title:
Unsupervised Domain Adaptation with Implicit Pseudo Supervision for
  Semantic Segmentation

abstract:
  Pseudo-labelling is a popular technique in unsuper-vised domain adaptation
for semantic segmentation. However, pseudo labels are noisy and inevitably have
confirmation bias due to the discrepancy between source and target domains and
training process. In this paper, we train the model by the pseudo labels which
are implicitly produced by itself to learn new complementary knowledge about
target domain. Specifically, we propose a tri-learning architecture, where
every two branches produce the pseudo labels to train the third one. And we
align the pseudo labels based on the similarity of the probability
distributions for each two branches. To further implicitly utilize the pseudo
labels, we maximize the distances of features for different classes and
minimize the distances for the same classes by triplet loss. Extensive
experiments on GTA5 to Cityscapes and SYNTHIA to Cityscapes tasks show that the
proposed method has considerable improvements.


title:
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via
  Intent Conditioning

abstract:
  Semantic parsing (SP) is a core component of modern virtual assistants like
Google Assistant and Amazon Alexa. While sequence-to-sequence-based
auto-regressive (AR) approaches are common for conversational semantic parsing,
recent studies employ non-autoregressive (NAR) decoders and reduce inference
latency while maintaining competitive parsing quality. However, a major
drawback of NAR decoders is the difficulty of generating top-k (i.e., k-best)
outputs with approaches such as beam search. To address this challenge, we
propose a novel NAR semantic parser that introduces intent conditioning on the
decoder. Inspired by the traditional intent and slot tagging parsers, we
decouple the top-level intent prediction from the rest of a parse. As the
top-level intent largely governs the syntax and semantics of a parse, the
intent conditioning allows the model to better control beam search and improves
the quality and diversity of top-k outputs. We introduce a hybrid
teacher-forcing approach to avoid training and inference mismatch. We evaluate
the proposed NAR on conversational SP datasets, TOP & TOPv2. Like the existing
NAR models, we maintain the O(1) decoding time complexity while generating more
diverse outputs and improving the top-3 exact match (EM) by 2.4 points. In
comparison with AR models, our model speeds up beam search inference by 6.7
times on CPU with competitive top-k EM.


title:
Dissipative particle dynamics simulation study on ATRP-brush
  modification of variably shaped surfaces and biopolymer adsorption

abstract:
  We present a dissipative particle dynamics (DPD) simulation study on the
surface modification of initiator embedded microparticles (MPs) of different
shapes via atom transfer radical polymerization (ATRP) brush growth. The
surface-initiated ATRP-brush growth leads to the formation of a more globular
MP shape. We perform the comparative analysis of ATRP-brush growth on three
different forms of particle surfaces: cup surface, spherical surface, and flat
surface (rectangular/disk-shaped). First, we establish the chemical kinetics of
the brush growth: the monomer conversion and the reaction rates. We next argue
the structure changes (shape-modification) of brush-modified surfaces by
computing the radial distribution function, spatial density distribution,
radius of gyration, hydrodynamic radius, and shape factor. The polymer
brush-modified particles are well known as the carrier materials for enzyme
immobilization. Finally, we study the biopolymer adsorption on ATRP-brush
modified particles in a compatible solution. In particular, we explore the
effect of ATRP-brush length, biopolymer chain length, and concentration on the
adsorption process. Our results illustrate the enhanced biopolymer adsorption
with increased brush length, initiator concentration, and biopolymer
concentration. Most importantly, the flat surface loads more biopolymers than
the other two surfaces when adsorption reaches saturation. The experimental
results verified the same, considering the disk-shaped flat surface particle,
cup, and spherical particles.


title: Conditions for superradiant instability of the Kerr-Newman black holes

abstract:
  We find a condition for superradiant instability of Kerr-Newman black holes
under a charged massive scalar perturbation by analyzing asymptotic scalar
potential and wave function. Actually, it corresponds to the condition for
getting a trapping well. Also, we obtain the condition for superradiant
stability of Kerr-Newman black holes which states that there is no trapping
well. The analysis is applied to Kerr black holes to find a condition for
superradiant instability.


title: The Burge correspondence and crystal graphs

abstract:
  The Burge correspondence yields a bijection between simple labelled graphs
and semistandard Young tableaux of threshold shape. We characterize the simple
graphs of hook shape by peak and valley conditions on Burge arrays. This is the
first step towards an analogue of Schensted's result for the RSK insertion
which states that the length of the longest increasing subword of a word is the
length of the largest row of the tableau under the RSK correspondence.
Furthermore, we give a crystal structure on simple graphs of hook shape. The
extremal vectors in this crystal are precisely the simple graphs whose degree
sequence are threshold and hook-shaped.


title:
Candidate high-redshift protoclusters and lensed galaxies in the Planck
  list of high-z sources overlapping with Herschel-SPIRE imaging

abstract:
  The Planck list of high-redshift source candidates (the PHz catalogue)
contains 2151 peaks in the cosmic infrared background, unresolved by Planck's 5
arcmin beam. Follow-up spectroscopic observations have revealed that some of
these objects are $z \approx 2$ protoclusters and strong gravitational lenses,
but an unbiased survey has not yet been carried out. To this end, we have used
archival Herschel-SPIRE observations to study a uniformly-selected sample of
187 PHz sources. In contrast with previous biased follow-up studies, we find
that only one of our PHz sources is a bright gravitationally-lensed galaxy
(peak flux $\gtrsim 300$ mJy), indicating that such objects are rarer in the
PHz catalogue than previously believed ($< 1$ per cent). The majority of our
PHz sources consist of many red, star-forming galaxies, demonstrating that
typical PHz sources are candidate protoclusters. However, our new PHz sources
are significantly less bright than found in previous studies and differ in
colour, suggesting possible differences in redshift and star-formation rate.
Nonetheless, 40 of our PHz sources contain $> 3\sigma$ galaxy overdensities,
comparable to the fraction of $> 3\sigma$ overdensities found in earlier biased
studies. We additionally use a machine-learning approach to identify less
extreme (peak flux $\sim 100$ mJy) gravitationally-lensed galaxies among
Herschel-SPIRE observations of PHz sources, finding a total of seven candidates
in our unbiased sample, and 13 amongst previous biased samples. Our new
uniformly-selected catalogues of $> 3\sigma$ candidate protoclusters and strong
gravitational lenses provide interesting targets for follow up with
higher-resolution facilities, such as ALMA and JWST.


title:
Rationality of meromorphic functions between real algebraic sets in the
  plane

abstract:
  We study one variable meromorphic functions mapping a planar real algebraic
set $A$ to another real algebraic set in the complex plane. By using the theory
of Schwarz reflection functions, we show that for certain $A$, these
meromorphic functions must be rational. In particular, when $A$ is the standard
unit circle, we obtain an one dimensional analog of Poincar\'e(1907),
Tanaka(1962) and Alexander(1974)'s rationality results for $2m-1$ dimensional
sphere in $\mathbb{C}^m$ when $m\ge 2$.


title: RecurSeed and CertainMix for Weakly Supervised Semantic Segmentation

abstract:
  Although weakly supervised semantic segmentation using only image-level
labels (WSSS-IL) is potentially useful, its low performance and implementation
complexity still limit its application. The main causes are (a) non-detection
and (b) false-detection phenomena: (a) The class activation maps refined from
existing WSSS-IL methods still only represent partial regions for large-scale
objects, and (b) for small-scale objects, over-activation causes them to
deviate from the object edges. We propose RecurSeed which alternately reduces
non- and false-detections through recursive iterations, thereby implicitly
finding an optimal junction that minimizes both errors. To maximize the
effectiveness of RecurSeed, we also propose a novel data augmentation (DA)
approach called CertainMix, which virtually creates object masks and further
expresses their edges in combining the segmentation results, thereby obtaining
a new DA method effectively reflecting object existence reliability through the
spatial information. We achieved new state-of-the-art performances on both the
PASCAL VOC 2012 and MS COCO 2014 benchmarks (VOC val 72.4%, COCO val 45.0%).
The code is available at https://github.com/OFRIN/RecurSeed_and_CertainMix.


title: Particle Deceleration for Collective QED Signatures

abstract:
  Frequency upshifts have been proposed as a first experimental signature of
collective effects in QED cascade generated electron-positron pair plasmas.
Since the high effective masses of generated pairs will reduce any frequency
change, stopped pairs at minimal Lorentz factor in the lab frame were thought
to be the dominant contribution to the the laser upshift. However, we
demonstrate that only considering stopped particles unduly neglects the
contributions of particles re-accelerated in the laser propagation direction.
Re-accelerated particles should, on a per particle basis, affect the laser more
strongly, and over a much longer timescale. To maximize particle contributions
to the laser upshift, we consider a Laguerre-Gaussian (LG) mode to better
reflect generated pairs. The LG mode doesn't have an advantage in particle
deceleration and re-acceleration when compared against a Gaussian beam, but the
LG mode can maintain particle contributions for a longer duration, allowing for
more pair density accumulation. Deceleration with a structured beam to keep
pairs within the laser should create a larger upshift, thereby lowering the
demands on the driving laser.


title:
A note on the effects of magnetic field on holographic fermions with
  dipole-like coupling

abstract:
  In this note, we study the effects of non-zero magnetic field on the fermion
spectral function coupled with a non-minimal coupling in the background of a
dyonic $AdS_4$ black hole. The system can be reduced to a non magnetic one with
the momentum being quantized into Landau levels $n$. By tuning the coupling
parameter, we study its effect on the Fermi level and the quasiparticle width.
We found an interesting scaling relation between the decay width and the
coupling parameter, with a universal scaling exponent which is independent of
the levels $n$.


title:
Bi-algebraicity in the rank one Riemann--Hilbert correspondence via
  o-minimality

abstract:
  For a smooth, projective, complex algebraic variety $X$, the Riemann--Hilbert
correspondence establishes a complex analytic isomorphism between the `Betti
moduli space' of rank $n$ local systems on $X^\mathrm{an}$ and the `de Rham
moduli space' of rank $n$ vector bundles with flat connection on $X$. In the
rank one case, C. Simpson precisely characterizes the subvarieties of these
moduli spaces that are `bi-algebraic' for this typically transcendental,
analytic isomorphism. In this short note, we give a new proof of this
characterization of Simpson, using methods from o-minimal geometry. We adapt
the o-minimal proof to a p-adic setting, namely that of Mumford curves.


title: Multi-label topic classification for COVID-19 literature with Bioformer

abstract:
  We describe Bioformer team's participation in the multi-label topic
classification task for COVID-19 literature (track 5 of BioCreative VII). Topic
classification is performed using different BERT models (BioBERT, PubMedBERT,
and Bioformer). We formulate the topic classification task as a sentence pair
classification problem, where the title is the first sentence, and the abstract
is the second sentence. Our results show that Bioformer outperforms BioBERT and
PubMedBERT in this task. Compared to the baseline results, our best model
increased micro, macro, and instance-based F1 score by 8.8%, 15.5%, 7.4%,
respectively. Bioformer achieved the highest micro F1 and macro F1 scores in
this challenge. In post-challenge experiments, we found that pretraining of
Bioformer on COVID-19 articles further improves the performance.


title:
Iterative Inner/outer Approximations for Scalable Semidefinite Programs
  using Block Factor-width-two Matrices

abstract:
  In this paper, we propose iterative inner/outer approximations based on a
recent notion of block factor-width-two matrices for solving semidefinite
programs (SDPs). Our inner/outer approximating algorithms generate a sequence
of upper/lower bounds of increasing accuracy for the optimal SDP cost. The
block partition in our algorithms offers flexibility in terms of both numerical
efficiency and solution quality, which includes the approach of scaled
diagonally dominance (SDD) approximation as a special case. We discuss both the
theoretical results and numerical implementation in detail. Our main theorems
guarantee that the proposed iterative algorithms generate monotonically
decreasing upper (increasing lower) bounds. Extensive numerical results confirm
our findings.


title:
HCFL: A High Compression Approach for Communication-Efficient Federated
  Learning in Very Large Scale IoT Networks

abstract:
  Federated learning (FL) is a new artificial intelligence concept that enables
Internet-of-Things (IoT) devices to learn a collaborative model without sending
the raw data to centralized nodes for processing. Despite numerous advantages,
low computing resources at IoT devices and high communication costs for
exchanging model parameters make applications of FL in massive IoT networks
very limited. In this work, we develop a novel compression scheme for FL,
called high-compression federated learning (HCFL), for very large scale IoT
networks. HCFL can reduce the data load for FL processes without changing their
structure and hyperparameters. In this way, we not only can significantly
reduce communication costs, but also make intensive learning processes more
adaptable on low-computing resource IoT devices. Furthermore, we investigate a
relationship between the number of IoT devices and the convergence level of the
FL model and thereby better assess the quality of the FL process. We
demonstrate our HCFL scheme in both simulations and mathematical analyses. Our
proposed theoretical research can be used as a minimum level of satisfaction,
proving that the FL process can achieve good performance when a determined
configuration is met. Therefore, we show that HCFL is applicable in any
FL-integrated networks with numerous IoT devices.


title: A Fixed-Parameter Algorithm for the Kneser Problem

abstract:
  The Kneser graph $K(n,k)$ is defined for integers $n$ and $k$ with $n \geq
2k$ as the graph whose vertices are all the $k$-subsets of $\{1,2,\ldots,n\}$
where two such sets are adjacent if they are disjoint. A classical result of
Lov\'asz asserts that the chromatic number of $K(n,k)$ is $n-2k+2$. In the
computational Kneser problem, we are given an oracle access to a coloring of
the vertices of $K(n,k)$ with $n-2k+1$ colors, and the goal is to find a
monochromatic edge. We present a randomized algorithm for the Kneser problem
with running time $n^{O(1)} \cdot k^{O(k)}$. This shows that the problem is
fixed-parameter tractable with respect to the parameter $k$. The analysis
involves structural results on intersecting families and on induced subgraphs
of Kneser graphs.
  We also study the Agreeable-Set problem of assigning a small subset of a set
of $m$ items to a group of $\ell$ agents, so that all agents value the subset
at least as much as its complement. As an application of our algorithm for the
Kneser problem, we obtain a randomized polynomial-time algorithm for the
Agreeable-Set problem for instances that satisfy $\ell \geq m - O(\frac{\log
m}{\log \log m})$. We further show that the Agreeable-Set problem is at least
as hard as a variant of the Kneser problem with an extended access to the input
coloring.


title:
Local interpretation of time-resolved X-ray absorption in Mott
  insulators: Insights from nonequilibrium dynamical mean-field theory

abstract:
  We present a formalism based on nonequilibrium dynamical mean field theory
(DMFT) which allows to compute the time-resolved X-ray absorption spectrum
(XAS) of photo-excited solids. By applying this formalism to the photo-doped
half-filled and quarter-filled two-orbital Hubbard models in the Mott
insulating regime we clarify how the time-resolved XAS signal reflects the
nonequilibrium population of different local states. Apart from the missing
broadening associated with continuum excitations, the atomic XAS spectrum
computed with the nonthermal state populations provides a good approximation to
the full nonequilibrium DMFT result. This suggest a route to combine the
accurate DMFT description of nonequilibrum states of solids with cluster
calculations of the XAS signal.


title:
Critical correlations of Ising and Yang-Lee critical points from Tensor
  RG

abstract:
  We examine feasibility of accurate estimations of universal critical data
using tensor renormalization group (TRG) algorithm introduced by Levin and
Nave. Specifically, we compute critical exponents $\gamma, \gamma/\nu, \delta,
\eta$ and amplitude ratio $A$ for the magnetic susceptibility from one- and
two-point correlation functions for three critical points in two dimensions --
isotropic and anisotropic Ising models and the Yang-Lee critical point at
finite imaginary magnetic field. While TRG performs quantitaviely well in all
three cases already at smaller bond dimension, $D=16$, the latter two appear to
show more rapid improvement in bond dimension, e.g. we are able to reproduce
exactly known results to better than one percent at bond dimension $D=24$. We
are able to reproduce exactly known values to better than 1 percent with modest
effort of bond dimension 28. We comment on the relationship between these
results and earlier results on conformal dimensions, fixed points of tensor RG,
and also compare computational costs of tensor renormalization vs. conventional
Monte-Carlo sampling.


title:
Supplementation of deep neural networks with simplified physics-based
  features to increase model prediction accuracy

abstract:
  To improve predictive models for STEM applications, supplemental
physics-based features computed from input parameters are introduced into
single and multiple layers of a deep neural network (DNN). While many studies
focus on informing DNNs with physics through differential equations or
numerical simulation, much may be gained through integration of simplified
relationships. To evaluate this hypothesis, a number of thin rectangular plates
simply-supported on all edges are simulated for five materials. With plate
dimensions and material properties as input features and fundamental natural
frequency as the sole output, predictive performance of a purely data-driven
DNN-based model is compared with models using additional inputs computed from
simplified physical relationships among baseline parameters, namely plate
weight, modulus of rigidity, and shear modulus. To better understand the
benefit to model accuracy, these additional features are injected into various
single and multiple DNN layers, and trained with four different dataset sizes.
When these physics-enhanced models are evaluated against independent data of
the same materials and similar dimensions to the training sets, supplementation
with simplified physics-based parameters provides little reduction in
prediction error over the baseline for models trained with dataset sizes of 60
and greater, although small improvement from 19.3% to 16.1% occurs when trained
with a sparse size of 30. Conversely, notable accuracy gains occur when the
independent test data is of material and dimensions not conforming to the
training set. Specifically, when physics-enhanced data is injected into
multiple DNN layers, reductions in error from 33.2% to 19.6%, 34.9% to 19.9%,
35.8% to 22.4%, and 43.0% to 28.4% are achieved for training dataset sizes of
261, 117, 60, and 30, respectively, demonstrating attainment of a degree of
generalizability.


title: High-performance Evolutionary Algorithms for Online Neuron Control

abstract:
  Recently, optimization has become an emerging tool for neuroscientists to
study neural code. In the visual system, neurons respond to images with graded
and noisy responses. Image patterns eliciting highest responses are diagnostic
of the coding content of the neuron. To find these patterns, we have used
black-box optimizers to search a 4096d image space, leading to the evolution of
images that maximize neuronal responses. Although genetic algorithm (GA) has
been commonly used, there haven't been any systematic investigations to reveal
the best performing optimizer or the underlying principles necessary to improve
them.
  Here, we conducted a large scale in silico benchmark of optimizers for
activation maximization and found that Covariance Matrix Adaptation (CMA)
excelled in its achieved activation. We compared CMA against GA and found that
CMA surpassed the maximal activation of GA by 66% in silico and 44% in vivo. We
analyzed the structure of Evolution trajectories and found that the key to
success was not covariance matrix adaptation, but local search towards
informative dimensions and an effective step size decay. Guided by these
principles and the geometry of the image manifold, we developed SphereCMA
optimizer which competed well against CMA, proving the validity of the
identified principles. Code available at
https://github.com/Animadversio/ActMax-Optimizer-Dev


title:
Multimodal spatiotemporal graph neural networks for improved prediction
  of 30-day all-cause hospital readmission

abstract:
  Measures to predict 30-day readmission are considered an important quality
factor for hospitals as accurate predictions can reduce the overall cost of
care by identifying high risk patients before they are discharged. While recent
deep learning-based studies have shown promising empirical results on
readmission prediction, several limitations exist that may hinder widespread
clinical utility, such as (a) only patients with certain conditions are
considered, (b) existing approaches do not leverage data temporality, (c)
individual admissions are assumed independent of each other, which is
unrealistic, (d) prior studies are usually limited to single source of data and
single center data. To address these limitations, we propose a multimodal,
modality-agnostic spatiotemporal graph neural network (MM-STGNN) for prediction
of 30-day all-cause hospital readmission that fuses multimodal in-patient
longitudinal data. By training and evaluating our methods using longitudinal
chest radiographs and electronic health records from two independent centers,
we demonstrate that MM-STGNN achieves AUROC of 0.79 on both primary and
external datasets. Furthermore, MM-STGNN significantly outperforms the current
clinical reference standard, LACE+ score (AUROC=0.61), on the primary dataset.
For subset populations of patients with heart and vascular disease, our model
also outperforms baselines on predicting 30-day readmission (e.g., 3.7 point
improvement in AUROC in patients with heart disease). Lastly, qualitative model
interpretability analysis indicates that while patients' primary diagnoses were
not explicitly used to train the model, node features crucial for model
prediction directly reflect patients' primary diagnoses. Importantly, our
MM-STGNN is agnostic to node feature modalities and could be utilized to
integrate multimodal data for triaging patients in various downstream resource
allocation tasks.


title: Learning Task-Aware Energy Disaggregation: a Federated Approach

abstract:
  We consider the problem of learning the energy disaggregation signals for
residential load data. Such task is referred as non-intrusive load monitoring
(NILM), and in order to find individual devices' power consumption profiles
based on aggregated meter measurements, a machine learning model is usually
trained based on large amount of training data coming from a number of
residential homes. Yet collecting such residential load datasets require both
huge efforts and customers' approval on sharing metering data, while load data
coming from different regions or electricity users may exhibit heterogeneous
usage patterns. Both practical concerns make training a single, centralized
NILM model challenging. In this paper, we propose a decentralized and
task-adaptive learning scheme for NILM tasks, where nested meta learning and
federated learning steps are designed for learning task-specific models
collectively. Simulation results on benchmark dataset validate proposed
algorithm's performance on efficiently inferring appliance-level consumption
for a variety of homes and appliances.


title:
Strategic Safety-Critical Attacks Against an Advanced Driver Assistance
  System

abstract:
  A growing number of vehicles are being transformed into semi-autonomous
vehicles (Level 2 autonomy) by relying on advanced driver assistance systems
(ADAS) to improve the driving experience. However, the increasing complexity
and connectivity of ADAS expose the vehicles to safety-critical faults and
attacks. This paper investigates the resilience of a widely-used ADAS against
safety-critical attacks that target the control system at opportune times
during different driving scenarios and cause accidents. Experimental results
show that our proposed Context-Aware attacks can achieve an 83.4% success rate
in causing hazards, 99.7% of which occur without any warnings. These results
highlight the intolerance of ADAS to safety-critical attacks and the importance
of timely interventions by human drivers or automated recovery mechanisms to
prevent accidents.


title:
Learning topological defects formation with neural networks in a quantum
  phase transition

abstract:
  The strong representing power of neural networks make it become a powerful
tool for solving quantum many-body systems. Except for static solutions,
nonequilibrium processes are more challenging for neural networks. We study
time evolutions and the universal statistics of topological defects beyond
Kibble-Zurek mechanism after a quantum phase transition in a transverse Ising
model by virtue of the state-of-the-art neural networks and machine learning
methods. The first three cumulants of the topological defects numbers and the
energy spectrum for this transverse Ising model are computed after a linear
quench. The resulting outcomes match the analytical predictions very well.


title:
De Sitter Spacetime from Holographic Flat Spacetime with Inexact Bulk
  Quantum Mechanics

abstract:
  We argue that the flat spacetime with inexact quantum mechanics in it is dual
to the de Sitter spacetime with exact quantum mechanics in it, and the positive
cosmological constant of this de Sitter spacetime is in the second order of the
degree of the violation of the bulk quantum mechanics in the flat spacetime.
The flat spacetime is holographic and has a dual time-contracted conformal
field theory. The vanishing smallness of the observed positive cosmological
constant suggests the extraordinary exactness of the bulk quantum mechanics in
the flat spacetime.


title: GLAD: Neural Predicate Synthesis to Repair Omission Faults

abstract:
  Existing template and learning-based APR tools have successfully found
patches for many benchmark faults. However, our analysis of existing results
shows that omission faults pose a significant challenge to these techniques.
For template based approaches, omission faults provide no location to apply
templates to; for learning based approaches that formulate repair as Neural
Machine Translation (NMT), omission faults similarly do not provide the faulty
code to translate. To address these issues, we propose GLAD, a novel
learning-based repair technique that specifically targets if-clause synthesis.
GLAD does not require a faulty line as it is based on generative Language
Models (LMs) instead of machine translation; consequently, it can repair
omission faults. GLAD intelligently constrains the language model using a
type-based grammar. Further, it efficiently reduces the validation cost by
performing dynamic ranking of candidate patches using a debugger. Thanks to the
shift from translation to synthesis, GLAD is highly orthogonal to existing
techniques: GLAD can correctly fix 16 Defects4J v1.2 faults that previous
NMT-based techniques could not, while maintaining a reasonable runtime cost,
underscoring its utility as an APR tool and potential to complement existing
tools in practice. An inspection of the bugs that GLAD fixes reveals that GLAD
can quickly generate expressions that would be challenging for other
techniques.


title: ViTOL: Vision Transformer for Weakly Supervised Object Localization

abstract:
  Weakly supervised object localization (WSOL) aims at predicting object
locations in an image using only image-level category labels. Common challenges
that image classification models encounter when localizing objects are, (a)
they tend to look at the most discriminative features in an image that confines
the localization map to a very small region, (b) the localization maps are
class agnostic, and the models highlight objects of multiple classes in the
same image and, (c) the localization performance is affected by background
noise. To alleviate the above challenges we introduce the following simple
changes through our proposed method ViTOL. We leverage the vision-based
transformer for self-attention and introduce a patch-based attention dropout
layer (p-ADL) to increase the coverage of the localization map and a gradient
attention rollout mechanism to generate class-dependent attention maps. We
conduct extensive quantitative, qualitative and ablation experiments on the
ImageNet-1K and CUB datasets. We achieve state-of-the-art MaxBoxAcc-V2
localization scores of 70.47% and 73.17% on the two datasets respectively. Code
is available on https://github.com/Saurav-31/ViTOL


title: Recommending Code Improvements Based on Stack Overflow Answer Edits

abstract:
  Background: Sub-optimal code is prevalent in software systems. Developers may
write low-quality code due to many reasons, such as lack of technical
knowledge, lack of experience, time pressure, management decisions, and even
unhappiness. Once sub-optimal code is unknowingly (or knowingly) integrated
into the codebase of software systems, its accumulation may lead to large
maintenance costs and technical debt. Stack Overflow is a popular website for
programmers to ask questions and share their code snippets. The crowdsourced
and collaborative nature of Stack Overflow has created a large source of
programming knowledge that can be leveraged to assist developers in their
day-to-day activities. Objective: In this paper, we present an exploratory
study to evaluate the usefulness of recommending code improvements based on
Stack Overflow answers' edits. Method: We propose Matcha, a code recommendation
tool that leverages Stack Overflow code snippets with version history and code
clone search techniques to identify sub-optimal code in software projects and
suggest their optimised version. By using SOTorrent and GitHub datasets, we
will quali-quantitatively investigate the usefulness of recommendations given
by \textsc{Matcha} to developers using manual categorisation of the
recommendations and acceptance of pull-requests to open-source projects.


title:
Discrete Boltzmann modeling of Rayleigh-Taylor instability: effects of
  interfacial tension, viscosity and heat conductivity

abstract:
  The Rayleigh-Taylor Instability (RTI) in compressible flow with
inter-molecular interactions is probed via the Discrete Boltzmann Method (DBM).
The effects of interfacial tension, viscosity and heat conduction are
investigated. It is found that the influences of interfacial tension on the
perturbation amplitude, bubble velocity, and two kinds of entropy production
rates all show differences at different stages of RTI evolution. It inhibits
the RTI evolution at the bubble acceleration stage, while at the asymptotic
velocity stage, it first promotes and then inhibits the RTI evolution.
Viscosity and heat conduction inhibit the RTI evolution. Viscosity shows a
suppressive effect on entropy generation rate related to heat flow at the early
stage but a first promotive and then suppressive effect on entropy generation
rate related to heat flow at a later stage. Heat conduction shows a promotive
effect on entropy generation rate related to heat flow at an early stage.
Still, it offers a first promotive and then suppressive effect on entropy
generation rate related to heat flow at a later stage. By introducing the
morphological boundary length, we found that the stage of exponential growth of
interface length with time corresponds to the bubble acceleration stage. The
first maximum point of interface length change rate and the first maximum point
of the change rate of entropy generation rate related to viscous stress can be
used as a new criterion for RTI to enter the asymptotic velocity stage.


title:
A novel activity choice and scheduling model to model activity schedules
  of customers and staff in Dutch restaurants

abstract:
  The Covid-19 pandemic has had a large impact on the world. The virus spreads
especially easily among people in indoor spaces such as restaurants. Hence,
tools that can assess if and how different restaurant settings can impact the
potential spread of the virus are of high value. The novel activity choice and
scheduling model for restaurants, presented in this paper, is a key part of a
larger model which combines pedestrian dynamics modelling and epidemiological
models to achieve exactly this. This novel activity model can produce activity
schedules for both customers and staff at a restaurant for a variety of
restaurant settings. A key feature of the model is that it requires little user
inputs to do so which is important as the intended users are restaurant owners.
These owners have neither the time nor the expertise to deal with complex
input. Tests show that the model can produce face-valid activity schedules for
both staff and customers for a variety of restaurant settings. The tests also
show that different restaurant settings lead to distinctly different contact
patterns between people in a restaurant. As such, the model can provide
valuable insights into how a restaurant setting relates to the risk of virus
transmission. This is especially the case when it is combined with data about
the virus. Hence, it shows the high relevance of pedestrian dynamics modelling
in these pandemic times and especially the relevance of activity choice and
scheduling models.


title:
Visual-Inertial Odometry with Online Calibration of Velocity-Control
  Based Kinematic Motion Models

abstract:
  Visual-inertial odometry (VIO) is an important technology for autonomous
robots with power and payload constraints. In this paper, we propose a novel
approach for VIO with stereo cameras which integrates and calibrates the
velocity-control based kinematic motion model of wheeled mobile robots online.
Including such a motion model can help to improve the accuracy of VIO. Compared
to several previous approaches proposed to integrate wheel odometer
measurements for this purpose, our method does not require wheel encoders and
can be applied when the robot motion can be modeled with velocity-control based
kinematic motion model. We use radial basis function (RBF) kernels to
compensate for the time delay and deviations between control commands and
actual robot motion. The motion model is calibrated online by the VIO system
and can be used as a forward model for motion control and planning. We evaluate
our approach with data obtained in variously sized indoor environments,
demonstrate improvements over a pure VIO method, and evaluate the prediction
accuracy of the online calibrated model.


title: Computational Vibrational Spectroscopy

abstract:
  Vibrational spectroscopy is a powerful technique to characterize the
near-equilibrium dynamics of molecules in the gas- and the condensed-phase.
This contribution summarizes efforts from computer-based methods to gain
insight into the relationship between structure and spectroscopic response.
Methods for this purpose include physics-based empirical energy functions,
machine-learned force fields, and methods that separate sampling conformational
space and determining the data for spectral analysis such as map-based
approaches.


title: ALMA Astrometry of the Objects within 0.5 pc of Sagittarius A$^\ast$

abstract:
  Atacama Large Millimeter/Submillimeter Array (ALMA) is promising to be a
powerful tool for precision astrometry of the area around Sagittarius A$^\ast$
(Sgr A$^\ast$) because it has the high angular resolution, high sensitivity,
and wide field of view. We have observed the area including the Nuclear Star
Cluster at 230 GHz with ALMA in October 2017. The angular resolution is ~0.03".
We determined the relative positions to Sgr A$^\ast$ of 65 compact objects in
the area with the accuracy of 0.001". We also analyzed the similar ALMA
archival data obtained in June 2019 and determined the 64 relative positions in
these objects. We derived the proper motions relative to Sgr A$^\ast$ by
comparing these positions. The derived proper motions are roughly described
with both clockwise and counterclockwise rotations around Sgr A$^\ast$. The
rotation velocities are reproduced by Kepler orbits bounded around Sgr
A$^\ast$. Moreover, the proper motions include co-moving clusters for example
IRS13E and IRS13N. The positions and proper motions are almost consistent with
those by previous infrared observations. Therefore the observational
demonstrations would prove that ALMA is a powerful tool for precision
astrometry of the region.


title:
3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of
  Transformer-MLP Paradigm for Dense Prediction in Medical Volume

abstract:
  Dense prediction in medical volume provides enriched guidance for clinical
analysis. CNN backbones have met bottleneck due to lack of long-range
dependencies and global context modeling power. Recent works proposed to
combine vision transformer with CNN, due to its strong global capture ability
and learning capability. However, most works are limited to simply applying
pure transformer with several fatal flaws (i.e., lack of inductive bias, heavy
computation and little consideration for 3D data). Therefore, designing an
elegant and efficient vision transformer learner for dense prediction in
medical volume is promising and challenging. In this paper, we propose a novel
3D Shuffle-Mixer network of a new Local Vision Transformer-MLP paradigm for
medical dense prediction. In our network, a local vision transformer block is
utilized to shuffle and learn spatial context from full-view slices of
rearranged volume, a residual axial-MLP is designed to mix and capture
remaining volume context in a slice-aware manner, and a MLP view aggregator is
employed to project the learned full-view rich context to the volume feature in
a view-aware manner. Moreover, an Adaptive Scaled Enhanced Shortcut is proposed
for local vision transformer to enhance feature along spatial and channel
dimensions adaptively, and a CrossMerge is proposed to skip-connects the
multi-scale feature appropriately in the pyramid architecture. Extensive
experiments demonstrate the proposed model outperforms other state-of-the-art
medical dense prediction methods.


title:
A Feedback Capacity-Achieving Coding Scheme for the $(d,\infty)$-RLL
  Input-Constrained Binary Erasure Channel

abstract:
  This paper considers the memoryless input-constrained binary erasure channel
(BEC). The channel input constraint is the $(d,\infty)$-runlength limited (RLL)
constraint, which mandates that any pair of successive $1$s in the input
sequence be separated by at least $d$ $0$s. We consider a scenario where there
is causal, noiseless feedback from the decoder. We demonstrate a simple,
labelling-based, zero-error feedback coding scheme, which we prove to be
feedback capacity-achieving, and, as a by-product, obtain an explicit
characterization of the feedback capacity. Our proof is based on showing that
the rate of our feedback coding scheme equals an upper bound on the feedback
capacity derived using the single-letter bounding techniques of Sabag et al.
(2017). Further, we note using the tools of Thangaraj (2017) that there is a
gap between the feedback and non-feedback capacities of the $(d,\infty)$-RLL
input constrained BEC, at least for $d=1,2$.


title: Spin-lattice coupling in an epitaxial NdFeO3 thin film

abstract:
  Rare-earth orthoferrite RFeO3 materials such as NdFeO3 are strongly studied
because of their fascinating magnetic properties and their potential
applications. Here, we show the successful epitaxial synthesis of
parasitic-free NFO thin film by pulsed laser deposition on (001)-SrTiO3.
High-resolution X-ray diffraction shows a coherent growth and a tetragonal-like
structure of a tensile strained 80 nm thick NFO film in contrast with the bulk
orthorhombic state. Room temperature magnetometry indicates a bulk-like
antiferromagnetic state for the NFO film. Temperature-dependent X-ray
diffraction and magnetometry highlight a significant spin-lattice coupling at
the Neel Temperature while a new magneto-structural instability is discovered
around 250{\deg}C that needs further investigation.


title: Time-time covariance for last passage percolation in half-space

abstract:
  This article studies several properties of the half-space last passage
percolation, in particular the two-time covariance. We show that, when the two
end-points are at small macroscopic distance, then the first order correction
to the covariance for the point-to-point model is the same as the one of the
stationary model. In order to obtain the result, we first derive comparison
inequalities of the last passage increments for different models. This is used
to prove tightness of the point-to-point process as well as localization of the
geodesics. Unlike for the full-space case, for half-space we have to overcome
the difficulty that the point-to-point model in half-space with generic start
and end points is not known.


title:
Explainable Analysis of Deep Learning Methods for SAR Image
  Classification

abstract:
  Deep learning methods exhibit outstanding performance in synthetic aperture
radar (SAR) image interpretation tasks. However, these are black box models
that limit the comprehension of their predictions. Therefore, to meet this
challenge, we have utilized explainable artificial intelligence (XAI) methods
for the SAR image classification task. Specifically, we trained
state-of-the-art convolutional neural networks for each polarization format on
OpenSARUrban dataset and then investigate eight explanation methods to analyze
the predictions of the CNN classifiers of SAR images. These XAI methods are
also evaluated qualitatively and quantitatively which shows that Occlusion
achieves the most reliable interpretation performance in terms of
Max-Sensitivity but with a low-resolution explanation heatmap. The explanation
results provide some insights into the internal mechanism of black-box
decisions for SAR image classification.


title: A crowdsourced implementation of ITU-T P.910

abstract:
  The gold standard for measuring video quality is the subjective test, and the
most prevalent is the ITU-T P.910, a lab-based subjective standard in use for
the past two decades. However, in practice using P.910 is slow, expensive, and
requires a lab, which all create barriers to usage. As a result, most research
papers that need to measure video quality don't use P.910 but rather metrics
that are not well correlated to subjective opinion. We provide an open-source
extension of P.910 based on crowdsourcing principles which address the speed,
usage cost, and barrier to usage issues. We implement Absolute Category Rating
(ACR), ACR with hidden reference (ACR-HR), and Degradation Category Rating
(DCR). It includes rater, environment, hardware, and network qualifications, as
well as gold and trapping questions to ensure quality. We have validated that
the implementation is both accurate and highly reproducible compared to
existing P.910 lab studies.


title:
Search for periodic emission of five gamma-ray pulsars at the frequency
  of 111 MHz

abstract:
  The search for pulsar (periodic) emission of five gamma-ray pulsars was
carried out using the summed power spectra and the summed periodograms. No
harmonics corresponding to the known pulsar periods were found. The upper
estimations of the integral flux density of the pulsars J0357+3205 (<0.5 mJy),
J0554+3107 (<0.5 mJy), J1958+2846 (<0.5 mJy), J2021+4026 (<0.4 mJy), and
J2055+2539 (<0.55 mJy) is obtained.


title:
Upside Risk Effect on Reliability of Microgrids Considering Demand
  Response Program and COVID-19: An Investigation on Health System and Power
  System Interactions

abstract:
  COVID-19 has a vast impact on the power systems considering the customers
demand and human resources. During this situation, the utilization of
microgrids (MGs) may help the power systems balance the generation and
consumption of power, which leads to customer satisfaction. In this paper, the
optimal power scheduling of energy sources in an islanded MG by considering the
upside risk (UR) is proposed for the very first time. The intended islanded MG
consists of various sources such as wind turbine (WT), photovoltaic (PV),
diesel generator (DGR), and battery. The goals of this work are minimizing the
energy not supplied (ENS) in islanded mode considering the COVID-19s effect and
implementing the demand response program (DRP). The difference between target
ENS and actual ENS when actual ENS is less than the target is defined as UR.
The results indicate that the UR related to the ENS of the islanded MG
decreases significantly by slightly increasing the ENS. Moreover, COVID-19
decreases the ENS considerably and has a bigger effect than the DRP.


title:
Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop
  All-reduce with Ultimate Compression

abstract:
  Traditional one-bit compressed stochastic gradient descent can not be
directly employed in multi-hop all-reduce, a widely adopted distributed
training paradigm in network-intensive high-performance computing systems such
as public clouds. According to our theoretical findings, due to the cascading
compression, the training process has considerable deterioration on the
convergence performance. To overcome this limitation, we implement a sign-bit
compression-based learning synchronization framework, Marsit. It prevents
cascading compression via an elaborate bit-wise operation for unbiased sign
aggregation and its specific global compensation mechanism for mitigating
compression deviation. The proposed framework retains the same theoretical
convergence rate as non-compression mechanisms. Experimental results
demonstrate that Marsit reduces up to 35% training time while preserving the
same accuracy as training without compression.


title: Pyramidal Attention for Saliency Detection

abstract:
  Salient object detection (SOD) extracts meaningful contents from an input
image. RGB-based SOD methods lack the complementary depth clues; hence,
providing limited performance for complex scenarios. Similarly, RGB-D models
process RGB and depth inputs, but the depth data availability during testing
may hinder the model's practical applicability. This paper exploits only RGB
images, estimates depth from RGB, and leverages the intermediate depth
features. We employ a pyramidal attention structure to extract multi-level
convolutional-transformer features to process initial stage representations and
further enhance the subsequent ones. At each stage, the backbone transformer
model produces global receptive fields and computing in parallel to attain
fine-grained global predictions refined by our residual convolutional attention
decoder for optimal saliency prediction. We report significantly improved
performance against 21 and 40 state-of-the-art SOD methods on eight RGB and
RGB-D datasets, respectively. Consequently, we present a new SOD perspective of
generating RGB-D SOD without acquiring depth data during training and testing
and assist RGB methods with depth clues for improved performance. The code and
trained models are available at
https://github.com/tanveer-hussain/EfficientSOD2


title:
Octave-spanning 1 emission across the visible spectrum from single
  crystalline 1,3,5,7-tetrakis-($p$-methoxyphenyl)adamantane

abstract:
  Sustainable efficient light emitter based solely on elements-of-hope are
needed to replace current compounds based on less-abundant materials.
Functionalized diamondoids are a potential solution for this challenge as they
offer efficient, octave-spanning emission across the visible spectrum in their
single-crystalline form. Its large quantum efficiency increases towards
higher-than-ambient temperatures to beyond 7 %. The stability beyond 200
{\deg}C renders such functionalized diamondoids as sustainable phosphors for
LED applications. Detailed structural and theoretical investigations suggest a
crucial role of exciton states accompanied by structural modifications
(self-trapped excitons) in the emission process.


title:
An Exploratory Study of Attestation Mechanisms for Trusted Execution
  Environments

abstract:
  Attestation is a fundamental building block to establish trust over software
systems. When used in conjunction with trusted execution environments, it
guarantees that genuine code is executed even when facing strong attackers,
paving the way for adoption in several sensitive application domains. This
paper reviews existing remote attestation principles and compares the
functionalities of current trusted execution environments as Intel SGX, Arm
TrustZone and AMD SEV, as well as emerging RISC-V solutions.


title:
Approximating Constraint Manifolds Using Generative Models for
  Sampling-Based Constrained Motion Planning

abstract:
  Sampling-based motion planning under task constraints is challenging because
the null-measure constraint manifold in the configuration space makes rejection
sampling extremely inefficient, if not impossible. This paper presents a
learning-based sampling strategy for constrained motion planning problems. We
investigate the use of two well-known deep generative models, the Conditional
Variational Autoencoder (CVAE) and the Conditional Generative Adversarial Net
(CGAN), to generate constraint-satisfying sample configurations. Instead of
precomputed graphs, we use generative models conditioned on constraint
parameters for approximating the constraint manifold. This approach allows for
the efficient drawing of constraint-satisfying samples online without any need
for modification of available sampling-based motion planning algorithms. We
evaluate the efficiency of these two generative models in terms of their
sampling accuracy and coverage of sampling distribution. Simulations and
experiments are also conducted for different constraint tasks on two robotic
platforms.


title: Background Shielding by Dense Samples in Low-Level Gamma Spectrometry

abstract:
  In low activity gamma spectrometric measurements of large, dense samples, the
bulk sample material shields the HPGe crystal from external background sources.
If not accounted for in studies that utilise background-subtraction methods,
this effect may result in systematic errors in the sample activity and
detection limit estimation. We introduce a Monte Carlo based method to minimise
the impact of this effect on sample gamma spectra. It is validated using
simulated detector backgrounds and applied to a measurement of low-activity
Gd$_2$(SO$_4$)$_3 \cdot$8H$_2$O. One main prerequisite for the correct
application of this method is to know in advance the nuclides which contribute
to the detector background spectrum and their spatial distribution. With a
thorough understanding of the detector backgrounds, the method improves the
accuracy of sensitive low-background measurements of low-activity samples. Even
without knowing the background sources and their distribution, conservative
results may still be presented that account for the potential systematic errors
introduced by this background shielding effect.


title: A general framework for nonlocal Neumann problems

abstract:
  Within the framework of Hilbert spaces, we solve nonlocal problems in bounded
domains with prescribed conditions on the complement of the domain. Our main
focus is on the inhomogeneous Neumann problem in a rather general setting. We
also study the transition from complement value problems to local boundary
value problems. Several results are new even for the fractional Laplace
operator. The setting also covers relevant models in the framework of
peridynamics.


title:
Structure of optimal control for planetary landing with control and
  state constraints

abstract:
  This paper studies a vertical powered descent problem in the context of
planetary landing, considering glide-slope and thrust pointing constraints and
minimizing any final cost. In a first time, it proves the Max-Min-Max or
Max-Singular-Max form of the optimal control using the Pontryagin Maximum
Principle, and it extends this result to a problem formulation considering the
effect of an atmosphere. It also shows that the singular structure does not
appear in generic cases. In a second time, it theoretically analyzes the
optimal trajectory for a more specific problem formulation to show that there
can be at most one contact or boundary interval with the state constraint on
each Max or Min arc.


title:
A slow-replicating mutant induces synchronized replication rates and
  oscillatory viral dynamics in an HIV mathematical model

abstract:
  HIV infection is an incurable disease that leads to the destruction of
immunity. Its clinical course is divided into the acute, latency, and acquired
immunodeficiency syndrome (AIDS) phases. Nowak and May proposed a simple
mathematical model that considered viral mutation and found the parameter
condition for the long-term development of AIDS [M. A. Nowak, R. M. May. J.
Theor. Biol 159, 329-342 (1992)]. However, the mechanism of the acute and
latency phase remains unclear from their study. In the present paper, we
consider the same model and investigate its capability to qualitatively
reproduce the time course of an actual HIV infection. We find that the three
phases robustly appear when there are two viral mutants with quite different
replication rates. The viral dynamics have the following two components: the
damped oscillation that forms the initial viral peak and the following slow
exponential growth that resembles the latency and AIDS phase. We analyze the
obtained dynamics utilizing linear stability analysis and subsequently
generalize for the many mutants case in which one mutant has a considerably
lower replication rate than the others. We demonstrate that (i) all replication
rates synchronize to the same value that is as small as the slowest-replicating
mutant's replication rate and (ii) an oscillation appears if the initial value
of the slowest-replicating mutant is sufficiently small. In particular, the
synchronization of replication rates emphasizes the role of a slow-replicating
mutant in maintaining the latency. Our findings explain the pathogenesis of HIV
from a mathematical aspect, possibly providing a clue for the cure of HIV
infection.


title: Scaling Limits of Controlled Branching Processes

abstract:
  In this paper, a special sequence of controlled branching processes is
considered. We provide a simple set of sufficient conditions for the weak
convergence of such processes to a weak solution to a kind of continuous
branching processes with dependent immigration.


title: A new avenue for Bayesian inference with INLA

abstract:
  Integrated Nested Laplace Approximations (INLA) has been a successful
approximate Bayesian inference framework since its proposal by Rue et al.
(2009). The increased computational efficiency and accuracy when compared with
sampling-based methods for Bayesian inference like MCMC methods, are some
contributors to its success. Ongoing research in the INLA methodology and
implementation thereof in the R package R-INLA, ensures continued relevance for
practitioners and improved performance and applicability of INLA. The era of
big data and some recent research developments, presents an opportunity to
reformulate some aspects of the classic INLA formulation, to achieve even
faster inference, improved numerical stability and scalability. The improvement
is especially noticeable for data-rich models. We demonstrate the efficiency
gains with various examples of data-rich models, like Cox's proportional
hazards model, an item-response theory model, a spatial model including
prediction, and a 3-dimensional model for fMRI data.


title:
Pion parton distribution functions with the nonrelativistic constituent
  quark model

abstract:
  We calculate the valence quark distribution functions of the $\pi$ meson
using the non-relativistic chiral constituent quark model. The $\pi$ wave
function is obtained by solving the two-body Schr\"odinger equation within the
framework of constituent quark model. We transform the $\pi$ wave function from
the rest frame to the light cone or infinite momentum frame based on the
Lorentz boost. The valence quark distributions at the initial evolution scale
are obtained. The QCD evolution are given with the DGLAP equations with
parton-parton recombination corrections. With tuning the valence up (down)
quark mass to 70 MeV, the calculated valence up quark distributions at $Q^2=20$
GeV$^2$ are in good agreement with the E615 experimental data. The structure
functions $\rm{F}_2^\pi(x,Q^2)$ of pion are also calculated which consist with
the H1 experimental data. The proposed mechanisms here could be also used to
study other hadrons.


title:
Environment Imitation: Data-Driven Environment Model Generation Using
  Imitation Learning for Efficient CPS Goal Verification

abstract:
  Cyber-Physical Systems (CPS) continuously interact with their physical
environments through software controllers that observe the environments and
determine actions. Engineers can verify to what extent the CPS under analysis
can achieve given goals by analyzing its Field Operational Test (FOT) logs.
However, it is challenging to repeat many FOTs to obtain statistically
significant results due to its cost and risk in practice. To address this
challenge, simulation-based verification can be a good alternative for
efficient CPS goal verification, but it requires an accurate virtual
environment model that can replace the real environment that interacts with the
CPS in a closed loop. This paper proposes a novel data-driven approach that
automatically generates the virtual environment model from a small amount of
FOT logs. We formally define the environment model generation problem and solve
it using Imitation Learning (IL) algorithms. In addition, we propose three
specific use cases of our approach in the evolutionary CPS development. To
validate our approach, we conduct a case study using a simplified autonomous
vehicle with a lane-keeping system. The case study results show that our
approach can generate accurate virtual environment models for CPS goal
verification at a low cost through simulations.


title:
9 $\times$ 4 = 6 $\times$ 6: Understanding the quantum solution to the
  Euler's problem of 36 officers

abstract:
  The famous combinatorial problem of Euler concerns an arrangement of $36$
officers from six different regiments in a $6 \times 6$ square array. Each
regiment consists of six officers each belonging to one of six ranks. The
problem, originating from Saint Petersburg, requires that each row and each
column of the array contains only one officer of a given rank and given
regiment. Euler observed that such a configuration does not exist. In recent
work, we constructed a solution to a quantum version of this problem assuming
that the officers correspond to quantum states and can be entangled. In this
paper, we explain the solution which is based on a partition of 36 officers
into nine groups, each with four elements. The corresponding quantum states are
locally equivalent to maximally entangled two-qubit states, hence each officer
is entangled with at most three out of his $35$ colleagues. The entire quantum
combinatorial design involves $9$ Bell bases in nine complementary
$4$-dimensional subspaces.


title:
Multiple measurements of gravitational waves acting as standard probes:
  model-independent constraints on the cosmic curvature with DECIGO

abstract:
  Although the spatial curvature has been precisely determined via the cosmic
microwave background (CMB) observation by Planck satellite, it still suffers
from the well-known cosmic curvature tension. As a standard siren,
gravitational waves (GWs) from binary neutron star mergers provide a direct way
to measure the luminosity distance. In addition, the accelerating expansion of
the universe may cause an additional phase shift in the gravitational waveform,
which allows us to measure the acceleration parameter. This measurement
provides an important opportunity to determine the curvature parameter
$\Omega_k$ in the GW domain based on the combination of two different
observables for the same objects at high redshifts. In this study, we
investigate how such an idea could be implemented with future generation of
space-based DECi-hertz Interferometer Gravitational-wave Observatory (DECIGO)
in the framework of two model-independent methods. Our results show that DECIGO
could provide a reliable and stringent constraint on the cosmic curvature at a
precision of $\Delta\Omega_k$=0.12, which is comparable to existing results
based on different electromagnetic data. Our constraints are more stringent
than the traditional electromagnetic method from the Pantheon SNe Ia sample,
which shows no evidence for the deviation from the flat universe at $z\sim
2.3$. More importantly, with our model-independent method, such a
second-generation space-based GW detector would also be able to explore the
possible evolution $\Omega_k$ with redshifts, through direct measurements of
cosmic curvature at different redshifts ($z\sim 5$). Such a model-independent
$\Omega_k$ reconstruction to the distance past can become a milestone in
gravitational-wave cosmology.


title: Signaling oscillations: molecular mechanisms and functional roles

abstract:
  Mounting evidence shows that oscillatory activity is widespread in cell
signaling. Here we review some of this recent evidence, focusing on both the
molecular mechanisms that potentially underlie such dynamical behavior, and the
potential advantages that signaling oscillations might have in cell function.
The biological processes considered include tissue maintenance, embryonic
development and wound healing. With the aid of mathematical modeling, we show
that a common principle, namely delayed negative feedback, underpins this wide
variety of phenomena.


title:
A Novel Approach for Cancellation of Non-Aligned Inter Spreading Factor
  Interference in LoRa Systems

abstract:
  Long Range (LoRa) has become a key enabler technology for low power wide area
networks. However, due to its ALOHA-based medium access scheme, LoRa has to
cope with collisions that limit the capacity and network scalability.
Collisions between randomly overlapped signals modulated with different
spreading factors (SFs) result in inter-SF interference, which increases the
packet loss likelihood when signal-to-interference ratio (SIR) is low. This
issue cannot be resolved by channel coding since the probability of error
distance is not concentrated around the adjacent symbol. In this paper, we
analytically model this interference, and propose an interference cancellation
method based on the idea of segmentation of the received signal. This scheme
has three steps. First, the SF of the interference signal is identified, then
the equivalent data symbol and complex amplitude of the interference are
estimated. Finally, the estimated interference signal is subtracted from the
received signal before demodulation. Unlike conventional serial interference
cancellation (SIC), this scheme can directly estimate and reconstruct the
non-aligned inter-SF interference without synchronization. Simulation results
show that the proposed method can significantly reduce the symbol error rate
(SER) under low SIR compared with the conventional demodulation. Moreover, it
also shows high robustness to fractional sample timing offset (STO) and carrier
frequency offset (CFO) of interference. The presented results clearly show the
effectiveness of the proposed method in terms of the SER performance.


title:
Additional evidence for a pulsar wind nebula in the heart of SN 1987A
  from multi-epoch X-ray data and MHD modeling

abstract:
  Since the day of its explosion, supernova (SN) 1987A has been closely
monitored to study its evolution and to detect its central compact relic. In
fact, the formation of a neutron star is strongly supported by the detection of
neutrinos from the SN. However, besides the detection in the Atacama Large
Millimeter/submillimeter Array (ALMA) data of a feature that is compatible with
the emission arising from a proto-pulsar wind nebula (PWN), the only hint for
the existence of such elusive compact object is provided by the detection of
hard emission in NuSTAR data up to ~ 20 keV. We report on the simultaneous
analysis of multi-epoch observations of SN 1987A performed with Chandra,
XMM-Newton and NuSTAR. We also compare the observations with a state-of-the-art
3D magnetohydrodynamic (MHD) simulation of SN 1987A. A heavily absorbed
power-law, consistent with the emission from a PWN embedded in the heart of SN
1987A, is needed to properly describe the high-energy part of the observed
spectra. The spectral parameters of the best-fit power-law are in agreement
with the previous estimate, and exclude diffusive shock acceleration as a
possible mechanism responsible for the observed non-thermal emission. The
information extracted from our analysis are used to infer the physical
characteristics of the pulsar and the broad-band emission of its nebula, in
agreement with the ALMA data. Analysis of the synthetic spectra also show that,
in the near future, the main contribution to Fe K emission line will originate
in the outermost shocked ejecta of SN 1987A.


title:
Genus-five hyperelliptic or trigonal curves with many rational points in
  characteristic three

abstract:
  The number $N_9(5)$, the maximal number of $\mathbb{F}_9$-rational points on
curves over $\mathbb{F}_9$ of genus $5$ is unknown, but it is known that $32
\le N_9(5)\le 35$. In this paper, we enumerate hyperelliptic curves and
trigonal curves over $\mathbb{F}_3$ which have many $\mathbb{F}_9$-rational
points (and $\mathbb{F}_3$-rational points), especially the maximal number of
$\mathbb{F}_9$-rational points of those curves is $30$. Kudo-Harashita studied
the nonhyperelliptic and nontrigonal case,where they found a new example of
curves (over $\mathbb{F}_3$) of genus five which attains $32$ and proved that
there is no example attaining more than $32$, among sextic plane curves with
mild singularities. We conclude from the main results in this paper that we
need to search sextic models (i.e., nonhyperelliptic and nontrigonal) with bad
singularities, in order to find a genus-five curve over $\mathbb{F}_3$ with at
least $33$ $\mathbb{F}_9$-rational points.


title:
YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object
  Keypoint Similarity Loss

abstract:
  We introduce YOLO-pose, a novel heatmap-free approach for joint detection,
and 2D multi-person pose estimation in an image based on the popular YOLO
object detection framework. Existing heatmap based two-stage approaches are
sub-optimal as they are not end-to-end trainable and training relies on a
surrogate L1 loss that is not equivalent to maximizing the evaluation metric,
i.e. Object Keypoint Similarity (OKS). Our framework allows us to train the
model end-to-end and optimize the OKS metric itself. The proposed model learns
to jointly detect bounding boxes for multiple persons and their corresponding
2D poses in a single forward pass and thus bringing in the best of both
top-down and bottom-up approaches. Proposed approach doesn't require the
postprocessing of bottom-up approaches to group detected keypoints into a
skeleton as each bounding box has an associated pose, resulting in an inherent
grouping of the keypoints. Unlike top-down approaches, multiple forward passes
are done away with since all persons are localized along with their pose in a
single inference. YOLO-pose achieves new state-of-the-art results on COCO
validation (90.2% AP50) and test-dev set (90.3% AP50), surpassing all existing
bottom-up approaches in a single forward pass without flip test, multi-scale
testing, or any other test time augmentation. All experiments and results
reported in this paper are without any test time augmentation, unlike
traditional approaches that use flip-test and multi-scale testing to boost
performance. Our training codes will be made publicly available at
https://github.com/TexasInstruments/edgeai-yolov5 and
https://github.com/TexasInstruments/edgeai-yolox


title: Constraining deviations from spherical symmetry using $ \gamma $-metric

abstract:
  The $ \gamma $-spacetime metric is a static and axially symmetric vacuum
solution of the Einstein equation. This spacetime represents a naked
singularity and it has an extra parameter $ \gamma $ which signifies deviations
from spherical symmetry. In this work, we study the possibility of constraining
the deformation parameter with astrophysical observations. We start with
gravitational lensing in the weak and strong-field limit and calculate the
respective deflection angles to show that only strong-field lensing
observations will be able to constrain $ \gamma $ independently. Later we study
two other classical tests of gravity: Shapiro time delay and precession of
perihelion. We show that, out of these two experiments, the deformation
parameter affects the observables only in perihelion shift.


title:
Tuning the coexistence regime of incomplete and tubular skyrmions in
  ferro/ferri/ferromagnetic trilayers

abstract:
  The development of skyrmionic devices requires a suitable tuning of material
parameters in order to stabilize skyrmions and control their density. It has
been demonstrated recently that different skyrmion types can be simultaneously
stabilized at room temperature in heterostructures involving ferromagnets,
ferrimagnets and heavy metals, offering a new platform of coding binary
information in the type of skyrmion instead of the presence/absence of
skyrmions. Here, we tune the energy landscape of the two skyrmion types in such
heterostructures by engineering the geometrical and material parameters of the
individual layers. We find that a fine adjustment of the ferromagnetic layer
thickness and thus its magnetic anisotropy, allows the trilayer system to
support either one of the skyrmion types or the coexistence of both and with
varying densities.


title:
Uplifting Interviews in Social Science with Individual Data
  Visualization: the case of Music Listening

abstract:
  Collecting accurate and fine-grain information about the music people like,
dislike and actually listen to has long been a challenge for sociologists. As
millions of people now use online music streaming services, research can build
upon the individual listening history data that are collected by these
platforms. Individual interviews in particular can benefit from such data, by
allowing the interviewers to immerse themselves in the musical universe of
consenting respondents, and thus ask them contextualized questions and get more
precise answers. Designing a visual exploration tool allowing such an immersion
is however difficult, because of the volume and heterogeneity of the listening
data, the unequal "visual literacy" of the prospective users, or the
interviewers' potential lack of knowledge of the music listened to by the
respondents. In this case study we discuss the design and evaluation of such a
tool. Designed with social scientists, its purpose is to help them in preparing
and conducting semi-structured interviews that address various aspects of the
listening experience. It was evaluated during thirty interviews with consenting
users of a streaming platform in France.


title:
Second-order statically screened exchange correction to the random phase
  approximation: Assessment for non-covalent interactions

abstract:
  With increasing inter-electronic distance, the screening of the
electron-electron interaction by the presence of other electrons becomes the
dominant source of electron correlation. This effect is described by the random
phase approximation (RPA) which is therefore a promising method for the
calculation of weak interactions. The success of the RPA relies on the
cancellation of errors, which can be traced back to the violation of the
crossing symmetry of the 4-point vertex, leading to strongly overestimated
total correlation energies. By addition of second-order screened exchange
(SOSEX) to the correlation energy, this issue is substantially reduced. In the
adiabatic connection (AC) SOSEX formalism, one of the two electron-electron
interaction lines in the second-order exchange term is dynamically screened. A
related SOSEX expression in which both electron-electron interaction lines are
statically screened is obtained from the $G3W2$ contribution to the electronic
self-energy. In contrast to AC-SOSEX, the evaluation of this correlation energy
expression does not require an expensive numerical frequency integration and is
therefore advantageous from a computational perspective. We compare the
accuracy of both RPA+SOSEX variants for the calculation of the interaction
energies of non-covalently bound complexes. Both variants, independently of the
input Kohn-Sham Green's function, result in significant improvements over RPA.
They are also comparable in accuracy, indicating that the dynamical screened
effects are of minor importance in the description of long-ranged interactions.
For dispersion interactions, both RPA+SOSEX variants are however outperformed
by dispersion corrected hybrid and double-hybrid functionals.


title: Electronic structure and magnetism of the Hund insulator CrI3

abstract:
  CrI3 is a two-dimensional ferromagnetic van der Waals material with a charge
gap of 1.1-1.2 eV. In this study, the electronic structure and magnetism of
CrI3 are investigated by using density functional theory and dynamical
mean-field theory. Our calculations successfully reproduce a charge gap of 1.1
eV in the paramagnetic state when a Hund coupling JH = 0.7 eV is included with
an on-site Hubbard U = 5 eV. In contrast, with a large U value of 8 eV and
negligible Hund coupling JH, CrI3 is predicted to be a moderately correlated
metal in the paramagnetic state. We conclude that CrI3 is a Mott-Hund insulator
due to the half-filled configuration of the Cr 3d t2g orbitals. The Cr 3d eg
orbitals are occupied by approximately one electron, which leads to strong
valence fluctuations so that the Cr 3d orbitals cannot be described by a single
state. Moreover, at finite temperature, the calculated ordered static magnetic
moment in the ferromagnetic state is significantly larger in the R3 phase than
in the C2/m phase. This observation indicates that the structural phase
transition from the C2/m phase to the R3 phase with decreasing temperature is
driven by ferromagnetic spin fluctuations.


title:
Learning to Generalize to More: Continuous Semantic Augmentation for
  Neural Machine Translation

abstract:
  The principal task in supervised neural machine translation (NMT) is to learn
to generate target sentences conditioned on the source inputs from a set of
parallel sentence pairs, and thus produce a model capable of generalizing to
unseen instances. However, it is commonly observed that the generalization
performance of the model is highly influenced by the amount of parallel data
used in training. Although data augmentation is widely used to enrich the
training data, conventional methods with discrete manipulations fail to
generate diverse and faithful training samples. In this paper, we present a
novel data augmentation paradigm termed Continuous Semantic Augmentation
(CsaNMT), which augments each training instance with an adjacency semantic
region that could cover adequate variants of literal expression under the same
meaning. We conduct extensive experiments on both rich-resource and
low-resource settings involving various language pairs, including WMT14
English-{German,French}, NIST Chinese-English and multiple low-resource IWSLT
translation tasks. The provided empirical evidences show that CsaNMT sets a new
level of performance among existing augmentation techniques, improving on the
state-of-the-art by a large margin. The core codes are contained in Appendix E.


title:
Transient obscuration event captured in NGC 3227 III. Photoionization
  modeling of the X-ray obscuration event in 2019

abstract:
  A growing number of transient X-ray obscuration events in type I AGN suggest
that our line-of-sight to the central engine is not always free. Multiple X-ray
obscuration events have been reported in the nearby Seyfert 1.5 galaxy NGC 3227
from 2000 to 2016. In late 2019, another X-ray obscuration event was identified
with Swift. Two coordinated target-of-opportunity observations with XMM-Newton,
NuSTAR, and HST/COS were triggered in Nov. and Dec. 2019 to study this
obscuration event. For each observation, we analyze the time-averaged X-ray
spectra. We perform photoionization modeling with the SPEX code, which allows
us to constrain the intrinsic continuum simultaneously with various
photoionized absorption and emission components. Similar to previous transient
X-ray obscuration events in NGC 3227, the one caught in late 2019 is
short-lived (less than five months). If the obscurer has only one photoionized
component, the two X-ray observations in late 2019 cannot be explained by the
same obscurer that responds to the varying ionizing continuum. Due to the
unknown geometry of the obscurer, its number density and distance to the black
hole cannot be well constrained. The inferred distance covers at least two
orders of magnitude, from the BLR to the dusty torus. Unlike some other X-ray
obscuration events in Seyfert galaxies like NGC 5548 and NGC 3783, no prominent
blueshifted broad absorption troughs were found in the 2019 HST/COS spectra of
NGC 3227 when compared with archival UV spectra. This might be explained if the
X-ray obscurer does not intercept our line of sight to (a significant portion
of) the UV emitting region. It is not straightforward to understand the variety
of the observational differences in the X-ray obscuration events observed so
far. Future observations with high-quality data are needed to unveil the nature
of the X-ray obscuration events. [shortend for arXiv]


title: Unbinned Angular Analysis of $B\to D^*(D\pi)\ell \nu_\ell$ and $C_{V_R}$

abstract:
  A sensitivity study of the unbinned angular analysis of the $B\to
D^*(D\pi)\ell \nu_\ell$ decay is presented. In the analysis, it is shown that
the Wilson coefficient of the right-handed vector current, $C_{V_R}$, can be
measured to precision of 2-4$\%$ using either the full set of normalised
angular observables or the forward-backward asymmetry $A_{FB}$ of the charged
lepton in 10 $w$ bins. Such angular measurements are independent of the
$V_{cb}$ puzzle.


title:
deep-significance - Easy and Meaningful Statistical Significance Testing
  in the Age of Neural Networks

abstract:
  A lot of Machine Learning (ML) and Deep Learning (DL) research is of an
empirical nature. Nevertheless, statistical significance testing (SST) is still
not widely used. This endangers true progress, as seeming improvements over a
baseline might be statistical flukes, leading follow-up research astray while
wasting human and computational resources. Here, we provide an easy-to-use
package containing different significance tests and utility functions
specifically tailored towards research needs and usability.


title:
Preliminary Results of a Survey on the Use of Self-Adaptation in
  Industry

abstract:
  Self-adaptation equips a software system with a feedback loop that automates
tasks that otherwise need to be performed by operators. Such feedback loops
have found their way to a variety of practical applications, one typical
example is an elastic cloud. Yet, the state of the practice in self-adaptation
is currently not clear. To get insights into the use of self-adaptation in
practice, we are running a large-scale survey with industry. This paper reports
preliminary results based on survey data that we obtained from 113
practitioners spread over 16 countries, 62 of them work with concrete
self-adaptive systems. We highlight the main insights obtained so far:
motivations for self-adaptation, concrete use cases, and difficulties
encountered when applying self-adaptation in practice. We conclude the paper
with outlining our plans for the remainder of the study.


title:
Boundary-induced Exotic Bulk Entropy in Strongly-correlated Systems at
  Finite Temperatures

abstract:
  Exploring the bulk-boundary correspondences and the boundary-induced
phenomena in the strongly-correlated systems belongs to the most fundamental
topics of condensed matter physics. In this work, we show that the
entanglement-bath Hamiltonian (EBH) can induce exotic thermodynamic properties
in the bulk of a quantum spin chain from the boundaries, analogous to the heat
bath. The EBH is defined as the local Hamiltonian located on the boundary of a
finite-size system, which approximately generates the bulk entanglement
Hamiltonian of the translational-invariant system in the thermodynamic limit. A
"boundary quench point" (BQP) is recognized by the discontinuity in the
coefficients of the EBH and the bulk entropy. The BQP distinguishes the point,
below which the thermal effects become insignificant and the bulk properties
are dominated by the ground state. More interestingly, for the topologically
non-trivial Haldane chain, an entropy trench emerges due to the competitions
between the topological degeneracy of the ground states and the boundary
effects from the EBH. We show that the trench is a typical boundary effect from
the EBH, and can be a thermal signature of the ground-state topological
degeneracy. Our work provides new opportunities on theoretically or
experimentally investigating the boundary-induced physics in quantum many-body
thermodynamics and on the possible applications in quantum technologies.


title:
Interpretable Vertebral Fracture Quantification via Anchor-Free
  Landmarks Localization

abstract:
  Vertebral body compression fractures are early signs of osteoporosis. Though
these fractures are visible on Computed Tomography (CT) images, they are
frequently missed by radiologists in clinical settings. Prior research on
automatic methods of vertebral fracture classification proves its reliable
quality; however, existing methods provide hard-to-interpret outputs and
sometimes fail to process cases with severe abnormalities such as highly
pathological vertebrae or scoliosis. We propose a new two-step algorithm to
localize the vertebral column in 3D CT images and then detect individual
vertebrae and quantify fractures in 2D simultaneously. We train neural networks
for both steps using a simple 6-keypoints based annotation scheme, which
corresponds precisely to the current clinical recommendation. Our algorithm has
no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and
provides an interpretable and verifiable output. The method approaches
expert-level performance and demonstrates state-of-the-art results in vertebrae
3D localization (the average error is 1 mm), vertebrae 2D detection (precision
and recall are 0.99), and fracture identification (ROC AUC at the patient level
is up to 0.96). Our anchor-free vertebra detection network shows excellent
generalizability on a new domain by achieving ROC AUC 0.95, sensitivity 0.85,
specificity 0.9 on a challenging VerSe dataset with many unseen vertebra types.


title: Configurational temperature in dry active-matter models

abstract:
  We show that the liquid-state-theory configurational temperature defines an
energy scale, which for dry active-matter models can be used to determine how
to adjust model parameters to achieve approximately invariant structure and
dynamics when density is changed. The required parameter changes are calculated
from the variation of a single configuration's configurational temperature upon
a uniform scaling of all coordinates. The formalism developed applies for
models involving a potential-energy function with hidden scale invariance. The
procedure is illustrated by computer simulations of the Kob-Andersen binary
Lennard-Jones model with active Ornstein-Uhlenbeck dynamics in three dimensions
and the two-dimensional single-component Yukawa model with active Brownian
particle dynamics. For the latter model we also show how the configurational
temperature may be applied for estimating the MIPS phase boundary, in effect
reducing by one the dimension of the parameter space. We finally propose that
the ratio between the equilibrium-system temperature corresponding to actual
potential energy and the configurational temperature provides a useful measure
of how far an active-matter system is from thermal equilibrium.


title: On allocations that give intersecting groups their fair share

abstract:
  We consider item allocation to individual agents who have additive
valuations, in settings in which there are protected groups, and the allocation
needs to give each protected group its "fair" share of the total welfare.
Informally, within each protected group we consider the total welfare that the
allocation gives the members of the group, and compare it to the maximum
possible welfare that an allocation can give to the group members. An
allocation is fair towards the group if the ratio between these two values is
no worse then the relative size of the group. For divisible items, our formal
definition of fairness is based on the proportional share, whereas for
indivisible items, it is based on the anyprice share.
  We present examples in which there are no fair allocations, and even not
allocations that approximate the fairness requirement within a constant
multiplicative factor. We then attempt to identify sufficient conditions for
fair or approximately fair allocations to exist. For example, for indivisible
items, when agents have identical valuations and the family of protected groups
is laminar, we show that if the items are chores, then an allocation that
satisfies every fairness requirement within a multiplicative factor no worse
than two exists and can be found efficiently, whereas if the items are goods,
no constant approximation can be guaranteed.


title: Justice in interaction design: preventing manipulation in interfaces

abstract:
  Designers incorporate values in the design process that raise risks for
vulnerable groups. Persuasion in user interfaces can quickly turn into
manipulation and become potentially harmful for those groups in the realm of
intellectual disabilities, class, or health, requiring proactive responsibility
approaches in design. Here we introduce the Capability Sensitive Design
Approach and explain how it can be used proactively to inform designers'
decisions when it comes to evaluating justice in their designs preventing the
risk of manipulation.


title:
Stream-based Active Learning with Verification Latency in Non-stationary
  Environments

abstract:
  Data stream classification is an important problem in the field of machine
learning. Due to the non-stationary nature of the data where the underlying
distribution changes over time (concept drift), the model needs to continuously
adapt to new data statistics. Stream-based Active Learning (AL) approaches
address this problem by interactively querying a human expert to provide new
data labels for the most recent samples, within a limited budget. Existing AL
strategies assume that labels are immediately available, while in a real-world
scenario the expert requires time to provide a queried label (verification
latency), and by the time the requested labels arrive they may not be relevant
anymore. In this article, we investigate the influence of finite,
time-variable, and unknown verification delay, in the presence of concept drift
on AL approaches. We propose PRopagate (PR), a latency independent utility
estimator which also predicts the requested, but not yet known, labels.
Furthermore, we propose a drift-dependent dynamic budget strategy, which uses a
variable distribution of the labelling budget over time, after a detected
drift. Thorough experimental evaluation, with both synthetic and real-world
non-stationary datasets, and different settings of verification latency and
budget are conducted and analyzed. We empirically show that the proposed method
consistently outperforms the state-of-the-art. Additionally, we demonstrate
that with variable budget allocation in time, it is possible to boost the
performance of AL strategies, without increasing the overall labeling budget.


title:
Self-organised quantum dots in marginally twisted MoSe$_2$/WSe$_2$ and
  MoS$_2$/WS$_2$ bilayers

abstract:
  Moir\'e superlattices in twistronic heterostructures are a powerful tool for
materials engineering. In marginally twisted (small misalignment angle,
$\theta$) bilayers of nearly lattice-matched two-dimensional (2D) crystals
moir\'e patterns take the form of domains of commensurate stacking, separated
by a network of domain walls (NoDW) with strain hot spots at the NoDW nodes.
Here, we show that, for type-II transition metal dichalcogenide bilayers
MoX$_2$/WX$_2$ (X=S, Se), the hydrostatic strain component in these hot spots
creates quantum dots for electrons and holes. We investigate the electron/hole
states bound by such objects, discussing their manifestations via the
intralayer intraband infrared transitions. The electron/hole confinement, which
is strongest for $\theta<0.5^{\circ}$, leads to a red-shift of their
recombination line producing single photon emitters (SPE) broadly tuneable
around 1\,eV by misalignment angle. These self-organised dots can form in
bilayers with both aligned and inverted MoX$_2$ and WX$_2$ unit cells, emitting
photons with different polarizations. We also find that the hot spots of strain
reduce the intralayer MoX$_2$ A-exciton energy, enabling selective population
of the quantum dot states.


title: Orbital-resolved Observation of Singlet Fission

abstract:
  Singlet fission may boost photovoltaic efficiency by transforming a singlet
exciton into two triplet excitons and thereby doubling the number of excited
charge carriers. The primary step of singlet fission is the ultrafast creation
of the correlated triplet pair. While several mechanisms have been proposed to
explain this step, none has emerged as a consensus. The challenge lies in
tracking the transient excitonic states. Here we use time- and angle-resolved
photoemission spectroscopy to observe the primary step of singlet fission in
crystalline pentacene and show that it occurs in a charge-transfer mediated
mechanism. We gained intimate knowledge about the localization and the orbital
character of the exciton wavefunctions recorded in momentum maps. This allowed
us to directly compare the localization of singlet and bitriplet excitons and
decompose energetically overlapping states based on their orbital character.
Orbital- and localization-resolved many-body dynamics promise deep insights
into the mechanics governing molecular systems and topological materials.


title: The Vision of Self-Evolving Computing Systems

abstract:
  Computing systems are omnipresent; their sustainability has become crucial
for our society. A key aspect of this sustainability is the ability of
computing systems to cope with the continuous change they face, ranging from
dynamic operating conditions, to changing goals, and technological progress.
While we are able to engineer smart computing systems that autonomously deal
with various types of changes, handling unanticipated changes requires system
evolution, which remains in essence a human-centered process. This will
eventually become unmanageable. To break through the status quo, we put forward
an arguable opinion for the vision of self-evolving computing systems that are
equipped with an evolutionary engine enabling them to evolve autonomously.
Specifically, when a self-evolving computing system detects conditions outside
its operational domain, such as an anomaly or a new goal, it activates an
evolutionary engine that runs online experiments to determine how the system
needs to evolve to deal with the changes, thereby evolving its architecture.
During this process the engine can integrate new computing elements that are
provided by computing warehouses. These computing elements provide
specifications and procedures enabling their automatic integration. We motivate
the need for self-evolving computing systems in light of the state of the art,
outline a conceptual architecture of self-evolving computing systems, and
illustrate the architecture for a future smart city mobility system that needs
to evolve continuously with changing conditions. To conclude, we highlight key
research challenges to realize the vision of self-evolving computing systems.


title: Exhaustive Survey of Rickrolling in Academic Literature

abstract:
  Rickrolling is an Internet cultural phenomenon born in the mid 2000s.
Originally confined to Internet fora, it has spread to other channels and
media. In this paper, we hypothesize that rickrolling has reached the formal
academic world. We design and conduct a systematic experiment to survey
rickrolling in the academic literature. As of March 2022, there are
\nbrealrickcoll academic documents intentionally rickrolling the reader.
Rickrolling happens in footnotes, code listings, references. We believe that
rickrolling in academia proves inspiration and facetiousness, which is healthy
for good science. This original research suggests areas of improvement for
academic search engines and calls for more investigations about academic pranks
and humor.


title:
How Gender Debiasing Affects Internal Model Representations, and Why It
  Matters

abstract:
  Common studies of gender bias in NLP focus either on extrinsic bias measured
by model performance on a downstream task or on intrinsic bias found in models'
internal representations. However, the relationship between extrinsic and
intrinsic bias is relatively unknown. In this work, we illuminate this
relationship by measuring both quantities together: we debias a model during
downstream fine-tuning, which reduces extrinsic bias, and measure the effect on
intrinsic bias, which is operationalized as bias extractability with
information-theoretic probing. Through experiments on two tasks and multiple
bias metrics, we show that our intrinsic bias metric is a better indicator of
debiasing than (a contextual adaptation of) the standard WEAT metric, and can
also expose cases of superficial debiasing. Our framework provides a
comprehensive perspective on bias in NLP models, which can be applied to deploy
NLP systems in a more informed manner. Our code will be made publicly
available.


title: Deep Vehicle Detection in Satellite Video

abstract:
  This work presents a deep learning approach for vehicle detection in
satellite video. Vehicle detection is perhaps impossible in single EO satellite
images due to the tininess of vehicles (4-10 pixel) and their similarity to the
background. Instead, we consider satellite video which overcomes the lack of
spatial information by temporal consistency of vehicle movement. A new
spatiotemporal model of a compact $3 \times 3$ convolutional, neural network is
proposed which neglects pooling layers and uses leaky ReLUs. Then we use a
reformulation of the output heatmap including Non-Maximum-Suppression (NMS) for
the final segmentation. Empirical results on two new annotated satellite videos
reconfirm the applicability of this approach for vehicle detection. They more
importantly indicate that pre-training on WAMI data and then fine-tuning on few
annotated video frames for a new video is sufficient. In our experiment only
five annotated images yield a $F_1$ score of 0.81 on a new video showing more
complex traffic patterns than the Las Vegas video. Our best result on Las Vegas
is a $F_1$ score of 0.87 which makes the proposed approach a leading method for
this benchmark.


title: Realistic Video Sequences for Subjective QoE Analysis

abstract:
  Multimedia streaming over the Internet (live and on demand) is the
cornerstone of modern Internet carrying more than 60% of all traffic. With such
high demand, delivering outstanding user experience is a crucial and
challenging task. To evaluate user QoE many researchers deploy subjective
quality assessments where participants watch and rate videos artificially
infused with various temporal and spatial impairments. To aid current efforts
in bridging the gap between the mapping of objective video QoE metrics to user
experience, we developed DashReStreamer, an open-source framework for
re-creating adaptively streamed video in real networks. DashReStreamer utilises
a log created by a HAS algorithm run in an uncontrolled environment (i.e.,
wired or wireless networks), encoding visual changes and stall events in one
video file. These videos are applicable for subjective QoE evaluation mimicking
realistic network conditions.
  To supplement DashReStreamer, we re-create 234 realistic video clips, based
on video logs collected from real mobile and wireless networks. In addition our
dataset contains both video logs with all decisions made by the HASalgorithm
and network bandwidth profile illustrating throughput distribution. We believe
this dataset and framework will permit other researchers in their pursuit for
the final frontier in understanding the impact of video QoE dynamics.