title:
Self-supervised learning unveils morphological clusters behind lung
  cancer types and prognosis

abstract:
  Histopathological images of tumors contain abundant information about how
tumors grow and how they interact with their micro-environment. Characterizing
and improving our understanding of phenotypes could reveal factors related to
tumor progression and their underpinning biological processes, ultimately
improving diagnosis and treatment. In recent years, the field of histological
deep learning applications has seen great progress, yet most of these
applications focus on a supervised approach, relating tissue and associated
sample annotations. Supervised approaches have their impact limited by two
factors. Firstly, high-quality labels are expensive in time and effort, which
makes them not easily scalable. Secondly, these methods focus on predicting
annotations from histological images, fundamentally restricting the discovery
of new tissue phenotypes. These limitations emphasize the importance of using
new methods that can characterize tissue by the features enclosed in the image,
without pre-defined annotation or supervision. We present Phenotype
Representation Learning (PRL), a methodology to extract histomorphological
phenotypes through self-supervised learning and community detection. PRL
creates phenotype clusters by identifying tissue patterns that share common
morphological and cellular features, allowing to describe whole slide images
through compositional representations of cluster contributions. We used this
framework to analyze histopathology slides of LUAD and LUSC lung cancer
subtypes from TCGA and NYU cohorts. We show that PRL achieves a robust lung
subtype prediction providing statistically relevant phenotypes for each lung
subtype. We further demonstrate the significance of these phenotypes in lung
adenocarcinoma overall and recurrence free survival, relating clusters with
patient outcomes, cell types, grown patterns, and omic-based immune signatures.


title: Early Detection of Spam Domains with Passive DNS and SPF

abstract:
  Spam domains are sources of unsolicited mails and one of the primary vehicles
for fraud and malicious activities such as phishing campaigns or malware
distribution. Spam domain detection is a race: as soon as the spam mails are
sent, taking down the domain or blacklisting it is of relative use, as spammers
have to register a new domain for their next campaign. To prevent malicious
actors from sending mails, we need to detect them as fast as possible and,
ideally, even before the campaign is launched. In this paper, using
near-real-time passive DNS data from Farsight Security, we monitor the DNS
traffic of newly registered domains and the contents of their TXT records, in
particular, the configuration of the Sender Policy Framework, an anti-spoofing
protocol for domain names and the first line of defense against devastating
Business Email Compromise scams. Because spammers and benign domains have
different SPF rules and different traffic profiles, we build a new method to
detect spam domains using features collected from passive DNS traffic. Using
the SPF configuration and the traffic to the TXT records of a domain, we
accurately detect a significant proportion of spam domains with a low false
positives rate demonstrating its potential in real-world deployments. Our
classification scheme can detect spam domains before they send any mail, using
only a single DNS query and later on, it can refine its classification by
monitoring more traffic to the domain name.


title: Adaptive constant-depth circuits for manipulating non-abelian anyons

abstract:
  We consider Kitaev's quantum double model based on a finite group $G$ and
describe quantum circuits for (a) preparation of the ground state, (b) creation
of anyon pairs separated by an arbitrary distance, and (c) non-destructive
topological charge measurement. We show that for any solvable group $G$ all
above tasks can be realized by constant-depth adaptive circuits with
geometrically local unitary gates and mid-circuit measurements. Each gate may
be chosen adaptively depending on previous measurement outcomes. Constant-depth
circuits are well suited for implementation on a noisy hardware since it may be
possible to execute the entire circuit within the qubit coherence time. Thus
our results could facilitate an experimental study of exotic phases of matter
with a non-abelian particle statistics. We also show that adaptiveness is
essential for our circuit construction. Namely, task (b) cannot be realized by
non-adaptive constant-depth local circuits for any non-abelian group $G$. This
is in a sharp contrast with abelian anyons which can be created and moved over
an arbitrary distance by a depth-$1$ circuit composed of generalized Pauli
gates.


title: An unexplored valley of binary packing: The loose jamming state

abstract:
  We present a theoretical prediction on random close packing factor \phi_RCP^b
of binary granular packings based on the hard-sphere fluid theory. An
unexplored regime is unravelled, where the packing fraction \phi_RCP^b is
smaller than that of the mono-sized one \phi_RCP^m, i.e., the so-called loose
jamming state. This is against our common perception that binary packings
should always reach a denser packing than mono-sized packings at the jamming
state. Numerical evidence further supports this prediction and confirms the
regime location in the size ratio and mole fraction (R_r-X_s) space, where the
size ratio R_r is close to 1, and the mole fraction of the smaller sphere X_s
close to 0. This extreme regime remains unreported in existing literature, yet
significant for our fundamental understanding of binary packing systems.


title:
Thin current sheet formation: comparison between Earth's magnetotail and
  coronal streamers

abstract:
  Magnetic field line reconnection is a universal plasma process responsible
for the magnetic field topology change and magnetic field energy dissipation
into charged particle heating and acceleration. In many systems, the conditions
leading to the magnetic reconnection are determined by the pre-reconnection
configuration of a thin layer with intense currents -- otherwise known as the
thin current sheet. In this study we investigate two such systems: Earth's
magnetotail and helmet streamers in the solar corona. The pre-reconnection
current sheet evolution has been intensely studied in the magnetotail, where
in-situ spacecraft observations are available; but helmet streamer current
sheets studies are fewer, due to lack of in-situ observations -- they are
mostly investigated with numerical simulations and information that can be
surmised from remote sensing instrumentation. Both systems exhibit
qualitatively the same behavior, despite their largely different Mach numbers,
much higher at the solar corona than at the magnetotail. Comparison of
spacecraft data (from the magnetotail) with numerical simulations (for helmet
streamers) shows that the pre-reconnection current sheet thinning, for both
cases, is primarily controlled by plasma pressure gradients. Scaling laws of
the current density, magnetic field, and pressure gradients are the same for
both systems. We discuss how magnetotail observations and kinetic simulations
can be utilized to improve our understanding and modeling of the helmet
streamer current sheets.


title:
Deviations from Tribimaximal and Golden Ratio mixings under radiative
  corrections of neutrino masses and mixings

abstract:
  The impact of renormalization group equations(RGEs) on neutrino masses and
mixings at high energy scales in Minimal Supersymmetric Standard Model(MSSM) is
studied using two different mixing patterns such as Tri-Bimaximal(TBM) mixing
and Golden Ratio(GR) mixing in consistent with cosmological bound of the sum of
three neutrino masses, $\sum _{i}|m_{i}|$. Magnifications of neutrino masses
and mixing angles at low energy scale, are obtained by giving proper input
masses, and mixing angles from TBM mixing matrix and GR mixing matrix at high
energy scales. High energy scales, $M_{R}$ such as
$10^{13}$GeV,$10^{14}$GeV,$10^{15}$GeV are employed in the analysis. The large
solar($\theta_{12}$) and atmospheric($\theta_{23}$) neutrino mixing angles with
zero reactor angle ($\theta_{13}$) from both TBM mixing matrix and GR mixing
matrix at high scale, can magnify the reactor angle($\theta_{13}$) at low
energy scale in 3$\sigma$ confidence level. Both cases of normal hierarchy(NH)
and inverted hierarchy(IH) are addressed here. In normal hierarchical case, it
is found that $\theta_{23}\simeq51.1^{\circ}$ and that in inverted hierarchical
case is $\theta_{23}\simeq39.1^{\circ}$ in both mixing patterns. Possibility of
$\theta_{23}>45^{\circ}$ or $\theta_{23}<45^{\circ}$ is observed at low scale.
The analysis shows the validity of the two mixing patterns at high energy
scale.


title: Homography-Based Loss Function for Camera Pose Regression

abstract:
  Some recent visual-based relocalization algorithms rely on deep learning
methods to perform camera pose regression from image data. This paper focuses
on the loss functions that embed the error between two poses to perform deep
learning based camera pose regression. Existing loss functions are either
difficult-to-tune multi-objective functions or present unstable reprojection
errors that rely on ground truth 3D scene points and require a two-step
training. To deal with these issues, we introduce a novel loss function which
is based on a multiplane homography integration. This new function does not
require prior initialization and only depends on physically interpretable
hyperparameters. Furthermore, the experiments carried out on well established
relocalization datasets show that it minimizes best the mean square
reprojection error during training when compared with existing loss functions.


title:
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning
  Programs

abstract:
  As Deep Learning (DL) systems are widely deployed for mission-critical
applications, debugging such systems becomes essential. Most existing works
identify and repair suspicious neurons on the trained Deep Neural Network
(DNN), which, unfortunately, might be a detour. Specifically, several existing
studies have reported that many unsatisfactory behaviors are actually
originated from the faults residing in DL programs. Besides, locating faulty
neurons is not actionable for developers, while locating the faulty statements
in DL programs can provide developers with more useful information for
debugging. Though a few recent studies were proposed to pinpoint the faulty
statements in DL programs or the training settings (e.g. too large learning
rate), they were mainly designed based on predefined rules, leading to many
false alarms or false negatives, especially when the faults are beyond their
capabilities.
  In view of these limitations, in this paper, we proposed DeepFD, a
learning-based fault diagnosis and localization framework which maps the fault
localization task to a learning problem. In particular, it infers the
suspicious fault types via monitoring the runtime features extracted during DNN
model training and then locates the diagnosed faults in DL programs. It
overcomes the limitations by identifying the root causes of faults in DL
programs instead of neurons and diagnosing the faults by a learning approach
instead of a set of hard-coded rules. The evaluation exhibits the potential of
DeepFD. It correctly diagnoses 52% faulty DL programs, compared with around
half (27%) achieved by the best state-of-the-art works. Besides, for fault
localization, DeepFD also outperforms the existing works, correctly locating
42% faulty programs, which almost doubles the best result (23%) achieved by the
existing works.


title:
Explicit and implicit measures of emotions: Data-science might help to
  account for data complexity and heterogeneity

abstract:
  Measuring emotions is a real challenge for fundamental and applied research,
especially in ecological contexts. de Wijk and Noldus propose combining two
types of measures-explicit to characterize a specific food, and
implicit-physiological-to capture the whole experience of a meal in real-life
situations. This raises several challenges including development of new and
miniaturized sensors and devices but also developing new ways of data analysis.
We suggest a path to follow for future studies regarding data analysis: to
include Data Science in the game. This field of research may enable developing
predictive but also explicative models that link subjective experience of
emotions and physiological responses in real-life contexts. We suggest that
food scientists should go out of their comfort zone by collaborating with
computer scientists and then be trained with the new tools of Data Science,
which will undoubtedly enable them 1/ to better manage complex and
heterogeneous data sets, 2/ to extract knowledge that will be essential to this
field of research.


title: Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs

abstract:
  This paper aims to theoretically analyze the complexity of feature
transformations encoded in DNNs with ReLU layers. We propose metrics to measure
three types of complexities of transformations based on the information theory.
We further discover and prove the strong correlation between the complexity and
the disentanglement of transformations. Based on the proposed metrics, we
analyze two typical phenomena of the change of the transformation complexity
during the training process, and explore the ceiling of a DNN's complexity. The
proposed metrics can also be used as a loss to learn a DNN with the minimum
complexity, which also controls the over-fitting level of the DNN and
influences adversarial robustness, adversarial transferability, and knowledge
consistency. Comprehensive comparative studies have provided new perspectives
to understand the DNN.


title: Lexical Knowledge Internalization for Neural Dialog Generation

abstract:
  We propose knowledge internalization (KI), which aims to complement the
lexical knowledge into neural dialog models. Instead of further conditioning
the knowledge-grounded dialog (KGD) models on externally retrieved knowledge,
we seek to integrate knowledge about each input token internally into the
model's parameters. To tackle the challenge due to the large scale of lexical
knowledge, we adopt the contrastive learning approach and create an effective
token-level lexical knowledge retriever that requires only weak supervision
mined from Wikipedia. We demonstrate the effectiveness and general
applicability of our approach on various datasets and diversified model
structures.


title:
Intra-pulse variability induced by plasmoid formation in pulsar
  magnetospheres

abstract:
  Pulsars show irregularities in their pulsed radio emission that originate
from propagation effects and the intrinsic activity of the source. In this
work, we investigate the role played by magnetic reconnection and the formation
of plasmoids in the pulsar wind current sheet as a possible source of intrinsic
pulse-to-pulse variability in the incoherent, high-energy emission pattern. We
used a two-dimensional particle-in-cell simulation of an orthogonal pulsar
magnetosphere restricted to the plane perpendicular to the star spin axis. We
evolved the solution for several tens of pulsar periods to gather a
statistically significant sample of synthetic pulse profiles. The formation of
plasmoids leads to strong pulse-to-pulse variability in the form of multiple
short, bright subpulses, which appear only on the leading edge of each main
pulse. These secondary peaks of emission are dominated by the dozen plasmoids
that can grow up to macroscopic scales. They emerge from the high end of the
hierarchical merging process occurring along the wind current layer. The flux
of the subpulses is correlated with their width in phase. Although the
full-scale separation is not realistic, we argue that the simulation correctly
captures the demographics and the properties of the largest plasmoids, and
therefore of the brightest subpulses. The prediction of subpulses at specific
pulse phases provides a new observational test of the magnetic reconnection
scenario as the origin of the pulsed incoherent emission. High-time-resolution
observations of the Crab pulsar in the optical range may be the most promising
source to target for this purpose.


title:
Low-field Feshbach resonances and three-body losses in a fermionic
  quantum gas of $^{161}$Dy

abstract:
  We report on high-resolution Feshbach spectroscopy on a degenerate,
spin-polarized Fermi gas of $^{161}$Dy atoms, measuring three-body
recombination losses at low magnetic field. For field strength up to 1\,G, we
identify as much as 44 resonance features and observe plateaus of very low
losses. For four selected typical resonances, we study the dependence of the
three-body recombination rate coefficient on the magnetic resonance detuning
and on the temperature. We observe a strong suppression of losses with
decreasing temperature already for small detunings from resonance. The
characterization of complex behavior of three-body losses of fermionic
$^{161}$Dy is important for future applications of this peculiar species in
research on atomic quantum gases.


title:
Joint Compute-Caching-Communication Control for Online Data-Intensive
  Service Delivery

abstract:
  Emerging Metaverse applications, designed to deliver highly interactive and
immersive experiences that seamlessly blend physical reality and digital
virtuality, are accelerating the need for distributed compute platforms with
unprecedented storage, computation, and communication requirements. To this
end, the integrated evolution of next-generation networks (e.g., 5G and beyond)
and distributed cloud technologies (e.g., fog and mobile edge computing), have
emerged as a promising paradigm to address the interaction- and
resource-intensive nature of Metaverse applications. In this paper, we focus on
the design of control policies for the joint orchestration of compute, caching,
and communication (3C) resources in next-generation distributed cloud networks
for the efficient delivery of Metaverse applications that require the real-time
aggregation, processing, and distribution of multiple live media streams and
pre-stored digital assets. We describe Metaverse applications via directed
acyclic graphs able to model the combination of real-time stream-processing and
content distribution pipelines. We design the first throughput-optimal control
policy that coordinates joint decisions around (i) routing paths and processing
locations for live data streams, together with (ii) cache selection and
distribution paths for associated data objects. We then extend the proposed
solution to include a max-throughput database placement policy and two
efficient replacement policies. In addition, we characterize the network
stability regions for all studied scenarios. Numerical results demonstrate the
superior performance obtained via the novel multi-pipeline flow control and 3C
resource orchestration mechanisms of the proposed policy, compared with
state-of-the-art algorithms that lack full 3C integrated control.


title:
Optimal Network Charge for Peer-to-Peer Energy Trading: A Grid
  Perspective

abstract:
  Peer-to-peer (P2P) energy trading is a promising market scheme to accommodate
the increasing distributed energy resources (DERs). However, how P2P to be
integrated into the existing power systems remains to be investigated. In this
paper, we apply network charge as a means for the grid operator to attribute
transmission loss and ensure network constraints for empowering P2P
transaction. The interaction between the grid operator and the prosumers is
modeled as a Stackelberg game, which yields a bi-level optimization problem. We
prove that the Stackelberg game admits an equilibrium network charge price.
Besides, we propose a method to obtain the network charge price by converting
the bi-level optimization into a single-level mixed-integer quadratic
programming (MIQP), which can handle a reasonable scale of prosumers
efficiently. Simulations on the IEEE bus systems show that the proposed optimal
network charge is favorable as it can benefit both the grid operator and the
prosumers for empowering the P2P market, and achieves near-optimal social
welfare. Moreover, the results show that the presence of energy storage will
make the prosumers more sensitive to the network charge price changes.


title:
Analysis of Wall Heat Flux of a Hypersonic Shock Wave / Boundary Layer
  Interaction with a Novel Decomposition Formula

abstract:
  The generation mechanism of wall heat flux is one of the fundamental problems
in supersonic/hypersonic turbulent boundary layers. A novel heat decomposition
formula under the curvilinear coordinate was proposed in this paper. The new
formula has wider application scope and can be applied in the configurations
with grid deformed. The wall heat flux of an interaction between shock wave and
the turbulent boundary layer over a compression corner is analyzed by the new
formula. The results indicated good performance of the formula in the complex
interaction region. The contributions of different energy transport processes
were obtained. The contributions by the turbulent fluctuations e.g., Reynolds
stresses and turbulent transport of heat flux, were significantly increased,
while the processes by the mean profile e.g., molecular stresses and heat
conduction, can be neglected. In addition, the pressure work is another
contributor to the wall heat flux and the streamwise component works mainly in
the shear layer and the reattachment point, while pressure in the wall-normal
direction is concentrated in the vicinity of the reattachment point.


title: EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking

abstract:
  The study of human gaze behavior in natural contexts requires algorithms for
gaze estimation that are robust to a wide range of imaging conditions. However,
algorithms often fail to identify features such as the iris and pupil centroid
in the presence of reflective artifacts and occlusions. Previous work has shown
that convolutional networks excel at extracting gaze features despite the
presence of such artifacts. However, these networks often perform poorly on
data unseen during training. This work follows the intuition that jointly
training a convolutional network with multiple datasets learns a generalized
representation of eye parts. We compare the performance of a single model
trained with multiple datasets against a pool of models trained on individual
datasets. Results indicate that models tested on datasets in which eye images
exhibit higher appearance variability benefit from multiset training. In
contrast, dataset-specific models generalize better onto eye images with lower
appearance variability.


title:
Dynamic Median Consensus for Marine Multi-Robot Systems Using Acoustic
  Communication

abstract:
  In this paper, we present a dynamic median consensus protocol for multi-agent
systems using acoustic communication. The motivating target scenario is a
multi-agent system consisting of underwater robots acting as intelligent
sensors, applied to continuous monitoring of the state of a marine environment.
The proposed protocol allows each agent to track the median value of individual
measurements of all agents through local communication with neighbouring
agents. Median is chosen as a measure robust to outliers, as opposed to average
value, which is usually used. In contrast to the existing consensus protocols,
the proposed protocol is dynamic, uses a switching communication topology and
converges to median of measured signals. Stability and correctness of the
protocol are theoretically proven. The protocol is tested in simulation, and
accuracy and influence of protocol parameters on the system output are
analyzed. The protocol is implemented and validated by a set of experiments on
an underwater group of robots comprising of aMussel units. This experimental
setup is one of the first deployments of any type of consensus protocol for an
underwater setting. Both simulation and experimental results confirm the
correctness of the presented approach.


title:
Compatible $L^2$ norm convergence of variable-step L1 scheme for the
  time-fractional MBE mobel with slope selection

abstract:
  The convergence of variable-step L1 scheme is studied for the time-fractional
molecular beam epitaxy (MBE) model with slope selection.A novel asymptotically
compatible $L^2$ norm error estimate of the variable-step L1 scheme is
established under a convergence-solvability-stability (CSS)-consistent
time-step constraint. The CSS-consistent condition means that the maximum
step-size limit required for convergence is of the same order to that for
solvability and stability (in certain norms) as the small interface parameter
$\epsilon\rightarrow 0^+$. To the best of our knowledge, it is the first time
to establish such error estimate for nonlinear subdiffusion problems. The
asymptotically compatible convergence means that the error estimate is
compatible with that of backward Euler scheme for the classical MBE model as
the fractional order $\alpha\rightarrow 1^-$. Just as the backward Euler scheme
can maintain the physical properties of the MBE equation, the variable-step L1
scheme can also preserve the corresponding properties of the time-fractional
MBE model, including the volume conservation, variational energy dissipation
law and $L^2$ norm boundedness. Numerical experiments are presented to support
our theoretical results.


title:
Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type
  Conscious Transformation

abstract:
  We propose an adversarial learning framework that deals with the
privacy-utility tradeoff problem under two types of conditions: data-type
ignorant, and data-type aware. Under data-type aware conditions, the privacy
mechanism provides a one-hot encoding of categorical features, representing
exactly one class, while under data-type ignorant conditions the categorical
variables are represented by a collection of scores, one for each class. We use
a neural network architecture consisting of a generator and a discriminator,
where the generator consists of an encoder-decoder pair, and the discriminator
consists of an adversary and a utility provider. Unlike previous research
considering this kind of architecture, which leverages autoencoders (AEs)
without introducing any randomness, or variational autoencoders (VAEs) based on
learning latent representations which are then forced into a Gaussian
assumption, our proposed technique introduces randomness and removes the
Gaussian assumption restriction on the latent variables, only focusing on the
end-to-end stochastic mapping of the input to privatized data. We test our
framework on different datasets: MNIST, FashionMNIST, UCI Adult, and US Census
Demographic Data, providing a wide range of possible private and utility
attributes. We use multiple adversaries simultaneously to test our privacy
mechanism -- some trained from the ground truth data and some trained from the
perturbed data generated by our privacy mechanism. Through comparative
analysis, our results demonstrate better privacy and utility guarantees than
the existing works under similar, data-type ignorant conditions, even when the
latter are considered under their original restrictive single-adversary model.


title: Proximal ADMM for Nonconvex and Nonsmooth Optimization

abstract:
  By enabling the nodes or agents to solve small-sized subproblems to achieve
coordination, distributed algorithms are favored by many networked systems for
efficient and scalable computation. While for convex problems, substantial
distributed algorithms are available, the results for the more broad nonconvex
counterparts are extremely lacking. This paper develops a distributed algorithm
for a class of nonconvex and nonsmooth problems featured by i) a nonconvex
objective formed by both separate and composite objective components regarding
the decision components of interconnected agents, ii) local bounded convex
constraints, and iii) coupled linear constraints. This problem is directly
originated from smart buildings and is also broad in other domains. To provide
a distributed algorithm with convergence guarantee, we revise the powerful tool
of alternating direction method of multiplier (ADMM) and proposed a proximal
ADMM. Specifically, noting that the main difficulty to establish the
convergence for the nonconvex and nonsmooth optimization within the ADMM
framework is to assume the boundness of dual updates, we propose to update the
dual variables in a discounted manner. This leads to the establishment of a
so-called sufficiently decreasing and lower bounded Lyapunov function, which is
critical to establish the convergence. We prove that the method converges to
some approximate stationary points. We besides showcase the efficacy and
performance of the method by a numerical example and the concrete application
to multi-zone heating, ventilation, and air-conditioning (HVAC) control in
smart buildings.


title:
A Generic Solution to Register-bounded Synthesis with an Application to
  Discrete Orders

abstract:
  We study synthesis of reactive systems interacting with environments using an
infinite data domain. A popular formalism for specifying and modelling such
systems is register automata and transducers. They extend finite-state automata
by adding registers to store data values and to compare the incoming data
values against stored ones. Synthesis from nondeterministic or universal
register automata is undecidable in general. However, its register-bounded
variant, where additionally a bound on the number of registers in a sought
transducer is given, is known to be decidable for universal register automata
which can compare data for equality, i.e., for data domain $(N,=)$. This paper
extends the decidability border to the domain $(N,<)$ of natural numbers with
linear order. Our solution is generic: we define a sufficient condition on data
domains (regular approximability) for decidability of register-bounded
synthesis. The condition is satisfied by natural data domains like $(N,<)$. It
allows one to use simple language-theoretic arguments and avoid technical
game-theoretic reasoning. Further, by defining a generic notion of reducibility
between data domains, we show the decidability of synthesis in the domain
$(N^d,<^d)$ of tuples of numbers equipped with the component-wise partial order
and in the domain $(\Sigma^*,\prec)$ of finite strings with the prefix
relation.


title: A Global Asymptotic Convergent Observer for SLAM

abstract:
  This paper examines the global convergence problem of SLAM algorithms, an
issue that faces topological obstructions. This is because the state-space of
attitude dynamics is defined on a non-contractible manifold: the special
orthogonal group of order three SO(3). Therefore, this paper presents a novel,
gradient-based hybrid observer to overcome these topological obstacles. The
Lyapunov stability theorem is used to prove the globally asymptotic convergence
of the proposed algorithm. Finally, comparative analyses of two simulations
were conducted to evaluate the performance of the proposed scheme and to
demonstrate the superiority of the proposed hybrid observer to a smooth
observer.


title:
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman
  Problem

abstract:
  Word embeddings are one of the most fundamental technologies used in natural
language processing. Existing word embeddings are high-dimensional and consume
considerable computational resources. In this study, we propose WordTour,
unsupervised one-dimensional word embeddings. To achieve the challenging goal,
we propose a decomposition of the desiderata of word embeddings into two parts,
completeness and soundness, and focus on soundness in this paper. Owing to the
single dimensionality, WordTour is extremely efficient and provides a minimal
means to handle word embeddings. We experimentally confirmed the effectiveness
of the proposed method via user study and document classification.


title: Fuzzy Simulations and Bisimulations between Fuzzy Automata

abstract:
  Simulations and bisimulations between two fuzzy automata over a complete
residuated lattice were defined by \'Ciri\'c et al. (2012) as fuzzy relations
between the sets of states of the automata. However, they act as a crisp
relationship between the automata. In particular, if there exists a (forward)
bisimulation between two fuzzy automata, then the fuzzy languages recognized by
them are crisply equal. Approximate simulations and bisimulations introduced by
Stanimirovi\'c et al. (2020) aim at fuzzifying this phenomenon. However, they
are defined only for fuzzy automata over a complete Heyting algebra and do not
give the exact relationship between states of the automata. In this article, we
introduce and study fuzzy simulations and bisimulations between fuzzy automata
over a complete residuated lattice. These notions are novel and have good
properties. They are defined for fuzzy automata over any complete residuated
lattice. We prove that the fuzzy language recognized by a fuzzy automaton is
fuzzily preserved by fuzzy simulations and fuzzily invariant under fuzzy
bisimulations. We also prove that the notions of fuzzy simulation and
bisimulation have the Hennessy-Milner properties, which are a logical
characterization of the greatest fuzzy simulation or bisimulation between two
fuzzy automata. In addition, we provide results showing that our notions of
fuzzy simulation and bisimulation are more general and refined than the notions
of simulation and bisimulation introduced by \'Ciri\'c et al. and the notions
of approximate simulation and bisimulation introduced by Stanimirovi\'c et al.


title: Limits of manifolds with a Kato bound on the Ricci curvature. II

abstract:
  We prove that metric measure spaces obtained as limits of closed Riemannian
manifolds with Ricci curvature satisfying a uniform Kato bound are rectifiable.
In the case of a non-collapsing assumption and a strong Kato bound, we
additionally show that for any $\alpha \in (0,1)$ the regular part of the space
lies in an open set with the structure of a $\mathcal{C}^\alpha$-manifold.


title:
Utilitarianism on the front lines: COVID-19, public ethics, and the
  "hidden assumption" problem

abstract:
  How should we think of the preferences of citizens? Whereas self-optimal
policy is relatively straightforward to produce, socially optimal policy often
requires a more detailed examination. In this paper, we identify an issue that
has received far too little attention in welfarist modelling of public policy,
which we name the "hidden assumptions" problem. Hidden assumptions can be
deceptive because they are not expressed explicitly and the social planner
(e.g. a policy maker, a regulator, a legislator) may not give them the critical
attention they need. We argue that ethical expertise has a direct role to play
in public discourse because it is hard to adopt a position on major issues like
public health policy or healthcare prioritisation without making contentious
assumptions about population ethics. We then postulate that ethicists are best
situated to critically evaluate these hidden assumptions, and can therefore
play a vital role in public policy debates.


title:
Cathode Side Transport Phenomena Investigation and Multi-Objective
  Optimization of a Tapered Parallel Flow Field PEMFC

abstract:
  A Proton Exchange Membrane Fuel Cell (PEMFC) provides stable, emission-free,
high-efficiency power. Water management and durability of PEMFCs are directly
affected by transport phenomena at the cathode side. In the present study,
transport phenomena are investigated and optimized in a tapered parallel flow
field. Main channels in the flow field are tapered, which increases limiting
current density by 41%. Two objectives, i.e. water saturation and transport
resistance, are considered metrics for transport phenomena in a tapered
parallel flow field PEMFC. Operating pressure, temperature, stoichiometries at
both sides, and the porosity of gas diffusion layers are selected as parameters
to be optimized. Two functions are generated for objectives by integrating 3D
multiphase-flow computational fluid dynamics and Response Surface Methodology.
Multi-Objective Optimization (MOO) is carried out with two different methods.
Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting
Genetic Algorithm II (NSGA-II) are employed to produce two challenging Pareto
fronts. The results demonstrate that MOPSO performs better than NSGA-II. MOPSO
recognized quite the same Pareto front with lower runtime. In the last step,
the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is
used to select an optimum point from the Pareto front. The results are compared
against experimental data, and good correspondence is observed. The optimum
features are temperature 323, pressure 1 atm, anode stoichiometry 3, cathode
stoichiometry 2.62, and porosity 0.68. The porosity and pressure played the
most significant roles in determining water saturation and resistance.


title:
Birkhoff-von Neumann Quantum Logic as an Assertion Language for Quantum
  Programs

abstract:
  A first-order logic with quantum variables is needed as an assertion language
for specifying and reasoning about various properties (e.g. correctness) of
quantum programs. Surprisingly, such a logic is missing in the literature, and
the existing first-order Birkhoff-von Neumann quantum logic deals with only
classical variables and quantifications over them. In this paper, we fill in
this gap by introducing a first-order extension of Birkhoff-von Neumann quantum
logic with universal and existential quantifiers over quantum variables.
Examples are presented to show our logic is particularly suitable for
specifying some important properties studied in quantum computation and quantum
information. We further incorporate this logic into quantum Hoare logic as an
assertion logic so that it can play a role similar to that of first-order logic
for classical Hoare logic and BI-logic for separation logic. In particular, we
show how it can be used to define and derive quantum generalisations of some
adaptation rules that have been applied to significantly simplify verification
of classical programs. It is expected that the assertion logic defined in this
paper - first-order quantum logic with quantum variables - can be combined with
various quantum program logics to serve as a solid logical foundation upon
which verification tools can be built using proof assistants such as Coq and
Isabelle/HOL.


title:
Search and detection of pulsars in the monitoring observations at the
  frequency 111 MHz

abstract:
  Pulsars with periods more than 0.4 seconds in the declination range -9o <
decj < 42o and in the right ascension range 0h < r.a.< 24h were searched in
parallel with the program of interplanetary scintillations monitoring of a
large number of sources with the radiotelescope LPA LPI. Four-year observations
carried out at the central frequency 110.25 MHz in the band 2.5 MHz and in six
frequency channels with the time resolution 100 ms were used for the search.
Initial detections of candidates for pulsars were based on the Fourier power
spectra averaged over the whole observations period. The candidates for pulsars
were checked in more details from observations analysis with higher
time-frequency resolution: 32 frequency channels with time resolution of 12.5
ms. 18 new pulsars was discovered, their main characteristics are presented.


title: Model robustness for feedback stabilization of open quantum systems

abstract:
  This paper generalizes the results in [32] concerning feedback stabilization
of target states for N-level quantum angular momentum systems undergoing
quantum non-demolition measurements (QND) in absence of the knowledge about
initial states and parameters. Our purpose is to study the stabilization toward
a chosen target subspace which is a common eigenspace of measurement operators.
We suppose that measurements satisfy QND conditions and we consider
misspecification of initial states and model parameters. In addition, we show
that with an approximate diagonal filter whose state is a N-dimensional vector,
the exponential stabilization toward target subspaces is still ensured. This
has important practical use as the dimension of quantum systems is usually
high. Beside the mentioned generalization, this paper initiates investigations
in this direction.


title: On the Diversity and Coded Modulation Design of Fluid Antenna Systems

abstract:
  Reconfigurability is a desired characteristic of future communication
networks. From a transceiver's standpoint, this can be materialized through the
implementation of fluid antennas (FAs). An FA consists of a dielectric holder,
in which a radiating liquid moves between pre-defined locations (called ports)
that serve as the transceiver's antennas. Due to the nature of liquids, FAs can
practically take any size and shape, making them both flexible and
reconfigurable. In this paper, we deal with the outage probability of FAs under
general fading channels, where a port is scheduled based on selection
combining. An analytical framework is provided for the performance with and
without estimation errors, as a result of post-scheduling delays. We show that
although FAs achieve maximum diversity, this cannot be realized in the presence
of delays. Hence, a linear prediction scheme is proposed that overcomes delays
and restores the lost diversity by predicting the next scheduled port.
Moreover, we design space-time coded modulations that exploit the FA's
sequential operation with space-time rotations and code diversity. The derived
expressions for the pairwise error probability and average word error rate give
an accurate estimate of the performance. We illustrate that the proposed design
attains maximum diversity, while keeping a low-complexity receiver, thereby
confirming the feasibility of FAs.


title:
Reliable and Broad-range Thickness Determination of for Au-assisted
  Exfoliated Large Area MoS$_2$ and WS$_2$ Using Reflection Spectroscopy

abstract:
  The emerging Au-assisted exfoliation technique provides a wealth of
large-area and high-quality ultrathin two-dimensional (2D) materials compared
with traditional tape-based exfoliation. Fast, damage-free, and reliable
determination of the layer number of such 2D films is essential to study
layer-dependent physics and promote device applications. Here, an optical
method has been developed for simple, high throughput, and accurate
determination of the layer number for Au-assisted exfoliated MoS$_2$ and WS$_2$
films in a broad thickness range. The method is based on quantitative analysis
of layer-dependent white light reflection spectra, revealing that the
reflection peak intensity can be used as a clear indicator for determining the
layer number. The simple yet robust method will facilitate the fundamental
study on layer-dependent optical, electrical, and thermal properties and device
applications of 2D materials. The technique can also be readily combined with
photoluminescence and Raman spectroscopies to study other layer-dependent
physical properties of 2D materials.


title:
On weighted Compactness of Commutators of square function and semi-group
  maximal function associated to Schrodinger operator

abstract:
  In this paper, the object of our investigation is the following
Littlewood-Paley square function $g$ associated with the Schr\"odinger operator
$L=-\Delta +V$ which is defined by:
$g(f)(x)=\Big(\int_{0}^{\infty}\Big|\frac{d}{dt}e^{-tL}(f)(x)\Big|^2tdt\Big)^{1/2},$
where $\Delta$ is the laplacian operator on $\mathbb{R}^n$ and $V$ is a
nonnegative potential. We show that the commutators of $g$ are compact
operators from $L^p(w)$ to $L^p(w)$ for $1<p<\infty$ if $b\in {\rm
CMO}_\theta(\rho)$ and $w\in A_p^{\rho,\theta}$, where ${\rm CMO}_\theta(\rho)$
is the closure of $\mathcal{C}_c^\infty(\mathbb{R}^n)$ in the ${\rm
BMO}_\theta(\rho)$ topology which is more larger than the classical ${\rm CMO}$
space and $A_p^{\rho,\theta}$ is a weights class which is more larger than
Muckenhoupt $A_p$ weight class. An extra weight condition in a privious
weighted compactness result is removed for the commutators of the semi-group
maximal function defined by $\mathcal{T}^*(f)(x)=\sup_{t>0}|e^{-tL}f(x)|.$


title:
State Representation Learning for Goal-Conditioned Reinforcement
  Learning

abstract:
  This paper presents a novel state representation for reward-free Markov
decision processes. The idea is to learn, in a self-supervised manner, an
embedding space where distances between pairs of embedded states correspond to
the minimum number of actions needed to transition between them. Compared to
previous methods, our approach does not require any domain knowledge, learning
from offline and unlabeled data. We show how this representation can be
leveraged to learn goal-conditioned policies, providing a notion of similarity
between states and goals and a useful heuristic distance to guide planning and
reinforcement learning algorithms. Finally, we empirically validate our method
in classic control domains and multi-goal environments, demonstrating that our
method can successfully learn representations in large and/or continuous
domains.


title: Non-Autoregressive Machine Translation: It's Not as Fast as it Seems

abstract:
  Efficient machine translation models are commercially important as they can
increase inference speeds, and reduce costs and carbon emissions. Recently,
there has been much interest in non-autoregressive (NAR) models, which promise
faster translation. In parallel to the research on NAR models, there have been
successful attempts to create optimized autoregressive models as part of the
WMT shared task on efficient translation. In this paper, we point out flaws in
the evaluation methodology present in the literature on NAR models and we
provide a fair comparison between a state-of-the-art NAR model and the
autoregressive submissions to the shared task. We make the case for consistent
evaluation of NAR models, and also for the importance of comparing NAR models
with other widely used methods for improving efficiency. We run experiments
with a connectionist-temporal-classification-based (CTC) NAR model implemented
in C++ and compare it with AR models using wall clock times. Our results show
that, although NAR models are faster on GPUs, with small batch sizes, they are
almost always slower under more realistic usage conditions. We call for more
realistic and extensive evaluation of NAR models in future work.


title: Complementarity-Entanglement Tradeoff in Quantum Gravity

abstract:
  Quantization of the gravity remains one of the most important, yet extremely
illusive, challenges at the heart of modern physics. Any attempt to resolve
this long-standing problem seems to be doomed, as the route to any direct
empirical evidence (i.e., detecting gravitons) for shedding light on the
quantum aspect of the gravity is far beyond the current capabilities. Recently,
it has been discovered that gravitationally-induced entanglement, tailored in
the interferometric frameworks, can be used to witness the quantum nature of
the gravity. Even though these schemes offer promising tools for investigating
quantum gravity, many fundamental and empirical aspects of the schemes are yet
to be discovered. Considering the fact that, beside quantum entanglement,
quantum uncertainty and complementarity principles are the two other
foundational aspects of quantum physics, the quantum nature of the gravity
needs to manifest all of these features. Here, we lay out an interferometric
platform for testing these three nonclassical aspects of quantum mechanics in
quantum gravity setting, which connects gravity and quantum physics in a
broader and deeper context. As we show in this work, all of these three
fundamental features of quantum gravity can be framed and fully analyzed in an
interferometric scheme.


title:
Numerical approximation of probabilistically weak and strong solutions
  of the stochastic total variation flow

abstract:
  We propose a fully practical numerical scheme for the simulation of the
stochastic total variation flow (STFV). The approximation is based on a stable
time-implicit finite element space-time approximation of a regularized STVF
equation. The approximation also involves a finite dimensional discretization
of the noise that makes the scheme fully implementable on physical hardware. We
show that the proposed numerical scheme converges to a solution that is defined
in the sense of stochastic variational inequalities (SVIs). As a by product of
our convergence analysis we provide a generalization of the concept of
probabilistically weak solutions of stochastic partial differential equation
(SPDEs) to the setting of SVIs. We also prove convergence of the numerical
scheme to a probabilistically strong solution in probability if pathwise
uniqueness holds. We perform numerical simulations to illustrate the behavior
of the proposed numerical scheme {as well as its non-conforming variant} in the
context of image denoising.


title: Positional Accuracy Assessment of Historical Google Earth Imagery

abstract:
  Google Earth is the most popular virtual globe in use today. Given its
popularity and usefulness, most users do not pay close attention to the
positional accuracy of the imagery, and there is limited information on the
subject. This study evaluates the horizontal accuracy of historical GE imagery
at four epochs between year 2000 and 2018, and the vertical accuracy of its
elevation data within Lagos State in Nigeria, West Africa. The horizontal
accuracies of the images were evaluated by comparison with a very high
resolution (VHR) digital orthophoto while the vertical accuracy was assessed by
comparison with a network of 558 ground control points. The GE elevations were
also compared to elevation data from two readily available 30m digital
elevation models (DEMs), the Shuttle Radar Topography Mission (SRTM) v3.0 and
the Advanced Land Observing Satellite World 3D (AW3D) DEM v2.1. The most recent
GE imagery (year 2018) was the most accurate while year 2000 was the least
accurate. This shows a continuous enhancement in the accuracy and reliability
of satellite imagery data sources which form the source of Google Earth data.
In terms of the vertical accuracy, GE elevation data had the highest RMSE of
6.213m followed by AW3D with an RMSE of 4.388m and SRTM with an RMSE of 3.682m.
Although the vertical accuracy of SRTM and AW3D are superior, Google Earth
still presents clear advantages in terms of its ease of use and contextual
awareness.


title: Nonstationary Bandit Learning via Predictive Sampling

abstract:
  We propose predictive sampling as an approach to selecting actions that
balance between exploration and exploitation in nonstationary bandit
environments. When specialized to stationary environments, predictive sampling
is equivalent to Thompson sampling. However, predictive sampling is effective
across a range of nonstationary environments in which Thompson sampling
suffers. We establish a general information-theoretic bound on the Bayesian
regret of predictive sampling. We then specialize this bound to study a
modulated Bernoulli bandit environment. Our analysis highlights a key advantage
of predictive sampling over Thompson sampling: predictive sampling
deprioritizes investments in exploration where acquired information will
quickly become less relevant.


title: Discrete Isothermic Nets Based on Checkerboard Patterns

abstract:
  This paper studies the discrete differential geometry of the checkerboard
pattern inscribed in a quadrilateral net by connecting edge midpoints. It turns
out to be a versatile tool which allows us to consistently define principal
nets, Koenigs nets and eventually isothermic nets as a combination of both.
  Principal nets are based on the notions of orthogonality and conjugacy and
can be identified with sphere congruences that are entities of Moebius
geometry. Discrete Koenigs nets are defined via the existence of the so called
conic of Koenigs. We find several interesting properties of Koenigs nets,
including their being dualizable and having equal Laplace invariants.
  Koenigs nets that are also principal are defined as isothermic nets. We prove
that the class of isothermic nets is invariant under both dualization and
Moebius transformations. Among other things, this allows a natural construction
of discrete minimal surfaces and their Goursat transformations.


title: Sequencer: Deep LSTM for Image Classification

abstract:
  In recent computer vision research, the advent of the Vision Transformer
(ViT) has rapidly revolutionized various architectural design efforts: ViT
achieved state-of-the-art image classification performance using self-attention
found in natural language processing, and MLP-Mixer achieved competitive
performance using simple multi-layer perceptrons. In contrast, several studies
have also suggested that carefully redesigned convolutional neural networks
(CNNs) can achieve advanced performance comparable to ViT without resorting to
these new ideas. Against this background, there is growing interest in what
inductive bias is suitable for computer vision. Here we propose Sequencer, a
novel and competitive architecture alternative to ViT that provides a new
perspective on these issues. Unlike ViTs, Sequencer models long-range
dependencies using LSTMs rather than self-attention layers. We also propose a
two-dimensional version of Sequencer module, where an LSTM is decomposed into
vertical and horizontal LSTMs to enhance performance. Despite its simplicity,
several experiments demonstrate that Sequencer performs impressively well:
Sequencer2D-L, with 54M parameters, realizes 84.6\% top-1 accuracy on only
ImageNet-1K. Not only that, we show that it has good transferability and the
robust resolution adaptability on double resolution-band.


title: V'CER: Efficient Certificate Validation in Constrained Networks

abstract:
  We address the challenging problem of efficient trust establishment in
constrained networks, i.e., networks that are composed of a large and dynamic
set of (possibly heterogeneous) devices with limited bandwidth, connectivity,
storage, and computational capabilities. Constrained networks are an integral
part of many emerging application domains, from IoT meshes to satellite
networks. A particularly difficult challenge is how to enforce timely
revocation of compromised or faulty devices. Unfortunately, current solutions
and techniques cannot cope with idiosyncrasies of constrained networks, since
they mandate frequent real-time communication with centralized entities,
storage and maintenance of large amounts of revocation information, and incur
considerable bandwidth overhead.
  To address the shortcomings of existing solutions, we design V'CER, a secure
and efficient scheme for certificate validation that augments and benefits a
PKI for constrained networks. V'CER utilizes unique features of Sparse Merkle
Trees (SMTs) to perform lightweight revocation checks, while enabling
collaborative operations among devices to keep them up-to-date when
connectivity to external authorities is limited. V'CER can complement any PKI
scheme to increase its flexibility and applicability, while ensuring fast
dissemination of validation information independent of the network routing or
topology. V'CER requires under 3KB storage per node covering 106 certificates.
We developed and deployed a prototype of V'CER on an in-orbit satellite and our
large-scale simulations demonstrate that V'CER decreases the number of requests
for updates from external authorities by over 93%, when nodes are
intermittently connected.


title: Restart expedites quantum walk hitting times

abstract:
  Classical first-passage times under restart are used in a wide variety of
models, yet the quantum version of the problem still misses key concepts. We
study the quantum first detected passage time under restart protocol using a
monitored quantum walk. The restart strategy eliminates the problem of dark
states, i.e. cases where the particle is not detected at all, while maintaining
the ballistic propagation which is important for fast search. We find profound
effects of quantum oscillations on the restart problem, namely a type of
instability of the mean detection time, and optimal restart times that form
staircases, with sudden drops as the rate of sampling is modified. In the
absence of restart and in the Zeno limit, the detection of the walker is not
possible and we examine how restart overcomes this well-known problem, showing
that the optimal restart time becomes insensitive to the sampling period.


title: Aligning to Social Norms and Values in Interactive Narratives

abstract:
  We focus on creating agents that act in alignment with socially beneficial
norms and values in interactive narratives or text-based games -- environments
wherein an agent perceives and interacts with a world through natural language.
Such interactive agents are often trained via reinforcement learning to
optimize task performance, even when such rewards may lead to agent behaviors
that violate societal norms -- causing harm either to the agent itself or other
entities in the environment. Social value alignment refers to creating agents
whose behaviors conform to expected moral and social norms for a given context
and group of people -- in our case, it means agents that behave in a manner
that is less harmful and more beneficial for themselves and others.
  We build on the Jiminy Cricket benchmark (Hendrycks et al. 2021), a set of 25
annotated interactive narratives containing thousands of morally salient
scenarios covering everything from theft and bodily harm to altruism. We
introduce the GALAD (Game-value ALignment through Action Distillation) agent
that uses the social commonsense knowledge present in specially trained
language models to contextually restrict its action space to only those actions
that are aligned with socially beneficial values. An experimental study shows
that the GALAD agent makes decisions efficiently enough to improve
state-of-the-art task performance by 4% while reducing the frequency of
socially harmful behaviors by 25% compared to strong contemporary value
alignment approaches.


title:
On chromatic vertex stability of 3-chromatic graphs with maximum degree
  4

abstract:
  The (independent) chromatic vertex stability ($\ivs(G)$) $\vs(G)$ is the
minimum size of (independent) set $S\subseteq V(G)$ such that
$\chi(G-S)=\chi(G)-1$. In this paper we construct infinitely many graphs $G$
with $\Delta(G)=4$, $\chi(G)=3$, $\ivs(G)=3$ and $\vs(G)=2$, which gives a
partial negative answer to a problem posed in \cite{ABKM}.


title:
Collision Resolution with Deep Reinforcement Learning for Random Access
  in Machine-Type Communication

abstract:
  Grant-free random access (RA) techniques are suitable for machine-type
communication (MTC) networks but they need to be adaptive to the MTC traffic,
which is different from the human-type communication. Conventional RA protocols
such as exponential backoff (EB) schemes for slotted-ALOHA suffer from a high
number of collisions and they are not directly applicable to the MTC traffic
models. In this work, we propose to use multi-agent deep Q-network (DQN) with
parameter sharing to find a single policy applied to all machine-type devices
(MTDs) in the network to resolve collisions. Moreover, we consider binary
broadcast feedback common to all devices to reduce signalling overhead. We
compare the performance of our proposed DQN-RA scheme with EB schemes for up to
500 MTDs and show that the proposed scheme outperforms EB policies and provides
a better balance between throughput, delay and collision rate


title: Small modules with interesting rank varieties

abstract:
  This paper focuses on the rank varieties for modules over a group algebra
$\mathbb{F}E$ where $E$ is an elementary abelian $p$-group and $p$ is the
characteristic of an algebraically closed field $\mathbb{F}$. In the first
part, we give a sufficient condition for a Green vertex of an indecomposable
module containing an elementary abelian $p$-group $E$ in terms of the rank
variety of the module restricted to $E$. In the second part, given a
homogeneous algebraic variety $V$ , we explore the problem on finding a small
module with rank variety $V$ . In particular, we examine the simple module
$D^{(kp-p+1,1^{p-1})}$ for the symmetric group $\mathfrak{S}_{kp}$.


title: ASP-Based Declarative Process Mining

abstract:
  We put forward Answer Set Programming (ASP) as a solution approach for three
classical problems in Declarative Process Mining: Log Generation, Query
Checking, and Conformance Checking. These problems correspond to different ways
of analyzing business processes under execution, starting from sequences of
recorded events, a.k.a. event logs. We tackle them in their data-aware variant,
i.e., by considering events that carry a payload (set of attribute-value
pairs), in addition to the performed activity, specifying processes
declaratively with an extension of linear-time temporal logic over finite
traces (LTLf). The data-aware setting is significantly more challenging than
the control-flow one: Query Checking is still open, while the existing
approaches for the other two problems do not scale well. The contributions of
the work include an ASP encoding schema for the three problems, their solution,
and experiments showing the feasibility of the approach.


title: EqVIO: An Equivariant Filter for Visual Inertial Odometry

abstract:
  Visual Inertial Odometry (VIO) is the problem of estimating a robot's
trajectory by combining information from an inertial measurement unit (IMU) and
a camera, and is of great interest to the robotics community. This paper
develops a novel Lie group symmetry for the VIO problem and applies the
recently proposed equivariant filter. The symmetry is shown to be compatible
with the invariance of the VIO reference frame, lead to exact linearisation of
bias-free IMU dynamics, and provide equivariance of the visual measurement
function. As a result, the equivariant filter (EqF) based on this Lie group is
a consistent estimator for VIO with lower linearisation error in the
propagation of state dynamics and a higher order equivariant output
approximation than standard formulations. Experimental results on the popular
EuRoC and UZH-FPV datasets demonstrate that the proposed system outperforms
other state-of-the-art VIO algorithms in terms of both speed and accuracy.


title: The Isabelle ENIGMA

abstract:
  We significantly improve the performance of the E automated theorem prover on
the Isabelle Sledgehammer problems by combining learning and theorem proving in
several ways. In particular, we develop targeted versions of the ENIGMA
guidance for the Isabelle problems, targeted versions of neural premise
selection, and targeted strategies for E. The methods are trained in several
iterations over hundreds of thousands untyped and typed first-order problems
extracted from Isabelle. Our final best single-strategy ENIGMA and premise
selection system improves the best previous version of E by 25.3% in 15
seconds, outperforming also all other previous ATP and SMT systems.


title:
Lifelong Ensemble Learning based on Multiple Representations for
  Few-Shot Object Recognition

abstract:
  Service robots are integrating more and more into our daily lives to help us
with various tasks. In such environments, robots frequently face new objects
while working in the environment and need to learn them in an open-ended
fashion. Furthermore, such robots must be able to recognize a wide range of
object categories. In this paper, we present a lifelong ensemble learning
approach based on multiple representations to address the few-shot object
recognition problem. In particular, we form ensemble methods based on deep
representations and handcrafted 3D shape descriptors. To facilitate lifelong
learning, each approach is equipped with a memory unit for storing and
retrieving object information instantly. The proposed model is suitable for
open-ended learning scenarios where the number of 3D object categories is not
fixed and can grow over time. We have performed extensive sets of experiments
to assess the performance of the proposed approach in offline, and open-ended
scenarios. For the evaluation purpose, in addition to real object datasets, we
generate a large synthetic household objects dataset consisting of 27000 views
of 90 objects. Experimental results demonstrate the effectiveness of the
proposed method on 3D object recognition tasks, as well as its superior
performance over the state-of-the-art approaches. Additionally, we demonstrated
the effectiveness of our approach in both simulated and real-robot settings,
where the robot rapidly learned new categories from limited examples.


title:
Ground and excited states from model-space quantum imaginary time
  evolution for strongly correlated systems

abstract:
  We present various extensions and modifications of quantum imaginary time
evolution (QITE), a recently proposed quantum-classical hybrid algorithm that
is guaranteed to reach the lowest state of system. We analyze the derivation of
the underlying QITE equation order-by-order, and suggest a modification that is
theoretically well found. Our results clearly indicate the soundness of the
here-derived equation, enabling to approximate more properly the imaginary time
propagation by a unitary. With the improved evaluation of the quantum Lanczos
introduced in this work, our implementations allow for stable applications of
QITE for larger systems. We also discuss how to obtain excited states by QITE,
and propose two such schemes. The folded-spectrum QITE is a straightforward
extension of QITE to excited state simulations, but is found to be impractical
due to considerably slow convergence. The other method, the model space QITE,
propagates a model space to the exact one in principle by keeping its
orthogonality, and is hence able to describe multiple states simultaneously.
Finally, we demonstrate that spin contamination can be effectively removed by
shifting the imaginary time propagator, and thus excited states with a
particular spin quantum number are efficiently captured without falling into
the different spin states that have lower energies. The effectiveness of all
these developments is illustrated by noiseless simulations, offering the
further capabilities and insights of quantum algorithms for imaginary time
evolution.


title: Shot noise in next-generation neural mass models

abstract:
  Recently, the so-called next-generation neural mass models have received a
lot of attention of the researchers in the field of mathematical neuroscience.
The ability of these models to account for the degree of synchrony in neural
populations proved useful in many contexts such as the modeling of brain
rhythms, working memory and spatio-temporal patterns of activity. In the
present Letter we study the effects of finite size on the collective behaviour
of neural networks and show that they can be captured by appropriately modified
neural mass models. Namely, the finite size of the network leads to the
emergence of the shot noise appearing as a stochastic term in the neural mass
model. We calculate the power spectrum of the shot noise and show that it might
demonstrate pronounced peaks in the frequencies comparable to the mean firing
rate. Although the shot noise is weak in large massively connected networks,
its impact on the collective dynamics might be crucial due to resonance
effects.


title: Sampling from the ferromagnetic Ising model with external fields

abstract:
  We study the sampling problem for ferromagnetic Ising models with consistent
external fields. We show that the edge-flipping dynamics for a corresponding
weighted random cluster model is rapidly mixing. Consequences include rapid
mixing of the Swendsen-Wang dynamics and perfect samplers for both models.


title: Cosmic rays and random magnetic traps

abstract:
  The spatial distribution of cosmic ray (CR) particles in the interstellar
medium (ISM) is of major importance in radio astronomy, where its knowledge is
essential for the interpretation of observations, and in theoretical
astrophysics, where CR contribute to the structure and dynamics of the ISM.
Local inhomogeneities in interstellar magnetic field strength and structure can
affect the local diffusivity and ensemble dynamics of the cosmic ray particles.
Magnetic traps (regions between magnetic mirrors located on the same magnetic
line) can lead to especially strong and persistent features in the CR spatial
distribution. Using test particle simulations, we study the spatial
distribution of an ensemble of CR particles (both protons and electrons) in
various magnetic field configurations, from an idealized axisymmetric trap to
those that emerge in intermittent (dynamo-generated) random magnetic fields. We
demonstrate that both the inhomogeneity in the CR sources and the energy losses
by the CR particles can lead to persistent local inhomogeneities in the CR
distribution and that the protons and electrons have different spatial
distributions. Our results can have profound implications for the
interpretation of the synchrotron emission from astronomical objects, and in
particular its random fluctuations.


title:
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource
  Speech Translation Tasks

abstract:
  This paper describes the ON-TRAC Consortium translation systems developed for
two challenge tracks featured in the Evaluation Campaign of IWSLT 2022:
low-resource and dialect speech translation. For the Tunisian Arabic-English
dataset (low-resource and dialect tracks), we build an end-to-end model as our
joint primary submission, and compare it against cascaded models that leverage
a large fine-tuned wav2vec 2.0 model for ASR. Our results show that in our
settings pipeline approaches are still very competitive, and that with the use
of transfer learning, they can outperform end-to-end models for speech
translation (ST). For the Tamasheq-French dataset (low-resource track) our
primary submission leverages intermediate representations from a wav2vec 2.0
model trained on 234 hours of Tamasheq audio, while our contrastive model uses
a French phonetic transcription of the Tamasheq audio as input in a Conformer
speech translation architecture jointly trained on automatic speech
recognition, ST and machine translation losses. Our results highlight that
self-supervised models trained on smaller sets of target data are more
effective to low-resource end-to-end ST fine-tuning, compared to large
off-the-shelf models. Results also illustrate that even approximate phonetic
transcriptions can improve ST scores.


title: Modelling calibration uncertainty in networks of environmental sensors

abstract:
  Networks of low-cost sensors are becoming ubiquitous, but often suffer from
low accuracies and drift. Regular colocation with reference sensors allows
recalibration but is often complicated and expensive. Alternatively the
calibration can be transferred using low-cost, mobile sensors, often at very
low cost. However inferring appropriate estimates of the calibration functions
(with uncertainty) for the network of sensors becomes difficult, especially as
the network of visits by the mobile, low-cost sensors becomes large. We propose
a variational approach to model the calibration across the network of sensors.
We demonstrate the approach on both synthetic and real air pollution data, and
find it can perform better than the state of the art (multi-hop calibration).
We extend it to categorical data, combining classifications of insects by
non-expert citizen scientists. Achieving uncertainty-quantified calibration has
been one of the major barriers to low-cost sensor deployment and
citizen-science research. We hope that the methods described will enable such
projects.


title: MM-Claims: A Dataset for Multimodal Claim Detection in Social Media

abstract:
  In recent years, the problem of misinformation on the web has become
widespread across languages, countries, and various social media platforms.
Although there has been much work on automated fake news detection, the role of
images and their variety are not well explored. In this paper, we investigate
the roles of image and text at an earlier stage of the fake news detection
pipeline, called claim detection. For this purpose, we introduce a novel
dataset, MM-Claims, which consists of tweets and corresponding images over
three topics: COVID-19, Climate Change and broadly Technology. The dataset
contains roughly 86000 tweets, out of which 3400 are labeled manually by
multiple annotators for the training and evaluation of multimodal models. We
describe the dataset in detail, evaluate strong unimodal and multimodal
baselines, and analyze the potential and drawbacks of current models.


title:
Supratransmission-induced travelling breathers in long Josephson
  junctions

abstract:
  The emergence of travelling sine-Gordon breathers due to the nonlinear
supratransmission effect is theoretically studied in a long Josephson junction
driven by suitable magnetic pulses, taking into account the presence of
dissipation, a current bias, and a thermal noise source. The simulations
clearly indicate that, depending on the pulse's shape and the values of the
main system parameters, such a configuration can effectively yield breather
excitations only. Furthermore, a nonmonotonic behavior of the breather-only
generation probability is observed as a function of the noise intensity.
Finally, the dynamics of the supratransmission-induced breathers is
characterized by looking at quantities such as their radiative decay lifetime
and the medium's energy.


title:
MateriApps LIVE! and MateriApps Installer: Environment for starting and
  scaling up materials science simulations

abstract:
  In our current era, numerical simulations have become indispensable
theoretical and experimental tools for use in daily research activities,
particularly in the materials science fields. However, the installation
processes for such simulations frequently become problematic because they
depend strongly on the device environment, and troubleshooting those processes
is a challenging task for beginners. To minimize such difficulties, we created
MateriApps LIVE! and MateriApps Installer, which can solve most of the related
issues. Specifically, MateriApps LIVE! offers a virtual environment in which
users can quickly try out computational materials science simulations on a
personal computer while MateriApps Installer provides a comprehensive set of
shell scripts for use when installing software on Unix, Linux, macOS, and
supercomputer systems. Herein, we provide detailed descriptions of MateriApps
LIVE! and MateriApps Installer together with illustrative examples of their
use.

categories: physics.comp-ph cond-mat.mtrl-sci cond-mat.str-el physics.ed-ph


title:
Wild Patterns Reloaded: A Survey of Machine Learning Security against
  Training Data Poisoning

abstract:
  The success of machine learning is fueled by the increasing availability of
computing power and large training datasets. The training data is used to learn
new models or update existing ones, assuming that it is sufficiently
representative of the data that will be encountered at test time. This
assumption is challenged by the threat of poisoning, an attack that manipulates
the training data to compromise the model's performance at test time. Although
poisoning has been acknowledged as a relevant threat in industry applications,
and a variety of different attacks and defenses have been proposed so far, a
complete systematization and critical review of the field is still missing. In
this survey, we provide a comprehensive systematization of poisoning attacks
and defenses in machine learning, reviewing more than 200 papers published in
the field in the last 15 years. We start by categorizing the current threat
models and attacks, and then organize existing defenses accordingly. While we
focus mostly on computer-vision applications, we argue that our systematization
also encompasses state-of-the-art attacks and defenses for other data
modalities. Finally, we discuss existing resources for research in poisoning,
and shed light on the current limitations and open research questions in this
research field.


title: Horizontally quasiconvex envelope in the Heisenberg group

abstract:
  This paper is concerned with a PDE-based approach to the horizontally
quasiconvex (h-quasiconvex for short) envelope of a given continuous function
in the Heisenberg group. We provide a characterization for upper
semicontinuous, h-quasiconvex functions in terms of the viscosity subsolution
to a first-order nonlocal Hamilton-Jacobi equation. We also construct the
corresponding envelope of a continuous function by iterating the nonlocal
operator. One important step in our arguments is to prove the uniqueness and
existence of viscosity solutions to the Dirichlet boundary problems for the
nonlocal Hamilton-Jacobi equation. Applications of our approach to the h-convex
hull of a given set in the Heisenberg group are discussed as well.


title:
Two-dimensional Obstructed Atomic Insulators with Fractional Corner
  Charge in MA$_2$Z$_4$ Family

abstract:
  According to topological quantum chemistry, a class of electronic materials
have been called obstructed atomic insulators (OAIs), in which a portion of
valence electrons necessarily have their centers located on some empty
$\textit{Wyckoff}$ positions without atoms occupation in the lattice. The
obstruction of centering these electrons coinciding with their host atoms is
nontrivial and results in metallic boundary states when the boundary is
properly cut. Here, on basis of first-principles calculations in combination
with topological quantum chemistry analysis, we propose two dimensional
MA$_2$Z$_4$ (M = Cr, Mo and W; A = Si and Ge, Z = N, P and As) monolayer family
are all OAIs. A typical case is the recently synthesized MoSi$_2$N$_4$.
Although it is a topological trivial insulator with the occupied electronic
states being integer combination of elementary band representations, it has
valence electrons centering empty $\textit{Wyckoff}$ positions. It exhibits
unique OAI-induced metallic edge states along the (1$\bar{1}$0) edge of
MoSi$_2$N$_4$ monolayer and the in-gap corner states at three vertices of
certain hexagonal nanodisk samples respecting C$_3$ rotation symmetry. The
readily synthesized MoSi$_2$N$_4$ is quite stable and has a large bulk band gap
of 1.94 eV, which makes the identification of these edge and corner states most
possible for experimental clarification.


title: Centralizers of commutators in finite groups

abstract:
  Let $G$ be a finite group. A coprime commutator in $G$ is any element that
can be written as a commutator $[x,y]$ for suitable $x,y\in G$ such that
$\pi(x)\cap\pi(y)=\emptyset$. Here $\pi(g)$ denotes the set of prime divisors
of the order of the element $g\in G$. An anti-coprime commutator is an element
that can be written as a commutator $[x,y]$, where $\pi(x)=\pi(y)$. The main
results of the paper are as follows.
  -- If $|x^G|\leq n$ whenever $x$ is a coprime commutator, then $G$ has a
nilpotent subgroup of $n$-bounded index.
  -- If $|x^G|\leq n$ for every anti-coprime commutator $x\in G$, then $G$ has
a subgroup $H$ of nilpotency class at most $4$ such that $[G : H]$ and
$|\gamma_4 (H)|$ are both $n$-bounded.
  We also consider finite groups in which the centralizers of coprime, or
anti-coprime, commutators are of bounded order.


title:
EmoBank: Studying the Impact of Annotation Perspective and
  Representation Format on Dimensional Emotion Analysis

abstract:
  We describe EmoBank, a corpus of 10k English sentences balancing multiple
genres, which we annotated with dimensional emotion metadata in the
Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a
bi-perspectival and bi-representational design. On the one hand, we distinguish
between writer's and reader's emotions, on the other hand, a subset of the
corpus complements dimensional VAD annotations with categorical ones based on
Basic Emotions. We find evidence for the supremacy of the reader's perspective
in terms of IAA and rating intensity, and achieve close-to-human performance
when mapping between dimensional and categorical formats.


title:
Impact of a DCT-driven Loss in Attention-based Knowledge-Distillation
  for Scene Recognition

abstract:
  Knowledge Distillation (KD) is a strategy for the definition of a set of
transferability gangways to improve the efficiency of Convolutional Neural
Networks. Feature-based Knowledge Distillation is a subfield of KD that relies
on intermediate network representations, either unaltered or depth-reduced via
maximum activation maps, as the source knowledge. In this paper, we propose and
analyse the use of a 2D frequency transform of the activation maps before
transferring them. We pose that\textemdash by using global image cues rather
than pixel estimates, this strategy enhances knowledge transferability in tasks
such as scene recognition, defined by strong spatial and contextual
relationships between multiple and varied concepts. To validate the proposed
method, an extensive evaluation of the state-of-the-art in scene recognition is
presented. Experimental results provide strong evidences that the proposed
strategy enables the student network to better focus on the relevant image
areas learnt by the teacher network, hence leading to better descriptive
features and higher transferred performance than every other state-of-the-art
alternative. We publicly release the training and evaluation framework used
along this paper at
http://www-vpu.eps.uam.es/publications/DCTBasedKDForSceneRecognition.


title:
Nonholonomic Controlled Hamiltonian System: Symmetric Reduction and
  Hamilton-Jacobi Equations

abstract:
  In order to describe the impact of nonholonomic constraints for the dynamics
of a regular controlled Hamiltonian (RCH) system, in this paper, for an RCH
system with nonholonomic constraint, we first derive its distributional RCH
system, by analyzing carefully the structure of dynamical vector field of the
nonholonomic RCH system. Secondly, we derive precisely the geometric constraint
conditions of the induced distributional two-form for the dynamical vector
field of the distributional RCH system, which are called the Type I and Type II
of Hamilton-Jacobi equations. Thirdly, we generalize the above results for the
nonholonomic reducible RCH system with symmetry, and prove two types of
Hamilton-Jacobi theorems for the nonholonomic reduced distributional RCH
system. Moreover, we consider the nonholonomic reducible RCH system with
momentum map, by combining with the regular point and regular orbit reduction
theory and the analysis of dynamics of RCH system, we give the geometric
formulations of the nonholonomic regular point reduced and orbit reduced
distributional RCH systems, and prove two types of Hamilton-Jacobi theorems for
these reduced distributional RCH systems. These researches reveal the deeply
internal relationships of the nonholonomic constraints, the induced (resp.
reduced) distributional two-forms, the dynamical vector fields and controls of
the nonholonomic RCH system and its (reduced) distributional RCH systems.


title: The Landscape of the Quantum Start-up Ecosystem

abstract:
  The second quantum revolution has been producing groundbreaking scientific
and technological outputs since the early 2000s; however, the scientific
literature on the impact of this revolution on the industry, specifically on
start-ups, is limited. In this paper, we present a landscaping study with a
gathered dataset of 421 companies from 42 countries that we identify as quantum
start-ups, meaning that they mainly focus on quantum technologies (QT) as their
primary priority business. We answer the following questions: (i) What are the
temporal and geographical distributions of the quantum start-ups? (ii) How can
we categorize them, and how are these categories populated? (iii) Are there any
patterns that we can derive from empirical data on trends? We found that more
than 92% of the start-ups have been founded within the last 10 years, and more
than 50% of the start-ups are located in the US, the UK, and Canada. We
categorized the QT start-ups into six fields: (i) complementary technologies,
(ii) quantum computing (hardware), (iii) quantum computing
(software/application/simulation), (iv) quantum cryptography/communication, (v)
quantum sensing and metrology, and (vi) supporting companies, and analyzed the
population of each field both for countries, and temporally. Finally, we argue
that low levels of quantum start-up activity in a country might be an indicator
of a national initiative to be adopted afterwards, which later sees both an
increase in the number of start-ups, and a diversification of activity in
different QT fields.


title: Perturbative Calculations of Deuteron Form Factors

abstract:
  We calculate the deuteron charge, electric quadrupole, and magnetic form
factors up to next-to-next-to-leading order in chiral effective field theory,
treating subleading corrections, especially that of chiral nuclear forces, in
perturbation theory. We examine the power counting based on naive dimensional
analysis by investigating the ultraviolet cutoff variation of these form
factors. We find that the N$^2$LO magnetic form factor shows significant cutoff
dependence, suggesting the contact current operator responsible be enhanced.
After promotion to N$^2$LO, it indeed renormalizes the magnetic form factor.
This is in agreement with a previous work based on renormalization-group
analysis. For the charge and quadrupole moments, perturbative calculation
allows us to study how they scale with multiple low-energy parameters such as
the pion mass, deuteron binding momentum, and momentum transfer.


title:
Design of a novel Korean learning application for efficient
  pronunciation correction

abstract:
  The Korean wave, which denotes the global popularity of South Korea's
cultural economy, contributes to the increasing demand for the Korean language.
However, as there does not exist any application for foreigners to learn
Korean, this paper suggested a design of a novel Korean learning application.
Speech recognition, speech-to-text, and speech-to-waveform are the three key
systems in the proposed system. The Google API and the librosa library will
transform the user's voice into a sentence and MFCC. The software will then
display the user's phrase and answer, with mispronounced elements highlighted
in red, allowing users to more easily recognize the incorrect parts of their
pronunciation. Furthermore, the Siamese network might utilize those translated
spectrograms to provide a similarity score, which could subsequently be used to
offer feedback to the user. Despite the fact that we were unable to collect
sufficient foreigner data for this research, it is notable that we presented a
novel Korean pronunciation correction method for foreigners.


title:
Pressure-sensitive ion conduction in a conical channel: optimal pressure
  and geometry

abstract:
  We theoretically investigate the electric conductivity of a conical channel,
which in accordance with recent experiments exhibits a strong non-linear
pressure dependence. This mechanosensitive diodic behavior stems from the
pressure-sensitive build-up or depletion of salt in the pore. We find that
optimal diodic behavior is achieved when the tip and base radii of the cone
have a ratio of 0.22 and for a pressure drop where the fluid flow vanishes.
This optimal pressure drop is proportional to the inverse square of the tip
radius, explaining the dramatic mechanosensitivity observed in experiments.


title:
Multi-subgoal Robot Navigation in Crowds with History Information and
  Interactions

abstract:
  Robot navigation in dynamic environments shared with humans is an important
but challenging task, which suffers from performance deterioration as the crowd
grows. In this paper, multi-subgoal robot navigation approach based on deep
reinforcement learning is proposed, which can reason about more comprehensive
relationships among all agents (robot and humans). Specifically, the next
position point is planned for the robot by introducing history information and
interactions in our work. Firstly, based on subgraph network, the history
information of all agents is aggregated before encoding interactions through a
graph neural network, so as to improve the ability of the robot to anticipate
the future scenarios implicitly. Further consideration, in order to reduce the
probability of unreliable next position points, the selection module is
designed after policy network in the reinforcement learning framework. In
addition, the next position point generated from the selection module satisfied
the task requirements better than that obtained directly from the policy
network. The experiments demonstrate that our approach outperforms
state-of-the-art approaches in terms of both success rate and collision rate,
especially in crowded human environments.


title: Geometric multiplicity of unitary non-backtracking eigenvalues

abstract:
  We completely characterize the conditions under which a complex unitary
number is an eigenvalue of the non-backtracking matrix of an undirected graph.
Further, we provide a closed formula to compute its geometric multiplicity and
describe an algorithm to compute this multiplicity without making a single
matrix computation. The algorithm has time complexity that is linear in the
size of the graph.


title:
A Framework to Generate High-Quality Datapoints for Multiple Novel
  Intent Detection

abstract:
  Systems like Voice-command based conversational agents are characterized by a
pre-defined set of skills or intents to perform user specified tasks. In the
course of time, newer intents may emerge requiring retraining. However, the
newer intents may not be explicitly announced and need to be inferred
dynamically. Thus, there are two important tasks at hand (a). identifying
emerging new intents, (b). annotating data of the new intents so that the
underlying classifier can be retrained efficiently. The tasks become specially
challenging when a large number of new intents emerge simultaneously and there
is a limited budget of manual annotation. In this paper, we propose MNID
(Multiple Novel Intent Detection) which is a cluster based framework to detect
multiple novel intents with budgeted human annotation cost. Empirical results
on various benchmark datasets (of different sizes) demonstrate that MNID, by
intelligently using the budget for annotation, outperforms the baseline methods
in terms of accuracy and F1-score.


title: Tur\'{a}n numbers of $r$-graphs on $r+1$ vertices

abstract:
  Let $H_k^r$ be an $r$-uniform hypergraph with $k$ edges and $r+1$ vertices.
In the case $k=3$, the known general bounds on the Tur\'{a}n density are
$2^{1-r} \leq \pi(H_3^r) \leq r^{-1}$. We provide a simple construction that
yields $\pi(H_3^r) \geq Cr^{-2}$. We also prove that for each $k \geq 4$,
$\pi(H_k^r) = \Theta(r^{-1})$ as $r\to\infty$.


title: A Computational Inflection for Scientific Discovery

abstract:
  We stand at the foot of a significant inflection in the trajectory of
scientific discovery. As society continues on its fast-paced digital
transformation, so does humankind's collective scientific knowledge and
discourse. We now read and write papers in digitized form, and a great deal of
the formal and informal processes of science are captured digitally --
including papers, preprints and books, code and datasets, conference
presentations, and interactions in social networks and communication platforms.
The transition has led to the growth of a tremendous amount of information,
opening exciting opportunities for computational models and systems that
analyze and harness it. In parallel, exponential growth in data processing
power has fueled remarkable advances in AI, including self-supervised neural
models capable of learning powerful representations from large-scale
unstructured text without costly human supervision. The confluence of societal
and computational trends suggests that computer science is poised to ignite a
revolution in the scientific process itself.
  However, the explosion of scientific data, results and publications stands in
stark contrast to the constancy of human cognitive capacity. While scientific
knowledge is expanding with rapidity, our minds have remained static, with
severe limitations on the capacity for finding, assimilating and manipulating
information. We propose a research agenda of task-guided knowledge retrieval,
in which systems counter humans' bounded capacity by ingesting corpora of
scientific knowledge and retrieving inspirations, explanations, solutions and
evidence synthesized to directly augment human performance on salient tasks in
scientific endeavors. We present initial progress on methods and prototypes,
and lay out important opportunities and challenges ahead with computational
approaches that have the potential to revolutionize science.


title:
The Effect of Junction Gutters for the Upscaling of Droplet Generation
  in a Microfluidic T-Junction

abstract:
  The influence of drop formation due to micro rib-like structures, viz., the
Junction Gutters (JGs) within a standard microfluidic T-junction, is
numerically investigated. Hydrodynamic conditions that lead to various flow
regimes are identified characterized by the Capillary number (Ca) and velocity
ratios of the dispersed and continuous phases (q) within a standard T-junction.
Subsequently, under such conditions, a range of gutter configurations is
introduced in the standard channel. The results predict that the introduction
of JGs can favourably alter the formation frequency and morphology of drops
and, consequently, promote upscaling significantly for the hydrodynamic
conditions associated with low Ca. Detailed flow maps are presented that reveal
a plethora of transitions during the formation of droplets with higher Ca and q
that would otherwise signify a dripping or a jetting regime in a standard
junction. However, specific gutter configurations are identified where JGs are
unfavourable for generating monodisperse droplets.


title:
Complete flow-preserving rewrite rules for MBQC patterns with Pauli
  measurements

abstract:
  In the one-way model of measurement-based quantum computation (MBQC),
computation proceeds via measurements on some standard resource state.
So-called flow conditions ensure that the overall computation is deterministic
in a suitable sense, with Pauli flow being the most general of these. Existing
work on rewriting MBQC patterns while preserving the existence of flow has
focused on rewrites that reduce the number of qubits.
  In this work, we show that introducing new $Z$-measured qubits, connected to
any subset of the existing qubits, preserves the existence of Pauli flow.
Furthermore, we give a unique canonical form for stabilizer ZX-diagrams
inspired by recent work of Hu & Khesin [arXiv:2109.10210]. We prove that any
MBQC-like stabilizer ZX-diagram with Pauli flow can be rewritten into this
canonical form using only rules which preserve the existence of Pauli flow and
that each of these rules can be reversed while also preserving the existence of
Pauli flow. Hence we have complete graphical rewriting for MBQC-like stabilizer
ZX-diagrams with Pauli flow.


title:
The Dynamics of the Hubbard Model through Stochastic Calculus and
  Girsanov Transformation

abstract:
  As a typical quantum many body problem, we consider the time evolution of
density matrix elements in the Bose-Hubbard model. For an arbitrary initial
state, these quantities can be obtained from an SDE or stochastic differential
equation system. To this SDE system, a Girsanov transformation can be applied.
This has the effect that all the information from the initial state moves into
the drift part, into the mean field part, of the transformed system. In the
large N limit with g=UN fixed, the diffusive part of the transformed system
vanishes and as a result, the exact quantum dynamics is given by an ODE system
which turns out to be the time dependent discrete Gross Pitaevskii equation.
For the two site Bose-Hubbard model, the GP equation reduces to the
mathematical pendulum and the particle imbalance is equal to the velocity of
that pendulum which is either oscillatory or it can have rollovers which then
corresponds to the self trapping or insulating phase. As a by-product, we also
find an equivalence of the mathematical pendulum with a quartic double well
potential. Collapse and revivals are a more subtle phenomenom, in order to see
these the diffusive part of the SDE system or quantum corrections have to be
taken into account. This can be done with an approximation and collapse and
revivals can be reproduced, numerically and also through an analytic
calculation. Since expectation values of Fresnel or Wiener diffusion processes,
we write the density matrix elements exactly in this way, can be obtained from
parabolic second order PDEs, we also obtain various exact PDE representations.
The paper has been written with the goal to come up with an efficient
calculation scheme for quantum many body systems and as such the formalism is
generic and applies to arbitrary dimension, arbitrary hopping matrices and,
with suitable adjustments, to fermionic models.

categories: math-ph cond-mat.quant-gas math.MP math.PR quant-ph


title:
Exchange bias in Sm$ _{2} $NiMnO$ _{6}$/BaTiO$ _{3}$
  ferromagnetic-diamagnetic heterostructure thin films

abstract:
  Exchange bias (EB) shifts are commonly reported for the ferromagnetic
(FM)/antiferromagnetic (AFM) bilayer systems. While stoichiometric ordered
Sm$_{2}$NiMnO$_{6}$ (SNMO) and BaTiO$_{3}$ (BTO) are known to possesses FM and
diamagnetic orderings respectively, here we have demonstrated the cooling field
dependent EB and training effects in epitaxial SNMO/BTO/SNMO (SBS)
heterostructure thin films. The polarized Raman spectroscopy and magnetometric
studies reveal the presence of anti-site cation disorders in background of
ordered lattice in SNMO layers, which introduces Ni-O-Ni or Mn-O-Mn local AFM
interactions in long range Ni-O-Mn FM ordered host matrix. We have also
presented growth direction manipulation of the degree of cation disorders in
the SNMO system. Polarization dependent X-ray absorption measurements, duly
combined with configuration interaction simulations suggest charge transfer
from Ni/Mn 3\textit{d} to Ti 3\textit{d} orbitals through O 2\textit{p}
orbitals across the SNMO/BTO (SB) interfaces, which can induce magnetism in the
BTO spacer layer. The observed exchange bias in SBS heterostructures is
discussed considering the pinning of moments due to exchange coupling at SB (or
BTO/SNMO) sandwich interface.