title:
Failure and success of the spectral bias prediction for Kernel Ridge
  Regression: the case of low-dimensional data

abstract:
  Recently, several theories including the replica method made predictions for
the generalization error of Kernel Ridge Regression. In some regimes, they
predict that the method has a `spectral bias': decomposing the true function
$f^*$ on the eigenbasis of the kernel, it fits well the coefficients associated
with the O(P) largest eigenvalues, where $P$ is the size of the training set.
This prediction works very well on benchmark data sets such as images, yet the
assumptions these approaches make on the data are never satisfied in practice.
To clarify when the spectral bias prediction holds, we first focus on a
one-dimensional model where rigorous results are obtained and then use scaling
arguments to generalize and test our findings in higher dimensions. Our
predictions include the classification case $f(x)=$sign$(x_1)$ with a data
distribution that vanishes at the decision boundary $p(x)\sim x_1^{\chi}$. For
$\chi>0$ and a Laplace kernel, we find that (i) there exists a cross-over ridge
$\lambda^*_{d,\chi}(P)\sim P^{-\frac{1}{d+\chi}}$ such that for $\lambda\gg
\lambda^*_{d,\chi}(P)$, the replica method applies, but not for
$\lambda\ll\lambda^*_{d,\chi}(P)$, (ii) in the ridge-less case, spectral bias
predicts the correct training curve exponent only in the limit
$d\rightarrow\infty$.


title: Conditional Gradients for the Approximately Vanishing Ideal

abstract:
  The vanishing ideal of a set of points $X\subseteq \mathbb{R}^n$ is the set
of polynomials that evaluate to $0$ over all points $\mathbf{x} \in X$ and
admits an efficient representation by a finite set of polynomials called
generators. To accommodate the noise in the data set, we introduce the
Conditional Gradients Approximately Vanishing Ideal algorithm (CGAVI) for the
construction of the set of generators of the approximately vanishing ideal. The
constructed set of generators captures polynomial structures in data and gives
rise to a feature map that can, for example, be used in combination with a
linear classifier for supervised learning. In CGAVI, we construct the set of
generators by solving specific instances of (constrained) convex optimization
problems with the Pairwise Frank-Wolfe algorithm (PFW). Among other things, the
constructed generators inherit the LASSO generalization bound and not only
vanish on the training but also on out-sample data. Moreover, CGAVI admits a
compact representation of the approximately vanishing ideal by constructing few
generators with sparse coefficient vectors.


title: Optimal Gathering over Meeting Nodes in Infinite Grid

abstract:
  The gathering over meeting nodes problem requires the robots to gather at one
of the pre-defined meeting nodes. This paper investigates the problem with
respect to the objective function that minimizes the total number of moves made
by all the robots. In other words, the sum of the distances traveled by all the
robots is minimized while accomplishing the gathering task. The robots are
deployed on the nodes of an anonymous two-dimensional infinite grid which has a
subset of nodes marked as meeting nodes. The robots do not agree on a global
coordinate system and operate under an asynchronous scheduler. A deterministic
distributed algorithm has been proposed to solve the problem for all those
solvable configurations, and the initial configurations for which the problem
is unsolvable have been characterized. The proposed gathering algorithm is
optimal with respect to the total number of moves performed by all the robots
in order to finalize the gathering.


title: Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model

abstract:
  This paper introduces a Threshold Asymmetric Conditional Autoregressive Range
(TACARR) formulation for modeling the daily price ranges of financial assets.
It is assumed that the process generating the conditional expected ranges at
each time point switches between two regimes, labeled as upward market and
downward market states. The disturbance term of the error process is also
allowed to switch between two distributions depending on the regime. It is
assumed that a self-adjusting threshold component that is driven by the past
values of the time series determines the current market regime. The proposed
model is able to capture aspects such as asymmetric and heteroscedastic
behavior of volatility in financial markets. The proposed model is an attempt
at addressing several potential deficits found in existing price range models
such as the Conditional Autoregressive Range (CARR), Asymmetric CARR (ACARR),
Feedback ACARR (FACARR) and Threshold Autoregressive Range (TARR) models.
Parameters of the model are estimated using the Maximum Likelihood (ML) method.
A simulation study shows that the ML method performs well in estimating the
TACARR model parameters. The empirical performance of the TACARR model was
investigated using IBM index data and results show that the proposed model is a
good alternative for in-sample prediction and out-of-sample forecasting of
volatility.
  Key Words: Volatility Modeling, Asymmetric Volatility, CARR Models, Regime
Switching.


title: Analog Secure Distributed Matrix Multiplication over Complex Numbers

abstract:
  This work considers the problem of distributing matrix multiplication over
the real or complex numbers to helper servers, such that the information
leakage to these servers is close to being information-theoretically secure.
These servers are assumed to be honest-but-curious, i.e., they work according
to the protocol, but try to deduce information about the data. The problem of
secure distributed matrix multiplication (SDMM) has been considered in the
context of matrix multiplication over finite fields, which is not always
feasible in real world applications. We present two schemes, which allow for
variable degree of security based on the use case and allow for colluding and
straggling servers. We analyze the security and the numerical accuracy of the
schemes and observe a trade-off between accuracy and security.


title:
Back action in quantum electro-optic sampling of electromagnetic vacuum
  fluctuations

abstract:
  The influence of measurement back action on electro-optic sampling of
electromagnetic quantum fluctuations is investigated. Based on a cascaded
treatment of the nonlinear interaction between a near-infrared coherent probe
and the mid-infrared vacuum, we account for the generated electric-field
contributions that lead to detectable back action. Specifically, we
theoretically address two realistic setups, exploiting one or two probe beams
for the nonlinear interaction with the quantum vacuum, respectively. The setup
parameters at which back action starts to considerably contaminate the measured
noise profiles are determined. Due to the vacuum fluctuations entering at the
beam splitter, the shot noise of two incoming probe pulses in different
channels is uncorrelated. This leads to the absence of the base-level shot
noise in the correlation, while further contributions due to nonlinear
shot-noise enhancement are still present. Ultimately, the regime in which
electro-optic sampling of quantum fields can be considered as effectively
back-action free is found.


title: Robust Dialogue State Tracking with Weak Supervision and Sparse Data

abstract:
  Generalising dialogue state tracking (DST) to new data is especially
challenging due to the strong reliance on abundant and fine-grained supervision
during training. Sample sparsity, distributional shift and the occurrence of
new concepts and topics frequently lead to severe performance degradation
during inference. In this paper we propose a training strategy to build
extractive DST models without the need for fine-grained manual span labels. Two
novel input-level dropout methods mitigate the negative impact of sample
sparsity. We propose a new model architecture with a unified encoder that
supports value as well as slot independence by leveraging the attention
mechanism. We combine the strengths of triple copy strategy DST and value
matching to benefit from complementary predictions without violating the
principle of ontology independence. Our experiments demonstrate that an
extractive DST model can be trained without manual span labels. Our
architecture and training strategies improve robustness towards sample
sparsity, new concepts and topics, leading to state-of-the-art performance on a
range of benchmarks. We further highlight our model's ability to effectively
learn from non-dialogue data.


title: A High Phase-Space Density Gas of NaCs Feshbach Molecules

abstract:
  We report on the creation of ultracold gases of bosonic Feshbach molecules of
NaCs. The molecules are associated from overlapping gases of Na and Cs using a
Feshbach resonance at 864.12(5) G. We characterize the Feshbach resonance using
bound state spectroscopy, in conjunction with a coupled-channel calculation. By
varying the temperature and atom numbers of the initial atomic mixtures, we
demonstrate the association of NaCs gases over a wide dynamic range of molecule
numbers and temperatures, reaching 70 nK for our coldest systems and a
phase-space density near 0.1. This is an important stepping-stone for the
creation of degenerate gases of strongly dipolar NaCs molecules in their
absolute ground state.


title: Optimal Direct-Connect Topologies for Collective Communications

abstract:
  We consider the problem of distilling optimal network topologies for
collective communications. We provide an algorithmic framework for constructing
direct-connect topologies optimized for the latency-bandwidth tradeoff given a
collective communication workload. Our algorithmic framework allows us to start
from small base topologies and associated communication schedules and use a set
of techniques that can be iteratively applied to derive much larger topologies
and associated schedules. Our approach allows us to synthesize many different
topologies and schedules for a given cluster size and degree constraint, and
then identify the optimal topology for a given workload. We provide an
analytical-model-based evaluation of the derived topologies and results on a
small-scale optical testbed that uses patch panels for configuring a topology
for the duration of an application's execution. We show that the derived
topologies and schedules provide significant performance benefits over existing
collective communications implementations.


title: A note on continuous entropy

abstract:
  Von Neumann entropy has a natural extension to the case of an arbitrary
semifinite von Neumann algebra, as was considered by I. E. Segal. We relate
this entropy to the relative entropy and show that the entropy increase for an
inclusion of von Neumann factors is bounded by the logarithm of the Jones
index. The bound is optimal if the factors are infinite dimensional.


title: The constant coefficient in precise Laplace asymptotics for gPAM

abstract:
  This article resumes the analysis of precise Laplace asymptotics for the
generalised Parabolic Anderson Model (gPAM) initiated by Peter Friz and the
author. More precisely, we provide an explicit formula for the constant
coefficient in the asymptotic expansion in terms of traces and
Carleman-Fredholm determinants of certain explicit operators. The proof
combines classical Gaussian analysis in abstract Wiener spaces with arguments
from the theory of regularity structures. As an ingredient, we prove that the
minimiser in the (extended) phase functional of gPAM has better than just
Cameron-Martin regularity.


title:
Convolutional Neural Networks for Mode On-Demand High Finesse Optical
  Resonator Design

abstract:
  We demonstrate the use of machine learning through convolutional neural
networks to solve inverse design problems of optical resonator engineering. The
neural network finds a harmonic modulation of a spherical mirror to generate a
resonator mode with a given target topology ("mode on-demand"). The procedure
allows us to optimize the shape of mirrors to achieve a significantly enhanced
coupling strength and cooperativity between a resonator photon and a quantum
emitter located at the center of the resonator. In a second example, a
double-peak mode is designed which would enhance the interaction between two
quantum emitters, e.g., for quantum information processing.


title:
Discrete-Event Controller Synthesis for Autonomous Systems with
  Deep-Learning Perception Components

abstract:
  We present DEEPDECS, a new method for the synthesis of
correct-by-construction discrete-event controllers for autonomous systems that
use deep neural network (DNN) classifiers for the perception step of their
decision-making processes. Despite major advances in deep learning in recent
years, providing safety guarantees for these systems remains very challenging.
Our controller synthesis method addresses this challenge by integrating DNN
verification with the synthesis of verified Markov models. The synthesised
models correspond to discrete-event controllers guaranteed to satisfy the
safety, dependability and performance requirements of the autonomous system,
and to be Pareto optimal with respect to a set of optimisation criteria. We use
the method in simulation to synthesise controllers for mobile-robot collision
avoidance, and for maintaining driver attentiveness in shared-control
autonomous driving.


title:
Holomorphic anomaly equations for the Hilbert scheme of points of a K3
  surface

abstract:
  We conjecture that the generating series of Gromov-Witten invariants of the
Hilbert schemes of $n$ points on a K3 surface are quasi-Jacobi forms and
satisfy a holomorphic anomaly equation. We prove the conjecture in genus $0$
and for at most $3$ markings - for all Hilbert schemes and for arbitrary curve
classes. In particular, for fixed $n$, the reduced quantum cohomologies of all
hyperk\"ahler varieties of $K3^{[n]}$-type are determined up to finitely many
coefficients.
  As an application we show that the generating series of $2$-point
Gromov-Witten classes are vector-valued Jacobi forms of weight $-10$, and that
the fiberwise Donaldson-Thomas partition functions of an order two CHL
Calabi-Yau threefold are Jacobi forms.


title: Random entire functions from random polynomials with real zeros

abstract:
  We point out a simple criterion for convergence of polynomials to a concrete
entire function in the Laguerre-P\'{o}lya ($\mathcal{LP}$) class (of all
functions arising as uniform limits of polynomials with only real roots). We
then use this to show that any random $\mathcal{LP}$ function can be obtained
as the uniform limit of rescaled characteristic polynomials of principal
submatrices of an infinite unitarily invariant random Hermitian matrix.
Conversely, the rescaled characteristic polynomials of principal submatrices of
any infinite random unitarily invariant Hermitian matrix converge uniformly to
a random $\mathcal{LP}$ function. This result also has a natural extension to
$\beta$-ensembles. Distinguished cases include random entire functions
associated to the $\beta$-Sine, and more generally $\beta$-Hua-Pickrell,
$\beta$-Bessel and $\beta$-Airy point processes studied in the literature.


title:
Second-harmonic generation in plasmonic waveguides with nonlocal
  response and electron spill-out

abstract:
  Plasmonic waveguides provide an integrated platform to develop efficient
nanoscale ultrafast photonic devices. Theoretical models that describe
nonlinear optical phenomena in plasmonic waveguides, usually, only incorporate
bulk nonlinearities, while nonlinearities that arise from metallic constituents
remained unexplored. In this work, we present a method that enables a
generalized treatment of the nonlinearities present in plasmonic waveguides and
use it to calculate second-harmonic generation from free electrons through a
hydrodynamic nonlocal description. As a general application of our method we
also consider nonlinearities arising from the quantum hydrodynamic theory with
electron spill-out. Our results may find applicability in design and analysis
of integrated photonic platforms for nonlinear optics incorporating wide
variety of nonlinear materials such as heavily doped semiconductors for
mid-infrared applications.


title:
The Cosmic Hitchhikers Hypothesis: Extraterrestrial Civilizations Using
  Free-Floating Planets for Interstellar Colonization

abstract:
  I propose the Cosmic Hitchhikers hypothesis as follows. Advanced
extraterrestrial civilizations may use free-floating planets as interstellar
transportation for space exploration and interstellar colonization. Large
groups or populations of their biological species, post-biological species, and
technologies may become Cosmic Hitchhikers when they ride free-floating planets
to reach, explore and colonize planetary systems. To get an interstellar ride,
Cosmic Hitchhikers may travel to free-floating planets passing close by their
home worlds. Otherwise, they may use astronomical engineering to steer
free-floating planets toward their home planetary systems. Cosmic Hitchhikers
may also ride objects native to the outer regions of their planetary systems,
which become free-floating planets when ejected by astronomical engineering or
by their stars during the asymptotic giant branch evolution. During
interstellar travel, Cosmic Hitchhikers may apply astronomical engineering to
steer their free-floating planets toward the planetary systems of their choice.
Whereas riding free-floating planets may not save travel time, it avoids the
technical challenges of interstellar spacecraft transporting large populations.
Each civilization of Cosmic Hitchhikers may colonize several planetary systems.
Its colonies may grow into autonomous civilizations, changing the number of
civilizations in the Galaxy. Over the last 4 billion years, Cosmic Hitchhikers
or their artifacts riding free-floating planets might have passed by the Solar
System. Therefore, their artifacts might exist in the Solar System or in our
stellar neighborhood. SETI and SETA should include the search for Cosmic
Hitchhikers and their artifacts. Keywords: SETI, SETA, free-floating planet,
extraterrestrial civilization, interstellar travel, interstellar colonization,
artifact, Cosmic Hitchhikers


title: Simple Control Baselines for Evaluating Transfer Learning

abstract:
  Transfer learning has witnessed remarkable progress in recent years, for
example, with the introduction of augmentation-based contrastive
self-supervised learning methods. While a number of large-scale empirical
studies on the transfer performance of such models have been conducted, there
is not yet an agreed-upon set of control baselines, evaluation practices, and
metrics to report, which often hinders a nuanced and calibrated understanding
of the real efficacy of the methods. We share an evaluation standard that aims
to quantify and communicate transfer learning performance in an informative and
accessible setup. This is done by baking a number of simple yet critical
control baselines in the evaluation method, particularly the blind-guess
(quantifying the dataset bias), scratch-model (quantifying the architectural
contribution), and maximal-supervision (quantifying the upper-bound). To
demonstrate how the evaluation standard can be employed, we provide an example
empirical study investigating a few basic questions about self-supervised
learning. For example, using this standard, the study shows the effectiveness
of existing self-supervised pre-training methods is skewed towards image
classification tasks versus dense pixel-wise predictions. In general, we
encourage using/reporting the suggested control baselines in evaluating
transfer learning in order to gain a more meaningful and informative
understanding.


title: Linked Cluster Expansions via Hypergraph Decompositions

abstract:
  We propose a hypergraph expansion which facilitates the direct treatment of
quantum spin models with many-site interactions via perturbative linked cluster
expansions. The main idea is to generate all relevant subclusters and sort them
into equivalence classes essentially governed by hypergraph isomorphism.
Concretely, a reduced K\"onig representation of the hypergraphs is used to make
the equivalence relation accessible by graph isomorphism. During this procedure
we determine the embedding factor for each equivalence class, which is used in
the final resummation in order to obtain the final result. As an instructive
example we calculate the ground-state energy and a particular excitation gap of
the plaquette Ising model in a transverse field on the three-dimensional cubic
lattice.


title:
A longitudinal case study on the effects of an evidence-based software
  engineering training

abstract:
  Context: Evidence-based software engineering (EBSE) can be an effective
resource to bridge the gap between academia and industry by balancing research
of practical relevance and academic rigor. To achieve this, it seems necessary
to investigate EBSE training and its benefits for the practice. Objective: We
sought both to develop an EBSE training course for university students and to
investigate what effects it has on the attitudes and behaviors of the trainees.
Method: We conducted a longitudinal case study to study our EBSE course and its
effects. For this, we collect data at the end of each EBSE course (2017, 2018,
and 2019), and in two follow-up surveys (one after 7 months of finishing the
last course, and a second after 21 months). Results: Our EBSE courses seem to
have taught students adequately and consistently. Half of the respondents to
the surveys report making use of the new skills from the course. The
most-reported effects in both surveys indicated that EBSE concepts increase
awareness of the value of research and evidence and EBSE methods improve
information gathering skills. Conclusions: As suggested by research in other
areas, training appears to play a key role in the adoption of evidence-based
practice. Our results indicate that our training method provides an
introduction to EBSE suitable for undergraduates. However, we believe it is
necessary to continue investigating EBSE training and its impact on software
engineering practice.


title: Locally mediated entanglement through gravity from first principles

abstract:
  Observing entanglement generation mediated by a local field certifies that
the field cannot be classical. This information-theoretic argument is at the
heart of the race to observe gravity-mediated entanglement in a `table-top'
experiment. Previous derivations of the effect assume the locality of
interactions, while using an instantaneous interaction to derive the effect. We
correct this by giving a first principles derivation of mediated entanglement
using linearised gravity. The framework is Lorentz covariant -- thus local --
and yields Lorentz and gauge invariant expressions for the relevant quantum
observables. For completeness we also cover the electromagnetic case. An
experimental consequence of our analysis is the possibility to observe
*retarded* mediated entanglement, which avoids the need of taking relativistic
locality as an assumption. This is a difficult experiment for gravity, but
could be feasible for electromagnetism. Our results confirm that the
entanglement is dynamically mediated by the gravitational field.


title: A nonparametric doubly robust test for a continuous treatment effect

abstract:
  The vast majority of literature on evaluating the significance of a treatment
effect based on observational data has been confined to discrete treatments.
These methods are not applicable to drawing inference for a continuous
treatment, which arises in many important applications. To adjust for
confounders when evaluating a continuous treatment, existing inference methods
often rely on discretizing the treatment or using (possibly misspecified)
parametric models for the effect curve. Recently, Kennedy et al. (2017)
proposed nonparametric doubly robust estimation for a continuous treatment
effect in observational studies. However, inference for the continuous
treatment effect is a significantly harder problem. To the best of our
knowledge, a completely nonparametric doubly robust approach for inference in
this setting is not yet available. We develop such a nonparametric doubly
robust procedure in this paper for making inference on the continuous treatment
effect curve. Using empirical process techniques for local U- and V-processes,
we establish the test statistic's asymptotic distribution. Furthermore, we
propose a wild bootstrap procedure for implementing the test in practice. We
illustrate the new method via simulations and a study of a constructed dataset
relating the effect of nurse staffing hours on hospital performance. We
implement and share code for our doubly robust dose response test in the R
package DRDRtest on CRAN.


title: DFT insights into the new Hf-based chalcogenide MAX phase Hf2SeC

abstract:
  The physical characteristics of the novel chalcogenide MAX phase Hf2SeC have
been investigated using the DFT method. The obtained lattice constant and
elastic constants (Cij) are compared with previous results to check the
consistency of our setting parameters during calculations Moreover, the elastic
properties such as elastic constants, moduli, anisotropy, and hardness are also
compared with preexisting MAX phases of its kind. The checking of mechanical
stability has been done based on Cij in accordance with the previously stated
stability criteria. The reason for the higher hardness of Hf2SeC compared to
Hf2SC is explained using the density of states (DOS). The brittleness character
of Hf2SeC has been revealed using the Pugh ratio, Poisson ratio, and Cauchy
pressure. The electronic properties (band structure and charge density mapping)
of Hf2SeC are studied to disclose the metallic nature as well as bonding nature
within the titled chalcogenide. The anisotropy in both electronic conductivity
and mechanical properties is investigated. The temperature and pressure
dependence of volume, Gr\"uneisen parameter, Debye temperature, thermal
expansion coefficient (TEC), and specific heat at constant volume (Cv) are
explored. In addition, minimum thermal conductivity (Kmin) and melting point
(Tm) are studied to explore its suitability for high-temperature applications.


title: Cedille: A large autoregressive French language model

abstract:
  Scaling up the size and training of autoregressive language models has
enabled novel ways of solving Natural Language Processing tasks using zero-shot
and few-shot learning. While extreme-scale language models such as GPT-3 offer
multilingual capabilities, zero-shot learning for languages other than English
remain largely unexplored. Here, we introduce Cedille, a large open source
auto-regressive language model, specifically trained for the French language.
Our results show that Cedille outperforms existing French language models and
is competitive with GPT-3 on a range of French zero-shot benchmarks.
Furthermore, we provide an in-depth comparison of the toxicity exhibited by
these models, showing that Cedille marks an improvement in language model
safety thanks to dataset filtering.


title: Polarized Antimatter in the Proton from Global QCD Analysis

abstract:
  We present a global QCD analysis of spin-dependent parton distribution
functions (PDFs) that includes the latest polarized $W$-lepton production data
from the STAR collaboration at RHIC. These data allow the first data-driven
extraction of a nonzero polarized light quark sea asymmetry $\Delta \bar{u} -
\Delta \bar{d}$ within a global QCD framework with minimal theoretical
assumptions. Within our simultaneous extraction of polarized PDFs, unpolarized
PDFs, and pion and kaon fragmentation functions, we also extract a
self-consistent set of antiquark polarization ratios $\Delta \bar{u}/\bar{u}$
and $\Delta \bar{d}/\bar{d}$.


title: LEDNet: Joint Low-light Enhancement and Deblurring in the Dark

abstract:
  Night photography typically suffers from both low light and blurring issues
due to the dim environment and the common use of long exposure. While existing
light enhancement and deblurring methods could deal with each problem
individually, a cascade of such methods cannot work harmoniously to cope well
with joint degradation of visibility and textures. Training an end-to-end
network is also infeasible as no paired data is available to characterize the
coexistence of low light and blurs. We address the problem by introducing a
novel data synthesis pipeline that models realistic low-light blurring
degradations. With the pipeline, we present the first large-scale dataset for
joint low-light enhancement and deblurring. The dataset, LOL-Blur, contains
12,000 low-blur/normal-sharp pairs with diverse darkness and motion blurs in
different scenarios. We further present an effective network, named LEDNet, to
perform joint low-light enhancement and deblurring. Our network is unique as it
is specially designed to consider the synergy between the two inter-connected
tasks. Both the proposed dataset and network provide a foundation for this
challenging joint task. Extensive experiments demonstrate the effectiveness of
our method on both synthetic and real-world datasets.


title: Boundary actions of CAT(0) spaces and their $C^*$-algebras

abstract:
  In this paper, we study boundary actions of CAT(0) spaces from a point of
view of topological dynamics and $C^*$-algebras. First, we investigate the
actions of right-angled Coexter groups and right-angled Artin groups with
finite defining graphs on the visual boundaries and the Nevo-Sageev boundaries
of their natural assigned CAT(0) cube complexes. In particular, we establish
(strongly) pure infiniteness results for reduced crossed product $C^*$-algebras
of these actions through investigating the corresponding $\cat$ cube complexes
and establishing necessary dynamical properties such as minimality, topological
freeness and pure infiniteness of the actions. In addition, we study actions of
fundamental groups of graphs of groups on the visual boundaries of their
Bass-Serre trees. We show that the existence of repeatable paths essentially
implies that the action is $2$-filling, from which, we also obtain a large
class of unital Kirchberg algebras. Furthermore, our result also provides a new
method in identifying $C^*$-simple generalized Baumslag-Solitar groups. The
examples of groups obtained from our method have $n$-paradoxical towers in the
sense of \cite{G-G-K-N}. This class particularly contains non-degenerated free
products, Baumslag-Solitar groups and fundamental groups of $n$-circles or
wedge sums of $n$-circles.


title: The simulations chain of the MURAVES experiment

abstract:
  The MUon RAdiography of VESuvius (MURAVES) project aims at the study of the
summital cone of Mt. Vesuvius, an active and hazardous volcano near Naples,
Italy. A detailed Monte Carlo simulation framework is necessary in order to
investigate the effects of the experimental constraints and to perform
comparisons with the actual observations. Our Monte Carlo setup combines a
variety of Monte Carlo programs that address different aspects of cosmic muon
simulation, from muon generation in the Earth's upper atmosphere to the
response of the detector, including the interactions with the material of the
volcano. We will elaborate on the rationale for our technical choices,
including trade-off between speed and accuracy, and on the lessons learned,
which are of general interest for similar use cases in muon radiography.


title: Message Passing Neural PDE Solvers

abstract:
  The numerical solution of partial differential equations (PDEs) is difficult,
having led to a century of research so far. Recently, there have been pushes to
build neural--numerical hybrid solvers, which piggy-backs the modern trend
towards fully end-to-end learned systems. Most works so far can only generalize
over a subset of properties to which a generic solver would be faced,
including: resolution, topology, geometry, boundary conditions, domain
discretization regularity, dimensionality, etc. In this work, we build a
solver, satisfying these properties, where all the components are based on
neural message passing, replacing all heuristically designed components in the
computation graph with backprop-optimized neural function approximators. We
show that neural message passing solvers representationally contain some
classical methods, such as finite differences, finite volumes, and WENO
schemes. In order to encourage stability in training autoregressive models, we
put forward a method that is based on the principle of zero-stability, posing
stability as a domain adaptation problem. We validate our method on various
fluid-like flow problems, demonstrating fast, stable, and accurate performance
across different domain topologies, discretization, etc. in 1D and 2D. Our
model outperforms state-of-the-art numerical solvers in the low resolution
regime in terms of speed and accuracy.


title: Benchmarking and Analyzing Point Cloud Classification under Corruptions

abstract:
  3D perception, especially point cloud classification, has achieved
substantial progress. However, in real-world deployment, point cloud
corruptions are inevitable due to the scene complexity, sensor inaccuracy, and
processing imprecision. In this work, we aim to rigorously benchmark and
analyze point cloud classification under corruptions. To conduct a systematic
investigation, we first provide a taxonomy of common 3D corruptions and
identify the atomic corruptions. Then, we perform a comprehensive evaluation on
a wide range of representative point cloud models to understand their
robustness and generalizability. Our benchmark results show that although point
cloud classification performance improves over time, the state-of-the-art
methods are on the verge of being less robust. Based on the obtained
observations, we propose several effective techniques to enhance point cloud
classifier robustness. We hope our comprehensive benchmark, in-depth analysis,
and proposed techniques could spark future research in robust 3D perception.


title:
Gravitational collapse in AdS: instabilities, turbulence, and
  information

abstract:
  The gravitational collapse in asymptotically AdS spacetimes is studied, using
the differential configurational entropy (DCE). The DCE is shown to comply with
the instability of the Einstein-Klein-Gordon system, leading to different
stages in the route to turbulence and final black hole formation, including
near the critical behavior.


title:
Randomization Inference for Cluster-Randomized Test-Negative Designs
  with Application to Dengue Studies: Unbiased estimation, Partial compliance,
  and Stepped-wedge design

abstract:
  In 2019, the World Health Organization identified dengue as one of the top
ten global health threats. For the control of dengue, the Applying Wolbachia to
Eliminate Dengue (AWED) study group conducted a cluster-randomized trial in
Yogyakarta, Indonesia, and used a novel design, called the cluster-randomized
test-negative design (CR-TND). This design can yield valid statistical
inference with data collected by a passive surveillance system and thus has the
advantage of cost-efficiency compared to traditional cluster-randomized trials.
We investigate the statistical assumptions and properties of CR-TND under a
randomization inference framework, which is known to be robust and efficient
for small-sample problems. We find that, when the differential
healthcare-seeking behavior comparing intervention and control varies across
clusters (in contrast to the setting of Dufault and Jewell, 2020 where the
differential healthcare-seeking behavior is constant across clusters), current
analysis methods for CR-TND can be biased and have inflated type I error. We
propose the log-contrast estimator that can eliminate such bias and improve
precision by adjusting for covariates. Furthermore, we extend our methods to
handle partial intervention compliance and a stepped-wedge design, both of
which appear frequently in cluster-randomized trials. Finally, we demonstrate
our results by simulation studies and re-analysis of the AWED study.


title: Perturbative hysteresis and emergent resummation scales

abstract:
  We investigate hysteresis effects in the perturbative solution of
renormalisation group equations (RGEs). We present examples for the QCD running
coupling and proton's parton distribution functions (PDFs), relevant to
precision physics at the Large Hadron Collider (LHC) and future collider
experiments. We propose the use of resummation scales to take into account the
theoretical uncertainties from the solution of the RGEs. As a case study, we
consider the $F_2$ structure function in a region relevant to the extraction of
PDFs.


title: High-Energy Neutrinos from Active Galactic Nuclei

abstract:
  Active Galactic Nuclei (AGN) are sources of high-energy gamma-rays and are
considered to be promising candidates to be sources of high-energy cosmic rays
and neutrinos as well. We present and discuss various models for ion
acceleration and their interactions with matter and radiation leading to
high-energy neutrino production. We consider neutrino production mechanisms in
both jetted and non-jetted AGN, focusing on disks and coronae in the vicinity
of the central black hole, jet regions, and magnetized environments surrounding
the AGN. The IceCube Collaboration has reported high-energy neutrino events
that may come from both the jetted AGN TXS 0506+056 and the non-jetted AGN NGC
1068. We discuss the implications of these observations themselves as well as
the the origins of the all-sky neutrino intensity.


title: Corrupted Image Modeling for Self-Supervised Visual Pre-Training

abstract:
  We introduce Corrupted Image Modeling (CIM) for self-supervised visual
pre-training. CIM uses an auxiliary generator with a small trainable BEiT to
corrupt the input image instead of using artificial mask tokens, where some
patches are randomly selected and replaced with plausible alternatives sampled
from the BEiT output distribution. Given this corrupted image, an enhancer
network learns to either recover all the original image pixels, or predict
whether each visual token is replaced by a generator sample or not. The
generator and the enhancer are simultaneously trained and synergistically
updated. After pre-training, the enhancer can be used as a high-capacity visual
encoder for downstream tasks. CIM is a general and flexible visual pre-training
framework that is suitable for various network architectures. For the first
time, CIM demonstrates that both ViT and CNN can learn rich visual
representations using a unified, non-Siamese framework. Experimental results
show that our approach achieves compelling results in vision benchmarks, such
as ImageNet classification and ADE20K semantic segmentation. For example,
300-epoch CIM pre-trained vanilla ViT-Base/16 and ResNet-50 obtain 83.3 and
80.6 Top-1 fine-tuning accuracy on ImageNet-1K image classification
respectively.


title: Asymptotic of Bergman Kernel (Master Thesis)

abstract:
  In this master thesis, we give a new proof on the pointwise asymptotic
expansion for Bergman kernel of a hermitian holomorphic line bundle on the
points where the curvature of the line bundle is positive and satisfy local
spectral gap condition. The main point is to introduce a suitable
semi-classical symbol space and related symbolic calculus inspired from recent
work of Hsiao and Savale. Particularly, we establish the existence of pointwise
asymptotic expansion on the positive part for certain semi-positive line
bundles.


title: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval

abstract:
  With the recent boom of video-based social platforms (e.g., YouTube and
TikTok), video retrieval using sentence queries has become an important demand
and attracts increasing research attention. Despite the decent performance,
existing text-video retrieval models in vision and language communities are
impractical for large-scale Web search because they adopt brute-force search
based on high-dimensional embeddings. To improve efficiency, Web search engines
widely apply vector compression libraries (e.g., FAISS) to post-process the
learned embeddings. Unfortunately, separate compression from feature encoding
degrades the robustness of representations and incurs performance decay. To
pursue a better balance between performance and efficiency, we propose the
first quantized representation learning method for cross-view video retrieval,
namely Hybrid Contrastive Quantization (HCQ). Specifically, HCQ learns both
coarse-grained and fine-grained quantizations with transformers, which provide
complementary understandings for texts and videos and preserve comprehensive
semantic information. By performing Asymmetric-Quantized Contrastive Learning
(AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and
multiple fine-grained levels. This hybrid-grained learning strategy serves as
strong supervision on the cross-view video quantization model, where
contrastive learning at different levels can be mutually promoted. Extensive
experiments on three Web video benchmark datasets demonstrate that HCQ achieves
competitive performance with state-of-the-art non-compressed retrieval methods
while showing high efficiency in storage and computation. Code and
configurations are available at https://github.com/gimpong/WWW22-HCQ.


title: Using Multiwinner Voting to Search for Movies

abstract:
  We show a prototype of a system that uses multiwinner voting to suggest
resources (such as movies) related to a given query set (such as a movie that
one enjoys). Depending on the voting rule used, the system can either provide
resources very closely related to the query set or a broader spectrum of
options. We show how this ability can be interpreted as a way of controlling
the diversity of the results. We test our system both on synthetic data and on
the real-life collection of movie ratings from the MovieLens dataset. We also
present a visual comparison of the search results corresponding to selected
diversity levels.


title:
Closed Ricci Flows with Singularities Modeled on Asymptotically Conical
  Shrinkers

abstract:
  Given an asymptotically conical, shrinking, gradient Ricci soliton, we show
that there exists a Ricci flow solution on a closed manifold that forms a
finite-time singularity modeled on the given soliton. No symmetry or Kahler
assumptions on the soliton are required. The proof provides a precise
asymptotic description of the singularity formation.


title: Generalized Pell-Fermat equations and Pascal triangle

abstract:
  Using Pascal triangle, we give a simple generalization to the so-called
STRAND Puzzle solved by Srinivasa Ramanujan. Thus we are interested in
computing the median, first and third quartiles of some integer valued
distributions, arising naturally when extending partial sums of the arithmetic
progression (triangular numbers) to tetrahedral numbers and beyond. We show
this reduces to equations of Pell-Fermat type of higher order, which admit very
few integer solutions, but for which, following Ramanujan's original idea, we
can always find integer sequences of best approximation, in the Diophantine
sense. In absence of a general theory on Pell-Fermat equation of higher order,
our procedure relies much on formal Calculus with Mathematica.


title: Distributed Differentially Private Ranking Aggregation

abstract:
  Ranking aggregation is commonly adopted in cooperative decision-making to
assist in combining multiple rankings into a single representative. To protect
the actual ranking of each individual, some privacy-preserving strategies, such
as differential privacy, are often used. This, however, does not consider the
scenario where the curator, who collects all rankings from individuals, is
untrustworthy. This paper proposed a mechanism to solve the above situation
using the distribute differential privacy framework. The proposed mechanism
collects locally differential private rankings from individuals, then randomly
permutes pairwise rankings using a shuffle model to further amplify the privacy
protection. The final representative is produced by hierarchical rank
aggregation. The mechanism was theoretically analysed and experimentally
compared against existing methods, and demonstrated competitive results in both
the output accuracy and privacy protection.


title: Sensitivity to Dijet Resonances at Proton-Proton Colliders

abstract:
  A significant benchmark for discovery at a proton-proton collider is the
sensitivity to a dijet resonance, X, the intermediate state of the s-channel
process $pp \rightarrow X \rightarrow 2\mbox{ jets}$. To probe the highest
resonance masses, hadron collider experiments have used the classic technique
of searching for bumps in the mass spectrum of two individually resolved jets.
In this Snowmass 2021 study, we explore the search sensitivity to multiple
benchmark models of dijet resonances at current and future proton-proton
colliders. We present the expected masses for $5\sigma$ discovery and 95\%
confidence level exclusion of diquarks, colorons, excited quarks,
$W^{\prime}s$, $Z^{\prime}s$ and Randall-Sundrum gravitons, resulting from
accumulation of integrated luminosities between 10 and $10^5$ fb$^{-1}$, at
proton-proton colliders operating at energies $\sqrt{s}=$ 13, 14, 27, 75, 100,
150, 300 and 500 TeV.


title: GMC -- Geometric Multimodal Contrastive Representation Learning

abstract:
  Learning representations of multimodal data that are both informative and
robust to missing modalities at test time remains a challenging problem due to
the inherent heterogeneity of data obtained from different channels. To address
it, we present a novel Geometric Multimodal Contrastive (GMC) representation
learning method comprised of two main components: i) a two-level architecture
consisting of modality-specific base encoder, allowing to process an arbitrary
number of modalities to an intermediate representation of fixed dimensionality,
and a shared projection head, mapping the intermediate representations to a
latent representation space; ii) a multimodal contrastive loss function that
encourages the geometric alignment of the learned representations. We
experimentally demonstrate that GMC representations are semantically rich and
achieve state-of-the-art performance with missing modality information on three
different learning problems including prediction and reinforcement learning
tasks.


title:
Gradient-Based Learning of Discrete Structured Measurement Operators for
  Signal Recovery

abstract:
  Countless signal processing applications include the reconstruction of
signals from few indirect linear measurements. The design of effective
measurement operators is typically constrained by the underlying hardware and
physics, posing a challenging and often even discrete optimization task. While
the potential of gradient-based learning via the unrolling of iterative
recovery algorithms has been demonstrated, it has remained unclear how to
leverage this technique when the set of admissible measurement operators is
structured and discrete. We tackle this problem by combining unrolled
optimization with Gumbel reparametrizations, which enable the computation of
low-variance gradient estimates of categorical random variables. Our approach
is formalized by GLODISMO (Gradient-based Learning of DIscrete Structured
Measurement Operators). This novel method is easy-to-implement, computationally
efficient, and extendable due to its compatibility with automatic
differentiation. We empirically demonstrate the performance and flexibility of
GLODISMO in several prototypical signal recovery applications, verifying that
the learned measurement matrices outperform conventional designs based on
randomization as well as discrete optimization baselines.


title:
Large-scale Personalized Video Game Recommendation via Social-aware
  Contextualized Graph Neural Network

abstract:
  Because of the large number of online games available nowadays, online game
recommender systems are necessary for users and online game platforms. The
former can discover more potential online games of their interests, and the
latter can attract users to dwell longer in the platform. This paper
investigates the characteristics of user behaviors with respect to the online
games on the Steam platform. Based on the observations, we argue that a
satisfying recommender system for online games is able to characterize:
personalization, game contextualization and social connection. However,
simultaneously solving all is rather challenging for game recommendation.
Firstly, personalization for game recommendation requires the incorporation of
the dwelling time of engaged games, which are ignored in existing methods.
Secondly, game contextualization should reflect the complex and high-order
properties of those relations. Last but not least, it is problematic to use
social connections directly for game recommendations due to the massive noise
within social connections. To this end, we propose a Social-aware
Contextualized Graph Neural Recommender System (SCGRec), which harnesses three
perspectives to improve game recommendation. We conduct a comprehensive
analysis of users' online game behaviors, which motivates the necessity of
handling those three characteristics in the online game recommendation.


title:
Link Prediction of Artificial Intelligence Concepts using Low
  Computational Power

abstract:
  This paper presents an approach proposed for the Science4cast 2021
competition, organized by the Institute of Advanced Research in Artificial
Intelligence, whose main goal was to predict the likelihood of future
associations between machine learning concepts in a semantic network. The
developed methodology corresponds to a solution for a scenario of availability
of low computational power only, exploiting the extraction of low order
topological features and its incorporation in an optimized classifier to
estimate the degree of future connections between the nodes. The reasons that
motivated the developed methodologies will be discussed, as well as some
results, limitations and suggestions of improvements.


title:
Local mass-conserving solution for a critical Coagulation-Fragmentation
  equation

abstract:
  The critical coagulation-fragmentation equation with multiplicative
coagulation and constant fragmentation kernels is known to not have global
mass-conserving solutions when the initial mass is greater than $1$. We show
that for any given positive initial mass with finite second moment, there is a
time $T^*>0$ such that the equation possesses a unique mass-conserving solution
up to $T^*$. The novel idea is to singularly perturb the constant fragmentation
kernel by small additive terms and study the limiting behavior of the solutions
of the perturbed system via the Bernstein transform.


title: The excursion set model a step beyond: Environmental dependence

abstract:
  In terms of the excursion set model, we used Monte Carlo methods in order to
study the non-Markovian stochastic evolution of the smoothed overdensity
$\delta$ at scale $S$. For a Gaussian density field, smoothed by the top-hat
filter, in real space, we used random walks, with the correct correlation
between scales, in order to calculate constrained probabilities and to connect
the number of structures with the overdensity of their environment. A constant
barrier is used, lower than the usual one but which improves the consistency of
our results with those of N-body simulations.We present comparisons of
multiplicity functions with those resulting from N-body simulations as well as
number densities of descendant haloes and of their progenitors and we connect
them to the density of their large environment.There exists a dependence of the
number of haloes on their environment which is different for different masses
of these haloes.This number increases for larger overdensity of the environment
for large haloes and decreases for small ones. The number of progenitors of
these haloes is insensitive to the overdensity of their large environment.


title:
Emission from HCN and CH$_3$OH in comets. Onsala 20-m observations and
  radiative transfer modelling

abstract:
  The aim of this work is to characterize HCN and CH$_3$OH emission from recent
comets.
  We used the Onsala 20-m telescope to search for millimetre transitions of HCN
towards a sample of 11 recent and mostly bright comets in the period December
2016 to November 2019. Also CH$_3$OH was searched for in two comets. The HCN
sample includes the interstellar comet 2I/Borisov. For the short-period comet
46P/Wirtanen we could monitor the variation of HCN emission over a time span of
about one month. We performed radiative transfer modelling for the observed
molecular emission by also including time-dependent effects due to the
outgassing of molecules.
  HCN was detected in 6 comets. Two of these are short-period comets and 4 of
them are long-period. Six methanol transitions were detected in 46P/Wirtanen,
enabling us to determine the gas kinetic temperature. From the observations, we
determined the molecular production rates using time-dependent radiative
transfer modelling. For 5 comets, we could determine that the HCN mixing ratios
lie near 0.1% using contemporary water production rates, $Q_\mathrm{H_2O}$,
taken from other studies. This HCN mixing ratio was also found typical in our
monitoring observations of 46P/Wirtanen but here we notice deviations, on a
daily time scale, up to 0.2% which could indicate short-time changes in the
outgassing activity. From our radiative transfer modelling of cometary comae,
we found that time-dependent effects on the HCN level populations are of the
order 5-15% when $Q_\mathrm{H_2O}$ is around $2\times 10^{28}\,\mathrm{mol\,
s^{-1}}$. The effects may be relatively stronger for comets with lower
$Q_\mathrm{H_2O}$. The exact details of the time-dependent effects depend on
the amount of neutral and electron collisions, radiative pumping, and molecular
parameters such as the spontaneous rate coefficient.


title:
Bilevel Optimization with a Lower-level Contraction: Optimal Sample
  Complexity without Warm-Start

abstract:
  We analyze a general class of bilevel problems, in which the upper-level
problem consists in the minimization of a smooth objective function and the
lower-level problem is to find the fixed point of a smooth contraction map.
This type of problems include instances of meta-learning, hyperparameter
optimization and data poisoning adversarial attacks. Several recent works have
proposed algorithms which warm-start the lower-level problem, i.e. they use the
previous lower-level approximate solution as a staring point for the
lower-level solver. This warm-start procedure allows one to improve the sample
complexity in both the stochastic and deterministic settings, achieving in some
cases the order-wise optimal sample complexity. We show that without
warm-start, it is still possible to achieve order-wise optimal and near-optimal
sample complexity for the stochastic and deterministic settings, respectively.
In particular, we propose a simple method which uses stochastic fixed point
iterations at the lower-level and projected inexact gradient descent at the
upper-level, that reaches an $\epsilon$-stationary point using
$O(\epsilon^{-2})$ and $\tilde{O}(\epsilon^{-1})$ samples for the stochastic
and the deterministic setting, respectively. Compared to methods using
warm-start, ours is better suited for meta-learning and yields a simpler
analysis that does not need to study the coupled interactions between the
upper-level and lower-level iterates.


title:
Beam-Recoil Transferred Polarization in $K^+Y$ Electroproduction in the
  Nucleon Resonance Region with CLAS12

abstract:
  Beam-recoil transferred polarizations for the exclusive electroproduction of
$K^+\Lambda$ and $K^+\Sigma^0$ final states from an unpolarized proton target
have been measured using the CLAS12 spectrometer at Jefferson Laboratory. The
measurements at beam energies of 6.535~GeV and 7.546~GeV span the range of
four-momentum transfer $Q^2$ from 0.3 to 4.5~GeV$^2$ and invariant energy $W$
from 1.6 to 2.4~GeV, while covering the full center-of-mass angular range of
the $K^+$. These new data extend the existing hyperon polarization data from
CLAS in a similar kinematic range but from a significantly larger dataset. They
represent an important addition to the world data, allowing for better
exploration of the reaction mechanism in strangeness production processes, for
further understanding of the spectrum and structure of excited nucleon states,
and for improved insight into the strong interaction in the regime of
non-perturbative dynamics.


title:
AirNN: Neural Networks with Over-the-Air Convolution via Reconfigurable
  Intelligent Surfaces

abstract:
  Over-the-air analog computation allows offloading computation to the wireless
environment through carefully constructed transmitted signals. In this paper,
we design and implement the first-of-its-kind over-the-air convolution and
demonstrate it for inference tasks in a convolutional neural network (CNN). We
engineer the ambient wireless propagation environment through reconfigurable
intelligent surfaces (RIS) to design such an architecture, which we call
'AirNN'. AirNN leverages the physics of wave reflection to represent a digital
convolution, an essential part of a CNN architecture, in the analog domain. In
contrast to classical communication, where the receiver must react to the
channel-induced transformation, generally represented as finite impulse
response (FIR) filter, AirNN proactively creates the signal reflections to
emulate specific FIR filters through RIS. AirNN involves two steps: first, the
weights of the neurons in the CNN are drawn from a finite set of channel
impulse responses (CIR) that correspond to realizable FIR filters. Second, each
CIR is engineered through RIS, and reflected signals combine at the receiver to
determine the output of the convolution. This paper presents a proof-of-concept
of AirNN by experimentally demonstrating over-the-air convolutions. We then
validate the entire resulting CNN model accuracy via simulations for an example
task of modulation classification.


title: Private Read Update Write (PRUW) with Storage Constrained Databases

abstract:
  We investigate the problem of private read update write (PRUW) in relation to
federated submodel learning (FSL) with storage constrained databases. In PRUW,
a user privately reads a submodel from a system of $N$ databases containing $M$
submodels, updates it locally, and writes the update back to the databases
without revealing the submodel index or the value of the update. The databases
considered in this problem are only allowed to store a given amount of
information specified by an arbitrary storage constraint. We provide a storage
mechanism that determines the contents of each database prior to the
application of the PRUW scheme, such that the total communication cost is
minimized. We show that the proposed storage scheme achieves a lower total cost
compared to what is achieved by using \emph{coded storage} or \emph{divided
storage} to meet the given storage constraint.


title: TESS Transit Timing of Hundreds of Hot Jupiters

abstract:
  We provide a database of transit times and updated ephemerides for 382
planets based on data from the NASA Transiting Exoplanet Survey Satellite
(TESS) and previously reported transit times which were scraped from the
literature in a semi-automated fashion. In total, our database contains 8,667
transit timing measurements for 382 systems. About 240 planets in the catalog
are hot Jupiters (i.e. planets with mass $>$0.3$M_{\rm Jup}$ and period $<$10
days) that have been observed by TESS. The new ephemerides are useful for
scheduling follow-up observations and searching for long-term period changes.
WASP-12 remains the only system for which a period change is securely detected.
We remark on other cases of interest, such as a few systems with suggestive
(but not yet convincing) evidence for period changes, and the detection of a
second transiting planet in the NGTS-11 system. The compilation of light
curves, transit times, ephemerides, and timing residuals are made available
online, along with the Python code that generated them (visit
https://transit-timing.github.io).


title: Preserving Privacy and Security in Federated Learning

abstract:
  Federated learning is known to be vulnerable to security and privacy issues.
Existing research has focused either on preventing poisoning attacks from users
or on protecting user privacy of model updates. However, integrating these two
lines of research remains a crucial challenge since they often conflict with
one another with respect to the threat model.
  In this work, we develop a framework to combine secure aggregation with
defense mechanisms against poisoning attacks from users, while maintaining
their respective privacy guarantees. We leverage zero-knowledge proof protocol
to let users run the defense mechanisms locally and attest the result to the
central server without revealing any information about their model updates.
Furthermore, we propose a new secure aggregation protocol for federated
learning using homomorphic encryption that is robust against malicious users.
Our framework enables the central server to identify poisoned model updates
without violating the privacy guarantees of secure aggregation. Finally, we
analyze the computation and communication complexity of our proposed solution
and benchmark its performance.


title: R-Matrix presentation of quantum affine algebra in type $A_{2n-1}^{(2)}$

abstract:
  In this paper, we give an RTT presentation of the twisted quantum affine
algebra of type $A_{2n-1}^{(2)}$ and show that it is isomorphic to the Drinfeld
new realization via the Gauss decomposition of the L-operators. This provides
the first such presentation for twisted quantum affine algebras with nontrivial
central element.


title:
Understanding nature's purpose in starting all new lives with compound
  growth -- New science for individual systems

abstract:
  We often associate compound growth with the Anthropocene and our overwhelming
economic impacts on the Earth. Today our actual choices for future society
appear to lie with studying the class of emergent natural systems that first
develop by compound growth, called 'new lives' here, or "natural complex
adaptive systems" (NCAS). The key to their success is first starting with
compound growth, a system for growing the system. Its self-organization needs
to shift resources from multiplying its scale to maturing and adapting its
design for its new environment, a challenging environmental coordination
process.
  We see that taking place in biological reproduction all the time, of course,
but it all needs to occur for businesses, economies, cultures, and even for
creative work and personal relationships to survive their initial periods of
compound growth. Why economic growth as a process that accelerates ever faster
still seems unlimited to some seems to come from representing growth as a
numerical rather than organizational development process. For example, all
kinds of adaptive system organization have limits to how fast they can
rearrange their systems.
  To understand the kinds of limits and adaptations that lead to a successful
growth climax and perhaps a long creative life to follow, we combine the most
notable observed milestones of successful growth found in nature, divided into
3 main development periods. Those milestones of new lives (MNL) teach us
alternative paths to follow The primary milestones include 3 critical events,
initiating 3 feedback periods, for 3 organizational development stages,
occurring in 3 environments. Together they can be called the A, B, C, egg,
nest, world, or natural growth pattern. By observing them in familiar cases, we
learn the patterns that also apply in others.


title: Half-automorphism group of a class of Bol loops

abstract:
  A Bol loop is a loop that satisfies the Bol identity $(xy.z)y=x(yz.y)$. If
$L$ is a loop and $f:L\to L$ is a bijection such that
$f(xy)\in\{f(x)f(y),f(y)f(x)\}$, for every $x$, $y\in L$, then $f$ is called a
half-automorphism of $L$. In this paper, we describe the half-automorphism
group of a class of Bol loops of order $4m$.


title: Dependence model assessment and selection with DecoupleNets

abstract:
  Neural networks are suggested for learning a map from $d$-dimensional samples
with any underlying dependence structure to multivariate uniformity in $d'$
dimensions. This map, termed DecoupleNet, is used for dependence model
assessment and selection. If the data-generating dependence model was known,
and if it was among the few analytically tractable ones, one such
transformation for $d'=d$ is Rosenblatt's transform. DecoupleNets only require
an available sample and are applicable to $d'<d$, in particular $d'=2$. This
allows for simpler model assessment and selection without loss of information,
both numerically and, because $d'=2$, graphically. Through simulation studies
based on data from various copulas, the feasibility and validity of this novel
approach is demonstrated. Applications to real world data illustrate its
usefulness for model assessment and selection.

categories: stat.ML cs.LG q-fin.CP q-fin.RM stat.AP stat.CO


title:
Investigating the fidelity of explainable artificial intelligence
  methods for applications of convolutional neural networks in geoscience

abstract:
  Convolutional neural networks (CNNs) have recently attracted great attention
in geoscience due to their ability to capture non-linear system behavior and
extract predictive spatiotemporal patterns. Given their black-box nature
however, and the importance of prediction explainability, methods of
explainable artificial intelligence (XAI) are gaining popularity as a means to
explain the CNN decision-making strategy. Here, we establish an intercomparison
of some of the most popular XAI methods and investigate their fidelity in
explaining CNN decisions for geoscientific applications. Our goal is to raise
awareness of the theoretical limitations of these methods and gain insight into
the relative strengths and weaknesses to help guide best practices. The
considered XAI methods are first applied to an idealized attribution benchmark,
where the ground truth of explanation of the network is known a priori, to help
objectively assess their performance. Secondly, we apply XAI to a
climate-related prediction setting, namely to explain a CNN that is trained to
predict the number of atmospheric rivers in daily snapshots of climate
simulations. Our results highlight several important issues of XAI methods
(e.g., gradient shattering, inability to distinguish the sign of attribution,
ignorance to zero input) that have previously been overlooked in our field and,
if not considered cautiously, may lead to a distorted picture of the CNN
decision-making strategy. We envision that our analysis will motivate further
investigation into XAI fidelity and will help towards a cautious implementation
of XAI in geoscience, which can lead to further exploitation of CNNs and deep
learning for prediction problems.


title: Sensitivity Analysis in the Generalization of Experimental Results

abstract:
  Randomized controlled trials (RCT's) allow researchers to estimate causal
effects in an experimental sample with minimal identifying assumptions.
However, to generalize or transport a causal effect from an RCT to a target
population, researchers must adjust for a set of treatment effect moderators.
In practice, it is impossible to know whether the set of moderators has been
properly accounted for. In the following paper, I propose a three parameter
sensitivity analysis for generalizing or transporting experimental results
using weighted estimators, with several advantages over existing methods.
First, the framework does not require assumptions on the underlying data
generating process for either the experimental sample selection mechanism or
treatment effect heterogeneity. Second, I show that the sensitivity parameters
are guaranteed to be bounded and propose several tools researchers can use to
perform sensitivity analysis: (1) graphical and numerical summaries for
researchers to assess how robust a point estimate is to killer confounders; (2)
an extreme scenario analysis; and (3) a formal benchmarking approach for
researchers to estimate potential sensitivity parameter values using existing
data. Finally, I demonstrate that the proposed framework can be easily extended
to the class of doubly robust, augmented weighted estimators. The sensitivity
analysis framework is applied to a set of Jobs Training Program experiments.


title:
Effects of local curvature on epithelia tissue -- coordinated rotational
  movement and other spatiotemporal arrangements

abstract:
  Coordinated movements of epithelia tissue are linked with active matter
processes. We here consider the influence of curvature on the spatiotemporal
arrangements and the shapes of the cells. The cells are represented by a
multiphase field model which is defined on the surface of a sphere. Besides the
classical solid and liquid phases, which depend on the curvature of the sphere,
on mechanical properties of the cells and the strength of activity, we identify
a phase of global rotation. This rotation provides a coordinated cellular
movement which can be linked to tissue morphogenesis. This investigation on a
sphere is a first step to investigate the delicate interplay between
topological constraints, geometric properties and collective motion. Besides
the rotational state we also analyse positional defects, identify global
nematic order and study the associated orientational defects.


title:
A high order unfitted hybridizable discontinuous Galerkin method for
  linear elasticity

abstract:
  This work analyzes a high order hybridizable discontinuous Galerkin (HDG)
method for the linear elasticity problem in a domain not necessarily
polyhedral. The domain is approximated by a polyhedral computational domain
where the HDG solution can be computed. The introduction of the rotation as one
of the unknowns allows us to use the gradient of the displacements to obtain an
explicit representation of the boundary data in the computational domain. The
boundary data is transferred from the true boundary to the computational
boundary by line integrals, where the integrand depends on the Cauchy stress
tensor and the rotation. Under closeness assumptions between the computational
and true boundaries, the scheme is shown to be well-posed and optimal error
estimates are provided even in the nearly incompressible. Numerical experiments
in two-dimensions are presented.


title:
The Scatman: an approximate method for fast wide-angle scattering
  simulations

abstract:
  Single-shot Coherent Diffraction Imaging (CDI) is a powerful approach to
characterize the structure and dynamics of isolated nanoscale objects such as
single viruses, aerosols, nanocrystals or droplets. Using X-ray wavelengths,
the diffraction images in CDI experiments usually cover only small scattering
angles of few degrees. These small-angle patterns represent the magnitude of
the Fourier transform of the two-dimensional projection of the sample's
electron density, which can be reconstructed efficiently but lacks any depth
information. In cases where the diffracted signal can be measured up to
scattering angles exceeding 10 degrees, i.e. in the wide-angle regime,
three-dimensional morphological information of the target is contained in a
single-shot diffraction pattern. However, the extraction of the 3D structural
information is no longer straightforward and defines the key challenge in
wide-angle CDI. So far, the most convenient approach relies on iterative
forward fitting of the scattering pattern using scattering simulations. Here we
present the Scatman, an approximate and fast numerical tool for the simulation
and iterative fitting of wide-angle scattering images of isolated samples.
Furthermore, we publish and describe in detail our Open Source software
implementation of the Scatman algorithm, PyScatman. The Scatman approach yields
quantitative results for weakly scattering samples. PyScatman is capable of
computing wide-angle scattering patterns in few milliseconds even on
consumer-level computing hardware. The high computational efficiency of
PyScatman enables effective data analysis based on model fitting, thus
representing an important step towards a systematic application of 3D Coherent
Diffraction Imaging from single wide-angle diffraction patterns in various
scientific communities.


title: Hierarchies of compatible maps and integrable difference systems

abstract:
  We present two non-equivalent hierarchies of non-Abelian $3D-$compatible maps
and we provide their Lax pair formulation. These hierarchies are naturally
associated with integrable difference systems with variables defined on edges
of an elementary cell of the $\mathbb{Z}^2$ graph, that in turn lead to
hierarchies of difference systems with variables defined on vertices of the
same cell.


title:
The Marginal Labor Supply Disincentives of Welfare: Evidence from
  Administrative Barriers to Participation

abstract:
  Existing research on the static effects of the manipulation of welfare
program benefit parameters on labor supply has allowed only restrictive forms
of heterogeneity in preferences. Yet preference heterogeneity implies that the
marginal effects on labor supply of welfare expansions and contractions may
differ in different time periods with different populations and which sweep out
different portions of the distribution of preferences. A new examination of the
heavily studied AFDC program uses variation in state-level administrative
barriers to entering the program in the late 1980s and early 1990s to estimate
the marginal labor supply effects of changes in program participation induced
by that variation. The estimates are obtained from a theory-consistent reduced
form model which allows for a nonparametric specification of how changes in
welfare program participation affect labor supply on the margin. Estimates
using a form of local instrumental variables show that the marginal treatment
effects are quadratic, rising and then falling as participation rates rise
(i.e., becoming more negative then less negative on hours of work). The average
work disincentive is not large but that masks some margins where effects are
close to zero and some which are sizable. Traditional IV which estimates a
weighted average of marginal effects gives a misleading picture of marginal
responses. A counterfactual exercise which applies the estimates to three
historical reform periods in 1967, 1981, and 1996 when the program tax rate was
significantly altered shows that marginal labor supply responses differed in
each period because of differences in the level of participation in the period
and the composition of who was on the program.


title:
Mini-jet quenching in a concurrent jet+hydro evolution and the
  non-equilibrium quark-gluon plasma

abstract:
  Mini-jets, created by perturbative hard QCD collisions at moderate energies,
can represent a significant portion of the total multiplicity of a heavy-ion
collision event. Since their transverse momenta are initially larger than the
typical saturation scale describing the bulk of the equilibrating QGP, their
typical stopping distances are larger than the usual hydrodynamization time, so
they do not in general hydrodynamize at the same pace than the bulk of the
collision. Therefore, in general mini-jets cannot be described solely by a
unique pre-equilibrium stage that bridges the initial, over-occupied glasma
state, with the hydrodynamical evolution. In this work we make use of a new
concurrent mini-jet+hydrodynamic framework in which the properties of the
hydrodynamically evolving QGP are modified due to the injection of energy and
momentum from the mini-jets. We study the system for different choices of the
minimum transverse momentum associated to mini-jet production. In order to
achieve a realistic description of charged particle multiplicity, the amount of
entropy associated to the low-$x$ initial state needs to be reduced. Moreover,
the fact that the injected momentum from the randomly oriented mini-jets is not
correlated with the spatial gradients of the system reduces overall flow, and
the value of the QGP transport coefficients needs to be reduced accordingly.
They are an important new source of fluctuations, resulting in a spikier,
notably modified hydrodynamical evolution. We discuss the impact of the
mini-jets on a number of observables, such as $p_T$ spectra and
$p_T$-differential flow $v_n$ for a wide range of centrality classes. In
contrast to elliptic, triangular or quadrangular flow, we find that directed
flow, $v_1$, has the strongest potential to discriminate between different
mini-jet production rates.


title: PopNet: Real-Time Population-Level Disease Prediction with Data Latency

abstract:
  Population-level disease prediction estimates the number of potential
patients of particular diseases in some location at a future time based on
(frequently updated) historical disease statistics. Existing approaches often
assume the existing disease statistics are reliable and will not change.
However, in practice, data collection is often time-consuming and has time
delays, with both historical and current disease statistics being updated
continuously. In this work, we propose a real-time population-level disease
prediction model which captures data latency (PopNet) and incorporates the
updated data for improved predictions. To achieve this goal, PopNet models
real-time data and updated data using two separate systems, each capturing
spatial and temporal effects using hybrid graph attention networks and
recurrent neural networks. PopNet then fuses the two systems using both spatial
and temporal latency-aware attentions in an end-to-end manner. We evaluate
PopNet on real-world disease datasets and show that PopNet consistently
outperforms all baseline disease prediction and general spatial-temporal
prediction models, achieving up to 47% lower root mean squared error and 24%
lower mean absolute error compared with the best baselines.


title:
Deep Impulse Responses: Estimating and Parameterizing Filters with Deep
  Networks

abstract:
  Impulse response estimation in high noise and in-the-wild settings, with
minimal control of the underlying data distributions, is a challenging problem.
We propose a novel framework for parameterizing and estimating impulse
responses based on recent advances in neural representation learning. Our
framework is driven by a carefully designed neural network that jointly
estimates the impulse response and the (apriori unknown) spectral noise
characteristics of an observed signal given the source signal. We demonstrate
robustness in estimation, even under low signal-to-noise ratios, and show
strong results when learning from spatio-temporal real-world speech data. Our
framework provides a natural way to interpolate impulse responses on a spatial
grid, while also allowing for efficiently compressing and storing them for
real-time rendering applications in augmented and virtual reality.


title: Fate of higher-order topological insulator under Coulomb interaction

abstract:
  In this article, we study the influence of long-range Coulomb interaction on
three-dimensional second-order topological insulator (TI) by renormalization
group theory. We find that both the analysis method and conclusion in the
recent Letter Phys. Rev. Lett. {\bf 127}, 176001 (2021) are unreliable. There
are two problems in this Letter. Firstly, the characteristic described by the
RG flows $m\rightarrow\infty$ and $D\rightarrow0$ can not be used as the
criterion for transition from second-order TI to TI, since this characteristic
could be essentially not induced by Coulomb interaction but only results from
the trivial power counting contribution of fermion action. Indeed, this
characteristic is satisfied even for free second-order TI. Second, the flow of
$B$ is not paid attention, which is very important and should be seriously
studied. In this article, we analyze carefully the corrections for the flows of
the model parameters induced by Coulomb interaction. We find that the sign of
$m$ changes but the sign of $B$ holds if the initial Coulomb strength is large
enough, while the sign of $m$ holds but the sign of $B$ changes if the initial
Coulomb strength takes small values. These results indicate that the
second-order TI is unstable to trivial band insulator not only under strong
Coulomb interaction but also under weak Coulomb interaction.


title: Diversify and Disambiguate: Learning From Underspecified Data

abstract:
  Many datasets are underspecified, which means there are several equally
viable solutions for the data. Underspecified datasets can be problematic for
methods that learn a single hypothesis because different functions that achieve
low training loss can focus on different predictive features and thus have
widely varying predictions on out-of-distribution data. We propose DivDis, a
simple two-stage framework that first learns a diverse collection of hypotheses
for a task by leveraging unlabeled data from the test distribution. We then
disambiguate by selecting one of the discovered hypotheses using minimal
additional supervision, in the form of additional labels or inspection of
function visualization. We demonstrate the ability of DivDis to find hypotheses
that use robust features in image classification and natural language
processing problems with underspecification.


title:
DarkFlux: A new tool to analyze indirect-detection spectra of
  next-generation dark matter models

abstract:
  We present DarkFlux, a software tool designed to analyze indirect-detection
signatures for next-generation models of dark matter (DM) with multiple
annihilation channels. Version 1.0 of this tool accepts user-generated models
with $2\to 2$ tree-level dark matter annihilation to pairs of Standard Model
(SM) particles and analyzes DM annihilation to $\gamma$ rays. The tool consists
of three modules -- the annihilation fraction module, the flux module, and the
analysis module -- which can be run in a loop in order to scan over DM mass if
desired. [A description of each module is available in the full abstract.]
DarkFlux v1.0 compares the total $\gamma$-ray flux to a joint-likelihood
analysis of fifteen dwarf spheroidal galaxies (dSphs) analyzed by the
$Fermi$-LAT collaboration. DarkFlux automatically provides data tables and can
plot the output of the three modules. In this manual, we briefly motivate this
indirect-detection computer tool and review the essential DM physics. We then
describe the several modules of DarkFlux in greater detail. Finally, we show
how to install and run DarkFlux and provide two worked examples demonstrating
its capabilities.


title: On some topological properties of normed Boolean algebras

abstract:
  This paper concerns the compactness and separability properties of the normed
Boolean algebras (N.B.A.) with respect to topology generated by a distance
equal to the square root of a measure of symmetric difference between two
elements. The motivation arises from studying random elements talking values in
N.B.A.
  Those topological properties are important assumptions that enable us to
avoid possible difficulties when generalising concepts of random variable
convergence, the definition of conditional law and others.
  For each N.B.A., there exists a finite measure space $(E, {\mathcal E}, \mu)$
such that the N.B.A. is isomorphic to $(\widetilde{\mathcal E},
\widetilde{\mu})$ resulting from the factorisation of initial $\sigma$-algebra
by the ideal of negligible sets. We focus on topological properties of
$(\widetilde{\mathcal E}, \widetilde{\mu})$ in general setting when $\mu$ can
be an infinite measure. In case when $\mu$ is infinite, we also consider
properties of $\widetilde{\mathcal E}_{fin} \subseteq \widetilde{\mathcal E}$
consisting of classes of measurable sets having finite measure.
  The compactness and separability of the N.B.A. are characterised using the
newly defined terms of approximability and uniform approximability of the
corresponding measure space.
  Finally, conditions on $(E,\mathcal E,\mu)$ are derived for separability and
compactness of $\widetilde{\mathcal E}$ and $\widetilde{\mathcal E}_{fin}.$


title:
The Effect of Adiabatic Compression on Dark Matter Halos and the Radial
  Acceleration Relation

abstract:
  We use a semi-empirical model to investigate the radial acceleration relation
(RAR) in a cold dark matter (CDM) framework. Specifically, we build 80 model
galaxies covering the same parameter space as the observed galaxies in the
SPARC database, assigning them to dark matter halos using abundance matching
and halo mass-concentration relations. We consider several abundance matching
relations, finding some to be a better match to the kinematic data than others.
We compute the unavoidable gravitational interactions between baryons and their
dark matter halos, leading to an overall compression of the original NFW halos.
Before halo compression, high-mass galaxies approximately lie on the observed
RAR whereas low-mass galaxies display up-bending "hooks" at small radii due to
DM cusps, making them deviate systematically from the observed relation. After
halo compression, the initial NFW halos become more concentrated at small
radii, making larger contributions to rotation curves. This increases the total
accelerations, moving all model galaxies away from the observed relation. These
systematic deviations suggest that the CDM model with abundance matching alone
cannot explain the observed RAR. Further effects (e.g., feedback) would need to
counteract the compression with precisely the right amount of halo expansion,
even in high mass galaxies with deep potential wells where such effects are
generally predicted to be negligible.


title:
Development of a deep learning platform for optimising sheet stamping
  geometries subject to manufacturing constraints

abstract:
  The latest sheet stamping processes enable efficient manufacturing of complex
shape structural components that have high stiffness to weight ratios, but
these processes can introduce defects. To assist component design for stamping
processes, this paper presents a novel deep-learning-based platform for
optimising 3D component geometries. The platform adopts a non-parametric
modelling approach that is capable of optimising arbitrary geometries from
multiple geometric parameterisation schema. This approach features the
interaction of two neural networks: 1) a geometry generator and 2) a
manufacturing performance evaluator. The generator predicts continuous 3D
signed distance fields (SDFs) for geometries of different classes, and each SDF
is conditioned on a latent vector. The zero-level-set of each SDF implicitly
represents a generated geometry. Novel training strategies for the generator
are introduced and include a new loss function which is tailored for sheet
stamping applications. These strategies enable the differentiable generation of
high quality, large scale component geometries with tight local features for
the first time. The evaluator maps a 2D projection of these generated
geometries to their post-stamping physical (e.g., strain) distributions.
Manufacturing constraints are imposed based on these distributions and are used
to formulate a novel objective function for optimisation. A new gradient-based
optimisation technique is employed to iteratively update the latent vectors,
and therefore geometries, to minimise this objective function and thus meet the
manufacturing constraints. Case studies based on optimising box geometries
subject to a sheet thinning constraint for a hot stamping process are presented
and discussed. The results show that expressive geometric changes are
achievable, and that these changes are driven by stamping performance.


title: Backdoor Defense via Decoupling the Training Process

abstract:
  Recent studies have revealed that deep neural networks (DNNs) are vulnerable
to backdoor attacks, where attackers embed hidden backdoors in the DNN model by
poisoning a few training samples. The attacked model behaves normally on benign
samples, whereas its prediction will be maliciously changed when the backdoor
is activated. We reveal that poisoned samples tend to cluster together in the
feature space of the attacked DNN model, which is mostly due to the end-to-end
supervised training paradigm. Inspired by this observation, we propose a novel
backdoor defense via decoupling the original end-to-end training process into
three stages. Specifically, we first learn the backbone of a DNN model via
\emph{self-supervised learning} based on training samples without their labels.
The learned backbone will map samples with the same ground-truth label to
similar locations in the feature space. Then, we freeze the parameters of the
learned backbone and train the remaining fully connected layers via standard
training with all (labeled) training samples. Lastly, to further alleviate
side-effects of poisoned samples in the second stage, we remove labels of some
`low-credible' samples determined based on the learned model and conduct a
\emph{semi-supervised fine-tuning} of the whole model. Extensive experiments on
multiple benchmark datasets and DNN models verify that the proposed defense is
effective in reducing backdoor threats while preserving high accuracy in
predicting benign samples. Our code is available at
\url{https://github.com/SCLBD/DBD}.


title:
Reinforcement learning for multi-item retrieval in the puzzle-based
  storage system

abstract:
  Nowadays, fast delivery services have created the need for high-density
warehouses. The puzzle-based storage system is a practical way to enhance the
storage density, however, facing difficulties in the retrieval process. In this
work, a deep reinforcement learning algorithm, specifically the Double&Dueling
Deep Q Network, is developed to solve the multi-item retrieval problem in the
system with general settings, where multiple desired items, escorts, and I/O
points are placed randomly. Additionally, we propose a general compact integer
programming model to evaluate the solution quality. Extensive numerical
experiments demonstrate that the reinforcement learning approach can yield
high-quality solutions and outperforms three related state-of-the-art heuristic
algorithms. Furthermore, a conversion algorithm and a decomposition framework
are proposed to handle simultaneous movement and large-scale instances
respectively, thus improving the applicability of the PBS system.


title:
Influence of different factors on survival of patients with colorectal
  cancer

abstract:
  Colorectal cancer refers to the cancer from the dentate line to the junction
of rectosigmoid colon, which is one of the most common malignant tumors of the
digestive tract. The treatment of colorectal cancer is controversial, so
understanding the risk factors and survival factors of colorectal cancer is of
great significance for the diagnosis of patients. This study sampled patients
with colorectal cancer from the SEER database. The factors affecting the
survival of colorectal cancer patients were analysed by combining principal
component analysis and competitive risk model, and the survival time of
patients was analysed by combining principal component analysis and linear
regression. Finally, the data were predicted. The results show that principal
component analysis can effectively reduce the number of variables, and the
combination of competitive risk model and linear regression model can
effectively analyse and predict the data.


title: A Vaidya-type spacetime with no singularities

abstract:
  A regular Vaidya-type line-element is proposed in this work. The mass
function depends both on the temporal and the spatial coordinates. The
curvature invariants and the source stress tensor $T^{a}_{~b}$ are finite in
the whole space. The energy conditions for $T^{a}_{~b}$ are satisfied if
$k^{2}<2vr$, where $k$ is a positive constant and $v,r$ are coordinates. It is
found that the radial pressure has a maximum very close to $r = 2m~ (r>2m), v =
2m$. The energy crossing a sphere of constant radius is akin to
Lundgren-Schmekel-York quasilocal energy. The Newtonian acceleration of the
timelike geodesics has an extra term (compared to the result of Piesnack and
Kassner) which leads to rejecting effects.


title: Performance Evaluation of Infrared Image Enhancement Techniques

abstract:
  Infrared (IR) images are widely used in many fields such as medical imaging,
object tracking, astronomy and military purposes for securing borders. Infrared
images can be captured day or night based on the type of capturing device. The
capturing devices use electromagnetic radiation with longer wavelengths. There
are several types of IR radiation based on the range of wavelength and
corresponding frequency. Due to noising and other artifacts, IR images are not
clearly visible. In this paper, we present a complete up-todate survey on IR
imaging enhancement techniques. The survey includes IR radiation types and
devices and existing IR datasets. The survey covers spatial enhancement
techniques, frequency-domain based enhancement techniques and Deep
learning-based techniques.


title:
A Reliable Data-transmission Mechanism using Blockchain in Edge
  Computing Scenarios

abstract:
  With the advent of the Internet of things (IoT) era, more and more devices
are connected to the IoT. Under the traditional cloud-thing centralized
management mode, the transmission of massive data is facing many difficulties,
and the reliability of data is difficult to be guaranteed. As emerging
technologies, blockchain technology and edge computing (EC) technology have
attracted the attention of academia in improving the reliability, privacy and
invariability of IoT technology. In this paper, we combine the characteristics
of the EC and blockchain to ensure the reliability of data transmission in the
IoT. First of all, we propose a data transmission mechanism based on
blockchain, which uses the distributed architecture of blockchain to ensure
that the data is not tampered with; secondly, we introduce the three-tier
structure in the architecture in turn; finally, we introduce the four working
steps of the mechanism, which are similar to the working mechanism of
blockchain. In the end, the simulation results show that the proposed scheme
can ensure the reliability of data transmission in the Internet of things to a
great extent.


title:
VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm
  Considering Multi-criteria Decision Making

abstract:
  With the development of science and technology and the need for
Multi-Criteria Decision-Making (MCDM), the optimization problem to be solved
becomes extremely complex. The theoretically accurate and optimal solutions are
often difficult to obtain. Therefore, meta-heuristic algorithms based on
multi-point search have received extensive attention. Aiming at these problems,
the design strategy of hybrid flower pollination algorithm for Virtual Network
Embedding (VNE) problem is discussed. Combining the advantages of the Genetic
Algorithm (GA) and FPA, the algorithm is optimized for the characteristics of
discrete optimization problems. The cross operation is used to replace the
cross-pollination operation to complete the global search and replace the
mutation operation with self-pollination operation to enhance the ability of
local search. Moreover, a life cycle mechanism is introduced as a complement to
the traditional fitness-based selection strategy to avoid premature
convergence. A chaotic optimization strategy is introduced to replace the
random sequence-guided crossover process to strengthen the global search
capability and reduce the probability of producing invalid individuals.


title: A Topology-Attention ConvLSTM Network and Its Application to EM Images

abstract:
  Structural accuracy of segmentation is important for finescale structures in
biomedical images. We propose a novel TopologyAttention ConvLSTM Network
(TACNet) for 3D image segmentation in order to achieve high structural accuracy
for 3D segmentation tasks. Specifically, we propose a Spatial
Topology-Attention (STA) module to process a 3D image as a stack of 2D image
slices and adopt ConvLSTM to leverage contextual structure information from
adjacent slices. In order to effectively transfer topology-critical information
across slices, we propose an Iterative-Topology Attention (ITA) module that
provides a more stable topology-critical map for segmentation. Quantitative and
qualitative results show that our proposed method outperforms various baselines
in terms of topology-aware evaluation metrics.


title: On the List Color Function Threshold

abstract:
  The chromatic polynomial of a graph $G$, denoted $P(G,m)$, is equal to the
number of proper $m$-colorings of $G$. The list color function of graph $G$,
denoted $P_{\ell}(G,m)$, is a list analogue of the chromatic polynomial that
has been studied since the early 1990s, primarily through comparisons with the
corresponding chromatic polynomial. It is known that for any graph $G$ there is
a $k \in \mathbb{N}$ such that $P_\ell(G,m) = P(G,m)$ whenever $m \geq k$. The
list color function threshold of $G$, denoted $\tau(G)$, is the smallest $k
\geq \chi(G)$ such that $P_{\ell}(G,m) = P(G,m)$ whenever $m \geq k$. In 2009,
Thomassen asked whether there is a universal constant $\alpha$ such that for
any graph $G$, $\tau(G) \leq \chi_{\ell}(G) + \alpha$, where $\chi_{\ell}(G)$
is the list chromatic number of $G$. We show that the answer to this question
is no by proving that there exists a constant $C$ such that $\tau(K_{2,l}) -
\chi_{\ell}(K_{2,l}) \ge C\sqrt{l}$ for $l \ge 16$.


title: Inference of captions from histopathological patches

abstract:
  Computational histopathology has made significant strides in the past few
years, slowly getting closer to clinical adoption. One area of benefit would be
the automatic generation of diagnostic reports from H\&E-stained whole slide
images which would further increase the efficiency of the pathologists' routine
diagnostic workflows. In this study, we compiled a dataset (PatchGastricADC22)
of histopathological captions of stomach adenocarcinoma endoscopic biopsy
specimens, which we extracted from diagnostic reports and paired with patches
extracted from the associated whole slide images. The dataset contains a
variety of gastric adenocarcinoma subtypes. We trained a baseline
attention-based model to predict the captions from features extracted from the
patches and obtained promising results. We make the captioned dataset of 262K
patches publicly available.