM-L on a six-robot team in a communication-restricted environment with
subway-like, mine-like, and cave-like characteristics, constructed for the 2021
DARPA Subterranean Challenge. Our findings indicate that PropEM-L can improve
signal strength prediction accuracy by up to 44% over a log-distance path loss
model.


title: A fractional Anderson model

abstract:
  We examine the interplay between disorder and fractionality in a
one-dimensional tight-binding Anderson model. In the absence of disorder, we
observe that the two lowest energy eigenvalues detach themselves from the
bottom of the band, as fractionality $s$ is decreased, becoming completely
degenerate at $s=0$, with a common energy equal to a half bandwidth, $V$. The
remaining $N-2$ states become completely degenerate forming a flat band with
energy equal to a bandwidth, $2V$. Thus, a gap is formed between the ground
state and the band. In the presence of disorder and for a fixed disorder width,
a decrease in $s$ reduces the width of the point spectrum while for a fixed
$s$, an increase in disorder increases the width of the spectrum. For all
disorder widths, the average participation ratio decreases with $s$ showing a
tendency towards localization. However, the average mean square displacement
(MSD) shows a hump at low $s$ values, signaling the presence of a population of
extended states, in agreement with what is found in long-range hopping models.


title:
Modus ponens and modus tollens for the compositional rule of inference
  with aggregation functions

abstract:
  The compositional rule of inference (CRI) proposed by Zadeh has been widely
applied in artificial intelligence, control, data mining, image processing,
decision making and so on. Recently, Li and Zeng [Li, D., Zeng, Q. Approximate
reasoning with aggregation functions satisfying GMP rules, Artificial
Intelligence Review (2022), https://doi.org/10.1007/s10462-022-10136-1] shown
an A-compositional rule of inference (ACRI) method in which generalizes the
t-norm to any aggregation function in CRI method and studied its validity using
GMP rules. In this paper, we continue to investigate the validity of ACRI
method from a logical view and an interpolative view. Specifically, to discuss
the modus ponens (MP) and modus tollens (MT) properties of ACRI method based on
well-known fuzzy implications with aggregation functions.


title: A Search for Annihilating Dark Matter in 47 Tucanae and Omega Centauri

abstract:
  A plausible formation scenario for the Galactic globular clusters 47 Tuc and
Omega Cen is that they are tidally stripped remnants of dwarf galaxies, in
which case they are likely to have retained a fraction of their dark matter
cores. In this study, we have used the ultra-wide band receiver on the Parkes
telescope (Murriyang) to place upper limits on the annihilation rate of exotic
Light Dark Matter particles (X) via the XX/$e^+e^-$ channel using measurements
of the recombination rate of positronium (Ps). This is an extension of a
technique previously used to search for Ps in the Galactic Centre. However, by
stacking of spectral data at multiple line frequencies, we have been able to
improve sensitivity. Our measurements have resulted in 3-sigma flux density
(recombination rate) upper limits of 1.7 mJy (1.4x10$^{43}$ s$^{-1}$) and 0.8
mJy (1.1x10$^{43}$ s$^{-1}$) for 47 Tuc and Omega Cen, respectively. Within the
Parkes beam at the cluster distances, which varies from 10 to 23 pc depending
on the frequency of the recombination line, we calculate upper limits to the
dark matter mass and rms dark matter density of <1.2-1.3x10$^5$ fn$^{-0.5}$
(mX/MeV c$^{-2}$) solar masses and <48-54 fn$^{-0.5}$ (mX/MeV c$^{-2}$) solar
masses pc$^{-3}$ for the clusters, where fn=Rn/Rp is the ratio of Ps
recombination transitions to annihilations, estimated to be 0.01. The radio
limits for Omega Cen suggest that, for a fiducial dark/luminous mass ratio of
0.05, any contribution from Light Dark Matter is small unless the cross section
<7.9x10$^{-28}$ (mX/MeV c$^{-2}$)$^2$ cm$^3$ s$^{-1}$. Owing to the compactness
and proximity of the clusters, archival 511-keV measurements suggest even
tighter limits than permitted by CMB anisotropies, <8.6x10$^{-31}$ (mX/MeV
c$^{-2}$)$^2$ cm$^3$ s$^{-1}$. Our recombination rate limits substantially
improve on previous radio limits for the Milky Way.


title: Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation

abstract:
  Pose estimation plays a critical role in human-centered vision applications.
However, it is difficult to deploy state-of-the-art HRNet-based pose estimation
models on resource-constrained edge devices due to the high computational cost
(more than 150 GMACs per frame). In this paper, we study efficient architecture
design for real-time multi-person pose estimation on edge. We reveal that
HRNet's high-resolution branches are redundant for models at the
low-computation region via our gradual shrinking experiments. Removing them
improves both efficiency and performance. Inspired by this finding, we design
LitePose, an efficient single-branch architecture for pose estimation, and
introduce two simple approaches to enhance the capacity of LitePose, including
Fusion Deconv Head and Large Kernel Convs. Fusion Deconv Head removes the
redundancy in high-resolution branches, allowing scale-aware feature fusion
with low overhead. Large Kernel Convs significantly improve the model's
capacity and receptive field while maintaining a low computational cost. With
only 25% computation increment, 7x7 kernels achieve +14.0 mAP better than 3x3
kernels on the CrowdPose dataset. On mobile platforms, LitePose reduces the
latency by up to 5.0x without sacrificing performance, compared with prior
state-of-the-art efficient pose estimation models, pushing the frontier of
real-time multi-person pose estimation on edge. Our code and pre-trained models
are released at https://github.com/mit-han-lab/litepose.


title:
Statistical-mechanical study of deep Boltzmann machine given weight
  parameters after training by singular value decomposition

abstract:
  Deep learning methods relying on multi-layered networks have been actively
studied in a wide range of fields in recent years, and deep Boltzmann
machines(DBMs) is one of them. In this study, a model of DBMs with some
properites of weight parameters obtained by learning is studied theoretically
by a statistical-mechanical approach based on the replica method. The phases
characterizing the role of DBMs as a generator and their phase diagram are
derived, depending meaningfully on the numbers of layers and units in each
layer. It is found, in particular, that the correlation between the weight
parameters in the hidden layers plays an essential role and that an increase in
the layer number has a negative effect on DBM's performance as a generator when
the correlation is smaller than a certain threshold value.


title: Few-Shot Musical Source Separation

abstract:
  Deep learning-based approaches to musical source separation are often limited
to the instrument classes that the models are trained on and do not generalize
to separate unseen instruments. To address this, we propose a few-shot musical
source separation paradigm. We condition a generic U-Net source separation
model using few audio examples of the target instrument. We train a few-shot
conditioning encoder jointly with the U-Net to encode the audio examples into a
conditioning vector to configure the U-Net via feature-wise linear modulation
(FiLM). We evaluate the trained models on real musical recordings in the
MUSDB18 and MedleyDB datasets. We show that our proposed few-shot conditioning
paradigm outperforms the baseline one-hot instrument-class conditioned model
for both seen and unseen instruments. To extend the scope of our approach to a
wider variety of real-world scenarios, we also experiment with different
conditioning example characteristics, including examples from different
recordings, with multiple sources, or negative conditioning examples.


title:
A Polyhedral Approach to Least Cost Influence Maximization in Social
  Networks

abstract:
  The least cost influence maximization problem aims to determine minimum cost
of partial (e.g., monetary) incentives initially given to the influential
spreaders on a social network, so that these early adopters exert influence
toward their neighbors and prompt influence propagation to reach a desired
penetration rate by the end of cascading processes. We first conduct polyhedral
analysis on a substructure that describes influence propagation assuming
influence weights are unequal, linear and additively separable. Two classes of
facet-defining inequalities based on a mixed 0-1 knapsack set contained in this
substructure are proposed. We characterize another exponential class of valid
and facet-defining inequalities utilizing the concept of minimum influencing
subset. We show that these inequalities can be separated in polynomial time
efficiently. Furthermore, a polynomial-time dynamic programming recursion is
presented to solve this problem on a simple cycle graph. For arbitrary graphs,
we propose a new exponential class of valid inequalities that dominates the
cycle elimination constraints and an efficient separation algorithm for them. A
compact convex hull description for a special case is presented. We illustrate
the effectiveness of these inequalities via a delayed cut generation algorithm
in the computational experiments.


title:
Unveiling polarized emission from interstellar dust of the Large
  Magellanic Cloud with Planck

abstract:
  Polarization of interstellar dust emission is a powerful probe of dust
properties and magnetic field structure. Yet studies of external galaxies are
hampered by foreground dust contribution. The aim of this study is to separate
the polarised signal from the Large Magellanic Cloud (LMC) from that of the
Milky Way (MW) in order to construct a wide-field, spatially complete map of
dust polarization using the Planck 353 GHz data. To estimate the foreground
polarization direction, we used velocity gradients in HI spectral line data and
assessed the performance of the output by comparing to starlight extinction
polarization. We estimate the foreground intensity using dust properties
derived previously from the Planck data and we assume the foreground
polarization to be uniform and equal to the average of the MW around the
galaxy. After foreground removal, the geometry of the plane-of-the-sky magnetic
field in the LMC tends to follow the structure of the atomic gas in the LMC.
This is notably the case along the molecular ridges extending south and
south-east of the 30 Doradus star-forming complex, and along more diffuse
southern arm extending towards the Small Magellanic Cloud. There is also an
alignment between the magnetic field and the outer arm in the western part. The
median polarization fraction in the LMC is slightly lower than that observed
for the MW while the anti-correlation between the polarization angle dispersion
function and the polarization fraction is slightly larger. Overall,
polarization fraction distribution is similar to that observed in the MW.


title:
Response to Comment on (Novel two-dimensional porous graphitic carbon
  nitride C6N7 monolayer: A First-principle calculations [Appl. Phys. Lett.
  2021, 119, 142102])

abstract:
  Recently, Bohayra Gohrazavi et al. reported a comments on the our paper
[Appl. Phys. Lett. 119, 142102 (2021)]. With our response, the APL journal
rejected their non scientific comments. There are some ambiguities about their
claim: 1-They can check the phonon dispersion of their structure to see ZA
out-of-plane mode. 2-They report the uniaxial stress-strain responses in Fig
2., which is unrelated to our paper. For a more helpful understanding of the
mechanical properties of the novel C6N7 monolayer, they can publish a paper.
3-They mentioned: Using the DFT method and with assuming a thickness of 3.35 A
for the C6N7 monolayer based on graphenes thickness. Why did they choose this
thickness while we know our C6N7 monolayer is at without buckling? The distance
of ZA out-of-plane movement of ions in C6N7 is different from Graphene.


title:
Tightly Confined Surface Acoustic Waves as Microwave-to-Optical
  Transduction Platforms in the Quantum Regime

abstract:
  Surface acoustic waves (SAWs) coupled to quantum dots (QDs), trapped atoms
and ions, and point defects have been proposed as quantum transduction
platforms, yet the requisite coupling rates and cavity lifetimes have not been
experimentally established. Although the interaction mechanism varies, small
acoustic cavities with large zero-point motion are required for high
efficiencies. We experimentally demonstrate the feasibility of this platform
through electro- and opto-mechanical characterization of tightly focusing,
single-mode Gaussian SAW cavities at ~3.6 GHz on GaAs in the quantum regime,
with mode volumes approaching 6{\lambda}^3. Employing strain-coupled single
InAs QDs as optomechanical intermediaries, we measure single-phonon
optomechanical coupling rates g_0 > 2{\pi} x 1 MHz, implying zero-point
displacements >1 fm. In semi-planar cavities, we obtain quality factors >18,000
and finesse >140. To demonstrate operation at mK temperatures required for
quantum transduction, we use a fiber-based confocal microscope in a dilution
refrigerator to perform single-QD resonance fluorescence sideband spectroscopy
showing conversion of microwave phonons to optical photons with sub-natural
linewidths. These devices approach or meet the limits required for
microwave-to-optical quantum transduction.


title:
Real-time Cooperative Vehicle Coordination at Unsignalized Road
  Intersections

abstract:
  Cooperative coordination at unsignalized road intersections, which aims to
improve the driving safety and traffic throughput for connected and automated
vehicles, has attracted increasing interests in recent years. However, most
existing investigations either suffer from computational complexity or cannot
harness the full potential of the road infrastructure. To this end, we first
present a dedicated intersection coordination framework, where the involved
vehicles hand over their control authorities and follow instructions from a
centralized coordinator. Then a unified cooperative trajectory optimization
problem will be formulated to maximize the traffic throughput while ensuring
the driving safety and long-term stability of the coordination system. To
address the key computational challenges in the real-world deployment, we
reformulate this non-convex sequential decision problem into a model-free
Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep
Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement
learning (DRL) framework. Simulation and practical experiments show that the
proposed strategy could achieve near-optimal performance in sub-static
coordination scenarios and significantly improve the traffic throughput in the
realistic continuous traffic flow. The most remarkable advantage is that our
strategy could reduce the time complexity of computation to milliseconds, and
is shown scalable when the road lanes increase.


title: Complementary Goodness of Fit Procedure for Crash Frequency Models

abstract:
  This paper presents a new procedure for evaluating the goodness of fit of
Generalized Linear Models (GLM) estimated with Roadway Departure (RwD) crash
frequency data for the State of Hawaii on two-lane two-way (TLTW) state roads.
The procedure is analyzed using ten years of RwD crash data (including all
severity levels) and roadway characteristics (e.g., traffic, geometry, and
inventory databases) that can be aggregated at the section level. The three
estimation methods evaluated using the proposed procedure include: Negative
Binomial (NB), Zero-Inflated Negative Binomial (ZINB), and Generalized Linear
Mixed Model-Negative Binomial (GLMM-NB). The procedure shows that the three
methodologies can provide very good fits in terms of the distributions of
crashes within narrow ranges of the predicted mean frequency of crashes and in
terms of observed vs. predicted average crash frequencies for those data
segments. The proposed procedure complements other statistics such as Akaike
Information Criterion, Bayesian Information Criterion, and Log-likelihood used
for model selection. It is consistent with those statistics for models without
random effects, but it diverges for GLMM-NB models. The procedure can aid model
selection by providing a clear visualization of the fit of crash frequency
models and allowing the computation of a pseudo R2 similar the one used in
linear regression. It is recommended to evaluate its use for evaluating the
trade-off between the number of random effects in GLMM-NB models and their
goodness of fit using more appropriate datasets that do not lead to convergence
problems.


title:
Improving Dual-Microphone Speech Enhancement by Learning Cross-Channel
  Features with Multi-Head Attention

abstract:
  Hand-crafted spatial features, such as inter-channel intensity difference
(IID) and inter-channel phase difference (IPD), play a fundamental role in
recent deep learning based dual-microphone speech enhancement (DMSE) systems.
However, learning the mutual relationship between artificially designed spatial
and spectral features is hard in the end-to-end DMSE. In this work, a novel
architecture for DMSE using a multi-head cross-attention based convolutional
recurrent network (MHCA-CRN) is presented. The proposed MHCA-CRN model includes
a channel-wise encoding structure for preserving intra-channel features and a
multi-head cross-attention mechanism for fully exploiting cross-channel
features. In addition, the proposed approach specifically formulates the
decoder with an extra SNR estimator to estimate frame-level SNR under a
multi-task learning framework, which is expected to avoid speech distortion led
by end-to-end DMSE module. Finally, a spectral gain function is adopted to
further suppress the unnatural residual noise. Experiment results demonstrated
superior performance of the proposed model against several state-of-the-art
models.


title:
A correlation structure for the analysis of Gaussian and non-Gaussian
  responses in crossover experimental designs with repeated measures

abstract:
  In this study, we propose a family of correlation structures for crossover
designs with repeated measures for both, Gaussian and non-Gaussian responses
using generalized estimating equations (GEE). The structure considers two
matrices: one that models between-period correlation and another one that
models within-period correlation. The overall correlation matrix, which is used
to build the GEE, corresponds to the Kronecker between these matrices. A
procedure to estimate the parameters of the correlation matrix is proposed, its
statistical properties are studied and a comparison with standard models using
a single correlation matrix is carried out. A simulation study showed a
superior performance of the proposed structure in terms of the quasi-likelihood
criterion, efficiency, and the capacity to explain complex correlation
phenomena/patterns in longitudinal data from crossover designs


title:
Witten-Reshetikhin-Turaev invariants and homological blocks for plumbed
  homology spheres

abstract:
  In this paper, we prove a conjecture by Gukov-Pei-Putrov-Vafa for a wide
class of plumbed 3-manifolds. Their conjecture states that
Witten-Reshetikhin-Turaev (WRT) invariants are radial limits of homological
blocks, which are $ q $-series introduced by them for plumbed 3-manifolds with
negative definite linking matrices. The most difficult point in our proof is to
prove the vanishing of weighted Gauss sums that appear in coefficients of
negative degree in asymptotic expansions of homological blocks. To deal with
it, we develop a new technique for asymptotic expansions, which enables us to
compare asymptotic expansions of rational functions and false theta functions
related to WRT invariants and homological blocks, respectively. In our
technique, our vanishing results follow from holomorphy of such rational
functions.

categories: math.GT hep-th math-ph math.MP math.NT math.QA


title: Extending the View Composition Algebra to Hierarchical Data

abstract:
  Comparison is a core task in visual analysis. Although there are numerous
guidelines to help users design effective visualizations to aid known
comparison tasks, there are few formalisms that define the semantics of
comparison operations in a way that can serve as the basis for a grammar of
comparison interactions. Recent work proposed a formalism called View
Composition Algebra (VCA) that enables ad hoc comparisons between any
combination of marks, trends, or charts in a visualization interface. However,
VCA limits comparisons to visual representations of data that have an identical
schema, or where the schemas form a strict subset relationship (e.g., comparing
price per state with price, but not with price per county). In contrast, the
majority of real-world data - temporal, geographical, organizational - are
hierarchical.
  To bridge this gap, this paper presents an extension to VCA (called VCAH)
that enables ad hoc comparisons between visualizations of hierarchical data.
VCAH leverages known hierarchical relationships to enable ad hoc comparison of
data at different hierarchical granularities. We illustrate applications to
hierarchical and Tableau visualizations.


title: Scalable Private Decision Tree Evaluation with Sublinear Communication

abstract:
  Private decision tree evaluation (PDTE) allows a decision tree holder to run
a secure protocol with a feature provider. By running the protocol, the feature
provider will learn a classification result. Nothing more is revealed to either
party. In most existing PDTE protocols, the required communication grows
exponentially with the tree's depth $d$, which is highly inefficient for large
trees. This shortcoming motivated us to design a sublinear PDTE protocol with
$O(d)$ communication complexity. The core of our construction is a shared
oblivious selection (SOS) functionality, allowing two parties to perform a
secret-shared oblivious read operation from an array. We provide two SOS
protocols, both of which achieve sublinear communication and propose
optimizations to further improve their efficiency. Our sublinear PDTE protocol
is based on the proposed SOS functionality and we prove its security under a
semi-honest adversary. We compare our protocol with the state-of-the-art, in
terms of communication and computation, under various network settings. The
performance evaluation shows that our protocol is practical and more scalable
over large trees than existing solutions.


title: A Flexible Approach for Predictive Biomarker Discovery

abstract:
  An endeavor central to precision medicine is predictive biomarker discovery;
they define patient subpopulations which stand to benefit most, or least, from
a given treatment. The identification of these biomarkers is often the
byproduct of the related but fundamentally different task of treatment rule
estimation. Using treatment rule estimation methods to identify predictive
biomarkers in clinical trials where the number of covariates exceeds the number
of participants often results in high false discovery rates. The higher than
expected number of false positives translates to wasted resources when
conducting follow-up experiments for drug target identification and diagnostic
assay development. Patient outcomes are in turn negatively affected. We propose
a variable importance parameter for directly assessing the importance of
potentially predictive biomarkers, and develop a flexible semiparametric
inference procedure for this estimand. We prove that our estimator is
double-robust and asymptotically linear under loose conditions on the
data-generating process, permitting valid inference about the importance
metric. The statistical guarantees of the method are verified in a thorough
simulation study representative of randomized control trials with moderate and
high-dimensional covariate vectors. Our procedure is then used to discover
predictive biomarkers from among the tumor gene expression data of metastatic
renal cell carcinoma patients enrolled in recently completed clinical trials.
We find that our approach more readily discerns predictive from non-predictive
biomarkers than procedures whose primary purpose is treatment rule estimation.
An open-source software implementation of the methodology, the uniCATE R
package, is briefly introduced.


title:
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for
  Sequential Recommendation

abstract:
  Sequential recommendation aims at identifying the next item that is preferred
by a user based on their behavioral history. Compared to conventional
sequential models that leverage attention mechanisms and RNNs, recent efforts
mainly follow two directions for improvement: multi-interest learning and graph
convolutional aggregation. Specifically, multi-interest methods such as ComiRec
and MIMN, focus on extracting different interests for a user by performing
historical item clustering, while graph convolution methods including TGSRec
and SURGE elect to refine user preferences based on multi-level correlations
between historical items. Unfortunately, neither of them realizes that these
two types of solutions can mutually complement each other, by aggregating
multi-level user preference to achieve more precise multi-interest extraction
for a better recommendation. To this end, in this paper, we propose a unified
multi-grained neural model(named MGNM) via a combination of multi-interest
learning and graph convolutional aggregation. Concretely, MGNM first learns the
graph structure and information aggregation paths of the historical items for a
user. It then performs graph convolution to derive item representations in an
iterative fashion, in which the complex preferences at different levels can be
well captured. Afterwards, a novel sequential capsule network is proposed to
inject the sequential patterns into the multi-interest extraction process,
leading to a more precise interest learning in a multi-grained manner.


title: SemAttack: Natural Textual Attacks via Different Semantic Spaces

abstract:
  Recent studies show that pre-trained language models (LMs) are vulnerable to
textual adversarial attacks. However, existing attack methods either suffer
from low attack success rates or fail to search efficiently in the
exponentially large perturbation space. We propose an efficient and effective
framework SemAttack to generate natural adversarial text by constructing
different semantic perturbation functions. In particular, SemAttack optimizes
the generated perturbations constrained on generic semantic spaces, including
typo space, knowledge space (e.g., WordNet), contextualized semantic space
(e.g., the embedding space of BERT clusterings), or the combination of these
spaces. Thus, the generated adversarial texts are more semantically close to
the original inputs. Extensive experiments reveal that state-of-the-art (SOTA)
large-scale LMs (e.g., DeBERTa-v2) and defense strategies (e.g., FreeLB) are
still vulnerable to SemAttack. We further demonstrate that SemAttack is general
and able to generate natural adversarial texts for different languages (e.g.,
English and Chinese) with high attack success rates. Human evaluations also
confirm that our generated adversarial texts are natural and barely affect
human performance. Our code is publicly available at
https://github.com/AI-secure/SemAttack.


title: Half-Wormholes and Ensemble Averages

abstract:
  We study "half-wormhole-like" saddle point contributions to spectral
correlators in a variety of ensemble average models, including various
statistical models, generalized 0d SYK models, 1d Brownian SYK models and an
extension of it. In statistical ensemble models, where more general
distributions of the random variables could be studied in great details, we
find the accuracy of the previously proposed approximation for the
half-wormholes could be improved when the distribution of the random variables
deviate significantly from Gaussian distributions. We propose a modified
approximation scheme of the half-wormhole contributions that also work well in
these more general theories. In various generalized 0d SYK models we identify
new half-wormhole-like saddle point contributions. In the 0d SYK model and 1d
Brownian SYK model, apart from the wormhole and half-wormhole saddles, we find
new non-trivial saddles in the spectral correlators that would potentially give
contributions of the same order as the trivial self-averaging saddles. However
after a careful Lefschetz-thimble analysis we show that these non-trivial
saddles should not be included. We also clarify the difference between "linked
half-wormholes" and "unlinked half-wormholes" in some models.


title: On Ranking Consistency of Pre-ranking Stage

abstract:
  Industrial ranking systems, such as advertising systems, rank items by
aggregating multiple objectives into one final objective to satisfy user demand
and commercial intent. Cascade architecture, composed of retrieval,
pre-ranking, and ranking stages, is usually adopted to reduce the computational
cost. Each stage may employ various models for different objectives and
calculate the final objective by aggregating these models' outputs. The
multi-stage ranking strategy causes a new problem - the ranked lists of the
ranking stage and previous stages may be inconsistent. For example, items that
should be ranked at the top of the ranking stage may be ranked at the bottom of
previous stages. In this paper, we focus on the ranking consistency between the
pre-ranking and ranking stages. Specifically, we formally define the problem of
ranking consistency and propose the Ranking Consistency Score (RCS) metric for
evaluation. We demonstrate that ranking consistency has a direct impact on
online performance. Compared with the traditional evaluation manner that mainly
focuses on the individual ranking quality of every objective, RCS considers the
ranking consistency of the fused final objective, which is more proper for
evaluation. Finally, to improve the ranking consistency, we propose several
methods from the perspective of sample selection and learning algorithms.
Experimental results on industrial datasets validate the efficacy of the
proposed metrics and methods. The proposed consistency methods have been
deployed on the display advertising system of Alibaba, obtaining a 6.7%
improvement on CTR (Click-Through Rate) and a 5.5% increase on RPM (Revenue Per
Mille).


title:
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured
  Electronic Health Records For Medicine Related Queries

abstract:
  This paper develops the first question answering dataset (DrugEHRQA)
containing question-answer pairs from both structured tables and unstructured
notes from a publicly available Electronic Health Record (EHR). EHRs contain
patient records, stored in structured tables and unstructured clinical notes.
The information in structured and unstructured EHRs is not strictly disjoint:
information may be duplicated, contradictory, or provide additional context
between these sources. Our dataset has medication-related queries, containing
over 70,000 question-answer pairs. To provide a baseline model and help analyze
the dataset, we have used a simple model (MultimodalEHRQA) which uses the
predictions of a modality selection network to choose between EHR tables and
clinical notes to answer the questions. This is used to direct the questions to
the table-based or text-based state-of-the-art QA model. In order to address
the problem arising from complex, nested queries, this is the first time
Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (RAT-SQL)
has been used to test the structure of query templates in EHR data. Our goal is
to provide a benchmark dataset for multi-modal QA systems, and to open up new
avenues of research in improving question answering over EHR structured data by
using context from unstructured clinical data.


title:
Cross Domain Object Detection by Target-Perceived Dual Branch
  Distillation

abstract:
  Cross domain object detection is a realistic and challenging task in the
wild. It suffers from performance degradation due to large shift of data
distributions and lack of instance-level annotations in the target domain.
Existing approaches mainly focus on either of these two difficulties, even
though they are closely coupled in cross domain object detection. To solve this
problem, we propose a novel Target-perceived Dual-branch Distillation (TDD)
framework. By integrating detection branches of both source and target domains
in a unified teacher-student learning scheme, it can reduce domain shift and
generate reliable supervision effectively. In particular, we first introduce a
distinct Target Proposal Perceiver between two domains. It can adaptively
enhance source detector to perceive objects in a target image, by leveraging
target proposal contexts from iterative cross-attention. Afterwards, we design
a concise Dual Branch Self Distillation strategy for model training, which can
progressively integrate complementary object knowledge from different domains
via self-distillation in two branches. Finally, we conduct extensive
experiments on a number of widely-used scenarios in cross domain object
detection. The results show that our TDD significantly outperforms the
state-of-the-art methods on all the benchmarks. Our code and model will be
available at https://github.com/Feobi1999/TDD.


title: Quantum-Optical Coherence Microscopy for Bio-imaging Applications

abstract:
  Quantum-optical coherence tomography (QOCT) is an optical sectioning modality
based on the quantum interference of photon pairs, obtained from a spontaneous
parametric downconversion (SPDC) source. The promise of QOCT derives from two
quantum-conferred advantages when compared to equivalent classical optical
coherence tomography (OCT) systems: a factor of 2 axial resolution enhancement,
as well as dispersion cancellation. However, the technique is far from being
competitive with current OCT devices due to the long required acquisition
times. Here, we on the one hand demonstrate a quantum optical coherence
microscopy (QOCM) technique that is designed to overcome some of the
limitations of previous QOCT implementations, and on the other hand test it on
representative samples, including glass layers with manufactured transverse
patterns and metal-coated biological specimens. We use a collinear SPDC source,
so that the entire emitted photon pair flux may contribute to the measurements,
together with a multi-mode detection design. We employ a Michelson
interferometer with the sample placed as end-mirror in one of the
interferometer arms, instead of the more typical Hong-Ou-Mandel used in QOCT
implementations. In order to probe biological samples we transition from a
Michelson to a Linnik interferometer by placing a microscope objective in the
sample arm. In our setup, while the idler photon is collected with a multi-mode
fiber, the signal photon is detected by an ICCD camera, leading to full-field
transverse reconstruction through a single axial acquisition sequence.
Interestingly, our setup permits concurrent OCT and QOCT trace acquisition, the
former with greater counts and the latter with the benefit of quantum-conferred
advantages. We hope that our current results will represent a significant step
forward towards the actual applicability of QOCT, e.g. in clinical settings.


title: A Survey of Deep Learning Models for Structural Code Understanding

abstract:
  In recent years, the rise of deep learning and automation requirements in the
software industry has elevated Intelligent Software Engineering to new heights.
The number of approaches and applications in code understanding is growing,
with deep learning techniques being used in many of them to better capture the
information in code data. In this survey, we present a comprehensive overview
of the structures formed from code data. We categorize the models for
understanding code in recent years into two groups: sequence-based and
graph-based models, further make a summary and comparison of them. We also
introduce metrics, datasets and the downstream tasks. Finally, we make some
suggestions for future research in structural code understanding field.


title: An R Package AZIAD for Analyzing Zero-Inflated and Zero-Altered Data

abstract:
  Sparse data with a large portion of zeros arise in many scientific
disciplines. Modeling sparse data is very challenging due to the skewness of
the distribution. We adopt bootstrapped Monte Carlo method to estimate the
p-value of the Kolmogorov-Smirnov test, as well as bootstrapped likelihood
ratio tests for zero-inflated and zero-altered (or hurdle) model selection. Our
new package AZIAD provides miscellaneous functions to simulate zero-inflated or
zero-altered data and calculate maximum likelihood estimates of unknown
parameters for a large class of discrete or continuous distributions. In
addition, we calculate the Fisher information matrix and the confidence
intervals of unknown parameters. Compared with other R packages available so
far, our package covers many more types of zero-inflated and zero-altered
distributions, provides more accurate estimates for unknown parameters, and
achieves higher power for model selection. To facilitate the potential users,
in this paper we provide theoretical justifications and detailed formulae for
functions in AZIAD and illustrate the use of them with executable R code and
real dataset.


title:
Subsets of $\mathbb{F}_p^n\times\mathbb{F}_p^n$ without L-shaped
  configurations

abstract:
  Fix a prime $p\geq 11$. We show that there exists a positive integer $m$ such
that any subset of $\mathbb{F}_p^n\times\mathbb{F}_p^n$ containing no
nontrivial configurations of the form $(x,y),(x,y+z),(x,y+2z),(x+z,y)$ must
have density $\ll 1/\log_{m}{n}$, where $\log_{m}$ denotes the $m$-fold
iterated logarithm. This gives the first reasonable bound in the
multidimensional Szemer\'edi theorem for a two-dimensional four-point
configuration in any setting.


title:
Visual Knowledge Discovery with Artificial Intelligence: Challenges and
  Future Directions

abstract:
  This volume is devoted to the emerging field of Integrated Visual Knowledge
Discovery that combines advances in Artificial Intelligence/Machine Learning
(AI/ML) and Visualization/Visual Analytics. Chapters included are extended
versions of the selected AI and Visual Analytics papers and related symposia at
the recent International Information Visualization Conferences (IV2019 and
IV2020). AI/ML face a long-standing challenge of explaining models to humans.
Models explanation is fundamentally human activity, not only an algorithmic
one. In this chapter we aim to present challenges and future directions within
the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to
discuss the role of visualization in visual AI/ML. In addition, we describe
progress in emerging Full 2D ML, natural language processing, and AI/ML in
multidimensional data aided by visual means.


title: RU-Net: Regularized Unrolling Network for Scene Graph Generation

abstract:
  Scene graph generation (SGG) aims to detect objects and predict the
relationships between each pair of objects. Existing SGG methods usually suffer
from several issues, including 1) ambiguous object representations, as graph
neural network-based message passing (GMP) modules are typically sensitive to
spurious inter-node correlations, and 2) low diversity in relationship
predictions due to severe class imbalance and a large number of missing
annotations. To address both problems, in this paper, we propose a regularized
unrolling network (RU-Net). We first study the relation between GMP and graph
Laplacian denoising (GLD) from the perspective of the unrolling technique,
determining that GMP can be formulated as a solver for GLD. Based on this
observation, we propose an unrolled message passing module and introduce an
$\ell_p$-based graph regularization to suppress spurious connections between
nodes. Second, we propose a group diversity enhancement module that promotes
the prediction diversity of relationships via rank maximization. Systematic
experiments demonstrate that RU-Net is effective under a variety of settings
and metrics. Furthermore, RU-Net achieves new state-of-the-arts on three
popular databases: VG, VRD, and OI. Code is available at
https://github.com/siml3/RU-Net.


title:
Mechanism for Sequestering Magnetic Energy at Large Scales in Shear-Flow
  Turbulence

abstract:
  Straining of magnetic fields by large-scale shear flow, generally assumed to
lead to intensification and generation of small scales, is re-examined in light
of the persistent observation of large-scale magnetic fields in astrophysics.
It is shown that in magnetohydrodynamic turbulence, unstable shear flows have
the unexpected effect of sequestering magnetic energy at large scales, due to
counteracting straining motion of nonlinearly excited large-scale stable
eigenmodes. This effect is quantified via dissipation rates, energy transfer
rates, and visualizations of magnetic field evolution by artificially removing
the stable modes.

categories: physics.plasm-ph astro-ph.GA astro-ph.SR physics.flu-dyn


title: Cayley graphs on non-isomorphic groups

abstract:
  A number of authors have studied the question of when a graph can be
represented as a Cayley graph on more than one nonisomorphic group. In this
paper we give conditions for when a Cayley graph on an abelian group can be
represented as a Cayley graph on a generalized dihedral group, and conditions
for when the converse is true.


title: Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction

abstract:
  Prediction of network traffic behavior is significant for the effective
management of modern telecommunication networks. However, the intuitive
approach of predicting network traffic using administrative experience and
market analysis data is inadequate for an efficient forecast framework. As a
result, many different mathematical models have been studied to capture the
general trend of the network traffic and predict accordingly. But the
comprehensive performance analysis of varying regression models and their
ensemble has not been studied before for analyzing real-world anomalous
traffic. In this paper, several regression models such as Extra Gradient Boost
(XGBoost), Light Gradient Boosting Machine (LightGBM), Stochastic Gradient
Descent (SGD), Gradient Boosting Regressor (GBR), and CatBoost Regressor were
analyzed to predict real traffic without and with outliers and show the
significance of outlier detection in real-world traffic prediction. Also, we
showed the outperformance of the ensemble regression model over the individual
prediction model. We compared the performance of different regression models
based on five different feature sets of lengths 6, 9, 12, 15, and 18. Our
ensemble regression model achieved the minimum average gap of 5.04% between
actual and predicted traffic with nine outlier-adjusted inputs. In general, our
experimental results indicate that the outliers in the data can significantly
impact the quality of the prediction. Thus, outlier detection and mitigation
assist the regression model in learning the general trend and making better
predictions.


title:
Remarks on Sharp Interface Limit for an Incompressible Navier-Stokes and
  Allen-Cahn Coupled System

abstract:
  We are concerned with the sharp interface limit for an incompressible
Navier-Stokes and Allen-Cahn coupled system in this paper. When the thickness
of the diffuse interfacial zone, which is parameterized by $\varepsilon$, goes
to zero, we prove that a solution of the incompressible Navier-Stokes and
Allen-Cahn coupled system converges to a solution of a sharp interface model in
the $L^\infty(L^2)\cap L^2(H^1)$ sense on a uniform time interval independent
of the small parameter $\varepsilon$. The proof consists of two parts: one is
the construction of a suitable approximate solution and another is the estimate
of the error functions in Sobolev spaces. Besides the careful energy estimates,
a spectral estimate of the linearized operator for the incompressible
Navier-Stokes and Allen-Cahn coupled system around the approximate solution is
essentially used to derive the uniform estimates of the error functions. The
convergence of the velocity is well expected due to the fact that the layer of
the velocity across the diffuse interfacial zone is relatively weak.


title: Capacity Variation in the Many-to-one Stable Matching

abstract:
  The many-to-one stable matching problem provides the fundamental abstraction
of several real-world matching markets such as school choice and
hospital-resident allocation. The agents on both sides are often referred to as
residents and hospitals. The classical setup assumes that the agents rank the
opposite side and that the capacities of the hospitals are fixed. It is known
that increasing the capacity of a single hospital improves the residents' final
allocation. On the other hand, reducing the capacity of a single hospital
deteriorates the residents' allocation. In this work, we study the
computational complexity of finding the optimal variation of hospitals'
capacities that leads to the best outcome for the residents, subject to
stability and a capacity variation constraint. First, we show that the decision
problem of finding the optimal capacity expansion is NP-complete and the
corresponding optimization problem is inapproximable within a certain factor.
This result holds under strict and complete preferences, and even if we
allocate extra capacities to disjoint sets of hospitals. Second, we obtain
analogous computational complexity results for the problem of capacity
reduction. Finally, we study the variants of these problems when the goal is to
maximize the size of the final matching under incomplete preference lists.


title:
Convergence of Stochastic Approximation via Martingale and Converse
  Lyapunov Methods

abstract:
  This paper is dedicated to Prof. Eduardo Sontag on the occasion of his
seventieth birthday. In this paper, we build upon the ideas first proposed in
Gladyshev (1965) to develop a very general framework for proving the almost
sure boundedness and the convergence of stochastic approximation algorithms.
These ideas are based on martingale methods and are in some ways simpler than
convergence proofs based on the ODE method, e.g., Borkar-Meyn (2000). First we
study the original version of the SA algorithm introduced in Robbins-Monro
(1951), where the objective is to determine a zero of a function, when only
noisy measurements of the function are available. The proof makes use of the
general framework developed here, together with a new theorem on converse
Lyapunov stability, which might be of independent interest. Next we study an
alternate version of SA, first introduced in Kiefer-Wolfowitz (1952). The
objective here is to find a stationary point of a scalar-valued function, using
first-order differences to approximate its gradient. This problem is analyzed
in Blum (1954), but with a very opaque proof. We reproduce Blum's conclusions
using the proposed framework.


title:
Efficient dynamic filter for robust and low computational feature
  extraction

abstract:
  Unseen noise signal which is not considered in a model training process is
difficult to anticipate and would lead to performance degradation. Various
methods have been investigated to mitigate unseen noise. In our previous work,
an Instance-level Dynamic Filter (IDF) and a Pixel Dynamic Filter (PDF) were
proposed to extract noise-robust features. However, the performance of the
dynamic filter might be degraded since simple feature pooling is used to reduce
the computational resource in the IDF part. In this paper, we propose an
efficient dynamic filter to enhance the performance of the dynamic filter.
Instead of utilizing the simple feature mean, we separate Time-Frequency (T-F)
features as non-overlapping chunks, and separable convolutions are carried out
for each feature direction (inter chunks and intra chunks). Additionally, we
propose Dynamic Attention Pooling that maps high dimensional features as low
dimensional feature embeddings. These methods are applied to the IDF for
keyword spotting and speaker verification tasks. We confirm that our proposed
method performs better in unseen environments (unseen noise and unseen
speakers) than state-of-the-art models.


title:
Radiative processes of single and entangled detectors on circular
  trajectories in $(2+1)$ dimensional Minkowski spacetime

abstract:
  We investigate the radiative processes involving two entangled Unruh-DeWitt
detectors that are moving on circular trajectories in $(2+1)$-dimensional
Minkowski spacetime. We assume that the detectors are coupled to a massless,
quantum scalar field, and calculate the transition probability rates of the
detectors in the Minkowski vacuum as well as in a thermal bath. We also
evaluate the transition probability rates of the detectors when they are
switched on for a finite time interval with the aid of a Gaussian switching
function. We begin by examining the response of a single detector before we go
on to consider the case of two entangled detectors. As we shall see, working in
$(2+1)$ spacetime dimensions makes the computations of the transition
probability rates of the detectors relatively simpler. We find that the cross
transition probability rates of the two entangled detectors can be comparable
to the auto transition probability rates of the individual detectors. We
discuss specific characteristics of the response of the entangled detectors for
different values of the parameters involved and highlight the effects of the
thermal bath as well as switching on the detector for a finite time interval.


title: CANShield: Signal-based Intrusion Detection for Controller Area Networks

abstract:
  Modern vehicles rely on a fleet of electronic control units (ECUs) connected
through controller area network (CAN) buses for critical vehicular control.
However, with the expansion of advanced connectivity features in automobiles
and the elevated risks of internal system exposure, the CAN bus is increasingly
prone to intrusions and injection attacks. The ordinary injection attacks
disrupt the typical timing properties of the CAN data stream, and the
rule-based intrusion detection systems (IDS) can easily detect them. However,
advanced attackers can inject false data to the time series sensory data
(signal), while looking innocuous by the pattern/frequency of the CAN messages.
Such attacks can bypass the rule-based IDS or any anomaly-based IDS built on
binary payload data. To make the vehicles robust against such intelligent
attacks, we propose CANShield, a signal-based intrusion detection framework for
the CAN bus. CANShield consists of three modules: a data preprocessing module
that handles the high-dimensional CAN data stream at the signal level and makes
them suitable for a deep learning model; a data analyzer module consisting of
multiple deep autoencoder (AE) networks, each analyzing the time-series data
from a different temporal perspective; and finally an attack detection module
that uses an ensemble method to make the final decision. Evaluation results on
two high-fidelity signal-based CAN attack datasets show the high accuracy and
responsiveness of CANShield in detecting wide-range of advanced intrusion
attacks.


title: Embedding Hallucination for Few-Shot Language Fine-tuning

abstract:
  Few-shot language learners adapt knowledge from a pre-trained model to
recognize novel classes from a few-labeled sentences. In such settings,
fine-tuning a pre-trained language model can cause severe over-fitting. In this
paper, we propose an Embedding Hallucination (EmbedHalluc) method, which
generates auxiliary embedding-label pairs to expand the fine-tuning dataset.
The hallucinator is trained by playing an adversarial game with the
discriminator, such that the hallucinated embedding is indiscriminative to the
real ones in the fine-tuning dataset. By training with the extended dataset,
the language learner effectively learns from the diverse hallucinated
embeddings to overcome the over-fitting issue. Experiments demonstrate that our
proposed method is effective in a wide range of language tasks, outperforming
current fine-tuning methods. Further, we show that EmbedHalluc outperforms
other methods that address this over-fitting problem, such as common data
augmentation, semi-supervised pseudo-labeling, and regularization. The code
will be made available at: https://github.com/yiren-jian/EmbedHalluc.


title: Contrastive Learning for Prompt-Based Few-Shot Language Learners

abstract:
  The impressive performance of GPT-3 using natural language prompts and
in-context learning has inspired work on better fine-tuning of moderately-sized
models under this paradigm. Following this line of work, we present a
contrastive learning framework that clusters inputs from the same class for
better generality of models trained with only limited examples. Specifically,
we propose a supervised contrastive framework that clusters inputs from the
same class under different augmented "views" and repel the ones from different
classes. We create different "views" of an example by appending it with
different language prompts and contextual demonstrations. Combining a
contrastive loss with the standard masked language modeling (MLM) loss in
prompt-based few-shot learners, the experimental results show that our method
can improve over the state-of-the-art methods in a diverse set of 15 language
tasks. Our framework makes minimal assumptions on the task or the base model,
and can be applied to many recent methods with little modification. The code
will be made available at: https://github.com/yiren-jian/LM-SupCon.


title:
Shifted quantum groups and matter multiplets in supersymmetric gauge
  theories

abstract:
  The notion of shifted quantum groups has recently played an important role in
algebraic geometry. This subtle modification of the original definition brings
more flexibility in the representation theory of quantum groups. The first part
of this paper presents new mathematical results for the shifted quantum
toroidal $\mathfrak{gl}(1)$ and quantum affine $\mathfrak{sl}(2)$ algebras
(resp. denoted $\ddot{U}_{q_1,q_2}^\boldsymbol{\mu}(\mathfrak{gl}(1))$ and
$\dot{U}_q^\boldsymbol{\mu}(\mathfrak{sl}(2))$). It defines several new
representations, including finite dimensional highest $\ell$-weight
representations for the toroidal algebra, and a vertex representation of
$\dot{U}_q^\boldsymbol{\mu}(\mathfrak{sl}(2))$ acting on Hall-Littlewood
polynomials. It also explores the relations between representations of
$\dot{U}_q^\boldsymbol{\mu}(\mathfrak{sl}(2))$ and
$\ddot{U}_{q_1,q_2}^\boldsymbol{\mu}(\mathfrak{gl}(1))$ in the limit
$q_1\to\infty$ ($q_2$ fixed), and present the construction of several new
intertwiners. These results are used in the second part to construct BPS
observables for 5d $\mathcal{N}=1$ and 3d $\mathcal{N}=2$ gauge theories. In
particular, it is shown that 5d hypermultiplets and 3d chiral multiplets can be
introduced in the algebraic engineering framework using shifted
representations, and the Higgsing procedure is revisited from this perspective.


title: FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning

abstract:
  Robustness is becoming another important challenge of federated learning in
that the data collection process in each client is naturally accompanied by
noisy labels. However, it is far more complex and challenging owing to varying
levels of data heterogeneity and noise over clients, which exacerbates the
client-to-client performance discrepancy. In this work, we propose a robust
federated learning method called FedRN, which exploits k-reliable neighbors
with high data expertise or similarity. Our method helps mitigate the gap
between low- and high-performance clients by training only with a selected set
of clean examples, identified by their ensembled mixture models. We demonstrate
the superiority of FedRN via extensive evaluations on three real-world or
synthetic benchmark datasets. Compared with existing robust training methods,
the results show that FedRN significantly improves the test accuracy in the
presence of noisy labels.


title:
Automatically Debugging AutoML Pipelines Using Maro: ML Automated
  Remediation Oracle (Extended Version)

abstract:
  Machine learning in practice often involves complex pipelines for data
cleansing, feature engineering, preprocessing, and prediction. These pipelines
are composed of operators, which have to be correctly connected and whose
hyperparameters must be correctly configured. Unfortunately, it is quite common
for certain combinations of datasets, operators, or hyperparameters to cause
failures. Diagnosing and fixing those failures is tedious and error-prone and
can seriously derail a data scientist's workflow. This paper describes an
approach for automatically debugging an ML pipeline, explaining the failures,
and producing a remediation. We implemented our approach, which builds on a
combination of AutoML and SMT, in a tool called Maro. Maro works seamlessly
with the familiar data science ecosystem including Python, Jupyter notebooks,
scikit-learn, and AutoML tools such as Hyperopt. We empirically evaluate our
tool and find that for most cases, a single remediation automatically fixes
errors, produces no additional faults, and does not significantly impact
optimal accuracy nor time to convergence.


title:
Photon and phonon statistics in a qubit-plasmon-phonon ultrastrong
  coupling system

abstract:
  We study photon/phonon statistics of a qubit-plasmon-phonon hybrid system in
the ultrastrong coupling regime. The introduced qubit coupling causes parity
conserving and non-conserving situations. We employ an analytic approximation
approach for the parity conserving case to reveal the statistical behaviors of
photons and phonons. It indicates that both photons and phonons show strong
antibunching at the same frequency. Even though the bunching properties of
photons/phonons occupy the dominant regions of the considered frequencies,
phonons tend to weakly antibunching within the photonic strong-bunching area.
In contrast, one can find that the configurations of correlation functions for
both photons and phonons in the parity conserving case are squeezed towards the
central frequency by parity breaking, which directly triggers the reverse
statistical behaviors for the different parties at the low-frequency regions
and the strong bunching properties at other frequency regions. The
photon-phonon cross-correlation function also demonstrates similar
parity-induced differences, indicating that the non-conserving parity induces
the photon-phonon bunching behavior. We finally analyze the delayed
second-order correlation function with different driving frequencies, which
illustrates striking oscillations revealing the occurrence of simultaneous
multiple excitations.


title: cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

abstract:
  Particle Swarm Optimization (PSO) is a stochastic technique for solving the
optimization problem. Attempts have been made to shorten the computation times
of PSO based algorithms with massive threads on GPUs (graphic processing
units), where thread groups are formed to calculate the information of
particles and the computed outputs for the particles are aggregated and
analyzed to find the best solution. In particular, the reduction-based method
is considered as a common approach to handle the data aggregation and analysis
for the calculated particle information. Nevertheless, based on our analysis,
the reduction-based method would suffer from excessive memory accesses and
thread synchronization overheads. In this paper, we propose a novel algorithm
to alleviate the above overheads with the atomic functions. The threads within
a thread group update the calculated results atomically to the intra-group data
queue conditionally, which prevents the frequent accesses to the memory as done
by the parallel reduction operations. Furthermore, we develop an enhanced
version of the algorithm to alleviate the synchronization barrier among the
thread groups, which is achieved by allowing the thread groups to run
asynchronously and updating to the global, lock-protected variables
occasionally if necessary. Our experimental results show that our proposed
algorithm running on the Nvidia GPU is about 200 times faster than the serial
version executed by the Intel Xeon CPU. Moreover, the novel algorithm
outperforms the state-of-the-art method (the parallel reduction approach) by a
factor of 2.2.


title:
Distilling Governing Laws and Source Input for Dynamical Systems from
  Videos

abstract:
  Distilling interpretable physical laws from videos has led to expanded
interest in the computer vision community recently thanks to the advances in
deep learning, but still remains a great challenge. This paper introduces an
end-to-end unsupervised deep learning framework to uncover the explicit
governing equations of dynamics presented by moving object(s), based on
recorded videos. Instead in the pixel (spatial) coordinate system of image
space, the physical law is modeled in a regressed underlying physical
coordinate system where the physical states follow potential explicit governing
equations. A numerical integrator-based sparse regression module is designed
and serves as a physical constraint to the autoencoder and coordinate system
regression, and, in the meanwhile, uncover the parsimonious closed-form
governing equations from the learned physical states. Experiments on simulated
dynamical scenes show that the proposed method is able to distill closed-form
governing equations and simultaneously identify unknown excitation input for
several dynamical systems recorded by videos, which fills in the gap in
literature where no existing methods are available and applicable for solving
this type of problem.


title:
Assessing Certainty of Activation or Inactivation in Test-Retest fMRI
  Studies

abstract:
  Functional Magnetic Resonance Imaging~(fMRI) is widely used to study
activation in the human brain. In most cases, data are commonly used to
construct activation maps corresponding to a given paradigm. Results can be
very variable, hence quantifying certainty of identified activation and
inactivation over studies is important. This paper provides a model-based
approach to certainty estimation from data acquired over several replicates of
the same experimental paradigm. Specifically, the $p$-values derived from the
statistical analysis of the data are explicitly modeled as a mixture of their
underlying distributions; thus, unlike methodology currently in use, there is
no subjective thresholding required in the estimation process. The parameters
governing the mixture model are easily obtained by the principle of maximum
likelihood. Further, the estimates can also be used to optimally identify
voxel-specific activation regions along with their corresponding certainty
measures. The methodology is applied to a study involving a motor paradigm
performed on a single subject several times over a period of two months.
Simulation experiments used to calibrate performance of the method are
promising. The methodology is also seen to be robust in determining areas of
activation and their corresponding certainties.


title: HL-Net: Heterophily Learning Network for Scene Graph Generation

abstract:
  Scene graph generation (SGG) aims to detect objects and predict their
pairwise relationships within an image. Current SGG methods typically utilize
graph neural networks (GNNs) to acquire context information between
objects/relationships. Despite their effectiveness, however, current SGG
methods only assume scene graph homophily while ignoring heterophily.
Accordingly, in this paper, we propose a novel Heterophily Learning Network
(HL-Net) to comprehensively explore the homophily and heterophily between
objects/relationships in scene graphs. More specifically, HL-Net comprises the
following 1) an adaptive reweighting transformer module, which adaptively
integrates the information from different layers to exploit both the
heterophily and homophily in objects; 2) a relationship feature propagation
module that efficiently explores the connections between relationships by
considering heterophily in order to refine the relationship representation; 3)
a heterophily-aware message-passing scheme to further distinguish the
heterophily and homophily between objects/relationships, thereby facilitating
improved message passing in graphs. We conducted extensive experiments on two
public datasets: Visual Genome (VG) and Open Images (OI). The experimental
results demonstrate the superiority of our proposed HL-Net over existing
state-of-the-art approaches. In more detail, HL-Net outperforms the second-best
competitors by 2.1$\%$ on the VG dataset for scene graph classification and
1.2$\%$ on the IO dataset for the final score. Code is available at
https://github.com/siml3/HL-Net.


title:
Open vs Closed-ended questions in attitudinal surveys -- comparing,
  combining, and interpreting using natural language processing

abstract:
  To improve the traveling experience, researchers have been analyzing the role
of attitudes in travel behavior modeling. Although most researchers use
closed-ended surveys, the appropriate method to measure attitudes is debatable.
Topic Modeling could significantly reduce the time to extract information from
open-ended responses and eliminate subjective bias, thereby alleviating analyst
concerns. Our research uses Topic Modeling to extract information from
open-ended questions and compare its performance with closed-ended responses.
Furthermore, some respondents might prefer answering questions using their
preferred questionnaire type. So, we propose a modeling framework that allows
respondents to use their preferred questionnaire type to answer the survey and
enable analysts to use the modeling frameworks of their choice to predict
behavior. We demonstrate this using a dataset collected from the USA that
measures the intention to use Autonomous Vehicles for commute trips.
Respondents were presented with alternative questionnaire versions (open- and
closed- ended). Since our objective was also to compare the performance of
alternative questionnaire versions, the survey was designed to eliminate
influences resulting from statements, behavioral framework, and the choice
experiment. Results indicate the suitability of using Topic Modeling to extract
information from open-ended responses; however, the models estimated using the
closed-ended questions perform better compared to them. Besides, the proposed
model performs better compared to the models used currently. Furthermore, our
proposed framework will allow respondents to choose the questionnaire type to
answer, which could be particularly beneficial to them when using voice-based
surveys.


title:
On multi-soliton solutions to a generalized inhomogeneous nonlinear
  Schrodinger equation for the Heisenberg ferromagnetic spin chain

abstract:
  A generalized inhomogeneous higher-order nonlinear Schrodinger (GIHNLS)
equation for the Heisenberg ferromagnetic spin chain system in (1+1)-dimensions
under zero boundary condition at infinity is taken into account. The spectral
analysis is first performed to generate a related matrix Riemann-Hilbert
problem on the real axis. Then, through solving the resulting matrix
Riemann-Hilbert problem by taking the jump matrix to be the identity matrix,
the general bright multi-soliton solutions to the GIHNLS equation are attained.
Furthermore, the one- and two-soliton solutions are written out and analyzed by
figures.


title: Multiple-cell cavity design for high mass axion searches

abstract:
  The invisible axion is a well-motivated hypothetical particle that could
address two fundamental questions in modern physics - the CP symmetry problem
in the strong interactions and the dark matter mystery of our universe. The
plausible mass (frequency) range of the axion as a dark matter candidate spans
from ueV to meV (GHz to THz). The axion haloscope using a resonant cavity has
provided the most sensitive search method in the microwave region. However,
experimental searches have been limited to relatively low mass regions mainly
due to the reduced cavity volume at high masses. As an effective approach for
high-mass axion searches, a unique cavity design featuring multiple identical
cells divided by equidistant thin metal partitions in a single cylindrical
cavity was proposed and successfully demonstrated. This report reviews the
characteristics of the multiple-cell cavity design and discusses the various
advantages it offers for high-mass axion searches.


title: Bernstein inequality on conic domains and triangle

abstract:
  We establish weighted Bernstein inequalities in $L^p$ space for the doubling
weight on the conic surface $\mathbb{V}_0^{d+1} = \{(x,t): \|x\| = t, x \in
\mathbb{R}^d, t\in [0,1]\}$ as well as on the solid cone bounded by the conic
surface and the hyperplane $t =1$, which becomes a triangle on the plane when
$d=1$. While the inequalities for the derivatives in the $t$ variable behave as
expected, there are inequalities for the derivatives in the $x$ variables that
are stronger than what one may have expected. As an example, on the triangle
$\{(x_1,x_2): x_1 \ge 0, \, x_2 \ge 0,\, x_1+x_2 \le 1\}$, the usual Bernstein
inequality for the derivative $\partial_1$ states that $\|\phi_1 \partial_1
f\|_{p,w} \le c n \|f\|_{p,w}$ with $\phi_1(x_1,x_2):= x_1(1-x_1-x_2)$, whereas
our new result gives $$\| (1-x_2)^{-1/2} \phi_1 \partial_1 f\|_{p,w} \le c n
\|f\|_{p,w}.$$
  The new inequality is stronger and points out a phenomenon unobserved
hitherto for polygonal domains.


title: Solvable non-Hermitian skin effect in many-body unitary dynamic

abstract:
  Studying unitary evolution of bipartite entanglement in circuit with
nearest-neighbor random gates we show that being exact is actually wrong, while
being slightly wrong is correct. Deriving a non-unitary description of purity
dynamics on qudits we find a transition in the purity relaxation rate whose
origin is in the underlying boundary localized eigenmodes -- the skin effect.
We provide the full solution of the problem, being one of the simplest
iterations of two-site matrices, namely, each is a sum of only two projectors.
This leads to rich dynamics influenced by the Jordan normal form of the kernel
and, most importantly, a spectrum that is completely discontinuous in the
thermodynamic limit. It provides a simple example of how a seemingly innocuous
many-body unitary evolution can harbor all oddities that mathematics can offer.


title:
Structural and electronic phase transitions in Zr$_{1.03}$Se$_{2}$ at
  high pressure

abstract:
  A detailed high pressure investigation is carried out using x-ray
diffraction, Raman spectroscopy and low temperature resistivity measurements on
hexagonal ZrSe$_{2}$ having an excess of 3 at.\% Zr. Structural studies show
that the sample goes through a gradual structural transition from hexagonal to
monoclinic phase, with a mixed phase in the pressure range 5.9 GPa to 14.8 GPa.
Presence of a minimum in the $c/a$ ratio in the hexagonal phase and a minimum
in the full width half maximum of the $A_{1g}$ mode at about the same pressure
indicates an electronic phase transition. The sample shows a metallic
characteristic in its low temperature resistivity data at ambient pressure,
which persist till about 5.1 GPa and can be related the presence of slight
excess Zr. At and above 7.3 GPa, the sample shows a metal to semiconductor
transition with the opening of a very small band gap, which increases with
pressure. The low temperature resistivity data show an upturn, which flattens
with an increase in pressure. The phenomenological analysis of the low
temperature resistivity data indicates the presence of Kondo effect in the
sample, which may be due to the excess Zr.


title: Two loop mass renormalization in heterotic string theory: NS states

abstract:
  In this work computation of the renormalized mass at two loop order for the
NS sector of heterotic string theory is attempted. We identify the 1PI region
of the moduli space after a choice of gluing compatible local coordinates
around the external punctures, for a genus 2 Riemann surface with 2 punctures.
We then present a prescription for choosing a section avoiding the spurious
poles due to the presence of a required number of picture changing operators.
In practice this amounts to breaking up the region of integration over the
moduli space into different parts and carrying out the vertical integration
procedure near the spurious poles. This requires moving around the PCO
locations. We identify the co-dimension 2 subspace of the moduli space where
the spurious poles lie and hence we know which arrangements of PCOs to avoid.
We are unable to perform the integration over moduli space in this note and
hope to report on it in a later instalment.


title:
Learning Discrete Structured Variational Auto-Encoder using Natural
  Evolution Strategies

abstract:
  Discrete variational auto-encoders (VAEs) are able to represent semantic
latent spaces in generative learning. In many real-life settings, the discrete
latent space consists of high-dimensional structures, and propagating gradients
through the relevant structures often requires enumerating over an
exponentially large latent space. Recently, various approaches were devised to
propagate approximated gradients without enumerating over the space of possible
structures. In this work, we use Natural Evolution Strategies (NES), a class of
gradient-free black-box optimization algorithms, to learn discrete structured
VAEs. The NES algorithms are computationally appealing as they estimate
gradients with forward pass evaluations only, thus they do not require to
propagate gradients through their discrete structures. We demonstrate
empirically that optimizing discrete structured VAEs using NES is as effective
as gradient-based approximations. Lastly, we prove NES converges for
non-Lipschitz functions as appear in discrete structured VAEs.


title:
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous
  Spread of Infection

abstract:
  Epidemics like Covid-19 and Ebola have impacted people's lives significantly.
The impact of mobility of people across the countries or states in the spread
of epidemics has been significant. The spread of disease due to factors local
to the population under consideration is termed the endogenous spread. The
spread due to external factors like migration, mobility, etc. is called the
exogenous spread. In this paper, we introduce the Exo-SIR model, an extension
of the popular SIR model and a few variants of the model. The novelty in our
model is that it captures both the exogenous and endogenous spread of the
virus. First, we present an analytical study. Second, we simulate the Exo-SIR
model with and without assuming contact network for the population. Third, we
implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We
found that endogenous infection is influenced by exogenous infection.
Furthermore, we found that the Exo-SIR model predicts the peak time better than
the SIR model. Hence, the Exo-SIR model would be helpful for governments to
plan policy interventions at the time of a pandemic.


title:
The pedagogical value of the four-dimensional picture III: Solutions to
  Maxwell's equations

abstract:
  We outline a regular way for solving Maxwell's equations. We take, as the
starting point, the notion of vector potentials. The rationale for introducing
this notion in electrodynamics is that the set of Maxwell's equations is
seemingly overdetermined. We demonstrate the existence of two fundamental
solutions to Maxwell's equations whose linear combinations comprise the whole
variety of classical electromagnetic field configurations.


title: Shotgun assembly threshold for lattice labeling model

abstract:
  We study the shotgun assembly problem for the lattice labeling model, where
i.i.d. uniform labels are assigned to each vertex in a $d$-dimensional box of
side length $n$. We wish to recover the labeling configuration on the whole box
given empirical profile of labeling configurations on all boxes of side length
$r$. We determine the threshold around which there is a sharp transition from
impossible to recover with probability tending to 1, to possible to recover
with an efficient algorithm with probability tending to 1. Our result sharpens
a constant factor in a previous work of Mossel and Ross (2019) and thus solves
a question therein.


title: A fast quantum algorithm for computing matrix permanent

abstract:
  Computing a matrix permanent is known to be a classically intractable
problem. This paper presents the first polynomial time quantum algorithm for
computing permanents of arbitrary $N$-dimensional square matrices with
multiplicative error and additive error protocols. We transformed the
well-known classical algorithm, Ryser's formula, into sums of quantum overlap
integrals to develop the quantum algorithm. This requires $\mathcal{O}(N)$ and
$\mathcal{O}(N^{3})$ evaluations of the real part of the quantum overlap
integrals for real and complex matrices, respectively, with polynomial depth
quantum circuits of $\mathcal{O}(N)$ qubits. The existence of the polynomial
time multiplicative error estimation of a matrix permanent would strongly imply
that quantum computers can access the #P-hard problem. Our quantum algorithm
attains the milestone of opening a new direction in quantum computing and
computational complexity theory. It could resolve crucial computational
complexity issues like locating the bounded error quantum polynomial time (BQP)
relative to the polynomial hierarchy, which has been a big complexity question.
Depending on the size of a matrix norm of interest, we can also obtain an
exponentially small additive error estimation and better estimation against
Gurvits' classical sampling algorithm. The additive error protocol is
practically implementable in noisy intermediate-scale quantum devices.
Moreover, our quantum algorithm directly shows the computational complexity
connection between the matrix permanent and the real-time partition function of
the quantum Ising Hamiltonian.

categories: quant-ph math-ph math.MP physics.atom-ph physics.comp-ph


title:
Deformed relativistic Hartree-Bogoliubov theory in continuum with a
  point-coupling functional. II. Examples of odd Nd isotopes

abstract:
  The aim of this work is to extend the deformed relativistic
Hartree-Bogoliubov theory in continuum (DRHBc) based on the point-coupling
density functionals to odd-$A$ and odd-odd nuclei and examine its applicability
by taking odd-$A$ Nd isotopes as examples. In the DRHBc theory, the densities
and potentials with axial deformation are expanded in terms of Legendre
polynomials, and the relativistic Hartree-Bogoliubov equations are solved in a
Dirac Woods-Saxon basis to include the continuum effects. For an odd-$A$ or
odd-odd nucleus, the blocking effect of unpaired nucleon(s) is taken into
account with the equal filling approximation. To determine its ground state, an
automatic blocking procedure is adopted, in which the orbital with the lowest
quasiparticle energy is blocked during the iteration. This procedure is
justified by comparing with the results from the orbital-fixed blocking
calculations, in which the blocked orbital near the Fermi surface is fixed
during the iteration. The ground states for both light and heavy nuclei can be
provided by the automatic blocking procedure as the orbital-fixed blocking
procedure, but with considerably reduced computational cost. The numerical
details for even-even nuclei are found to be valid for odd-$A$ and odd-odd
nuclei as well. Taking Nd isotopes including both even-even and odd-$A$ ones as
examples, the calculated ground-state properties with PC-PK1 are in good
agreement with the available experimental data. This work paves the way to
construct the DRHBc mass table including all even-even, odd-$A$ and odd-odd
nuclei in the nuclear chart.


title:
Computing the gravitational acceleration withing the World Pendulum
  Alliance: an application of the remote laboratory methodology implemented by
  UNAD

abstract:
  In this paper, we calculate the gravitational acceleration by using simple
pendulums within the designated World Pendulum Alliance: a network constituted
by fourteen institutions in eight countries, each provided by a pendulum that
can be accessed remotely via the Internet. As a pedagogical option for remote
laboratory experiences, we show how to access them and measure the pendulum's
period for $N$ oscillations. We discuss the role of the repetitions and samples
to minimize the experimental uncertainty, and by using the averaged value of
the period, we compute the gravitational acceleration locally. Also, we present
the corrections due to the moment of inertia of the system. Finally, by taking
into account the geographical location of each pendulum, we report gravity's
dependence on latitude, longitude, and altitude by discussing the implications
on the Earth's shape. We hope this tool will be helpful for introductory and
university physics courses due to its remote access features and its relation
to fundamental concepts.


title:
GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of
  Communities. Detection of Scholarly Disputes in Adversarial Information
  Routes

abstract:
  The cognitive manifold of published content is currently expanding in all
areas of science. However, Scientific Knowledge Graphs (SKGs) only provide poor
pictures of the adversarial directions and scientific controversies that feed
the production of knowledge. In this Article, we tackle the understanding of
the design of the information space of a cognitive representation of research
activities, and of related bottlenecks that affect search interfaces, in the
mapping of structured objects into graphs. We propose, with SKG GRAPHYP, a
novel graph designed geometric architecture which optimizes both the detection
of the knowledge manifold of "cognitive communities", and the representation of
alternative paths to adversarial answers to a research question, for instance
in the context of academic disputes. With a methodology for designing "Manifold
Subnetworks of Cognitive Communities", GRAPHYP provides a classification of
distinct search paths in a research field. Users are detected from the variety
of their search practices and classified in "Cognitive communities" from the
analysis of the search history of their logs of scientific documentation. The
manifold of practices is expressed from metrics of differentiated uses by
triplets of nodes shaped into symmetrical graph subnetworks, with the following
three parameters: Mass, Intensity, and Variety.


title:
Large-scale Virtual Clinical Trials of Closed-loop Treatments for People
  with Type 1 Diabetes

abstract:
  We propose a virtual clinical trial for assessing the safety and efficacy of
closed-loop diabetes treatments prior to an actual clinical trial. Such virtual
trials enable rapid and risk-free pretrial testing of algorithms, and they can
be used to compare different treatment variations for large and diverse
populations. The participants are represented by multiple mathematical models,
consisting of stochastic differential equations, and we use Monte Carlo
closed-loop simulations to compute detailed statistics of the closed-loop
treatments. We implement the virtual clinical trial using high-performance
software and hardware, and we present an example trial with two mathematical
models of one~million participants over 52~weeks (i.e., two~million
simulations), which can be completed in 2~h 9~min.


title:
Engineering Annotations: A Generic Framework For Gluing Design Artefacts
  in Models of Interactive Systems

abstract:
  Along the design process of interactive system many intermediate artefacts
(such as user interface prototypes, task models describing user work and
activities, dialog models specifying system behavior, interaction models
describing user interactions {\ldots}) are created, tested, revised and
improved until the development team produces a validated version of the
full-fledged system. Indeed, to build interactive systems there is a need to
use multiple artefacts/models (as they provide a complementary view). However,
relevant information for describing the design solution and/or supporting
design decisions (such as rational about the design, decisions made,
recommendations, etc.) is not explicitly capturable in the models/artefacts,
hence the need for annotations. Multi-artefacts approaches usually argue that a
given information should only be present in one artefact to avoid duplication
and increase maintainability of the artefacts. Nonetheless, annotations created
on one artefact are usually relevant to other artefacts/models. So that, there
is a need for tools and techniques to coordinate annotations across
artefacts/models which is the contribution of the present work. In this paper,
we propose a model-based approach that was conceived to handle annotations in a
systematic way along the development process of interactive systems. As part of
the solution, we propose an annotation model built upon the W3C's Web
Annotation Data Model. The feasibility of the approach is demonstrated by means
of a tool suite featuring a plugin, which has been deployed and tested over the
multi-artefacts. The overall approach is illustrated on the design of an
interactive cockpit application performing two design iterations. The
contribution brings two main benefits for interactive systems engineering: i)
it presents a generic pattern for integrating information in multiple usually
heterogenous artefacts throughout the design process of interactive systems;
and ii) it highlights the need for tools helping to rationalize and to document
the various artefacts and the related decisions made during interactive systems
design. CCS CONCEPTS $\bullet$ Human-centered computing $\bullet$ Human
computer interaction (HCI)


title:
Precise determination of the 2s22p5-2s2p6 transition energy in
  fluorine-like nickel utilizing a low-lying dielectronic resonance

abstract:
  High precision spectroscopy of the low-lying dielectronic resonances in
fluorine-like nickel ions were determined by employing the merged electron-ion
beam at the heavy-ion storage ring CSRm. The measured dielectronic resonances
are identified by comparing with the most recent relativistic calculation
utilizing the FAC code. The first resonance at about 86 meV due to the
dielectronic recombination via (2s2p6[2S1/2]6s)J=1 intermediate state was
recognized. The experimental determination of the resonance position at 86 meV
reaches an uncertainty of 4 meV, which allows precise determination of the
2s22p5[2P3/2] - 2s2p6[2S1/2] transition energy. The Rydberg binding energy of
the 6s electron in the (2s2p6[2S1/2]6s)J=1 state is calculated by the
multi-configurational Dirac-HartreeFock and stabilization methods. The
determined transition energies are 149.056(4)exp(10)theo and
149.032(4)exp(6)theo, respectively. Moreover, the transition energy has also
been calculated by fully relativistic and ab initio approaches. Individual
theoretical contributions are evaluated by employing the core-Hartree and
Kohn-Sham screening potentials, respectively. High-order QED and correlation
effects contribute prominently to the total transition energy. The present DR
precision spectroscopy study at the CSRm paves the way for future precision
measurements of atomic energy levels with heavier highly charged ions.


title: Predicting Issue Types with seBERT

abstract:
  Pre-trained transformer models are the current state-of-the-art for natural
language models processing. seBERT is such a model, that was developed based on
the BERT architecture, but trained from scratch with software engineering data.
We fine-tuned this model for the NLBSE challenge for the task of issue type
prediction. Our model dominates the baseline fastText for all three issue types
in both recall and precisio} to achieve an overall F1-score of 85.7%, which is
an increase of 4.1% over the baseline.


title: More on Fake GUT

abstract:
  It is remarkable that the matter fields in the Standard Model (SM) are
apparently unified into the $\mathrm{SU}(5)$ representations. A straightforward
explanation of this fact is to embed all the SM gauge groups into a simple
group containing $\mathrm{SU}(5)$, i.e., the grand unified theory (GUT).
Recently, however, a new framework "fake GUT" has been proposed. In this new
framework, the apparent matter unification can be explained by a chiral gauge
group $G$, $ G \supset \mathrm{SU}(5)$. We emphasize that the SM matter fields
are not necessarily embedded into the chiral representations to explain the
apparent unification. In this paper, we discuss details of concrete
realizations of the fake GUT model. We first study the model based on
$\mathrm{SU}(5) \times \mathrm{U}(2)_H$, where $\mathrm{SU}(3)_c$ in the SM is
from $\mathrm{SU}(5)$ while $\mathrm{SU}(2)_L\times \mathrm{U}(1)_Y$ are from
the diagonal subgroups of $\mathrm{SU}(5) \times \mathrm{U}(2)_H$. We also
extend this model to the one based on a semi-simple group, $\mathrm{SU}(5)
\times \mathrm{SU}(3)_H$, so that $\mathrm{U}(2)_H$ is embedded in
$\mathrm{SU}(3)_H$. We also show that this framework predicts rather different
decay patterns of the proton, compared to the conventional GUT.


title:
Complexity-based Encoded Information Quantification in
  Neurophysiological Recordings

abstract:
  Brain activity differs vastly between sleep, cognitive tasks, and action.
Information theory is an appropriate concept to analytically quantify these
brain states. Based on neurophysiological recordings, this concept can handle
complex data sets, is free of any requirements about the data structure, and
can infer the present underlying brain mechanisms. Specifically, by utilizing
algorithmic information theory, it is possible to estimate the absolute
information contained in brain responses. While current approaches that apply
this theory to neurophysiological recordings can discriminate between different
brain states, they are limited in directly quantifying the degree of similarity
or encoded information between brain responses. Here, we propose a method
grounded in algorithmic information theory that affords direct statements about
responses' similarity by estimating the encoded information through a
compression-based scheme. We validated this method by applying it to both
synthetic and real neurophysiological data and compared its efficiency to the
mutual information measure. This proposed procedure is especially suited for
task paradigms contrasting different event types because it can precisely
quantify the similarity of neuronal responses.


title:
MMSE Signal Detection for MIMO Systems based on Ordinary Differential
  Equation

abstract:
  Motivated by emerging technologies for energy efficient analog computing and
continuous-time processing, this paper proposes continuous-time minimum mean
squared error estimation for multiple-input multiple-output (MIMO) systems
based on an ordinary differential equation. Mean squared error (MSE) is a
principal detection performance measure of estimation methods for MIMO systems.
We derive an analytical MSE formula that indicates the MSE at any time. The MSE
of the proposed method depends on a regularization parameter which affects the
convergence property of the MSE. Furthermore, we extend the proposed method by
using a time-dependent regularization parameter to achieve better convergence
performance. Numerical experiments indicated excellent agreement with the
theoretical values and improvement in the convergence performance owing to the
use of the time-dependent parameter.


title: Long geodesics in the space of K\"ahler metrics

abstract:
  We give some remarks on geodesics in the space of K\"ahler metrics that are
defined for all time. Such curves are conjecturally induced by holomorphic
vector fields, and we show that this is indeed so for regular geodesics,
whereas the question for generalized geodesics is still open (as far as we
know). We also give a result about the derivative of such geodesics which
implies a variant of a theorem of Atiyah and Guillemin-Sternberg on convexity
of the image of certain moment maps.


title:
On the Design of Locking Free Ghost Penalty Stabilization and the
  Relation to CutFEM with Discrete Extension

abstract:
  In this note, we develop a new stabilization mechanism for cut finite element
methods that generalizes previous approaches of ghost penalty type in two ways:
(1) The quantity that is stabilized and (2) The choice of elements that are
connected in the stabilization. In particular, we can stabilize functionals of
the discrete function such as finite element degrees of freedom. We
subsequently show that the kernel of our ghost penalty operator defines a
finite element space based on discrete extensions in the spirit of those
introduced in Burman, E.; Hansbo, P. and Larson, M. G., CutFEM Based on
Extended Finite Element Spaces, arXiv2101.10052, 2021.