title:
Revisiting Gaussian Neurons for Online Clustering with Unknown Number of
  Clusters

abstract:
  Despite the recent success of artificial neural networks, more biologically
plausible learning methods may be needed to resolve the weaknesses of
backpropagation trained models such as catastrophic forgetting and adversarial
attacks. A novel local learning rule is presented that performs online
clustering with a maximum limit of the number of cluster to be found rather
than a fixed cluster count. Instead of using orthogonal weight or output
activation constraints, activation sparsity is achieved by mutual repulsion of
lateral Gaussian neurons ensuring that multiple neuron centers cannot occupy
the same location in the input domain. An update method is also presented for
adjusting the widths of the Gaussian neurons in cases where the data samples
can be represented by means and variances. The algorithms were applied on the
MNIST and CIFAR-10 datasets to create filters capturing the input patterns of
pixel patches of various sizes. The experimental results demonstrate stability
in the learned parameters across a large number of training samples.


title:
Home health care planning with considering flexible starting/ending
  points and service features

abstract:
  One of the recently proposed strategies in health systems is providing
services to patients at home, improving the service quality, besides reducing
the health system costs. In the real world, some services, such as biological
tests or blood sampling, force the nurses to start or end his/her route from/at
the laboratory instead of the depot, changing the whole optimal planning. The
effect of these special service requirements and features has not been
considered so far. In this study, a new mathematical model is suggested
considering the flexibility of starting/ending places of each nurse's route
according to the specific characteristics of each service. Then several sets of
problems in various sizes are solved using the proposed model, where the
results confirm the efficiency of the proposed approach. In addition, some
sensitivity analyses are performed on the parameters of the required features
of the services, followed by some managerial insights and directions for future
studies.


title:
ARK: Fully Homomorphic Encryption Accelerator with Runtime Data
  Generation and Inter-Operation Key Reuse

abstract:
  Homomorphic Encryption (HE) is one of the most promising post-quantum
cryptographic schemes which enable privacy-preserving computation on servers.
However, noise accumulates as we perform operations on HE-encrypted data,
restricting the number of possible operations. Fully HE (FHE) removes this
restriction by introducing the bootstrapping operation, which refreshes the
data; however, FHE schemes are highly memory-bound. Bootstrapping, in
particular, requires loading GBs of evaluation keys and plaintexts from
off-chip memory, which makes FHE acceleration fundamentally bottlenecked by the
off-chip memory bandwidth.
  We propose ARK, an Accelerator for FHE with Runtime data generation and
inter-operation Key reuse. ARK enables practical FHE workloads with a novel
algorithm-hardware codesign to accelerate bootstrapping. We first eliminate the
off-chip memory bandwidth bottleneck through runtime data generation and
inter-operation key reuse. This approach enables ARK to fully exploit on-chip
memory by a substantial reduction in the working set size. On top of such
algorithmic innovations, we build ARK microarchitecture that minimizes on-chip
data movement through an efficient, alternating data distribution policy based
on the data access patterns and a streamlined dataflow organization of the
tailored functional units - including base conversion, number-theoretic
transform, and automorphism units. Overall, our co-design effectively handles
the heavy computation and data movement overheads of FHE, drastically reducing
the cost of HE operations including bootstrapping.


title: Recording and replaying psychomotor user actions in VR

abstract:
  We introduce a novel method that describes the functionality and
characteristics of an efficient VR recorder with replay capabilities,
implemented in a modern game engine, publicly available for free.


title:
Evaluation of the credibility of the Brazilian inflation targeting
  system using mixed causal-noncausal models

abstract:
  This paper uses predictive densities obtained via mixed causal-noncausal
autoregressive models to evaluate the statistical sustainability of Brazilian
inflation targeting system with the tolerance bounds. The probabilities give an
indication of the short-term credibility of the targeting system without
requiring modelling people's beliefs. We also investigate the added value of
including experts predictions of key macroeconomic variables.


title:
$D$-meson production in high energy $pA$ collisions within the QCD color
  dipole transverse momentum representation

abstract:
  The $D$-meson production is investigated by considering the unintegrated
gluon distribution within the dipole approach in the momentum representation.
We analyze the $D$-meson spectrum accounting for the effects of nonlinear
behavior of the QCD dynamics which can be accordingly addressed in the dipole
framework. The unintegrated gluon distribution is obtained by using geometric
scaling property and the results are compared to the Glauber-Gribov framework.
The absolute transverse momentum spectra and the nuclear modification ratios
are investigated. Predictions are compared with the experimental measurements
by the ALICE and LHCb Collaborations in $pA$ collisions for different rapidity
bins.


title: ORCAS-I: Queries Annotated with Intent using Weak Supervision

abstract:
  User intent classification is an important task in information retrieval. In
this work, we introduce a revised taxonomy of user intent. We take the widely
used differentiation between navigational, transactional and informational
queries as a starting point, and identify three different sub-classes for the
informational queries: instrumental, factual and abstain. The resulting
classification of user queries is more fine-grained, reaches a high level of
consistency between annotators, and can serve as the basis for an effective
automatic classification process. The newly introduced categories help
distinguish between types of queries that a retrieval system could act upon,
for example by prioritizing different types of results in the ranking.We have
used a weak supervision approach based on Snorkel to annotate the ORCAS dataset
according to our new user intent taxonomy, utilising established heuristics and
keywords to construct rules for the prediction of the intent category. We then
present a series of experiments with a variety of machine learning models,
using the labels from the weak supervision stage as training data, but find
that the results produced by Snorkel are not outperformed by these competing
approaches and can be considered state-of-the-art. The advantage of a
rule-based approach like Snorkel's is its efficient deployment in an actual
system, where intent classification would be executed for every query issued.
The resource released with this paper is the ORCAS-I dataset: a labelled
version of the ORCAS click-based dataset of Web queries, which provides 18
million connections to 10 million distinct queries.


title: Self-similar solutions to flows by negative powers of curvature

abstract:
  We consider self-similar solutions to curvature flows in some warped product
manifolds, which corresponding speed function is of negative power and
satisfies suitable conditions. For all negative powers, we prove such
self-similar solutions must be a slice in warped product manifolds if the
hypersurface is closed and strictly convex.


title:
The NICER "Reverberation Machine": A Systematic Study of Time Lags in
  Black Hole X-Ray Binaries

abstract:
  We perform the first systematic search of all NICER archival observations of
black hole (and candidate) low-mass X-ray binaries for signatures of
reverberation. Reverberation lags result from the light travel time difference
between the direct coronal emission and the reflected disk component, and
therefore their properties are a useful probe of the disk-corona geometry. We
detect new signatures of reverberation lags in 8 sources, increasing the total
sample from 3 to 11, and study the evolution of reverberation lag properties as
the sources evolve in outbursts. We find that in all of the 9 sources with more
than 1 reverberation lag detection, the reverberation lags become longer and
dominate at lower Fourier frequencies during the hard-to-soft state transition.
This result shows that the evolution in reverberation lags is a global property
of the state transitions of black hole low-mass X-ray binaries, which is
valuable in constraining models of such state transitions. The reverberation
lag evolution suggests that the corona is the base of a jet which vertically
expands and/or gets ejected during state transition. We also discover that in
the hard state, the reverberation lags get shorter, just as the QPOs move to
higher frequencies, but then in the state transition, while the QPOs continue
to higher frequencies, the lags get longer. We discuss implications for the
coronal geometry and physical models of QPOs in light of this new finding.


title:
On Double H\"older Regularity of the Hydrodynamic Pressure in Bounded
  Domains

abstract:
  We prove that the hydrodynamic pressure $p$ associated to the velocity $u\in
C^\theta(\Omega)$, $\theta\in(0,1)$, of an inviscid incompressible fluid in a
bounded and simply connected domain $\Omega\subset \mathbb{R}^d$ with $C^{2+}$
boundary satisfies $p\in C^{\theta}(\Omega)$ for $\theta \leq \frac12$ and
$p\in C^{1,2\theta-1}(\Omega)$ for $\theta>\frac12$. This extends the recent
result of Bardos and Titi obtained in the planar case to every dimension
$d\ge2$ and it also doubles the pressure regularity for $\theta>\frac12$.
Differently from Bardos and Titi, we do not introduce a new boundary condition
for the pressure, but instead work with the natural one. In the boundary-free
case of the $d$-dimensional torus, we show that the double regularity of the
pressure can be actually achieved under the weaker assumption that the
divergence of the velocity is sufficiently regular, thus not necessarily zero.


title: Multiple hypothesis screening using mixtures of non-local distributions

abstract:
  The analysis of large-scale datasets, especially in biomedical contexts,
frequently involves a principled screening of multiple hypotheses. The
celebrated two-group model jointly models the distribution of the test
statistics with mixtures of two competing densities, the null and the
alternative distributions. We investigate the use of weighted densities and, in
particular, non-local densities as working alternative distributions, to
enforce separation from the null and thus refine the screening procedure. We
show how these weighted alternatives improve various operating characteristics,
such as the Bayesian False Discovery rate, of the resulting tests for a fixed
mixture proportion with respect to a local, unweighted likelihood approach.
Parametric and nonparametric model specifications are proposed, along with
efficient samplers for posterior inference. By means of a simulation study, we
exhibit how our model outperforms both well-established and state-of-the-art
alternatives in terms of various operating characteristics. Finally, to
illustrate the versatility of our method, we conduct three differential
expression analyses with publicly-available datasets from genomic studies of
heterogeneous nature.


title:
Creative Uses of AI Systems and their Explanations: A Case Study from
  Insurance

abstract:
  Recent works have recognized the need for human-centered perspectives when
designing and evaluating human-AI interactions and explainable AI methods. Yet,
current approaches fall short at intercepting and managing unexpected user
behavior resulting from the interaction with AI systems and explainability
methods of different stake-holder groups. In this work, we explore the use of
AI and explainability methods in the insurance domain. In an qualitative case
study with participants with different roles and professional backgrounds, we
show that AI and explainability methods are used in creative ways in daily
workflows, resulting in a divergence between their intended and actual use.
Finally, we discuss some recommendations for the design of human-AI
interactions and explainable AI methods to manage the risks and harness the
potential of unexpected user behavior.


title: Understanding CNNs from excitations

abstract:
  For instance-level explanation, in order to reveal the relations between
high-level semantics and detailed spatial information, this paper proposes a
novel cognitive approach to neural networks, which named PANE. Under the
guidance of PANE, a novel saliency map representation method, named IOM, is
proposed for CNN-like models. We make the comparison with eight
state-of-the-art saliency map representation methods. The experimental results
show that IOM far outperforms baselines. The work of this paper may bring a new
perspective to understand deep neural networks.


title: Entanglement Forging with generative neural network models

abstract:
  The optimal use of quantum and classical computational techniques together is
important to address problems that cannot be easily solved by quantum
computations alone. This is the case of the ground state problem for quantum
many-body systems. We show here that probabilistic generative models can work
in conjunction with quantum algorithms to design hybrid quantum-classical
variational ans\"atze that forge entanglement to lower quantum resource
overhead. The variational ans\"atze comprise parametrized quantum circuits on
two separate quantum registers, and a classical generative neural network that
can entangle them by learning a Schmidt decomposition of the whole system. The
method presented is efficient in terms of the number of measurements required
to achieve fixed precision on expected values of observables. To demonstrate
its effectiveness, we perform numerical experiments on the transverse field
Ising model in one and two dimensions, and fermionic systems such as the t-V
Hamiltonian of spinless fermions on a lattice.


title: Assessing unconstrained surgical cuttings in VR using CNNs

abstract:
  We present a Convolutional Neural Network (CNN) suitable to assess
unconstrained surgical cuttings, trained on a dataset created with a data
augmentation technique.


title: The Ruelle Invariant And Convexity In Higher Dimensions

abstract:
  We construct the Ruelle invariant of a volume preserving flow and a
symplectic cocycle in any dimension and prove several properties. In the
special case of the linearized Reeb flow on the boundary of a convex domain $X$
in $\mathbb{R}^{2n}$, we prove that the Ruelle invariant $\text{Ru}(X)$, the
period of the systole $c(X)$ and the volume $\text{vol}{X}$ satisfy
\[\text{Ru}(X) \cdot c(X) \le C(n) \cdot \text{vol}{X}\] Here $C(n) > 0$ is an
explicit constant dependent on $n$. As an application, we construct dynamically
convex contact forms on $S^{2n-1}$ that are not convex, disproving the
equivalence of convexity and dynamical convexity in every dimension.


title:
Necessity of Feedback Control for Quantum Maxwell Demon in Finite-Time
  Steady Feedback Cycle

abstract:
  We revisit quantum Maxwell demon in thermodynamic feedback cycle in the
steady-state regime. We derive a generalized version of the Clausius inequality
for a finite-time steady feedback cycle with a single heat bath. It is shown to
be tighter than previously known ones, and allows us to clarify that feedback
control is necessary to violate the standard Clausius inequality.


title:
Studying Retrievability of Publications and Datasets in an Integrated
  Retrieval System

abstract:
  In this paper, we investigate the retrievability of datasets and publications
in a real-life Digital Library (DL). The measure of retrievability was
originally developed to quantify the influence that a retrieval system has on
the access to information. Retrievability can also enable DL engineers to
evaluate their search engine to determine the ease with which the content in
the collection can be accessed. Following this methodology, in our study, we
propose a system-oriented approach for studying dataset and publication
retrieval. A speciality of this paper is the focus on measuring the
accessibility biases of various types of DL items and including a metric of
usefulness. Among other metrics, we use Lorenz curves and Gini coefficients to
visualize the differences of the two retrievable document types (specifically
datasets and publications). Empirical results reported in the paper show a
distinguishable diversity in the retrievability scores among the documents of
different types.


title: Thermal excitations within and among mesospins in artificial spin ice

abstract:
  We provide experimental and numerical evidence for thermal excitations within
and among magnetic mesospins, forming artificial spin ice structures. At low
temperatures, a decrease in magnetization and increase in susceptibility is
observed with increasing temperature, interpreted as an onset of thermal
fluctuations of the magnetic texture within the mesospins. At elevated
temperatures a pronounced susceptibility peak is observed, related to thermally
induced flipping of the mesospins and a collapse of the remanent state. The
fluctuations, while occurring at distinct length- and energy-scales, are shown
to be tunable by the interaction strength of the mesospins.


title:
Is gravitational entanglement evidence for the quantization of
  spacetime?

abstract:
  Experiments witnessing the entanglement between two particles interacting
only via the gravitational field have been proposed as a test whether gravity
must be quantized. In the language of quantum information, a non-quantum
gravitational force would be modeled by local operations with classical
communication (LOCC), which cannot generate entanglement in an initially
unentangled state. This idea is criticized as too constraining on possible
alternatives to quantum gravity. We present a parametrized model for the
gravitational interaction of quantum matter on a classical spacetime, inspired
by the de Broglie-Bohm formulation of quantum mechanics, which results in
entanglement and thereby provides an explicit counterexample to the claim that
only a quantized gravitational field possesses this capability.


title: Axion-like Particles Implications for High-Energy Astrophysics

abstract:
  We offer a pedagogical introduction to axion-like particles (ALPs) as far as
their relevance for high-energy astrophysics is concerned, from a few MeV to
1000 TeV. This review is self-contained, in such a way to be understandable
even to non-specialists. Among other things, we discuss two strong hints at a
specific ALP that emerge from two very different astrophysical situations. More
technical matters are contained in three Appendices.


title:
Music Interpretation Analysis. A Multimodal Approach To Score-Informed
  Resynthesis of Piano Recordings

abstract:
  This Thesis discusses the development of technologies for the automatic
resynthesis of music recordings using digital synthesizers. First, the main
issue is identified in the understanding of how Music Information Processing
(MIP) methods can take into consideration the influence of the acoustic context
on the music performance. For this, a novel conceptual and mathematical
framework named "Music Interpretation Analysis" (MIA) is presented. In the
proposed framework, a distinction is made between the "performance" - the
physical action of playing - and the "interpretation" - the action that the
performer wishes to achieve. Second, the Thesis describes further works aiming
at the democratization of music production tools via automatic resynthesis: 1)
it elaborates software and file formats for historical music archiving and
multimodal machine-learning datasets; 2) it explores and extends MIP
technologies; 3) it presents the mathematical foundations of the MIA framework
and shows preliminary evaluations to demonstrate the effectiveness of the
approach


title: Charge transfer between rotating complex scalar fields

abstract:
  We consider the transfer of a U(1) charge density between Bose-Einstein
condensates of complex scalar fields coupled to a thermal bath, focusing on the
case of a homogeneous Affleck-Dine field transmitting the charge stored in its
angular motion to an axion field. We demonstrate that in the absence of
additional symmetries this charge transfer, aided by cosmic expansion as well
as the thermal effective potential of the Affleck-Dine field, can be very
efficient. The charge redistribution between the scalar fields becomes possible
if the interactions with the thermal bath break the original U(1) x U(1)
symmetry down to a single U(1) symmetry; the charge distribution between the
two fields is then determined by minimizing the free energy. We discuss
implications for cosmological setups involving complex scalars, with
applications to axion dark matter, baryogenesis, kination domination, and
gravitational wave production.


title:
CCLF: A Contrastive-Curiosity-Driven Learning Framework for
  Sample-Efficient Reinforcement Learning

abstract:
  In reinforcement learning (RL), it is challenging to learn directly from
high-dimensional observations, where data augmentation has recently been shown
to remedy this via encoding invariances from raw pixels. Nevertheless, we
empirically find that not all samples are equally important and hence simply
injecting more augmented inputs may instead cause instability in Q-learning. In
this paper, we approach this problem systematically by developing a
model-agnostic Contrastive-Curiosity-Driven Learning Framework (CCLF), which
can fully exploit sample importance and improve learning efficiency in a
self-supervised manner. Facilitated by the proposed contrastive curiosity, CCLF
is capable of prioritizing the experience replay, selecting the most
informative augmented inputs, and more importantly regularizing the Q-function
as well as the encoder to concentrate more on under-learned data. Moreover, it
encourages the agent to explore with a curiosity-based reward. As a result, the
agent can focus on more informative samples and learn representation
invariances more efficiently, with significantly reduced augmented inputs. We
apply CCLF to several base RL algorithms and evaluate on the DeepMind Control
Suite, Atari, and MiniGrid benchmarks, where our approach demonstrates superior
sample efficiency and learning performances compared with other
state-of-the-art methods.


title: A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network

abstract:
  We propose a system that transcribes the conversation of a typical meeting
scenario that is captured by a set of initially unsynchronized microphone
arrays at unknown positions. It consists of subsystems for signal
synchronization, including both sampling rate and sampling time offset
estimation, diarization based on speaker and microphone array position
estimation, multi-channel speech enhancement, and automatic speech recognition.
With the estimated diarization information, a spatial mixture model is
initialized that is used to estimate beamformer coefficients for source
separation. Simulations show that the speech recognition accuracy can be
improved by synchronizing and combining multiple distributed microphone arrays
compared to a single compact microphone array. Furthermore, the proposed
informed initialization of the spatial mixture model delivers a clear
performance advantage over random initialization.


title:
Cavity-induced Non-Adiabatic Dynamics and Spectroscopy of Molecular
  Rovibrational Polaritons studied by Multi-Mode Quantum Models

abstract:
  We study theoretically the quantum dynamics and spectroscopy of rovibrational
polaritons formed in a single rovibrating diatomic molecule, which interacts
with two degenerate, orthogonally polarized modes of an optical Fabry-P\'erot
cavity. We employ an effective rovibrational Pauli-Fierz Hamiltonian in length
gauge representation and identify three-state vibro-polaritonic conical
intersections (VPCIs) between singly-excited vibro-polaritonic states in a
two-dimensional angular coordinate branching space. The lower and upper
vibrational polaritons are of mixed light-matter hybrid character, whereas the
intermediate state is purely photonic in nature. The VPCIs provide effective
population transfer channels between singly-excited vibrational polaritons,
which manifest in rich interference patterns in rotational densities.
Spectroscopically, three bright singly-excited states are identified, when an
external infrared laser field couples to both a molecular and a cavity mode.
The non-trivial VPCI topology manifests as pronounced multi-peak progression in
the spectral region of the upper vibrational polariton, which is traced back to
the emergence of rovibro-polaritonic light-matter hybrid states. Experimentally
ubiquitous spontaneous emission from cavity modes induces a dissipative
reduction of intensity and peak broadening, which mainly influences the purely
photonic intermediate state peak as well as the rovibro-polaritonic
progression.


title: Geometric modularity for algebraic and non-algebraic weights

abstract:
  In this short paper we generalise a result of Diamond--Sasaki connecting
geometric modularity of algebraic weights to geometric modularity of
non-algebraic weights and vice versa. In particular, we show that geometric
modularity of non-algebraic weights implies geometric modularity of multiple
algebraic weights, which is easy to see. More significantly, we show that
geometric modularity of multiple algebraic weights sometimes implies geometric
modularity of a non-algebraic weight. This connects work of Diamond--Sasaki to
earlier work on generalisations of the weight part of Serre's conjecture.


title: On total stability conditions for Dynkin quivers

abstract:
  We show that for a Dynkin quiver $Q$ of type $E_7$ with a specific
orientation, the path algebra $KQ$ has no slope function of the form
$\mu=\frac{\theta}{\dim}$ that defines a total stability condition. This gives
a counterexample to a conjecture of Reineke.


title: Theoretical study of the $\gamma d \to \pi^0\eta d$ reaction

abstract:
  We have done a theoretical study of the $\gamma d \to \pi^0 \eta d$ reaction
starting with a realistic model for the $\gamma N \to \pi^0 \eta N$ reaction
that reproduces cross sections and polarization observables at low energies and
involves the $\gamma N \to \Delta(1700)\to \eta \Delta(1232) \to \eta \pi^0 N$
process. For the coherent reaction in the deuteron we considered the impulse
approximation together with the rescattering of the $\pi^0$and the $\eta$ on a
different nucleon than the one where they are produced. We found this second
mechanism very important since it helps share between two nucleons the
otherwise large momentum transfer of the reaction. We find a natural
explanation, tied to the dynamics of our model, for the shift of the $\eta-d$
mass distribution to lower invariant masses, and of the $\pi^0-d$ mass
distribution to larger invariant masses, compared to a phase space calculation.
We also study theoretical uncertainties related to the large momenta of the
deuteron wave function involved in the process. Striking differences are found
with the experimental angular distribution and a discussion is done around this
issue.


title: Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering

abstract:
  We present Answer-Me, a task-aware multi-task framework which unifies a
variety of question answering tasks, such as, visual question answering, visual
entailment, visual reasoning. In contrast to previous works using contrastive
or generative captioning training, we propose a novel and simple recipe to
pre-train a vision-language joint model, which is multi-task as well. The
pre-training uses only noisy image captioning data, and is formulated to use
the entire architecture end-to-end with both a strong language encoder and
decoder. Our results show state-of-the-art performance, zero-shot
generalization, robustness to forgetting, and competitive single-task results
across a variety of question answering tasks. Our multi-task mixture training
learns from tasks of various question intents and thus generalizes better,
including on zero-shot vision-language tasks. We conduct experiments in the
challenging multi-task and open-vocabulary settings and across a variety of
datasets and tasks, such as VQA2.0, SNLI-VE, NLVR2, GQA, VizWiz. We observe
that the proposed approach is able to generalize to unseen tasks and that more
diverse mixtures lead to higher accuracy in both known and novel tasks.


title:
Evidence for charge and spin order in single crystals of
  La$_3$Ni$_2$O$_7$ and La$_3$Ni$_2$O$_6$

abstract:
  Charge and spin order is intimately related to superconductivity in copper
oxide superconductors. To elucidate the competing orders in various nickel
oxide compounds are crucial given the fact that superconductivity has been
discovered in Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ films. Herein, we report structural,
electronic transport, magnetic, and thermodynamic characterizations on single
crystals of La$_3$Ni$_2$O$_7$ and La$_3$Ni$_2$O$_6$. La$_3$Ni$_2$O$_7$ is
metallic with mixed Ni$^{2+}$ and Ni$^{3+}$ valent states. Resistivity
measurements yield two transition-like kinks at $\sim$110 and 153 K,
respectively. The kink at 153 K is further revealed from magnetization and
specific heat measurements, indicative of the formation of charge and spin
order. La$_3$Ni$_2$O$_6$ single crystals obtained from topochemical reduction
of La$_3$Ni$_2$O$_7$ are insulating and show an anomaly at $\sim$176 K on
magnetic susceptibility. The transition-like behaviors of La$_3$Ni$_2$O$_7$ and
La$_3$Ni$_2$O$_6$ are analogous to the charge and spin order observed in
La$_4$Ni$_3$O$_{10}$ and La$_4$Ni$_3$O$_8$, suggesting charge and spin order is
a common feature in the ternary La-Ni-O system with mixed-valent states of
nickel.


title: Multi-quandles of topological pairs

abstract:
  In generalization of knot quandles we introduce similar algebraic structures
associated with arbitrary pairs consisting of a path-connected topological
space and its path-connected subspace.


title: Leaf Tar Spot Detection Using RGB Images

abstract:
  Tar spot disease is a fungal disease that appears as a series of black
circular spots containing spores on corn leaves. Tar spot has proven to be an
impactful disease in terms of reducing crop yield. To quantify disease
progression, experts usually have to visually phenotype leaves from the plant.
This process is very time-consuming and is difficult to incorporate in any
high-throughput phenotyping system. Deep neural networks could provide quick,
automated tar spot detection with sufficient ground truth. However, manually
labeling tar spots in images to serve as ground truth is also tedious and
time-consuming. In this paper we first describe an approach that uses automated
image analysis tools to generate ground truth images that are then used for
training a Mask R-CNN. We show that a Mask R-CNN can be used effectively to
detect tar spots in close-up images of leaf surfaces. We additionally show that
the Mask R-CNN can also be used for in-field images of whole leaves to capture
the number of tar spots and area of the leaf infected by the disease.


title: BERTops: Studying BERT Representations under a Topological Lens

abstract:
  Proposing scoring functions to effectively understand, analyze and learn
various properties of high dimensional hidden representations of large-scale
transformer models like BERT can be a challenging task. In this work, we
explore a new direction by studying the topological features of BERT hidden
representations using persistent homology (PH). We propose a novel scoring
function named "persistence scoring function (PSF)" which: (i) accurately
captures the homology of the high-dimensional hidden representations and
correlates well with the test set accuracy of a wide range of datasets and
outperforms existing scoring metrics, (ii) captures interesting post
fine-tuning "per-class" level properties from both qualitative and quantitative
viewpoints, (iii) is more stable to perturbations as compared to the baseline
functions, which makes it a very robust proxy, and (iv) finally, also serves as
a predictor of the attack success rates for a wide category of black-box and
white-box adversarial attack methods. Our extensive correlation experiments
demonstrate the practical utility of PSF on various NLP tasks relevant to BERT.


title:
Virtual Element Method for the Navier--Stokes Equation coupled with the
  Heat Equation

abstract:
  We consider the Virtual Element discretization of the Navier-Stokes equations
coupled with the heat equation where the viscosity depends on the temperature.
We present the Virtual Element discretization of the coupled problem, show its
well-posedness, and prove optimal error estimates. Numerical experiments which
confirm the theoretical error bounds are also presented.


title:
Coordination-free Multi-robot Path Planning for Congestion Reduction
  Using Topological Reasoning

abstract:
  We consider the problem of multi-robot path planning in a complex, cluttered
environment with the aim of reducing overall congestion in the environment,
while avoiding any inter-robot communication or coordination. Such limitations
may exist due to lack of communication or due to privacy restrictions (for
example, autonomous vehicles may not want to share their locations or intents
with other vehicles or even to a central server). The key insight that allows
us to solve this problem is to stochastically distribute the robots across
different routes in the environment by assigning them paths in different
topologically distinct classes, so as to lower congestion and the overall
travel time for all robots in the environment. We outline the computation of
topologically distinct paths in a spatio-temporal configuration space and
propose methods for the stochastic assignment of paths to the robots. A fast
replanning algorithm and a potential field based controller allow robots to
avoid collision with nearby agents while following the assigned path. Our
simulation and experiment results show a significant advantage over shortest
path following under such a coordination-free setup.


title: Defending Against Advanced Persistent Threats using Game-Theory

abstract:
  Advanced persistent threats (APT) combine a variety of different attack forms
ranging from social engineering to technical exploits. The diversity and usual
stealthiness of APT turns them into a central problem of contemporary practical
system security, since information on attacks, the current system status or the
attacker's incentives is often vague, uncertain and in many cases even
unavailable. Game theory is a natural approach to model the conflict between
the attacker and the defender, and this work investigates a generalized class
of matrix games as a risk mitigation tool for an APT defense. Unlike standard
game and decision theory, our model is tailored to capture and handle the full
uncertainty that is immanent to APT, such as disagreement among qualitative
expert risk assessments, unknown adversarial incentives and uncertainty about
the current system state (in terms of how deeply the attacker may have
penetrated into the system's protective shells already). Practically,
game-theoretic APT models can be derived straightforwardly from topological
vulnerability analysis, together with risk assessments as they are done in
common risk management standards like the ISO 31000 family. Theoretically,
these models come with different properties than classical game theoretic
models, whose technical solution presented in this work may be of independent
interest.


title:
Decisions with Uncertain Consequences -- A Total Ordering on
  Loss-Distributions

abstract:
  Decisions are often based on imprecise, uncertain or vague information.
Likewise, the consequences of an action are often equally unpredictable, thus
putting the decision maker into a twofold jeopardy. Assuming that the effects
of an action can be modeled by a random variable, then the decision problem
boils down to comparing different effects (random variables) by comparing their
distribution functions. Although the full space of probability distributions
cannot be ordered, a properly restricted subset of distributions can be totally
ordered in a practically meaningful way. We call these loss-distributions,
since they provide a substitute for the concept of loss-functions in decision
theory. This article introduces the theory behind the necessary restrictions
and the hereby constructible total ordering on random loss variables, which
enables decisions under uncertainty of consequences. Using data obtained from
simulations, we demonstrate the practical applicability of our approach.


title:
On the source algebra equivalence class of blocks with cyclic defect
  groups, I

abstract:
  We investigate the source algebra class of a p-block with cyclic defect
groups of the group algebra of a finite group. By the work of Linckelmann this
class is parametrized by the Brauer tree of the block together with a sign
function on its vertices and an endo-permutation module of a defect group. We
prove that this endo-permutation module can be read off from the character
table of the group. We also show that this module is trivial for all cyclic
p-blocks of quasisimple groups with a simple quotient which is a sporadic
group, an alternating group, a group of Lie type in defining characteristic, or
a group of Lie type in cross-characteristic for which the prime p is large
enough in a certain sense.


title:
Tuning the domain wall conductivity in bulk lithium niobate by uniaxial
  stress

abstract:
  Conductive domain walls (CDWs) in insulating ferroelectrics have recently
attracted considerable attention due to their unique topological, optical, and
electronic properties, and offer potential applications such as in memory
devices or re-writable circuitry. The electronic properties of domain walls
(DWs) can be tuned by the application of strain, hence controlling the charge
carrier density at DWs. In this work, we study the influence of uniaxial stress
on the conductivity of DWs in the bulk single crystal lithium niobate
(LiNbO$_3$). Using conductive atomic force microscopy (cAFM), we observe a
large asymmetry in the conductivity of DWs, where only negatively screened
walls, so called head-to-head DWs, are becoming increasingly conductive, while
positively screened, tail-to-tails DWs, show a decrease in conductivity. This
asymmetry of DW conductivity agrees with our theoretical model based on the
piezoelectric effect. In addition, we observed that the current in the DW
increases up to an order of magnitude for smaller compressive stresses of 100
MPa. This response of DWs remained intact for multiple stress cycles over 2
months, opening a path for future applications.


title:
On the solution manifold of a differential equation with a delay which
  has a zero

abstract:
  For a differential equation with a state-dependent delay we show that the
associated solution manifold $X_f$ of codimnsion 1 in the space
$C^1([-r,0],\mathbb {R})$ is an almost graph over a hyperplane, which implies
that $X_f$ is diffeomorphic to the hyperplane. For the case considered previous
results only provide a covering by 2 almost graphs.


title:
Test beam performance of a CBC3-based mini-module for the Phase-2 CMS
  Outer Tracker before and after neutron irradiation

abstract:
  The Large Hadron Collider (LHC) at CERN will undergo major upgrades to
increase the instantaneous luminosity up to 5-7.5$\times10^{34}$
cm$^{-2}$s$^{-1}$. This High Luminosity upgrade of the LHC (HL-LHC) will
deliver a total of 3000-4000 fb$^{-1}$ of proton-proton collisions at a
center-of-mass energy of 13-14 TeV. To cope with these challenging
environmental conditions, the strip tracker of the CMS experiment will be
upgraded using modules with two closely-spaced silicon sensors to provide
information to include tracking in the Level-1 trigger selection. This paper
describes the performance, in a test beam experiment, of the first prototype
module based on the final version of the CMS Binary Chip front-end ASIC before
and after the module was irradiated with neutrons. Results demonstrate that the
prototype module satisfies the requirements, providing efficient tracking
information, after being irradiated with a total fluence comparable to the one
expected through the lifetime of the experiment.


title:
Non-linearities in the Lyman-$\alpha$ forest and in its
  cross-correlation with dark matter halos

abstract:
  Three-dimensional correlations of the Lyman-$\alpha$ (Ly$\alpha$) forest and
cross correlations between the Ly$\alpha$ forest and quasars have been measured
on large scales, allowing a precise measurement of the baryon acoustic
oscillation (BAO) feature at redshifts $z>2$. These 3D correlations are often
modelled using linear perturbation theory, but full-shape analyses to extract
cosmological information beyond BAO will require more realistic models capable
of describing non-linearities present at smaller scales. We present a
measurement of the Ly$\alpha$ forest flux power spectrum from large
hydrodynamic simulations -- the Sherwood simulations -- and compare it to
different models describing the small-scale deviations from linear theory. We
confirm that the model presented in Arinyo-i-Prats et al. (2015) fits the
measured 3D power up to $k=10\, h\rm{Mpc^{-1}}$ with an accuracy better than
5%, and show that the same model can also describe the 1D correlations with
similar precision. We also present, for the first time, an equivalent study for
the cross-power spectrum of halos with the Ly$\alpha$ forest, and we discuss
different challenges we face when modelling the cross-power spectrum beyond
linear scales. We make all our measured power spectra public in
\url{https://github.com/andreufont/sherwood_p3d}. This study is a step towards
joint analyses of 1D and 3D flux correlations, and towards using the
quasar-Ly$\alpha$ cross-correlation beyond BAO analyses.


title: The existence of balanced neighborly polynomials

abstract:
  Inspired by the definition of balanced neighborly spheres, we define balanced
neighborly polynomials and study the existence of these polynomials. The goal
of this article is to construct balanced neighborly polynomials of type
$(k,k,k,k)$ over any field $K$ for all $k \neq 2$, and show that a balanced
neighborly polynomial of type $(2,2,2,2)$ exists if and only if ${\rm char}(K)
\neq 2$. Besides, we also discuss a relation between balanced neighborly
polynomials and balanced neighborly simplicial spheres.


title:
Pre-hydrodynamic evolution and its impact on quark-gluon plasma
  signatures

abstract:
  State-of-the-art hydrodynamic models of heavy-ion collisions have
considerable theoretical model uncertainties in the description of the very
early pre-hydrodynamic stage. We add a new computational module,
K$_\mathrm{T}$Iso, that describes the pre-hydrodynamic evolution kinetically,
based on the relativistic Boltzmann equation with collisions treated in the
Isotropization Time Approximation. As a novelty, K$_\mathrm{T}$Iso allows for
the inclusion and evolution of initial-state momentum anisotropies. To maintain
computational efficiency K$_\mathrm{T}$Iso assumes strict longitudinal boost
invariance and allows collisions to isotropize only the transverse momenta. We
use it to explore the sensitivity of hadronic observables measured in
relativistic heavy-ion collisions to initial-state momentum anisotropies and
microscopic scattering during the pre-hydrodynamic stage.


title: State-of-the-art in Open-domain Conversational AI: A Survey

abstract:
  We survey SoTA open-domain conversational AI models with the purpose of
presenting the prevailing challenges that still exist to spur future research.
In addition, we provide statistics on the gender of conversational AI in order
to guide the ethics discussion surrounding the issue. Open-domain
conversational AI are known to have several challenges, including bland
responses and performance degradation when prompted with figurative language,
among others. First, we provide some background by discussing some topics of
interest in conversational AI. We then discuss the method applied to the two
investigations carried out that make up this study. The first investigation
involves a search for recent SoTA open-domain conversational AI models while
the second involves the search for 100 conversational AI to assess their
gender. Results of the survey show that progress has been made with recent SoTA
conversational AI, but there are still persistent challenges that need to be
solved, and the female gender is more common than the male for conversational
AI. One main take-away is that hybrid models of conversational AI offer more
advantages than any single architecture. The key contributions of this survey
are 1) the identification of prevailing challenges in SoTA open-domain
conversational AI, 2) the unusual discussion about open-domain conversational
AI for low-resource languages, and 3) the discussion about the ethics
surrounding the gender of conversational AI.


title:
Pressure tuning of structure, magnetic frustration and carrier
  conduction in Kitaev spin liquid candidate Cu$_2$IrO$_3$: X-ray, Raman,
  magnetic susceptibility, resistivity and first-principles analysis

abstract:
  The layered honeycomb lattice iridate Cu$_2$IrO$_3$ is the closest
realization of the Kitaev quantum spin liquid, primarily due to the enhanced
interlayer separation and nearly ideal honeycomb lattice. We report
pressure-induced structural evolution of Cu$_2$IrO$_3$ by powder x-ray
diffraction (PXRD) up to $\sim$17 GPa and Raman scattering measurements up to
$\sim$25 GPa. A structural phase transition (monoclinic $C2/c \: \rightarrow$
triclinic $P\bar{1}$) is observed with a broad mixed phase pressure range
($\sim$4 to 15 GPa). The triclinic phase consists of heavily distorted
honeycomb lattice with Ir-Ir dimer formation and a collapsed interlayer
separation. In the stability range of the low-pressure monoclinic phase,
structural evolution maintains the Kitaev configuration up to 4 GPa. This is
supported by the observed enhanced magnetic frustration in dc susceptibility
without emergence of any magnetic ordering and an enhanced dynamic Raman
susceptibility. High-pressure resistance measurements up to 25 GPa in the
temperature range 1.4--300 K show resilient non-metallic $R$($T$) behaviour
with significantly reduced resistivity in the high-pressure phase. The Mott 3D
variable-range-hopping conduction with much reduced characteristic energy scale
$T_0$ suggests that the high-pressure phase is at the boundary of
localized-itinerant crossover. Using first-principles density functional
theoretical (DFT) calculations, we find that at ambient pressure $\rm
Cu_2IrO_3$ exists in monoclinic $P2_1/c$ phase which is energetically lower
than $C2/c$ phase (both the structures are consistent with experimental XRD
pattern). DFT reveals structural transition from $P2_1/c$ to $P\bar{1}$
structure at 7 GPa (involving dimerization of Ir-Ir bonds) in agreement with
experimentally observed transition pressure.


title: Monocular 3D Fingerprint Reconstruction and Unwarping

abstract:
  Compared with contact-based fingerprint acquisition techniques, contactless
acquisition has the advantages of less skin distortion, larger fingerprint
area, and hygienic acquisition. However, perspective distortion is a challenge
in contactless fingerprint recognition, which changes ridge orientation,
frequency, and minutiae location, and thus causes degraded recognition
accuracy. We propose a learning based shape from texture algorithm to
reconstruct a 3D finger shape from a single image and unwarp the raw image to
suppress perspective distortion. Experimental results on contactless
fingerprint databases show that the proposed method has high 3D reconstruction
accuracy. Matching experiments on contactless-contact and
contactless-contactless matching prove that the proposed method improves
matching accuracy.


title:
Detection Recovery in Online Multi-Object Tracking with Sparse Graph
  Tracker

abstract:
  Joint object detection and online multi-object tracking (JDT) methods have
been proposed recently to achieve one-shot tracking. Yet, existing works
overlook the importance of detection itself and often result in missed
detections when confronted by occlusions or motion blurs. The missed detections
affect not only detection performance but also tracking performance due to
inconsistent tracklets. Hence, we propose a new JDT model that recovers the
missed detections while associating the detection candidates of consecutive
frames by learning object-level spatio-temporal consistency through edge
features in a Graph Neural Network (GNN). Our proposed model Sparse Graph
Tracker (SGT) converts video data into a graph, where the nodes are top-$K$
scored detection candidates, and the edges are relations between the nodes at
different times, such as position difference and visual similarity. Two nodes
are connected if they are close in either a Euclidean or feature space,
generating a sparsely connected graph. Without motion prediction or
Re-Identification (ReID), the association is performed by predicting an edge
score representing the probability that two connected nodes refer to the same
object. Under the online setting, our SGT achieves state-of-the-art (SOTA) on
the MOT17/20 Detection and MOT16/20 benchmarks in terms of AP and MOTA,
respectively. Especially, SGT surpasses the previous SOTA on the crowded
dataset MOT20 where partial occlusion cases are dominant, showing the
effectiveness of detection recovery against partial occlusion. Code will be
released at https://github.com/HYUNJS/SGT.


title:
Dissecting flux balances to measure energetic costs in cell biology:
  techniques and challenges

abstract:
  Life is a nonequilibrium phenomenon: metabolism provides a continuous supply
of energy that drives nearly all cellular processes. However, very little is
known about how much energy different cellular processes use, i.e. their
energetic costs. The most direct experimental measurements of these costs
involve modulating the activity of cellular processes and determining the
resulting changes in energetic fluxes. In this review, we present a flux
balance framework to aid in the design and interpretation of such experiments,
and discuss the challenges associated with measuring the relevant metabolic
fluxes. We then describe selected techniques that enable measurement of these
fluxes. Finally, we review prior experimental and theoretical work that has
employed techniques from biochemistry and nonequilibrium physics to determine
the energetic costs of cellular processes.


title:
LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense
  Passage Retrieval

abstract:
  Text retrieval using dense embeddings generated from deep neural models is
called "dense passage retrieval". Dense passage retrieval systems normally
deploy a deep neural model followed by an approximate nearest neighbor (ANN)
search module. The model generates text embeddings, which are then indexed by
the ANN module. With the increasing data scale, the ANN module unavoidably
becomes the bottleneck on efficiency, because of its linear or sublinear time
complexity with data scale. An alternative is the learned index which has a
theoretically constant time complexity. But most of the existing learned
indexes are designed for low dimensional data. Thus they are not suitable for
dense passage retrieval tasks with high-dimensional dense embeddings. We
propose LIDER, an efficient high-dimensional Learned Index for large-scale
DEnse passage Retrieval. LIDER has a clustering-based hierarchical architecture
formed by two layers of core models. As the basic unit of LIDER to index and
search data, each core model includes an adapted recursive model index (RMI)
and a dimension reduction component which consists of an extended
SortingKeys-LSH (SK-LSH) and a key re-scaling module. The dimension reduction
component reduces the high-dimensional dense embeddings into one-dimensional
keys and sorts them in a specific order, which are then used by the RMI. And
the RMI consists of multiple simple linear regression models that make fast
prediction in only O(1) time. We successfully optimize and combine SK-LSH and
RMI together into the core model, and organize multiple core models into a
two-layer structure based on a clustering-based partitioning of the whole data
space. Experiments show that LIDER has a higher search speed with high
retrieval quality comparing to the state-of-the-art ANN indexes commonly used
in dense passage retrieval. Furthermore, LIDER has a better capability of
speed-quality trade-off.


title: Complex moment-based methods for differential eigenvalue problems

abstract:
  This paper considers computing partial eigenpairs of differential eigenvalue
problems (DEPs) such that eigenvalues are in a certain region on the complex
plane. Recently, based on a "solve-then-discretize" paradigm, an operator
analogue of the FEAST method has been proposed for DEPs without discretization
of the coefficient operators. Compared to conventional "discretize-then-solve"
approaches that discretize the operators and solve the resulting matrix
problem, the operator analogue of FEAST exhibits much higher accuracy; however,
it involves solving a large number of ordinary differential equations (ODEs).
In this paper, to reduce the computational costs, we propose operation
analogues of Sakurai-Sugiura-type complex moment-based eigensolvers for DEPs
using higher-order complex moments and analyze the error bound of the proposed
methods. We show that the number of ODEs to be solved can be reduced by a
factor of the degree of complex moments without degrading accuracy, which is
verified by numerical results. Numerical results demonstrate that the proposed
methods are over five times faster compared with the operator analogue of FEAST
for several DEPs while maintaining almost the same high accuracy. This study is
expected to promote the "solve-then-discretize" paradigm for solving DEPs and
contribute to faster and more accurate solutions in real-world applications.


title: Multiplicative functions resembling the M\"{o}bius funciton

abstract:
  A multiplicative function $f$ is said to be resembling the M\"{o}bius
function if $f$ is supported on the square-free integers, and $f(p)=\pm 1$ for
each prime $p$. Aymone has studied a class of these $f$. The purpose of this
paper is to give some improvements.


title: Wireless LAN sensing with smart antennas

abstract:
  The paper targets the problem of human motion detection using Wireless Local
Area Network devices (WiFi) equipped with pattern reconfigurable antennas.
Motion sensing is obtained by monitoring the body-induced alterations of the
ambient WiFi signals originated from smart antennas supporting the
beam-steering technology, thus allowing to channelize the antenna radiation
pattern to pre-defined spots of interest. We first discuss signal and Channel
State Information (CSI) processing and sanitization. Next, we describe the
motion detection algorithm based on Angle-of-Arrival (AoA) monitoring. Proposed
algorithms are validated experimentally inside a large size smart home
environment.


title: Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction

abstract:
  Blockchain finance has become a part of the world financial system, most
typically manifested in the attention to the price of Bitcoin. However, a great
deal of work is still limited to using technical indicators to capture Bitcoin
price fluctuation, with little consideration of historical relationships and
interactions between related cryptocurrencies. In this work, we propose a
generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can
effectively capture the synchronous and asynchronous impact factors between
Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping
algorithm to extract the lead-lag relationship, yielding Lead-lag Variance
Kernel, which will be used for aggregating the information of Altcoins to form
relational impact factors. Comprehensive experimental results demonstrate that
our C2RM can help existing price prediction methods achieve significant
performance improvement, suggesting the effectiveness of Cross-Cryptocurrency
interactions on benefitting Bitcoin price prediction.


title:
A portfolio management of a small RES utility with a Structural Vector
  Autoregressive model of German electricity markets

abstract:
  The changes in electricity markets expose RES producers and electricity
traders to various risks, among which the price and the volume risk play a very
important role. In this research, a portfolio building strategies are
presented, which allow to dynamically choose a proportion of electricity traded
in different electricity markets (day-ahead and intraday) and hence to optimize
the behavior of an utility. Two types of approaches are considered: simple,
assuming that the proportions are fixed, and data driven, which allows for
thier fluctuation. In order to explore the market information, Structural
Vector Autoregressive (SVAR) model is applied, which allows to estimate the
relationship between variables of interest and to simulate their future
distribution. The presented methods are evaluated with data coming from German
electricity market. The results indicate that data driven trading strategies
allow to increase the utility revenue and at the same time reduce the trading
risk, measured by the predictability of the next day income and the revenue
Value at Risk. It turns out that the approach based on Sharp Ratio provides the
most robust results.


title: Knowledge Graph Contrastive Learning for Recommendation

abstract:
  Knowledge Graphs (KGs) have been utilized as useful side information to
improve recommendation quality. In those recommender systems, knowledge graph
information often contains fruitful facts and inherent semantic relatedness
among items. However, the success of such methods relies on the high quality
knowledge graphs, and may not learn quality representations with two
challenges: i) The long-tail distribution of entities results in sparse
supervision signals for KG-enhanced item representation; ii) Real-world
knowledge graphs are often noisy and contain topic-irrelevant connections
between items and entities. Such KG sparsity and noise make the item-entity
dependent relations deviate from reflecting their true characteristics, which
significantly amplifies the noise effect and hinders the accurate
representation of user's preference.
  To fill this research gap, we design a general Knowledge Graph Contrastive
Learning framework (KGCL) that alleviates the information noise for knowledge
graph-enhanced recommender systems. Specifically, we propose a knowledge graph
augmentation schema to suppress KG noise in information aggregation, and derive
more robust knowledge-aware representations for items. In addition, we exploit
additional supervision signals from the KG augmentation process to guide a
cross-view contrastive learning paradigm, giving a greater role to unbiased
user-item interactions in gradient descent and further suppressing the noise.
Extensive experiments on three public datasets demonstrate the consistent
superiority of our KGCL over state-of-the-art techniques. KGCL also achieves
strong performance in recommendation scenarios with sparse user-item
interactions, long-tail and noisy KG entities. Our implementation codes are
available at https://github.com/yuh-yang/KGCL-SIGIR22


title: Disorder-averaged Binder ratio in site-diluted Heisenberg models

abstract:
  It is demonstrated via a numerical experiment (a Monte Carlo simulation) in
the context of three-dimensional site-diluted Heisenberg spin systems that a
functional dependence of the Binder ratio ($V_4$) on the order parameter
correlation length ($\xi / L$) requires a modification to the usual definition
of $V_4$ in disordered systems. An appropriate disorder averaging procedure is
proposed.


title: Quality-Aware Decoding for Neural Machine Translation

abstract:
  Despite the progress in machine translation quality estimation and evaluation
in the last years, decoding in neural machine translation (NMT) is mostly
oblivious to this and centers around finding the most probable translation
according to the model (MAP decoding), approximated with beam search. In this
paper, we bring together these two lines of research and propose quality-aware
decoding for NMT, by leveraging recent breakthroughs in reference-free and
reference-based MT evaluation through various inference methods like $N$-best
reranking and minimum Bayes risk decoding. We perform an extensive comparison
of various possible candidate generation and ranking methods across four
datasets and two model classes and find that quality-aware decoding
consistently outperforms MAP-based decoding according both to state-of-the-art
automatic metrics (COMET and BLEURT) and to human assessments. Our code is
available at https://github.com/deep-spin/qaware-decode.


title: Real-Time BDI Agents: a model and its implementation

abstract:
  The BDI model proved to be effective for developing applications requiring
high-levels of autonomy and to deal with the complexity and unpredictability of
real-world scenarios. The model, however, has significant limitations in
reacting and handling contingencies within the given real-time constraints.
Without an explicit representation of time, existing real-time BDI
implementations overlook the temporal implications during the agent's decision
process that may result in delays or unresponsiveness of the system when it
gets overloaded. In this paper, we redefine the BDI agent control loop inspired
by well established algorithms for real-time systems to ensure a proper
reaction of agents and their effective application in typical real-time
domains. Our model proposes an effective real-time management of goals, plans,
and actions with respect to time constraints and resources availability. We
propose an implementation of the model for a resource-collection video-game and
we validate the approach against a set of significant scenarios.


title:
Multi-dimensional parameter-space partitioning of spatio-temporal
  simulation ensembles

abstract:
  Numerical simulations are commonly used to understand the parameter
dependence of given spatio-temporal phenomena. Sampling a multi-dimensional
parameter space and running the respective simulations leads to an ensemble of
a large number of spatio-temporal simulation runs. A main objective for
analyzing the ensemble is to partition (or segment) the multi-dimensional
parameter space into connected regions of simulation runs with similar
behavior. To facilitate such an analysis, we propose a novel visualization
method for multi-dimensional parameter-space partitions. Our visualization is
based on the concept of a hyper-slicer, which allows for undistorted views of
the parameter-space segments' extent and transitions. For navigation within the
parameter space, interactions with a 2D embedding of the parameter-space
samples, including their segment memberships, are supported. Parameter-space
partitions are generated in a semi-automatic fashion by analyzing the
similarity space of the ensemble's simulation runs. Clusters of similar
simulation runs induce the segments of the parameter-space partition. We link
the parameter-space partitioning visualizations to similarity-space
visualizations of the ensemble's simulation runs and embed them into an
interactive visual analysis tool that supports the analysis of all facets of
the spatio-temporal simulation ensemble targeted at the overarching goal of
analyzing the parameter-space partitioning. The partitioning can then be
visually analyzed and interactively refined. We evaluated our approach with
experts within case studies from three different domains.


title:
Fermionic correlation functions from randomized measurements in
  programmable atomic quantum devices

abstract:
  We provide a measurement protocol to estimate 2- and 4-point fermionic
correlations in ultra-cold atom experiments. Our approach is based on combining
random atomic beam splitter operations, which can be realized with programmable
optical landscapes, with high-resolution imaging systems such as quantum gas
microscopes. We illustrate our results in the context of the variational
quantum eigensolver algorithm for solving quantum chemistry problems.


title: On algebraic properties of power monoids of numerical monoids

abstract:
  Let $S \subset \mathbb{N}_0$ be a numerical monoid and let $\mathcal
P_{\mathrm{fin}} (S)$, resp $\mathcal P_{\mathrm{fin},0}(S)$, denote the power
monoid, resp. the restricted power monoid, of $S$, that is the set of all
finite nonempty subsets of $S$, resp. the set of all finite nonempty subsets of
$S$ containing 0, with set addition as operation. The arithmetic of power
monoids received some attention in recent literature. We complement these
investigations by studying algebraic properties of power monoids, such as their
prime spectrum. Moreover, we show that almost all elements of $\mathcal
P_{\mathrm{fin},0} (S)$ are irreducible (i.e., they are not proper sumsets).


title: Operads and categories in representation stability theory

abstract:
  We make precise the relation between operads and some of the categories that
have appeared in representation stability theory. We give new examples of such
categories and show that these categories are quasi-Gr\"obner. We also show
that several operadic categories of Batanin--Markl that encode generalized
operads are quasi-Gr\"obner.


title: A Sharp Memory-Regret Trade-Off for Multi-Pass Streaming Bandits

abstract:
  The stochastic $K$-armed bandit problem has been studied extensively due to
its applications in various domains ranging from online advertising to clinical
trials. In practice however, the number of arms can be very large resulting in
large memory requirements for simultaneously processing them. In this paper we
consider a streaming setting where the arms are presented in a stream and the
algorithm uses limited memory to process these arms. Here, the goal is not only
to minimize regret, but also to do so in minimal memory. Previous algorithms
for this problem operate in one of the two settings: they either use
$\Omega(\log \log T)$ passes over the stream (Rathod, 2021; Chaudhuri and
Kalyanakrishnan, 2020; Liau et al., 2018), or just a single pass (Maiti et al.,
2021).
  In this paper we study the trade-off between memory and regret when $B$
passes over the stream are allowed, for any $B \geq 1$, and establish tight
regret upper and lower bounds for any $B$-pass algorithm. Our results uncover a
surprising *sharp transition phenomenon*: $O(1)$ memory is sufficient to
achieve $\widetilde\Theta\Big(T^{\frac{1}{2} + \frac{1}{2^{B+2}-2}}\Big)$
regret in $B$ passes, and increasing the memory to any quantity that is $o(K)$
has almost no impact on further reducing this regret, unless we use $\Omega(K)$
memory. Our main technical contribution is our lower bound which requires the
use of information-theoretic techniques as well as ideas from round elimination
to show that the *residual problem* remains challenging over subsequent passes.


title:
Switching of the information backflow between a helical spin system and
  non-Markovian bath

abstract:
  The dissipative dynamics of the spin chain coupled to the non-Markovian
magnonic reservoir was studied. The chirality of the chain is formed due to the
magnetoelectric coupling. We explored the sign of the trace distance derivative
and found the alternating positive/negative periods in system's time evolution.
The negative sign is associated with the flow of information from the system to
the bath and decrease in states distinguishability, while the positive sign is
related to the flow of the information in the opposite direction and increase
in distinguishability. We found the distinct effect of the applied electric and
magnetic fields. While the Dzyaloshinskii-Moriya interaction and external
electric field lead to reshuffling of the periods, the applied magnetic field
leads to the swift positive-negative transitions. Thus, in the helical quantum
rings coupled to the non-Markovian magnonic baths, it is possible to control
the directions of information flow through the external fields.


title: A walk through of time series analysis on quantum computers

abstract:
  Because of the rotational components on quantum circuits, some quantum neural
networks based on variational circuits can be considered equivalent to the
classical Fourier networks. In addition, they can be used to predict Fourier
coefficients of continuous functions. Time series data indicates a state of a
variable in time. Since some time series data can be also considered as
continuous functions, we can expect quantum machine learning models to do do
many data analysis tasks successfully on time series data. Therefore, it is
important to investigate new quantum logics for temporal data processing and
analyze intrinsic relationships of data on quantum computers.
  In this paper, we go through the quantum analogues of classical data
preprocessing and forecasting with ARIMA models by using simple quantum
operators requiring a few number of quantum gates. Then we discuss future
directions and some of the tools/algorithms that can be used for temporal data
analysis on quantum computers.


title: Distinguished Representations of $\mathrm{GL}_{n}(\mathbb{F}_{q})$

abstract:
  Let $\mathrm{G} = \mathrm{Gl}_{n}(K)$, and $\mathrm{H} = \mathrm{G}^{\sigma}$
for $\sigma$ an involution of the form $g\rightarrow aga^{-1}$, It is known
that for $K =\mathbb{Q}_q$ any irreducible representation of $\mathrm{G}$ with
an $\mathrm{H}$ invariant functional, is self dual, we prove an analogous
result for $\mathbb{F}_{q}$.


title:
Stability and convergence of dynamical decoupling with finite amplitude
  control

abstract:
  Dynamical decoupling is a key method to mitigate errors in a quantum
mechanical system, and we studied it in a series of papers dealing in
particular with the problems arising from unbounded Hamiltonians. The standard
bangbang model of dynamical decoupling, which we also used in those papers,
requires decoupling operations with infinite amplitude, which is strictly
speaking unrealistic from a physical point of view. In this paper we look at
decoupling operations of finite amplitude, discuss under what assumptions
dynamical decoupling works with such finite amplitude operations, and show how
the bangbang description arises as a limit, hence justifying it as a reasonable
approximation.


title: A note on the adapted weak topology in discrete time

abstract:
  The adapted weak topology is an extension of the weak topology for stochastic
processes designed to adequately capture properties of underlying filtrations.
With the recent work of Bart--Beiglb\"ock-P. as starting point, the purpose of
this note is to recover with topological arguments the intriguing result by
Backhoff-Bartl-Beiglb\"ock-Eder that all adapted topologies in discrete time
coincide. We also derive new characterizations of this topology including
descriptions of its trace on the sets of Markov processes and processes
equipped with their natural filtration. To emphasize the generality of the
argument, we also describe the classical weak topology for measures on $\mathbb
R^d$ by a weak Wasserstein metric based on the theory of weak optimal transport
initiated by Gozlan-Roberto-Samson-Tetali.


title: The spectral even cycle problem

abstract:
  In this paper, we study the maximum adjacency spectral radii of graphs of
large order that do not contain an even cycle of given length. For $n>k$, let
$S_{n,k}$ be the join of a clique on $k$ vertices with an independent set of
$n-k$ vertices and denote by $S_{n,k}^+$ the graph obtained from $S_{n,k}$ by
adding one edge. In 2010, Nikiforov conjectured that for $n$ large enough, the
$C_{2k+2}$-free graph of maximum spectral radius is $S_{n,k}^+$ and that the
$\{C_{2k+1},C_{2k+2}\}$-free graph of maximum spectral radius is $S_{n,k}$. We
solve this two-part conjecture.


title: Accurate modelling of extragalactic microlensing by compact objects

abstract:
  Microlensing of extragalactic sources, in particular the probability of
significant amplifications, is a potentially powerful probe of the abundance of
compact objects outside the halo of the Milky Way. Accurate experimental
constraints require an equally accurate theoretical model for the amplification
statistics produced by such a population. In this article, we argue that the
simplest (strongest-lens) model does not meet this demanding requirement. We
thus propose an elaborate practical modelling scheme for extragalactic
microlensing. We derive from first principles an expression for the
amplification probability that consistently allows for: (i) the coupling
between microlenses; (ii) realistic perturbations from the cosmic large-scale
structure; (iii) extended-source corrections. An important conclusion is that
the external shear applied on the dominant microlens, both by the other lenses
and by the large-scale structure, is practically negligible. Yet, the
predictions of our approach can still differ by a factor of a few with respect
to existing models of the literature. Updated constraints on the abundance of
compact objects accounting for such discrepancies may be required.


title:
Discussion of 'Multivariate Fisher's independence test for multivariate
  dependence'

abstract:
  Invited discussion for Biometrika of 'Multivariate Fisher's independence test
for multivariate dependence' by Gorsky and Ma (2022).


title:
A systematic analysis of biotech startups that went public in the first
  half of 2021

abstract:
  Biotechnologies are being commercialized at historic rates. In 2020, 74
biotech startups went public through an Initial Public Offering (IPO), and 60
went through the IPO process in the first six months of 2021. However, the
traits associated with biotech startups obtaining recent IPOs have not been
reported. Here we build a database of biotechs that underwent an IPO in the
first half of 2021. By analyzing leadership, technological focus, clinical
trials, and financing, we found that advanced degrees among the leadership,
clinical trials, and intellectual property are important factors for biotech
startups. The data also suggest that large private rounds can decrease
time-to-IPO and affect post-IPO stock performance. Notably, these traits were
often exhibited by the 138 biotech IPOs in 2018-2019, suggesting 2021 data were
not driven by COVID.


title:
Non-zero constraints in elliptic PDE with random boundary values and
  applications to hybrid inverse problems

abstract:
  Hybrid inverse problems are based on the interplay of two types of waves, in
order to allow for imaging with both high resolution and high contrast. The
inversion procedure often consists of two steps: first, internal measurements
involving the unknown parameters and some related quantities are obtained, and,
second, the unknown parameters have to be reconstructed from the internal data.
The reconstruction in the second step requires the solutions of certain PDE to
satisfy some non-zero constraints, such as the absence of nodal or critical
points, or a non-vanishing Jacobian.
  In this work, we consider a second-order elliptic PDE and show that it is
possible to satisfy these constraints with overwhelming probability by choosing
the boundary values randomly, following a sub-Gaussian distribution. The proof
is based on a new quantitative estimate for the Runge approximation, a result
of independent interest.


title: Birds on a Wire

abstract:
  We investigate the occupancy statistics of birds on a wire and on
higher-dimensional substrates. In one dimension, birds land one by one on a
wire and rest where they land. Whenever a newly arriving bird lands within a
fixed distance of already resting birds, these resting birds immediately fly
away. We determine the steady-state occupancy of the wire, the distribution of
gaps between neighboring birds, and other basic statistical features of this
process. We discuss conjectures for corresponding observables in higher
dimensions.


title: Efficient Numerical Optimal Control for Highly Oscillatory Systems

abstract:
  We present an efficient transcription method for highly oscillatory optimal
control problems. For these problems, the optimal state trajectory consists of
fast oscillations that change slowly over the time horizon. Out of a large
number of oscillations, we only simulate a subset to approximate the slow
change by constructing a semi-explicit differential-algebraic equation that can
be integrated with integration steps much larger than one period. For the
solution of optimal control problems with direct methods, we provide a way to
parametrize and regularize the controls. Finally, we utilize the method to find
a fuel-optimal orbit transfer of a low-thrust satellite. Using the novel
method, we reduce the size of the resulting nonlinear program by more than one
order of magnitude.


title:
Connecting Higher-Order Topology with the Orbital Hall Effect in
  Monolayers of Transition Metal Dichalcogenides

abstract:
  Monolayers of transition metal dichalcogenides (TMDs) in the 2H structural
phase have been recently classified as higher-order topological insulators
(HOTI), protected by $C_3$ rotation symmetry. In addition, theoretical
calculations show an orbital Hall plateau in the insulating gap of TMDs,
characterized by an orbital Chern number. We explore the correlation between
these two phenomena in TMD monolayers in two structural phases: the
noncentrosymmetric 2H and the centrosymmetric 1T. Using density functional
theory, we confirm the characteristics of 2H-TMDs and reveal that 1T-TMDs are
identified by a $\mathbb{Z}_4$ topological invariant. As a result, when cut
along appropriate directions, they host conducting edge-states, which cross
their bulk energy-band gaps and can transport orbital angular momentum. Our
linear response calculations thus indicate that the HOTI phase is accompanied
by an orbital Hall effect. Using general symmetry arguments, we establish a
connection between the two phenomena with potential implications for
spin-orbitronics.


title:
Complex network growth model: Possible isomorphism between nonextensive
  statistical mechanics and random geometry

abstract:
  In the realm of Boltzmann-Gibbs statistical mechanics there are three well
known isomorphic connections with random geometry, namely (i) the
Kasteleyn-Fortuin theorem which connects the $\lambda \to 1$ limit of the
$\lambda$-state Potts ferromagnet with bond percolation, (ii) the isomorphism
which connects the $\lambda \to 0$ limit of the $\lambda$-state Potts
ferromagnet with random resistor networks, and (iii) the de Gennes isomorphism
which connects the $n \to 0$ limit of the $n$-vector ferromagnet with
self-avoiding random walk in linear polymers. We provide here strong numerical
evidence that a similar isomorphism appears to emerge connecting the energy
$q$-exponential distribution $\propto e_q^{-\beta_q \varepsilon}$ (with $q=4/3$
and $\beta_q \omega_0 =10/3$) optimizing, under simple constraints, the
nonadditive entropy $S_q$ with a specific geographic growth random model based
on preferential attachment through exponentially-distributed weighted links,
$\omega_0$ being the characteristic weight.


title:
Updated QCD global analysis of single transverse-spin asymmetries I:
  Extracting $\tilde{H}$, and the role of the Soffer bound and lattice QCD

abstract:
  We present an update to the QCD global analysis of single transverse-spin
asymmetries presented in Cammarota, et al., PRD 102, 054002 (2020) (JAM3D-20).
JAM3D-20 simultaneously included transverse momentum dependent and collinear
twist-3 observables, both of which are sensitive to quark-gluon-quark
correlations in hadrons. In this study we extract for the first time the
twist-3 chiral odd fragmentation function $\tilde{H}$ by incorporating the
$\sin\phi_s$ modulation data from SIDIS along with its contribution to the
single transverse-spin asymmetry in pion production from proton-proton
collisions. We also explore the impact of lattice QCD tensor charge
calculations and the Soffer bound on our global analysis. We find that both
constraints can be accommodated within our results, with $\tilde{H}$ playing a
key role in maintaining agreement with the data from proton-proton collisions.


title:
Ap\'{e}ry-Type Series with Summation Indices of Mixed Parities and
  Colored Multiple Zeta Values, III

abstract:
  Ap\'{e}ry-type (inverse) binomial series have appeared prominently in the
calculations of Feynman integrals in recent years. In our previous work, we
showed that a few large classes of the non-alternating Ap\'ery-type (inverse)
central binomial series can be evaluated using colored multiple zeta values of
level four (i.e., special values of multiple polylogarithms at fourth roots of
unity) by expressing them in terms of iterated integrals. In this sequel, we
shall prove that for several classes of the alternating versions we need to
raise the level to eight. Our main idea is to adopt hyperbolic trigonometric
1-forms to replace the ordinary trigonometric ones used in the non-alternating
setting.


title: Infinite Variance in Monte Carlo Sampling of Lattice Field Theories

abstract:
  In Monte Carlo calculations of expectation values in lattice quantum field
theories, the stochastic variance of the sampling procedure that is used
defines the precision of the calculation for a fixed number of samples. If the
variance of an estimator of a particular quantity is formally infinite, or in
practice very large compared to the square of the mean, then that quantity can
not be reliably estimated using the given sampling procedure. There are
multiple scenarios in which this occurs, including in Lattice Quantum
Chromodynamics, and a particularly simple example is given by the Gross-Neveu
model where Monte Carlo calculations involve the introduction of auxiliary
bosonic variables through a Hubbard-Stratonovich (HS) transformation. Here, it
is shown that the variances of HS estimators for classes of operators involving
fermion fields are divergent in this model and an even simpler zero-dimensional
analogue. To correctly estimate these observables, two alternative sampling
methods are proposed and numerically investigated.


title: Refined enumeration of $k$-plane trees and $k$-noncrossing trees

abstract:
  A $k$-plane tree is a plane tree whose vertices are assigned labels between
$1$ and $k$ in such a way that the sum of the labels along any edge is no
greater than $k+1$. These trees are known to be related to $(k+1)$-ary trees,
and they are counted by a generalised version of the Catalan numbers. We prove
a surprisingly simple refined counting formula, where we count trees with a
prescribed number of labels of each kind. Several corollaries are derived from
this formula, and an analogous theorem is proven for $k$-noncrossing trees, a
similarly defined family of labelled noncrossing trees that are related to
$(2k+1)$-ary trees.


title: Chirality in 2d pAQFT

abstract:
  In this article, which builds upon the work done in our previous paper, the
chiral aspects of 2dcft on globally hyperbolic Lorentzian manifolds are
developed and explored within the perturbative algebraic quantum field theory
(pAQFT) framework. In the example of the massless scalar field on globally
hyperbolic 2-dimensional spacetimes, we identify the subalgebras of a given
theory comprising only chiral (or anti-chiral) observables. These subalgebras
are constructed explicitly, with the help of structures naturally associated to
a Cauchy surface, for both the classical and the quantised theory, and it is
shown that they then embed naturally into the algebra of the full theory.
Finally, it is demonstrated that the construction of these subalgebras is
independent of the choice of Cauchy surface and that they unambiguously define
a covariant theory on the spaces of null-geodesics.


title: Auto-scaling HTCondor pools using Kubernetes compute resources

abstract:
  HTCondor has been very successful in managing globally distributed,
pleasantly parallel scientific workloads, especially as part of the Open
Science Grid. HTCondor system design makes it ideal for integrating compute
resources provisioned from anywhere, but it has very limited native support for
autonomously provisioning resources managed by other solutions. This work
presents a solution that allows for autonomous, demand-driven provisioning of
Kubernetes-managed resources. A high-level overview of the employed
architectures is presented, paired with the description of the setups used in
both on-prem and Cloud deployments in support of several Open Science Grid
communities. The experience suggests that the described solution should be
generally suitable for contributing Kubernetes-based resources to existing
HTCondor pools.


title:
What Factors Should Paper-Reviewer Assignments Rely On? Community
  Perspectives on Issues and Ideals in Conference Peer-Review

abstract:
  Both scientific progress and individual researcher careers depend on the
quality of peer review, which in turn depends on paper-reviewer matching.
Surprisingly, this problem has been mostly approached as an automated
recommendation problem rather than as a matter where different stakeholders
(area chairs, reviewers, authors) have accumulated experience worth taking into
account. We present the results of the first survey of the NLP community,
identifying common issues and perspectives on what factors should be considered
by paper-reviewer matching systems. This study contributes actionable
recommendations for improving future NLP conferences, and desiderata for
interpretable peer review assignments.


title: Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding

abstract:
  Semantic understanding of 3D point cloud relies on learning models with
massively annotated data, which, in many cases, are expensive or difficult to
collect. This has led to an emerging research interest in semi-supervised
learning (SSL) for 3D point cloud. It is commonly assumed in SSL that the
unlabeled data are drawn from the same distribution as that of the labeled
ones; This assumption, however, rarely holds true in realistic environments.
Blindly using out-of-distribution (OOD) unlabeled data could harm SSL
performance. In this work, we propose to selectively utilize unlabeled data
through sample weighting, so that only conducive unlabeled data would be
prioritized. To estimate the weights, we adopt a bi-level optimization
framework which iteratively optimizes a metaobjective on a held-out validation
set and a task-objective on a training set. Faced with the instability of
efficient bi-level optimizers, we further propose three regularization
techniques to enhance the training stability. Extensive experiments on 3D point
cloud classification and segmentation tasks verify the effectiveness of our
proposed method. We also demonstrate the feasibility of a more efficient
training strategy.


title: Resonant string behavior in a Gravitational Wave burst

abstract:
  We investigate the behavior of classical closed strings in a gravitational
wave burst and discover an intriguing resonant behavior where the energy
absorbed by the strings is crucially dependent on the amplitude and frequency
of the gravitational wave. This behavior can be traced to the well-known
behavior of the solutions to the Mathieu equation.


title: A first look at the submillimeter Sun with ALMA

abstract:
  We present the first full-disk solar images obtained with the Atacama Large
Millimeter/submillimeter Array (ALMA) in Band 7 (0.86 mm; 347 GHz). In spite of
the low spatial resolution (21"), several interesting results were obtained.
During our observation, the sun was practically devoid of active regions. Quiet
Sun structures on the disk are similar to those in Atmospheric Imaging Assembly
(AIA) images at 1600 A and 304 A, after the latter are smoothed to the ALMA
resolution, as noted previously for Band 6 (1.26 mm) and Band 3 (3 mm) images;
they are also similar to negative H$\alpha$ images of equivalent resolution.
Polar coronal holes, which are clearly seen in the 304 A band and small
H$\alpha$ filaments, are not detectable at 0.86 mm. We computed the
center-to-limb variation (CLV) of the brightness temperature, $T_b$, in Band 7,
as well as in Bands 6 and 3, which were obtained during the same campaign, and
we combined them to a unique curve of $T_b(\log\mu_{100})$, where $\mu_{100}$
is the cosine of the heliocentric angle reduced to 100 GHz. Assuming that the
absolute calibration of the Band 3 commissioning observations is accurate, we
deduced a brightness temperature at the center of the disk of 6085 K for Band
7, instead of the value of 5500 K, extrapolated from the recommended values for
Bands 3 and 6. More importantly, the $T_b(\log\mu_{100})$ curve flattens at
large values of $\mu_{100}$, and so does the corresponding
$T_e(\log\tau_{100})$ at large $\tau_{100}$. This is probably an indication
that we are approaching the temperature minimum.


title: Token sliding on graphs of girth five

abstract:
  In the Token Sliding problem we are given a graph $G$ and two independent
sets $I_s$ and $I_t$ in $G$ of size $k \geq 1$. The goal is to decide whether
there exists a sequence $\langle I_1, I_2, \ldots, I_\ell \rangle$ of
independent sets such that for all $i \in \{1,\ldots, \ell\}$ the set $I_i$ is
an independent set of size $k$, $I_1 = I_s$, $I_\ell = I_t$ and $I_i \triangle
I_{i + 1} = \{u, v\} \in E(G)$. Intuitively, we view each independent set as a
collection of tokens placed on the vertices of the graph. Then, the problem
asks whether there exists a sequence of independent sets that transforms $I_s$
into $I_t$ where at each step we are allowed to slide one token from a vertex
to a neighboring vertex. In this paper, we focus on the parameterized
complexity of Token Sliding parameterized by $k$. As shown by Bartier et al.,
the problem is W[1]-hard on graphs of girth four or less, and the authors posed
the question of whether there exists a constant $p \geq 5$ such that the
problem becomes fixed-parameter tractable on graphs of girth at least $p$. We
answer their question positively and prove that the problem is indeed
fixed-parameter tractable on graphs of girth five or more, which establishes a
full classification of the tractability of Token Sliding parameterized by the
number of tokens based on the girth of the input graph.


title:
The effect of the rapid growth of covid-19 publications on citation
  indicators

abstract:
  A concern has been raised that "covidization" of research would cause an
overemphasizes on covid-19 and pandemics at the expense of other research. The
rapid growth of publications related to the Covid-19 pandemic renders a vast
amount of citations from this literature. This growth may affect bibliometric
indicators. In this paper I explored how the growth of covid-19 publications
influences bibliometric indicators commonly used in university rankings,
research evaluation and research allocation, namely the field normalized
citation score and the journal impact factor. I found that the burst of
publications in the early stage of the covid-19 pandemic affects
field-normalized citation scores and will affect the journal impact factor.
Publications unrelated to covid-19 are also heavily affected. I conclude that
there is a considerable risk to draw misleading conclusions from citation
indicators spanning over the beginning of the covid-19 pandemic, in particular
when time series are used and when the biomedical literature is assessed.


title:
The Potential of Detecting Radio-flaring Ultracool Dwarfs at L band in
  the FAST Drift-scan Survey

abstract:
  The Five-hundred-meter Aperture Spherical radio Telescope (FAST) completed
its commissioning and began the Commensal Radio Astronomy FasT Survey (CRAFTS),
a multi-year survey to cover 60% of the sky, in 2020. We present predictions
for the number of radio-flaring ultracool dwarfs (UCDs) that are likely to be
detected by CRAFTS. Based on the observed flaring UCDs from a number of
unbiased, targeted radio surveys in the literature, we derive a detection rate
of $\ge$3%. Assuming a flat radio spectrum $\nu L _{\nu}\propto \nu^{\beta+1} $
with $\beta$ = -1.0 for UCD flares, we construct a flare luminosity function $d
N/d L \propto L^{-1.96 \pm 0.45}$ (here $L=\nu L_\nu$). CRAFTS is found to be
sensitive enough for flares from UCDs up to $\sim$180 pc. Considering the
Galactic thin disk, we carry out a 3D Monte Carlo simulation of the UCD
population, which is then fed to mock CRAFTS observations. We estimate that
$\sim$170 flaring UCDs would be detected through transient searches in circular
polarization. Though only marginally sensitive to the scale height of UCDs, the
results are very sensitive to the assumed spectral index $\beta$. For $\beta$
from 0 to -2.5, the number of expected detections increases dramatically from
$\sim$20 to $\sim$3460. We also contemplate the strategies for following up
candidates of flaring UCDs, and discuss the implications of survey results for
improving our knowledge of UCD behavior at $L$ band and dynamos.


title: Excess Out-of-Sample Risk and Fleeting Modes

abstract:
  Using Random Matrix Theory, we propose a universal and versatile tool to
reveal the existence of "fleeting modes", i.e. portfolios that carry
statistically significant excess risk, signalling ex-post a change in the
correlation structure in the underlying asset space. Our proposed test is
furthermore independent of the "true" (but unknown) underlying correlation
structure. We show empirically that such fleeting modes exist both in futures
markets and in equity markets. We proposed a metric to quantify the alignment
between known factors and fleeting modes and identify momentum as a source of
excess risk in the equity space.


title:
Crossing numbers and rotation numbers of cycles in a plane immersed
  graph

abstract:
  For any generic immersion of a Petersen graph into a plane, the number of
crossing points between two edges of distance one is odd. The sum of the
crossing numbers of all $5$-cycles is odd. The sum of the rotation numbers of
all $5$-cycles is even. We show analogous results for $6$-cycles, $8$-cycles
and $9$-cycles. For any Legendrian spatial embedding of a Petersen graph, there
exists a $5$-cycle that is not an unknot with maximal Thurston-Bennequin
number, and the sum of all Thurston-Bennequin numbers of the cycles is $7$
times the sum of all Thurston-Bennequin numbers of the $5$-cycles. We show
analogous results for a Heawood graph. We also show some other results for some
graphs. We characterize abstract graphs that has a generic immersion into a
plane whose all cycles have rotation number $0$.


title: Local Immunodeficiency: Transformation of Viruses' Functions

abstract:
  We analyze three aspects of the phenomenon of local immunodeficiency. In all
cases only a stable (i.e. observable) state of local immunodeficiency is
considered. At first, we show that the appearance of a new virus may result in
a change in the roles played by previously existed viruses in the in-host
cross-immunoreactivity network. Particularly a persistent virus may become
neutral and vice versa. Then we study what happens if two
cross-immunoreactivity networks with stable local immunodeficiency get
connected. It is shown that already for minimal (just three-viruses) networks,
various transformations of viruses functions may occur. Namely, the resulting
network will carry the state of stable local immunodeficiency, and indeed
functions of different viruses may change. But, on top of that, some viruses
may become inactive or organize their own cross-immunoreactivity network. The
results of these two parts of the paper are rigorous. We also study numerically
time evolution (dynamics) of cross-immunoreactivity networks after the addition
of a new virus. It allows to see how the processes uncovered in the first part
evolve in time, i.e. how, for instance, a new virus becomes persistent.
Clearly, it does not happen instantly but involves some complicated processes
of competition and cooperation. One of the most interesting findings is that
appearance of a new virus in a network, which did not have a state of stable
local immunodeficiency, may actually create such state.


title: The Origin of Ultramassive White Dwarfs: Hints from Gaia EDR3

abstract:
  Gaia Data Release 2 revealed the presence of a population of ultramassive
white dwarfs on the Q branch that are moving anomalously fast for a local disc
population with the cooling ages determined using white dwarf cooling models,
which has been presented as evidence of an extra cooling delay experienced by
these white dwarfs. In this work, we investigate the kinematics of ultramassive
white dwarfs in the solar neighbourhood using the improved Gaia Early Data
Release 3 observations to explore this cooling delay explanation and alternate
possibilities. We performed a kinematic analysis of the transverse motions of
0.95-1.25 $M_\odot$ white dwarfs within 200 pc of the Sun subdivided by mass
and age and compared the result to the expectation based on the observed
kinematics of main sequence stars. The distributions of the transverse velocity
component in the direction of Galactic rotation for different mass and age
ranges reveal a population of photometrically young ($\sim$ 0.5-1.5 Gyr)
ultramassive ($\sim$ 1.15-1.25 $M_\odot$) white dwarfs that lags the local
Galactic rotation and that is too dispersed in this velocity component to be
explained by a cooling delay in white dwarfs originating from the local
Galactic disc. The dispersion ratio of the velocity components in the Galactic
plane for this population is also inconsistent with a local disc origin. We
discuss some possible explanations of this kinematically anomalous population
such as a halo origin or production through dynamical effects of stellar triple
systems.