title:
Masked Spectrogram Modeling using Masked Autoencoders for Learning
  General-purpose Audio Representation

abstract:
  Recent general-purpose audio representations show state-of-the-art
performance on various audio tasks. These representations are pre-trained by
self-supervised learning methods that create training signals from the input.
For example, typical audio contrastive learning uses temporal relationships
among input sounds to create training signals, whereas some methods use a
difference among input views created by data augmentations. However, these
training signals do not provide information derived from the intact input
sound, which we think is suboptimal for learning representation that describes
the input as it is.
  In this paper, we seek to learn audio representations from the input itself
as supervision using a pretext task of auto-encoding of masked spectrogram
patches, Masked Spectrogram Modeling (MSM, a variant of Masked Image Modeling
applied to audio spectrogram). To implement MSM, we use Masked Autoencoders
(MAE), an image self-supervised learning method. MAE learns to efficiently
encode the small number of visible patches into latent representations to carry
essential information for reconstructing a large number of masked patches.
While training, MAE minimizes the reconstruction error, which uses the input as
training signal, consequently achieving our goal.
  We conducted experiments on our MSM using MAE (MSM-MAE) models under the
evaluation benchmark of the HEAR 2021 NeurIPS Challenge. Our MSM-MAE models
outperformed the HEAR 2021 Challenge results on seven out of 15 tasks (e.g.,
accuracies of 73.4% on CREMA-D and 85.8% on LibriCount), while showing top
performance on other tasks where specialized models perform better. We also
investigate how the design choices of MSM-MAE impact the performance and
conduct qualitative analysis of visualization outcomes to gain an understanding
of learned representations. We make our code available online.


title: Managing Reliability Skew in DNA Storage

abstract:
  DNA is emerging as an increasingly attractive medium for data storage due to
a number of important and unique advantages it offers, most notably the
unprecedented durability and density. While the technology is evolving rapidly,
the prohibitive cost of reads and writes, the high frequency and the peculiar
nature of errors occurring in DNA storage pose a significant challenge to its
adoption. In this work we make a novel observation that the probability of
successful recovery of a given bit from any type of a DNA-based storage system
highly depends on its physical location within the DNA molecule. In other
words, when used as a storage medium, some parts of DNA molecules appear
significantly more reliable than others. We show that large differences in
reliability between different parts of DNA molecules lead to highly inefficient
use of error-correction resources, and that commonly used techniques such as
unequal error-correction cannot be used to bridge the reliability gap between
different locations in the context of DNA storage. We then propose two
approaches to address the problem. The first approach is general and applies to
any types of data; it stripes the data and ECC codewords across DNA molecules
in a particular fashion such that the effects of errors are spread out evenly
across different codewords and molecules, effectively de-biasing the underlying
storage medium. The second approach is application-specific, and seeks to
leverage the underlying reliability bias by using application-aware mapping of
data onto DNA molecules such that data that requires higher reliability is
stored in more reliable locations, whereas data that needs lower reliability is
stored in less reliable parts of DNA molecules. We show that the proposed data
mapping can be used to achieve graceful degradation in the presence of high
error rates, or to implement the concept of approximate storage in DNA.


title:
Efficient and scalable Path Integral Monte Carlo Simulations with
  worm-type updates for Bose-Hubbard and XXZ models

abstract:
  We present a novel and open-source implementation of the worm algorithm,
which is an algorithm to simulate Bose-Hubbard and sign-positive spin models
using a path integral representation of the partition function. The code can
deal with arbitrary lattice structures and assumes spin-exchange terms, or
bosonic hopping amplitudes, between nearest-neighbor sites, and local or
nearest-neighbor interactions of the density-density type. We explicitly
demonstrate the near-linear scaling of the algorithm with respect to the system
volume and the inverse temperature and analyze the autocorrelation times in the
vicinity of a U(1) second order phase transition. The code is written in such a
way that extensions to other lattice models as well as closely-related
sign-positive models can be done straightforwardly on top of the provided
framework.


title:
Science Checker: Extractive-Boolean Question Answering For Scientific
  Fact Checking

abstract:
  With the explosive growth of scientific publications, making the synthesis of
scientific knowledge and fact checking becomes an increasingly complex task. In
this paper, we propose a multi-task approach for verifying the scientific
questions based on a joint reasoning from facts and evidence in research
articles. We propose an intelligent combination of (1) an automatic information
summarization and (2) a Boolean Question Answering which allows to generate an
answer to a scientific question from only extracts obtained after
summarization. Thus on a given topic, our proposed approach conducts structured
content modeling based on paper abstracts to answer a scientific question while
highlighting texts from paper that discuss the topic. We based our final system
on an end-to-end Extractive Question Answering (EQA) combined with a three
outputs classification model to perform in-depth semantic understanding of a
question to illustrate the aggregation of multiple responses. With our light
and fast proposed architecture, we achieved an average error rate of 4% and a
F1-score of 95.6%. Our results are supported via experiments with two QA models
(BERT, RoBERTa) over 3 Million Open Access (OA) articles in the medical and
health domains on Europe PMC.


title:
Energy Efficient Beamforming Optimization for Integrated Sensing and
  Communication

abstract:
  This paper investigates the optimization of beamforming design in a system
with integrated sensing and communication (ISAC), where the base station (BS)
sends signals for simultaneous multiuser communication and radar sensing. We
aim at maximizing the energy efficiency (EE) of the multiuser communication
while guaranteeing the sensing requirement in terms of individual radar
beampattern gains. The problem is a complicated nonconvex fractional program
which is challenging to be solved. By appropriately reformulating the problem
and then applying the techniques of successive convex approximation (SCA) and
semidefinite relaxation (SDR), we propose an iterative algorithm to address
this problem. In theory, we prove that the introduced relaxation of the SDR is
rigorously tight. Numerical results validate the effectiveness of the proposed
algorithm.


title: PyNAPLE: Lunar Surface Impact Crater Detection

abstract:
  In the last 20 years, over 600 impact flashes have been documented on the
lunar surface. This wealth of data presents a unique opportunity to study the
meteoroid flux of the Earth-Moon environment, and in recent years the physical
properties of the impactors. However, other than through serendipitous events,
there has not been yet a systematic search and discovery of the craters
associated to these events. Such a meteoroid-crater link would allow us to get
insight into the crater formation via these live observations of collisions.
Here we present the PyNAPLE (Python NAC Automated Pair Lunar Evaluator)
software pipeline for locating newly formed craters using the location and
epoch of an observed impact flash. We present the first results from PyNAPLE,
having been implemented on the 2017-09-27 impact flash.
  A rudimentary analysis on the impact flash and linked impact crater is also
performed, finding that the crater's ejecta pattern indicates an impact angle
between 10-30\degree, and although the rim-to-rim diameter of the crater is not
resolvable in current LRO NAC images, using crater scaling laws we predict this
diameter to be 24.1-55.3~m, and using ejecta scaling predict a diameter of
27.3-37.7~m. We discuss how PyNAPLE will enable large scale analyses of
sub-kilometer scale cratering rates and refinement of both scaling laws, and
the luminous efficiency.


title: Attentive Fine-Grained Structured Sparsity for Image Restoration

abstract:
  Image restoration tasks have witnessed great performance improvement in
recent years by developing large deep models. Despite the outstanding
performance, the heavy computation demanded by the deep models has restricted
the application of image restoration. To lift the restriction, it is required
to reduce the size of the networks while maintaining accuracy. Recently, N:M
structured pruning has appeared as one of the effective and practical pruning
approaches for making the model efficient with the accuracy constraint.
However, it fails to account for different computational complexities and
performance requirements for different layers of an image restoration network.
To further optimize the trade-off between the efficiency and the restoration
accuracy, we propose a novel pruning method that determines the pruning ratio
for N:M structured sparsity at each layer. Extensive experimental results on
super-resolution and deblurring tasks demonstrate the efficacy of our method
which outperforms previous pruning methods significantly. PyTorch
implementation for the proposed methods will be publicly available at
https://github.com/JungHunOh/SLS_CVPR2022.


title: Sentiment Analysis of Cybersecurity Content on Twitter and Reddit

abstract:
  Sentiment Analysis provides an opportunity to understand the subject(s),
especially in the digital age, due to an abundance of public data and effective
algorithms. Cybersecurity is a subject where opinions are plentiful and
differing in the public domain. This descriptive research analyzed
cybersecurity content on Twitter and Reddit to measure its sentiment, positive
or negative, or neutral. The data from Twitter and Reddit was amassed via
technology-specific APIs during a selected timeframe to create datasets, which
were then analyzed individually for their sentiment by VADER, an NLP (Natural
Language Processing) algorithm. A random sample of cybersecurity content (ten
tweets and posts) was also classified for sentiments by twenty human annotators
to evaluate the performance of VADER. Cybersecurity content on Twitter was at
least 48% positive, and Reddit was at least 26.5% positive. The positive or
neutral content far outweighed negative sentiments across both platforms. When
compared to human classification, which was considered the standard or source
of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in
assessing the sentiment; in other words, some agreement between algorithm and
human classifiers. Overall, the goal was to explore an uninhibited research
topic about cybersecurity sentiment


title: Representations and cohomologies of Kleinian 4-rings

abstract:
  We introduce a new class of algebras over discrete valuation rings, called
Kleinian 4-rings, which generalize the group algebra of the Kleinian 4-group.
For these algebras we describe the lattices and their cohomologies. In the case
of regular lattices we obtain an explicit form of cocycles defining the
cohomology classes.


title: Observer Design for an Inertia Wheel Pendulum with Static Friction

abstract:
  A state observer design for the inertia wheel pendulum considering static
friction of the actuated inertia disc is presented. The frictional force is
modeled by the Stribeck effect, with two separate differential equations
describing the sticking and non-sticking system, respectively. The transition
between the two scenarios is in general determined by a static friction
condition. Three proposed observers are designed for both the sticking and
non-sticking model. Depending on the probability of the respective model given
a current measurement, the more likely is selected for state estimation. The
performance is demonstrated on a laboratory demonstrator, where the observers
including the proposed probabilistic model selection are compared.


title: Graph Neural Networks for Microbial Genome Recovery

abstract:
  Microbes have a profound impact on our health and environment, but our
understanding of the diversity and function of microbial communities is
severely limited. Through DNA sequencing of microbial communities
(metagenomics), DNA fragments (reads) of the individual microbes can be
obtained, which through assembly graphs can be combined into long contiguous
DNA sequences (contigs). Given the complexity of microbial communities, single
contig microbial genomes are rarely obtained. Instead, contigs are eventually
clustered into bins, with each bin ideally making up a full genome. This
process is referred to as metagenomic binning.
  Current state-of-the-art techniques for metagenomic binning rely only on the
local features for the individual contigs. These techniques therefore fail to
exploit the similarities between contigs as encoded by the assembly graph, in
which the contigs are organized. In this paper, we propose to use Graph Neural
Networks (GNNs) to leverage the assembly graph when learning contig
representations for metagenomic binning. Our method, VaeG-Bin, combines
variational autoencoders for learning latent representations of the individual
contigs, with GNNs for refining these representations by taking into account
the neighborhood structure of the contigs in the assembly graph. We explore
several types of GNNs and demonstrate that VaeG-Bin recovers more high-quality
genomes than other state-of-the-art binners on both simulated and real-world
datasets.


title:
Statistical features of multiple Compton scattering in a strong magnetic
  field

abstract:
  Compton scattering is a key process shaping spectra formation and accretion
flow dynamics in accreting strongly magnetized neutron stars. A strong magnetic
field affects the scattering cross section and makes it dependent on photon
energy, momentum, and polarization state. Using Monte Carlo simulations, we
investigate statistical features of Compton scattering of polarized X-ray
radiation in a strong magnetic field. {Our analysis is focused on photon gas
behaviour well inside the scattering region.} We take into account the resonant
scattering at the fundamental cyclotron frequency, thermal distribution of
electrons at the ground Landau level, and bulk velocity of the electron gas. We
show that (i) the photons scattered around the cyclotron energy by the electron
gas at rest tend to acquire the final energy close to the cyclotron one with a
very small dispersion measure; (ii) the redistribution of photons within the
Doppler core of cyclotron resonance differs significantly from the complete
redistribution; (iii) the efficiency of momentum transfer from photons to the
electron gas is affected by the temperature of electron gas both for photons at
cyclotron energy and below it; (iv) the momentum transfer from photons to the
electron gas of non-zero bulk velocity is more efficient in the case of
magnetic scattering.


title: Radio spectra of protostellar jets: Thermal and non-thermal emission

abstract:
  Protostellar jets and outflows are pointers of star-formation and serve as
important sources of momentum and energy transfer to the interstellar medium.
Radio emission from ionized jets have been detected towards a number of
protostellar objects. In few cases, negative spectral indices and polarized
emission have also been observed suggesting the presence of synchrotron
emission from relativistic electrons. In this work, we develop a numerical
model that incorporates both thermal free-free and non-thermal synchrotron
emission mechanisms in the jet geometry. The flux densities include
contribution from an inner thermal jet, and a combination of emission from
thermal and non-thermal distributions along the edges and extremities, where
the jet interacts with the interstellar medium. We also include the effect of
varying ionization fraction laterally across the jet. An investigation of radio
emission and spectra along the jet shows the dependence of the emission process
and optical depth along the line of sight. We explore the effect of various
parameters on the turnover frequencies and the radio spectral indices (between
10 MHz and 300 GHz) associated with them.


title:
Higher Br\'ezin-Gross-Witten tau-functions and intersection theory of
  Witten's and Norbury's classes

abstract:
  In this paper, we consider the higher Br\'ezin-Gross-Witten tau-functions,
given by the matrix integrals. For these tau-functions we construct the
canonical Kac-Schwarz operators, quantum spectral curves, and
$W^{(3)}$-constraints. For the simplest representative we construct the
cut-and-join operators, which describe the algebraic version of the topological
recursion. We also investigate a one-parametric generalization of the the
higher Br\'ezin-Gross-Witten tau-functions. We claim that the higher
Br\'ezin-Gross-Witten tau-functions govern the intersection theory for the
product of Witten's and Norbury's classes.


title:
Socio-technical constraints and affordances of virtual collaboration --
  A study of four online hackathons

abstract:
  Hackathons and similar time-bounded events have become a popular form of
collaboration. They are commonly organized as in-person events during which
teams engage in intense collaboration over a short period of time to complete a
project that is of interest to them. Most research to date has focused on
studying how teams collaborate in a co-located setting, pointing towards the
advantages of radical co-location. The global pandemic of 2020, however, has
led to many hackathons moving online, which challenges our current
understanding of how they function. In this paper, we address this gap by
presenting findings from a multiple-case study of 10 hackathon teams that
participated in 4 hackathons across two continents. By analyzing the collected
data, we found that teams merged synchronous and asynchronous means of
communication to maintain a common understanding of work progress as well as to
maintain awareness of each other's tasks. Task division was self-assigned based
on individual skills or interests, while leaders emerged from different
strategies (e.g., participant experience, the responsibility of registering the
team in an event). Some of the affordances of in-person hackathons, such as the
radical co-location of team members, could be partially reproduced in teams
that kept synchronous communication channels while working (i.e., shared audio
territories), in a sort of "radical virtual co-location". However, others, such
as interactions with other teams, easy access to mentors, and networking with
other participants, decreased. In addition, the technical constraints of the
different communication tools and platforms brought technical problems and were
overwhelming to participants. Our work contributes to understanding the virtual
collaboration of small teams in the context of online hackathons and how
technologies and event structures proposed by organizers imply this
collaboration.


title: GRMHD simulations of accreting neutron stars I: nonrotating dipoles

abstract:
  We study the general-relativistic dynamics of matter being accreted onto and
ejected by a magnetised and nonrotating neutron star. The dynamics is followed
in the framework of fully general relativistic magnetohydrodynamics (GRMHD)
within the ideal-MHD limit and in two spatial dimensions. More specifically,
making use of the numerical code BHAC, we follow the evolution of a
geometrically thick matter torus driven into accretion by the development of a
magnetorotational instability. By making use of a number of simulations in
which we vary the strength of the stellar dipolar magnetic field, we can
determine self-consistently the location of the magnetospheric (or Alfv\'en)
radius $r_{\rm msph}$ and study how it depends on the magnetic moment $\mu$ and
on the accretion rate. Overall, we recover the analytic Newtonian scaling
relation, i.e. $r_{\rm msph} \propto B^{4/7}$, but also find that the
dependence on the accretion rate is very weak. Furthermore, we find that the
material torque correlates linearly with the mass-accretion rate, although both
of them exhibit rapid fluctuations. Interestingly, the total torque fluctuates
drastically in strong magnetic field simulations and these unsteady torques
observed in the simulations could be associated with the spin fluctuations
observed in X-ray pulsars.


title:
Evidence of phase stability, topological phonon and temperature-induced
  topological phase transition in rocksalt SnS and SnSe

abstract:
  Both SnS and SnSe have been experimentally and theoretically confirmed as
topological crystalline insulators in native rocksalt structure. Here, phononic
structure, thermodynamical properties and temperature dependent electron-phonon
interaction (EPI) have investigated for both the materials in rocksalt phase.
Previously performed theoretical studies have predicted the phase instability
of SnS in this crystal structure at ambient condition. But, after a detailed
study performing on the phonon calculation of SnS, we have predicted the phase
stability of SnS with considering the Sn 4$p$ orbitals as valence states in
$ab-initio$ calculation. The importance of long range Coulomb forces along with
the themodynamical properties are also described in detailed for both
materials. The preliminary evidence of topological phonon is found along X-W
direction, where the linear band touching is observed as compared to type II
Weyl phononic material ZnSe. The topological phase transition is seen for these
materials due to EPI, where non-linear temperature dependent bandgap is
estimated. The predicted value of transition temperature for SnS (SnSe) is
found to be $\sim$700 K, where after this temperature the non-trivial to
trivial topological phase is seen. The strength of EPI shows more stronger
impact on the electronic structure of SnS than SnSe material. The reason of
non-linear behaviour of bandgap with rise in temperature is discussed with the
help of temperature dependent linewidths and lineshifts of conduction band and
valence band due to EPI. The present study reveals the phase stability of SnS
along with the comparative study of thermal effect on EPI of SnS and SnSe.
Further, the possibility of temperature induced topological phase transition
provides one of important behaviour to apply these two materials for device
making application.


title:
On regularity and existence of weak solutions to nonlinear
  Kolmogorov-Fokker-Planck type equations with rough coefficients

abstract:
  We consider nonlinear Kolmogorov-Fokker-Planck type equations of the form
\begin{equation}\label{abeqn}
(\partial_t+X\cdot\nabla_Y)u=\nabla_X\cdot(A(\nabla_X u,X,Y,t)). \end{equation}
The function $A=A(\xi,X,Y,t):\R^m\times\R^m\times\R^m\times\R\to\R^m$ is
assumed to be continuous with respect to $\xi$, and measurable with respect to
$X,Y$ and $t$. $A=A(\xi,X,Y,t)$ is allowed to be nonlinear but with linear
growth. We establish higher integrability and local boundedness of weak
sub-solutions, weak Harnack and Harnack inequalities, and H{\"o}lder continuity
with quantitative estimates. In addition we establish existence and uniqueness
of weak solutions to a Dirichlet problem in certain bounded $X$, $Y$ and $t$
dependent domains.


title: Antineutrino sensitivity at THEIA

abstract:
  We present the sensitivity of the THEIA experiment to low-energy
antineutrinos. For the purposes of this study, we consider one of the possible
proposed designs, a 25-ktonne THEIA-25 detector filled with a water-based
liquid scintillator (WbLS) placed at Sanford Underground Research Facility
(SURF). Currently, only two detectors in the world have measured geoneutrinos,
antineutrinos originating from the radioactive decays of long-lived isotopes
naturally present in the crust and mantle. We demonstrate THEIA's sensitivity
to measure the antineutrino signal via Inverse-Beta Decay (IBD) interactions
after one year of data taking. The expected number of detected geo- and reactor
antineutrinos is $218\,({+28})\,({-20})$ and $170\,({+24})\,({-20})$,
respectively. The precision of the fitting procedure, using
$(\text{Th}/\text{U})_\text{SURF}=4.24$, has been evaluated to be 6.72% and
8.55% for geo- and reactor antineutrinos, respectively. Assuming the crust
contribution and considering the detector efficiencies, we evaluate the mantle
signal to be $S_\text{mantle} = 8.8\,({+1.2})\,({-0.8})$ NIU from total
$S_\text{geo} = 45.9\,({+5.9})\,({-4.2})$ NIU. We also demonstrate the
sensitivity towards fitting individual Th and U contributions, and obtain
$(\text{Th}/\text{U})=4.3\,({+2.6})\,({-2.2})$ after one year of data taking.
Within ten years, the relative precision of the $(\text{Th}/\text{U})$ mass
ratio will be reduced to 15%.


title:
Low-dimensional representation of infant and adult vocalization
  acoustics

abstract:
  During the first years of life, infant vocalizations change considerably, as
infants develop the vocalization skills that enable them to produce speech
sounds. Characterizations based on specific acoustic features, protophone
categories, or phonetic transcription are able to provide a representation of
the sounds infants make at different ages and in different contexts but do not
fully describe how sounds are perceived by listeners, can be inefficient to
obtain at large scales, and are difficult to visualize in two dimensions
without additional statistical processing. Machine-learning-based approaches
provide the opportunity to complement these characterizations with purely
data-driven representations of infant sounds. Here, we use spectral features
extraction and unsupervised machine learning, specifically Uniform Manifold
Approximation (UMAP), to obtain a novel 2-dimensional spatial representation of
infant and caregiver vocalizations extracted from day-long home recordings.
UMAP yields a continuous and well-distributed space conducive to certain
analyses of infant vocal development. For instance, we found that the
dispersion of infant vocalization acoustics within the 2-D space over a day
increased from 3 to 9 months, and then decreased from 9 to 18 months. The
method also permits analysis of similarity between infant and adult
vocalizations, which also shows changes with infant age.


title: The variance-penalized stochastic shortest path problem

abstract:
  The stochastic shortest path problem (SSPP) asks to resolve the
non-deterministic choices in a Markov decision process (MDP) such that the
expected accumulated weight before reaching a target state is maximized. This
paper addresses the optimization of the variance-penalized expectation (VPE) of
the accumulated weight, which is a variant of the SSPP in which a multiple of
the variance of accumulated weights is incurred as a penalty. It is shown that
the optimal VPE in MDPs with non-negative weights as well as an optimal
deterministic finite-memory scheduler can be computed in exponential space. The
threshold problem whether the maximal VPE exceeds a given rational is shown to
be EXPTIME-hard and to lie in NEXPTIME. Furthermore, a result of interest in
its own right obtained on the way is that a variance-minimal scheduler among
all expectation-optimal schedulers can be computed in polynomial time.


title: Data-Efficient Backdoor Attacks

abstract:
  Recent studies have proven that deep neural networks are vulnerable to
backdoor attacks. Specifically, by mixing a small number of poisoned samples
into the training set, the behavior of the trained model can be maliciously
controlled. Existing attack methods construct such adversaries by randomly
selecting some clean data from the benign set and then embedding a trigger into
them. However, this selection strategy ignores the fact that each poisoned
sample contributes inequally to the backdoor injection, which reduces the
efficiency of poisoning. In this paper, we formulate improving the poisoned
data efficiency by the selection as an optimization problem and propose a
Filtering-and-Updating Strategy (FUS) to solve it. The experimental results on
CIFAR-10 and ImageNet-10 indicate that the proposed method is effective: the
same attack success rate can be achieved with only 47% to 75% of the poisoned
sample volume compared to the random selection strategy. More importantly, the
adversaries selected according to one setting can generalize well to other
settings, exhibiting strong transferability.


title: Non-separably valued Orlicz spaces

abstract:
  For a measure space $\Omega$ we extend the theory of Orlicz spaces generated
by an even convex integrand $\varphi \colon \Omega \times X \to \left[ 0,
\infty \right]$ to the case when the range Banach space $X$ is arbitrary.
Besides settling fundamental structural properties such as completeness, we
characterize separability, reflexivity and represent the dual space. This
representation includes for the first time the case when $X'$ has no
Radon-Nikodym property. Moreover, we characterize relatively sequentially
compact sets in weak topologies that subspaces of the Orlicz space and
subspaces of its dual induce on each other. We apply our theory to represent
convex conjugates and subdifferentials of general integral functionals, leading
to the first such result on function spaces with non-separable range space. For
this, we prove a new general interchange criterion between infimum and integral
for non-separable range spaces, which we consider of independent interest.


title:
A Novel Framework for Characterization of Tumor-Immune Spatial
  Relationships in Tumor Microenvironment

abstract:
  Understanding the impact of tumor biology on the composition of nearby cells
often requires characterizing the impact of biologically distinct tumor
regions. Biomarkers have been developed to label biologically distinct tumor
regions, but challenges arise because of differences in the spatial extent and
distribution of differentially labeled regions. In this work, we present a
framework for systematically investigating the impact of distinct tumor regions
on cells near the tumor borders, accounting their cross spatial distributions.
We apply the framework to multiplex immunohistochemistry (mIHC) studies of
pancreatic cancer and show its efficacy in demonstrating how biologically
different tumor regions impact the immune response in the tumor
microenvironment. Furthermore, we show that the proposed framework can be
extended to largescale whole slide image analysis.


title: Federated Stochastic Primal-dual Learning with Differential Privacy

abstract:
  Federated learning (FL) is a new paradigm that enables many clients to
jointly train a machine learning (ML) model under the orchestration of a
parameter server while keeping the local data not being exposed to any third
party. However, the training of FL is an interactive process between local
clients and the parameter server. Such process would cause privacy leakage
since adversaries may retrieve sensitive information by analyzing the overheard
messages. In this paper, we propose a new federated stochastic primal-dual
algorithm with differential privacy (FedSPD-DP). Compared to the existing
methods, the proposed FedSPD-DP incorporates local stochastic gradient descent
(local SGD) and partial client participation (PCP) for addressing the issues of
communication efficiency and straggler effects due to randomly accessed
clients. Our analysis shows that the data sampling strategy and PCP can enhance
the data privacy whereas the larger number of local SGD steps could increase
privacy leakage, revealing a non-trivial tradeoff between algorithm
communication efficiency and privacy protection. Specifically, we show that, by
guaranteeing $(\epsilon, \delta)$-DP for each client per communication round,
the proposed algorithm guarantees $(\mathcal{O}(q\epsilon \sqrt{p T}),
\delta)$-DP after $T$ communication rounds while maintaining an
$\mathcal{O}(1/\sqrt{pTQ})$ convergence rate for a convex and non-smooth
learning problem, where $Q$ is the number of local SGD steps, $p$ is the client
sampling probability, $q=\max_{i} q_i/\sqrt{1-q_i}$ and $q_i$ is the data
sampling probability of each client under PCP. Experiment results are presented
to evaluate the practical performance of the proposed algorithm and comparison
with state-of-the-art methods.


title:
Law of Total Probability in Quantum Theory and Its Application in
  Wigner's Friend Scenario

abstract:
  It is well-known that the law of total probability does not hold in general
in quantum theory. However, the recent arguments on some of the fundamental
assumptions in quantum theory based on the extended Wigner's Friend scenario
show a need to clarify how the law of total probability should be formulated in
quantum theory and under what conditions it still holds. In this work, the
definition of conditional probability in quantum theory is extended to POVM
measurements. Rule to assign two-time conditional probability is proposed for
incompatible POVM operators, which leads to a more general and precise
formulation of the law of total probability. Sufficient conditions under which
the law of total probability holds are identified. Applying the theory
developed here to analyze several quantum no-go theorems related to the
extended Wigner's friend scenario reveals logical loopholes in these no-go
theorems. The loopholes exist as a consequence of taking for granted the
validity of the law of total probability without verifying the sufficient
conditions. Consequently, the contradictions in these no-go theorems just
reconfirm the invalidity of the law of total probability in quantum theorem,
instead of due to the desired statements.


title:
A Note on Tamely Ramified Class Field Theory of Two Dimensional Local
  Rings

abstract:
  In this note, we treat two dimensional complete local rings which are the
completion of $\mathcal{O}_{K}[x, y] = \mathcal{O}_{K}[X, Y]/(XY-\pi)$ at a
maximal ideal $(x, y)$, where $\mathcal{O}_{K}$ is an integer ring of a finite
extension field $K$ of $\mathbb{Q}_{p}$ or $\mathbb{F}_{p}((t))$ which contains
$\pi^{1/m}$, and $\pi=p$ if $\mathbb{Q}_{p}\subset \mathcal{O}_{K}$, $\pi=t$ if
$\mathbb{F}_{p}((t))\subset \mathcal{O}_{K}$, and we assume that the order of
the group of root of unity of $\mathcal{O}_{K}$ is $m$ and $m$ is not divisible
by $p$. We discuss the tamely ramified class field theory of the fractional
field $K_{R}$ of R. This class field theory allows $x$ and $y$ to ramify tamely
and it is unramified at height 1 prime ideals of $R$ other than $x$ and $y$.


title: Quantitative results of the Romanov type representation functions

abstract:
  Let $f(n)$ be the number of the representations of $n$ by the sum of a prime
and a power of $2$. In 1950, Erd\H os proved the following remarkable result
$$\limsup_{n\rightarrow\infty}\frac{f(n)}{\log\log n}>0.$$ Erd\H os conjectured
$f(n)=o(\log n)$ and believed this to be rather deep. In this article, we make
some progress towards this old issue by showing that
$$\limsup_{n\rightarrow\infty}\frac{f(n)}{\log\log n}=\infty.$$ Our proof is
based on the new achievements in the distributions of primes due to Maynard.
Furthermore, the argument displayed here is actually valid for much more
general settings which cover the above result as a special case.


title: Empowering Next POI Recommendation with Multi-Relational Modeling

abstract:
  With the wide adoption of mobile devices and web applications, location-based
social networks (LBSNs) offer large-scale individual-level location-related
activities and experiences. Next point-of-interest (POI) recommendation is one
of the most important tasks in LBSNs, aiming to make personalized
recommendations of next suitable locations to users by discovering preferences
from users' historical activities. Noticeably, LBSNs have offered unparalleled
access to abundant heterogeneous relational information about users and POIs
(including user-user social relations, such as families or colleagues; and
user-POI visiting relations). Such relational information holds great potential
to facilitate the next POI recommendation. However, most existing methods
either focus on merely the user-POI visits, or handle different relations based
on over-simplified assumptions while neglecting relational heterogeneities. To
fill these critical voids, we propose a novel framework, MEMO, which
effectively utilizes the heterogeneous relations with a multi-network
representation learning module, and explicitly incorporates the inter-temporal
user-POI mutual influence with the coupled recurrent neural networks. Extensive
experiments on real-world LBSN data validate the superiority of our framework
over the state-of-the-art next POI recommendation methods.


title: A dynamic that evolves toward a Nash equilibrium

abstract:
  In this paper, we study an exponentiated multiplicative weights dynamic based
on Hedge, a well-known algorithm in theoretical machine learning and
algorithmic game theory. The empirical average (arithmetic mean) of the
iterates Hedge generates is known to approach a minimax equilibrium in zero-sum
games. We generalize that result to show that a weighted version of the
empirical average converges to an equilibrium in the class of symmetric
bimatrix games for a diminishing learning rate parameter. Our dynamic is the
first dynamical system (whether continuous or discrete) shown to evolve toward
a Nash equilibrium without assuming monotonicity of the payoff structure or
that a potential function exists. Although our setting is somewhat restricted,
it is also general as the class of symmetric bimatrix games captures the entire
computational complexity of the PPAD class (even to approximate an
equilibrium).


title:
On Machine Learning-Driven Surrogates for Sound Transmission Loss
  Simulations

abstract:
  Surrogate models are data-based approximations of computationally expensive
simulations that enable efficient exploration of the model's design space and
informed decision-making in many physical domains. The usage of surrogate
models in the vibroacoustic domain, however, is challenging due to the
non-smooth, complex behavior of wave phenomena. This paper investigates four
Machine Learning (ML) approaches in the modelling of surrogates of Sound
Transmission Loss (STL). Feature importance and feature engineering are used to
improve the models' accuracy while increasing their interpretability and
physical consistency. The transfer of the proposed techniques to other problems
in the vibroacoustic domain and possible limitations of the models are
discussed.


title:
Large Interferometer For Exoplanets (LIFE): VII. Practical
  implementation of a kernel-nulling beam combiner with a discussion on
  instrumental uncertainties and redundancy benefits

abstract:
  (Abridged)
  Context: In the previous paper in this series, we identified that a
pentagonal arrangement of five telescopes, using a kernel-nulling beam
combiner, shows notable advantages for some important performance metrics for a
space-based mid-infrared nulling interferometer over several other considered
configurations for the detection of Earth-like exoplanets around solar-type
stars.
  Aims: We aim to produce a practical implementation of a kernel-nulling beam
combiner for such a configuration, as well as a discussion of systematic and
stochastic errors associated with the instrument.
  Methods: We develop the mathematical framework around a beam-combiner based
on a nulling combiner first suggested by Guyon et al. (2013), and then use it
along with the simulator developed in the previous paper to identify the
effects of systematic uncertainties.
  Results: We find that errors in the beam combiner optics, systematic phase
errors and the RMS fringe tracking errors result in instrument limited
performance at $\sim$4-7 $\mu$m, and zodiacal limited at $\gtrsim$10 $\mu$m.
Assuming a beam splitter reflectance error of $|\Delta R| = 5\%$ and phase
shift error of $\Delta\phi = 3$ degrees, we find that the fringe tracking RMS
should be kept to less than 3 nm in order to be photon limited, and the
systematic piston error be less than 0.5 nm to be appropriately sensitive to
planets with a contrast of 1$\times 10^{-7}$ over a 4-19 $\mu$m bandpass. We
also identify that the beam combiner design, with the inclusion of a well
positioned shutter, provides an ability to produce robust kernel observables
even if one or two collecting telescopes were to fail. The resulting four
telescope combiner, when put into an X-array formation, results in a
transmission map with an relative SNR equivalent to 80% of the fully
functioning X-array combiner.


title:
Thermal and dimensional evaluation of a test plate for assessing the
  measurement capability of a thermal imager within nuclear decommissioning
  storage

abstract:
  In this laboratory-based study, a plate was designed, manufactured and then
characterised thermally and dimensionally using a thermal imager. This plate
comprised a range of known scratch, dent, thinning and pitting artefacts as
mimics of possible surface anomalies, as well as an arrangement of higher
emissivity targets. The thermal and dimensional characterisation of this plate
facilitated surface temperature determination. This was verified through
thermal models and successful defect identification of the scratch and pitting
artefacts at temperatures from \SIrange{30}{170}{\celsius}.
  These laboratory measurements demonstrated the feasibility of deploying
in-situ thermal imaging to the thermal and dimensional characterisation of
special nuclear material containers. Surface temperature determination
demonstrated uncertainties from \SIrange{1.0}{6.8}{\celsius} (\(k = 2\)). The
principle challenges inhibiting successful deployment are a lack of suitable
emissivity data and a robust defect identification algorithm suited to both
static and transient datasets.


title: Contrastive Language-Action Pre-training for Temporal Localization

abstract:
  Long-form video understanding requires designing approaches that are able to
temporally localize activities or language. End-to-end training for such tasks
is limited by the compute device memory constraints and lack of temporal
annotations at large-scale. These limitations can be addressed by pre-training
on large datasets of temporally trimmed videos supervised by class annotations.
Once the video encoder is pre-trained, it is common practice to freeze it
during fine-tuning. Therefore, the video encoder does not learn temporal
boundaries and unseen classes, causing a domain gap with respect to the
downstream tasks. Moreover, using temporally trimmed videos prevents to capture
the relations between different action categories and the background context in
a video clip which results in limited generalization capacity. To address these
limitations, we propose a novel post-pre-training approach without freezing the
video encoder which leverages language. We introduce a masked contrastive
learning loss to capture visio-linguistic relations between activities,
background video clips and language in the form of captions. Our experiments
show that the proposed approach improves the state-of-the-art on temporal
action localization, few-shot temporal action localization, and video language
grounding tasks.


title:
Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked
  Claims

abstract:
  False information has a significant negative influence on individuals as well
as on the whole society. Especially in the current COVID-19 era, we witness an
unprecedented growth of medical misinformation. To help tackle this problem
with machine learning approaches, we are publishing a feature-rich dataset of
approx. 317k medical news articles/blogs and 3.5k fact-checked claims. It also
contains 573 manually and more than 51k automatically labelled mappings between
claims and articles. Mappings consist of claim presence, i.e., whether a claim
is contained in a given article, and article stance towards the claim. We
provide several baselines for these two tasks and evaluate them on the manually
labelled part of the dataset. The dataset enables a number of additional tasks
related to medical misinformation, such as misinformation characterisation
studies or studies of misinformation diffusion between sources.


title: Strong Liouville theorems for discrete averaging operators

abstract:
  We introduce averaging operators on lattices $\mathbb{Z}^d$ and study the
Liouville property for the related discrete PDEs, enclosing the discrete
harmonic, $p$-harmonic, $p$-harmonious and $\infty$-harmonic functions. Our
approach generalizes a previous elementary argument for the discrete harmonic
case and provides an alternative proof for $p$-harmonic functions on
$\mathbb{Z}^2$.


title:
Unsupervised Segmentation of Hyperspectral Remote Sensing Images with
  Superpixels

abstract:
  In this paper, we propose an unsupervised method for hyperspectral remote
sensing image segmentation. The method exploits the mean-shift clustering
algorithm that takes as input a preliminary hyperspectral superpixels
segmentation together with the spectral pixel information. The proposed method
does not require the number of segmentation classes as input parameter, and it
does not exploit any a-priori knowledge about the type of land-cover or
land-use to be segmented (e.g. water, vegetation, building etc.). Experiments
on Salinas, SalinasA, Pavia Center and Pavia University datasets are carried
out. Performance are measured in terms of normalized mutual information,
adjusted Rand index and F1-score. Results demonstrate the validity of the
proposed method in comparison with the state of the art.


title:
Brain Tumor Detection and Classification Using a New Evolutionary
  Convolutional Neural Network

abstract:
  A definitive diagnosis of a brain tumour is essential for enhancing treatment
success and patient survival. However, it is difficult to manually evaluate
multiple magnetic resonance imaging (MRI) images generated in a clinic.
Therefore, more precise computer-based tumour detection methods are required.
In recent years, many efforts have investigated classical machine learning
methods to automate this process. Deep learning techniques have recently
sparked interest as a means of diagnosing brain tumours more accurately and
robustly. The goal of this study, therefore, is to employ brain MRI images to
distinguish between healthy and unhealthy patients (including tumour tissues).
As a result, an enhanced convolutional neural network is developed in this
paper for accurate brain image classification. The enhanced convolutional
neural network structure is composed of components for feature extraction and
optimal classification. Nonlinear L\'evy Chaotic Moth Flame Optimizer (NLCMFO)
optimizes hyperparameters for training convolutional neural network layers.
Using the BRATS 2015 data set and brain image datasets from Harvard Medical
School, the proposed model is assessed and compared with various optimization
techniques. The optimized CNN model outperforms other models from the
literature by providing 97.4% accuracy, 96.0% sensitivity, 98.6% specificity,
98.4% precision, and 96.6% F1-score, (the mean of the weighted harmonic value
of CNN precision and recall).


title:
Linear TDOA-based Measurements for Distributed Estimation and Localized
  Tracking

abstract:
  We propose a linear time-difference-of-arrival (TDOA) measurement model to
improve \textit{distributed} estimation performance for localized target
tracking. We design distributed filters over sparse (possibly large-scale)
communication networks using consensus-based data-fusion techniques. The
proposed distributed and localized tracking protocols considerably reduce the
sensor network's required connectivity and communication rate. We, further,
consider $\kappa$-redundant observability and fault-tolerant design in case of
losing communication links or sensor nodes. We present the minimal conditions
on the remaining sensor network (after link/node removal) such that the
distributed observability is still preserved and, thus, the sensor network can
track the (single) maneuvering target. The motivation is to reduce the
communication load versus the processing load, as the computational units are,
in general, less costly than the communication devices. We evaluate the
tracking performance via simulations in MATLAB.

categories: eess.SY cs.MA cs.RO cs.SY eess.SP math.DS


title: Deuterated ammonia in Galactic massive star-forming regions

abstract:
  We present sensitive observations of NH2D at 110.153599 GHz toward 50
Galactic massive star-forming regions with IRAM 30-m telescope. The NH2D
transition is detected toward 36 objects, yielding a detection rate of 72%.
Column densities of NH2D, HC3N and C18O for each source are derived by assuming
local thermal equilibrium conditions with a fixed excitation temperature. The
deuterium ratio of NH$_3$, defined as the abundance ratio of NH2D to NH3, for
19 sources is also obtained with the information of NH3 from the literature.
The range of deuterium fractionation bends to be large in the late-stage
star-forming regions in this work, with the value from 0.043 to 0.0006. The
highest deuterium ratio of NH3 is 0.043 in G081.75+00.78 (DR21). We also find
that the deuterium ratio of NH3 increases with the Galactocentric distances and
decreases with the line width.


title: Unified GCNs: Towards Connecting GCNs with CNNs

abstract:
  Graph Convolutional Networks (GCNs) have been widely demonstrated their
powerful ability in graph data representation and learning. Existing graph
convolution layers are mainly designed based on graph signal processing and
transform aspect which usually suffer from some limitations, such as
over-smoothing, over-squashing and non-robustness, etc. As we all know that
Convolution Neural Networks (CNNs) have received great success in many computer
vision and machine learning. One main aspect is that CNNs leverage many
learnable convolution filters (kernels) to obtain rich feature descriptors and
thus can have high capacity to encode complex patterns in visual data analysis.
Also, CNNs are flexible in designing their network architecture, such as
MobileNet, ResNet, Xception, etc. Therefore, it is natural to arise a question:
can we design graph convolutional layer as flexibly as that in CNNs?
Innovatively, in this paper, we consider connecting GCNs with CNNs deeply from
a general perspective of depthwise separable convolution operation.
Specifically, we show that GCN and GAT indeed perform some specific depthwise
separable convolution operations. This novel interpretation enables us to
better understand the connections between GCNs (GCN, GAT) and CNNs and further
inspires us to design more Unified GCNs (UGCNs). As two showcases, we implement
two UGCNs, i.e., Separable UGCN (S-UGCN) and General UGCN (G-UGCN) for graph
data representation and learning. Promising experiments on several graph
representation benchmarks demonstrate the effectiveness and advantages of the
proposed UGCNs.


title: Designing Perceptual Puzzles by Differentiating Probabilistic Programs

abstract:
  We design new visual illusions by finding "adversarial examples" for
principled models of human perception -- specifically, for probabilistic
models, which treat vision as Bayesian inference. To perform this search
efficiently, we design a differentiable probabilistic programming language,
whose API exposes MCMC inference as a first-class differentiable function. We
demonstrate our method by automatically creating illusions for three features
of human vision: color constancy, size constancy, and face perception.


title:
From Limited Annotated Raw Material Data to Quality Production Data: A
  Case Study in the Milk Industry (Technical Report)

abstract:
  Industry 4.0 offers opportunities to combine multiple sensor data sources
using IoT technologies for better utilization of raw material in production
lines. A common belief that data is readily available (the big data
phenomenon), is oftentimes challenged by the need to effectively acquire
quality data under severe constraints. In this paper we propose a design
methodology, using active learning to enhance learning capabilities, for
building a model of production outcome using a constrained amount of raw
material training data. The proposed methodology extends existing active
learning methods to effectively solve regression-based learning problems and
may serve settings where data acquisition requires excessive resources in the
physical world. We further suggest a set of qualitative measures to analyze
learners performance. The proposed methodology is demonstrated using an actual
application in the milk industry, where milk is gathered from multiple small
milk farms and brought to a dairy production plant to be processed into cottage
cheese.


title: On converses to the polynomial method

abstract:
  A surprising 'converse to the polynomial method' of Aaronson et al. (CCC'16)
shows that any bounded quadratic polynomial can be computed exactly in
expectation by a 1-query algorithm up to a universal multiplicative factor
related to the famous Grothendieck constant. A natural question posed there
asks if bounded quartic polynomials can be approximated by $2$-query quantum
algorithms. Arunachalam, Palazuelos and the first author showed that there is
no direct analogue of the result of Aaronson et al. in this case. We improve on
this result in the following ways: First, we point out and fix a small error in
the construction that has to do with a translation from cubic to quartic
polynomials. Second, we give a completely explicit example based on techniques
from additive combinatorics. Third, we show that the result still holds when we
allow for a small additive error. For this, we apply an SDP characterization of
Gribling and Laurent (QIP'19) for the completely-bounded approximate degree.


title: Is singularity resolution trivial?

abstract:
  Many non-trivial ideas have been proposed to resolve singularities in quantum
gravity. In this short note I argue that singularity resolution can be trivial
in gravitational path integrals, because geodesically incomplete singular
spacetimes are usually not included in the sum. For theories where this holds,
there is no need to develop non-trivial ideas on singularity resolution.
Instead, efforts should better be directed to understand tunneling processes
and complex-valued spacetimes.


title:
Angular distributions of nonlinear Thomson scattering in combined fields
  with a general elliptical polarization laser and a background magnetic field

abstract:
  Nonlinear Thomson scattering of an electron motion in a combined fields is
investigated. The fields are constituted by an intense elliptically polarized
laser field and a strong background magnetic field. It is found that the
angular distributions of scattering spectra with respect to the azimuthal angle
exhibits the symmetry for the laser intensity, the magnetic resonance
parameter, the initial axial momentum of electron as well as the order of
harmonics in emission spectra. Meanwhile the distribution of polar angle
approaches the direction of laser propagation more and more with parameters
increase. The optimal angle for maximum radiated power per unit of solid, the
corresponding photon number and its brightness can be obtained, which implies
that the high quality XUV or/and X-ray light can be generated by the studied
scheme when the suitable parameters are choosen.


title: Tentative Evidence for Transit Timing Variations of WASP-161b

abstract:
  We report on the detection of transit timing variations (TTV) of WASP-161b by
using the combination of TESS data and archival data. The midpoint of the
transits in TESS data are offset by $\sim$67 minutes in Jan. 2019, and
$\sim$203 minutes in Jan. 2021, based on the ephemeris published in previous
work. We are able to reproduce the transit timings from the archival light
curve (SSO-Europa; Jan. 2018) and find SSO-Europa timing is consistent with the
published ephemeris under a constant period assumption. Conversely, we find
that the SSO-Europa transit midpoint indicates a 6.62-minute variation at 4.40
$\sigma$ compared to the prediction obtained from TESS timings, and a constant
orbit period assumption. The TTVs could be modeled with a quadratic function,
yielding a constant period change. The period derivative $\dot{P}$ is
-1.16$\times$10$^{-7}\pm$2.25$\times$10$^{-8}$ days per day (or $-3.65$
s/year), derived from SSO-Europa and TESS timing. Different scenarios,
including a decaying period and apsidal precession can potentially explain
these TTVs but they both introduce certain inconsistencies. We have obtained
CHEOPS observations for two transits in Jan. 2022 to distinguish between
different TTV scenarios. We expect the timing to vary by 5 minutes, compared to
the timing predicted from SSO-Europa and TESS with a constant period
assumption.


title:
Scheduling of Sensor Transmissions Based on Value of Information for
  Summary Statistics

abstract:
  The optimization of Value of Information (VoI) in sensor networks integrates
awareness of the measured process in the communication system. However, most
existing scheduling algorithms do not consider the specific needs of monitoring
applications, but define VoI as a generic Mean Square Error (MSE) of the whole
system state regardless of the relevance of individual components. In this
work, we consider different summary statistics, i.e., different functions of
the state, which can represent the useful information for a monitoring process,
particularly in safety and industrial applications. We propose policies that
minimize the estimation error for different summary statistics, showing
significant gains by simulation.


title:
Supervised Attention in Sequence-to-Sequence Models for Speech
  Recognition

abstract:
  Attention mechanism in sequence-to-sequence models is designed to model the
alignments between acoustic features and output tokens in speech recognition.
However, attention weights produced by models trained end to end do not always
correspond well with actual alignments, and several studies have further argued
that attention weights might not even correspond well with the relevance
attribution of frames. Regardless, visual similarity between attention weights
and alignments is widely used during training as an indicator of the models
quality. In this paper, we treat the correspondence between attention weights
and alignments as a learning problem by imposing a supervised attention loss.
Experiments have shown significant improved performance, suggesting that
learning the alignments well during training critically determines the
performance of sequence-to-sequence models.


title:
Information Retrieval in Friction Stir Welding of Aluminum Alloys by
  using Natural Language Processing based Algorithms

abstract:
  Text summarization is a technique for condensing a big piece of text into a
few key elements that give a general impression of the content. When someone
requires a quick and precise summary of a large amount of information, it
becomes vital. If done manually, summarizing text can be costly and
time-consuming. Natural Language Processing (NLP) is the sub-division of
Artificial Intelligence that narrows down the gap between technology and human
cognition by extracting the relevant information from the pile of data. In the
present work, scientific information regarding the Friction Stir Welding of
Aluminum alloys was collected from the abstract of scholarly research papers.
For extracting the relevant information from these research abstracts four
Natural Language Processing based algorithms i.e. Latent Semantic Analysis
(LSA), Luhn Algorithm, Lex Rank Algorithm, and KL-Algorithm were used. In order
to evaluate the accuracy score of these algorithms, Recall-Oriented Understudy
for Gisting Evaluation (ROUGE) was used. The results showed that the Luhn
Algorithm resulted in the highest f1-Score of 0.413 in comparison to other
algorithms.


title:
Observation of large scale precursor correlations between cosmic rays
  and earthquakes

abstract:
  The search for correlations between secondary cosmic ray detection rates and
seismic effects has long been a subject of investigation motivated by the hope
of identifying a new precursor type that could feed a global early warning
system against earthquakes. Here we show for the first time that the average
variation of the cosmic ray detection rates correlates with the global seismic
activity to be observed with a time lag of approximately two weeks, and that
the significance of the effect varies with a periodicity resembling the
undecenal solar cycle, with a shift in phase of around three years, exceeding 6
sigma at local maxima. The precursor characteristics of the observed
correlations point to a pioneer perspective of an early warning system against
earthquakes.

categories: physics.geo-ph astro-ph.EP astro-ph.HE astro-ph.SR


title: Formalizing a Diophantine Representation of the Set of Prime Numbers

abstract:
  The DPRM (Davis-Putnam-Robinson-Matiyasevich) theorem is the main step in the
negative resolution of Hilbert's 10th problem. Almost three decades of work on
the problem have resulted in several equally surprising results. These include
the existence of diophantine equations with a reduced number of variables, as
well as the explicit construction of polynomials that represent specific sets,
in particular the set of primes. In this work, we formalize these constructions
in the Mizar system. We focus on the set of prime numbers and its explicit
representation using 10 variables. It is the smallest representation known
today. For this, we show that the exponential function is diophantine, together
with the same properties for the binomial coefficient and factorial. This
formalization is the next step in the research on formal approaches to
diophantine sets following the DPRM theorem.


title: Curvature loci of 3-manifolds

abstract:
  We refine the affine classification of real nets of quadrics in order to
obtain generic curvature loci of regular $3$-manifolds in $\mathbb{R}^6$ and
singular corank $1$ $3$-manifolds in $\mathbb{R}^5$. For this, we characterize
the type of the curvature locus by the number and type of solutions of a system
of equations given by 4 ternary cubics (which is a determinantal variety in
some cases). We also study how singularities of the curvature locus of a
regular 3-manifold can go to infinity when the manifold is projected
orthogonally in a tangent direction.


title:
Reconstruction of decays to merged photons using end-to-end deep
  learning with domain continuation in the CMS detector

abstract:
  A novel technique based on machine learning is introduced to reconstruct the
decays of highly Lorentz-boosted particles. Using an end-to-end deep learning
strategy, the technique bypasses existing rule-based particle reconstruction
methods typically used in high energy physics analyses. It uses minimally
processed detector data as input and directly outputs particle properties of
interest. The new technique is demonstrated for the reconstruction of the
invariant mass of particles decaying in the CMS detector. The decay of a
hypothetical scalar particle $\mathcal{A}$ into two photons,
$\mathcal{A}\to\gamma\gamma$, is chosen as a benchmark decay. Lorentz boosts
$\gamma_\mathrm{L}$ = 60-600 are considered, ranging from regimes where both
photons are resolved to those where the photons are closely merged as one
object. A training method using domain continuation is introduced, enabling the
invariant mass reconstruction of unresolved photon pairs in a novel way. The
new technique is validated using $\pi^0\to$gg decays in LHC collision data.


title: Gravitational Effects on Quantum Coherence in Neutrino Oscillation

abstract:
  In this paper, we investigate the quantum coherence for two flavor neutrinos
propagating in a Schwarzschild metric. In fact, this issue is explored both
qualitatively via calculating the parameter $K_{3}$ in Leggett-Garg inequality
(LGI) and also quantitatively by evaluating the $l_{1}$-norm, ${\cal C}(\rho)$.
Using the weak field approximations, we show that the gravitational effects
decrease the maximum value of $K_{3}$ for some intervals of energy such a way
that there is no violation, while it leaves the maximum amount of the quantum
coherence, ${\cal C}(\rho)$ unchanged.


title: Nonlocal $H$-convergence for topologically nontrivial domains

abstract:
  The notion of nonlocal $H$-convergence is extended to domains with nontrivial
topology, that is, domains with non-vanishing harmonic Dirichlet and/or Neumann
fields. If the space of harmonic Dirichlet (or Neumann) fields is
infinite-dimensional, there is an abundance of choice of pairwise incomparable
topologies generalising the one for topologically trivial $\Omega$. It will be
demonstrated that if the domain satisfies the Maxwell's compactness property
the corresponding natural version of the corresponding (generalised) nonlocal
$H$-convergence topology has no such ambiguity. Moreover, on multiplication
operators the nonlocal $H$-topology coincides with the one induced by (local)
$H$-convergence introduced by Murat and Tartar. The topology is used to obtain
nonlocal homogenisation results including convergence of the associated energy
for electrostatics. The derived techniques prove useful to deduce a new
compactness criterion relevant for nonlinear static Maxwell problems.


title:
Systematicity, Compositionality and Transitivity of Deep NLP Models: a
  Metamorphic Testing Perspective

abstract:
  Metamorphic testing has recently been used to check the safety of neural NLP
models. Its main advantage is that it does not rely on a ground truth to
generate test cases. However, existing studies are mostly concerned with
robustness-like metamorphic relations, limiting the scope of linguistic
properties they can test. We propose three new classes of metamorphic
relations, which address the properties of systematicity, compositionality and
transitivity. Unlike robustness, our relations are defined over multiple source
inputs, thus increasing the number of test cases that we can produce by a
polynomial factor. With them, we test the internal consistency of
state-of-the-art NLP models, and show that they do not always behave according
to their expected linguistic properties. Lastly, we introduce a novel graphical
notation that efficiently summarises the inner structure of metamorphic
relations.


title:
Revisiting the transient coarsening kinetics: a new framework in the
  Lifshitz-Slyozov-Wagner space

abstract:
  Phase coarsening is a fundamental process of microstructure evolution in
multiphase materials. A thorough understanding of its kinetics is of great
significance for material processing and performance. Generally, coarsening can
be divided into the transient stage and the steady stage. Compared with steady
coarsening kinetics, the current understanding of transient coarsening is
rather limited and contradictory. In the present work, a new framework in the
dimensionless Lifshitz-Slyozov-Wagner space is developed to study transient
coarsening kinetics co-controlled by interface migration/reaction and matrix
diffusion, where the dynamic equation for individual particles is derived from
the thermodynamic extremal principle.


Evaluating the Quality of a Synthesized Motion with the Fr\'echet Motion
  Distance


title:
Discussion of Multiscale Fisher's Independence Test for Multivariate
  Dependence

abstract:
  The multiscale Fisher's independence test (MULTIFIT hereafter) proposed by
Gorsky & Ma (2022) is a novel method to test independence between two random
vectors. By its design, this test is particularly useful in detecting local
dependence. Moreover, by adopting a resampling-free approach, it can easily
accommodate massive sample sizes. Another benefit of the proposed method is its
ability to interpret the nature of dependency. We congratulate the authors,
Shai Gorksy and Li Ma, for their very interesting and elegant work. In this
comment, we would like to discuss a general framework unifying the MULTIFIT and
other tests and compare it with the binary expansion randomized ensemble test
(BERET hereafter) proposed by Lee et al. (In press). We also would like to
contribute our thoughts on potential extensions of the method.


title: Design Methodologies for Integrated Quantum Frequency Processors

abstract:
  Frequency-encoded quantum information offers intriguing opportunities for
quantum communications and networking, with the quantum frequency processor
paradigm -- based on electro-optic phase modulators and Fourier-transform pulse
shapers -- providing a path for scalable construction of quantum gates. Yet all
experimental demonstrations to date have relied on discrete fiber-optic
components that occupy significant physical space and impart appreciable loss.
In this article, we introduce a model for the design of quantum frequency
processors comprising microring resonator-based pulse shapers and integrated
phase modulators. We estimate the performance of single and parallel
frequency-bin Hadamard gates, finding high fidelity values that extend to
frequency bins with relatively wide bandwidths. By incorporating multi-order
filter designs as well, we explore the limits of tight frequency spacings, a
regime extremely difficult to obtain in bulk optics. Overall, our model is
general, simple to use, and extendable to other material platforms, providing a
much-needed design tool for future frequency processors in integrated
photonics.


title: Statistically unbounded p-convergence in lattice-normed Riesz spaces

abstract:
  The statistically unbounded $p$-convergence is an abstraction of the
statistical order, unbounded order, and $p$-convergences. We investigate the
concept of the statistically unbounded convergence on lattice-normed Riesz
spaces with respect to statistical p-decreasing sequences. Also, we get some
relations between this concept and the other kinds of statistical convergences
on Riesz spaces.


title:
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training
  Quantization

abstract:
  We introduce a Power-of-Two post-training quantization( PTQ) method for deep
neural network that meets hardware requirements and does not call for long-time
retraining. PTQ requires a small set of calibration data and is easier for
deployment, but results in lower accuracy than Quantization-Aware Training(
QAT). Power-of-Two quantization can convert the multiplication introduced by
quantization and dequantization to bit-shift that is adopted by many efficient
accelerators. However, the Power-of-Two scale has fewer candidate values, which
leads to more rounding or clipping errors. We propose a novel Power-of-Two PTQ
framework, dubbed RAPQ, which dynamically adjusts the Power-of-Two scales of
the whole network instead of statically determining them layer by layer. It can
theoretically trade off the rounding error and clipping error of the whole
network. Meanwhile, the reconstruction method in RAPQ is based on the BN
information of every unit. Extensive experiments on ImageNet prove the
excellent performance of our proposed method. Without bells and whistles, RAPQ
can reach accuracy of 65% and 48% on ResNet-18 and MobileNetV2 respectively
with weight INT2 activation INT4. We are the first to propose PTQ for the more
constrained but hardware-friendly Power-of-Two quantization and prove that it
can achieve nearly the same accuracy as SOTA PTQ method. The code will be
released.


title: Learning reversible symplectic dynamics

abstract:
  Time-reversal symmetry arises naturally as a structural property in many
dynamical systems of interest. While the importance of hard-wiring symmetry is
increasingly recognized in machine learning, to date this has eluded
time-reversibility. In this paper we propose a new neural network architecture
for learning time-reversible dynamical systems from data. We focus in
particular on an adaptation to symplectic systems, because of their importance
in physics-informed learning.


title: The chirality-flow formalism for standard model calculations

abstract:
  Scattering amplitudes are often split up into their color (su(N)) and
kinematic components. Since the su(N) gauge part can be described using flows
of color, one may anticipate that the double su(2) kinematic part can be
described in terms of flows of chirality. In two recent papers we showed that
this is indeed the case, introducing the chirality-flow formalism for standard
model calculations. Using the chirality-flow method -- which builds on and
further simplifies the spinor-helicity formalism -- Feynman diagrams can be
directly written down in terms of Lorentz-invariant spinor inner products,
allowing the simplest and most direct path from a Feynman diagram to a complex
number. In this presentation, we introduce this method and show some examples.


title:
Lepton flavor violation, lepton $(g-2)_{\mu,\,e}$ and electron EDM in
  the modular symmetry

abstract:
  We study the lepton flavor violation (LFV), the leptonic magnetic moments
$(g-2)_{\mu,\,e} $ and the electric dipole moment (EDM) of the electron in the
Standard-Model Effective Field Theory with the $\Gamma_N$ modular flavor
symmetry. We employ the stringy Ansatz on coupling structure that 4-point
couplings of matter fields are written by a product of 3-point couplings of
matter fields. We take the level 3 finite modular group, $\Gamma_3$ for the
flavor symmetry, and discuss the dipole operators at nearby fixed point
$\tau=i$, where observed lepton masses and mixing angles are well reproduced.
Suppose the anomaly of the anomalous magnetic moment of the muon to be evidence
of the new physics (NP), we have related it with $(g-2)_{e}$, LFV decays, and
the electron EDM. It is found that the NP contribution to $(g-2)_{e}$ is
proportional to the lepton masses squared likewise the naive scaling. We also
discuss the correlations among the LFV processes $\mu\to e\gamma$, $\tau\to
\mu\gamma$ and $\tau\to e\gamma$, which are testable in the future. The
electron EDM requires the tiny imaginary part of the relevant Wilson
coefficient in the basis of real positive charged lepton masses, which is
related to the $\mu\to e\gamma$ transition in our framework.


title:
Investigating Accuracy-Novelty Performance for Graph-based Collaborative
  Filtering

abstract:
  Recent years have witnessed the great accuracy performance of graph-based
Collaborative Filtering (CF) models for recommender systems. By taking the
user-item interaction behavior as a graph, these graph-based CF models borrow
the success of Graph Neural Networks (GNN), and iteratively perform
neighborhood aggregation to propagate the collaborative signals. While
conventional CF models are known for facing the challenges of the popularity
bias that favors popular items, one may wonder "Whether the existing
graph-based CF models alleviate or exacerbate popularity bias of recommender
systems?" To answer this question, we first investigate the two-fold
performances w.r.t. accuracy and novelty for existing graph-based CF methods.
The empirical results show that symmetric neighborhood aggregation adopted by
most existing graph-based CF models exacerbate the popularity bias and this
phenomenon becomes more serious as the depth of graph propagation increases.
Further, we theoretically analyze the cause of popularity bias for graph-based
CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to
achieve an accuracy-novelty trade-off by controlling the normalization strength
in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly
applied to the existing graph-based CF backbones without additional
computation. Finally, experimental results on three benchmark datasets show
that our proposed method can improve novelty without sacrificing accuracy under
various graph-based CF backbones.


title:
$L^p$-boundedness of Pseudo differential operators on rank one
  Riemannian symmetric spaces of noncompact type

abstract:
  The aim of this paper is to study $L^p$-boundedness property of the pseudo
differential operator associated with a symbol, on rank one Riemannian
symmetric spaces of noncompact type, where the symbol satisfies
H\"ormander-type conditions near infinity.


title:
On the nonlinear Schr\"odinger equation with a toroidal trap in the
  strong confinement regime

abstract:
  We consider the 3D cubic nonlinear Schr\"odinger equation (NLS) with a strong
toroidal trap. In the first part, we show that as the confinement is
strengthened, a large class of global solutions to the time-dependent model can
be described by 1D flows solving the 1D periodic NLS (Theorem 1.4). In the
second part, we construct a steady state as a constrained energy minimizer, and
prove its dimension reduction to the well-known 1D periodic ground state
(Theorem 1.6 and 1.7). Then, employing the dimension reduction limit, we
establish the local uniqueness and the orbital stability of the 3D ring soliton
(Theorem 1.8). These results justify the emergence of stable quasi-1D periodic
dynamics for Bose-Einstein condensates on a ring in physics experiments.


title: A supplement on feathered gyrogroups

abstract:
  A topological gyrogroup is a gyrogroup endowed with a topology such that the
binary operation is jointly continuous and the inverse mapping is also
continuous. It is shown that each compact subset of a topological gyrogroup
with an $\omega^{\omega}$-base is metrizable, which deduces that if $G$ is a
topological gyrogroup with an $\omega^{\omega}$-base and is a $k$-space, then
it is sequential. Moreover, for a feathered strongly topological gyrogroup $G$,
based on the characterization of feathered strongly topological gyrogroups, we
show that if $G$ has countable $cs^{*}$-character, then it is metrizable; and
it is also shown that $G$ has a compact resolution swallowing the compact sets
if and only if $G$ contains a compact $L$-subgyrogroup $H$ such that the
quotient space $G/H$ is a Polish space.


title: Twin-width VII: groups

abstract:
  Twin-width is a recently introduced graph parameter with applications in
algorithmics, combinatorics, and finite model theory. For graphs of bounded
degree, finiteness of twin-width is preserved by quasi-isometry. Thus, through
Cayley graphs, it defines a group invariant. We prove that groups which are
abelian, hyperbolic, ordered, solvable, or with polynomial growth, have finite
twin-width. Twin-width can be characterised by excluding patterns in the
self-action by product of the group elements. Based on this characterisation,
we propose a strengthening called uniform twin-width, which is stable under
constructions such as group extensions, direct products, and direct limits.
  The existence of finitely generated groups with infinite twin-width is not
immediate. We construct one using a result of Osajda on embeddings of graphs
into groups. This implies the existence of a class of finite graphs with
unbounded twin-width but containing $2^{O(n)} \cdot n!$ graphs on vertex set
$\{1,\dots,n\}$, settling a question asked in a previous work.


title:
Brightening of a dark monolayer semiconductor via strong light-matter
  coupling in a cavity

abstract:
  Engineering the properties of quantum materials via strong light-matter
coupling is a compelling research direction with a multiplicity of modern
applications. Those range from modifying charge transport in organic molecules,
steering particle correlation and interactions, and even controlling chemical
reactions. Here, we study the modification of the material properties via
strong coupling and demonstrate an effective inversion of the excitonic
band-ordering in a monolayer of WSe2 with spin-forbidden, optically dark ground
state. In our experiments, we harness the strong light-matter coupling between
cavity photon and the high energy, spin-allowed bright exciton, and thus
creating two bright polaritonic modes in the optical bandgap with the lower
polariton mode pushed below the WSe2 dark state. We demonstrate that in this
regime the commonly observed luminescence quenching stemming from the fast
relaxation to the dark ground state is prevented, which results in the
brightening of this intrinsically dark material. We probe this effective
brightening by temperature-dependent photoluminescence, and we find an
excellent agreement with a theoretical model accounting for the inversion of
the band ordering and phonon-assisted polariton relaxation.


title: Quantum coherence in neutrino oscillation in matter

abstract:
  A closer and more detailed study of neutrino oscillation, in addition to
assisting us in founding physics beyond the standard model, can potentially be
used to understand the fundamental aspects of quantum mechanics. In particular,
we know that the neutrino oscillation occurs because the quantum states of the
produced and detected neutrinos are a coherent superposition of the mass
eigenstates, and this coherency is maintained during the propagation due to the
small mass difference of neutrinos. In this paper, we consider the decoherence
due to the neutrino interaction in the material medium with constant density in
addition to the decoherence coming from the localization properties. For this
purpose, we use $l_1\text{-norm}$ in order to quantify the coherence and
investigate its dependence on the matter density. According to our results, in
general, the coherence in material medium is less than vacuum. However, there
exist exceptions; for some matter densities, the localization coherence lengths
become infinite. So, for these cases, $l_1\text{-norm}$ in matter is more than
the vacuum.


title:
An Algorithm for the Labeling and Interactive Visualization of the
  Cerebrovascular System of Ischemic Strokes

abstract:
  During the diagnosis of ischemic strokes, the Circle of Willis and its
surrounding vessels are the arteries of interest. Their visualization in case
of an acute stroke is often enabled by Computed Tomography Angiography (CTA).
Still, the identification and analysis of the cerebral arteries remain time
consuming in such scans due to a large number of peripheral vessels which may
disturb the visual impression. In previous work we proposed VirtualDSA++, an
algorithm designed to segment and label the cerebrovascular tree on CTA scans.
Especially with stroke patients, labeling is a delicate procedure, as in the
worst case whole hemispheres may not be present due to impeded perfusion.
Hence, we extended the labeling mechanism for the cerebral arteries to identify
occluded vessels. In the work at hand, we place the algorithm in a clinical
context by evaluating the labeling and occlusion detection on stroke patients,
where we have achieved labeling sensitivities comparable to other works between
92\,\% and 95\,\%. To the best of our knowledge, ours is the first work to
address labeling and occlusion detection at once, whereby a sensitivity of
67\,\% and a specificity of 81\,\% were obtained for the latter. VirtualDSA++
also automatically segments and models the intracranial system, which we
further used in a deep learning driven follow up work. We present the generic
concept of iterative systematic search for pathways on all nodes of said model,
which enables new interactive features. Exemplary, we derive in detail,
firstly, the interactive planning of vascular interventions like the mechanical
thrombectomy and secondly, the interactive suppression of vessel structures
that are not of interest in diagnosing strokes (like veins). We discuss both
features as well as further possibilities emerging from the proposed concept.


title:
Deep simultaneous limits on optical emission from FRB 20190520B by 24.4
  fps observations with Tomo-e Gozen

abstract:
  We conduct 24.4~fps optical observations of repeating Fast Radio Burst (FRB)
20190520B using Tomo-e Gozen, a high-speed CMOS camera mounted on the Kiso
105-cm Schmidt telescope, simultaneously with radio observations carried out
using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We
succeeded in the simultaneous optical observations of 11 radio bursts that FAST
detected. However, no corresponding optical emission was found. The optical
fluence limits as deep as 0.068 Jy ms are obtained for the individual bursts
(0.029 Jy ms on the stacked data) corrected for the dust extinction in the
Milky Way. The fluence limit is deeper than those obtained in the previous
simultaneous observations for an optical emission with a duration $\gtrsim 0.1$
ms. Although the current limits on radio--optical spectral energy distribution
(SED) of FRBs are not constraining, we show that SED models based on observed
SEDs of radio variable objects such as optically detected pulsars, and a part
of parameter spaces of theoretical models in which FRB optical emission is
produced by inverse-Compton scattering in a pulsar magnetosphere or a strike of
a magnetar blastwave into a hot wind bubble, can be ruled out once a similar
fluence limit as in our observation is obtained for a bright FRB with a radio
fluence $\gtrsim 5$ Jy ms.


title: Linear-response approach to critical quantum many-body systems

abstract:
  The characterization of quantum critical phenomena is pivotal for the
understanding and harnessing of quantum many-body physics. However, their
complexity makes the inference of such fundamental processes difficult. Thus,
efficient and experimentally non-demanding methods for their diagnosis are
strongly desired. Here, we introduce a general scheme, based on the combination
of finite-size scaling and the linear response of a given observable to a
time-dependent perturbation, to efficiently extract the energy gaps to the
lowest excited states of the system, and thus infer its dynamical critical
exponents. Remarkably, the scheme is able to tackle both integrable and
non-integrable models, prepared away from their ground states. It thus holds
the potential to embody a valuable diagnostic tool for experimentally
significant problems in quantum many-body physics.


title:
Moir\'e-pattern evolution couples rotational and translational friction
  at crystalline interfaces

abstract:
  The sliding motion of objects is typically governed by their friction with
the underlying surface. Compared to translational friction, however, rotational
friction has received much less attention. Here, we experimentally and
theoretically study the rotational depinning and orientational dynamics of
two-dimensional colloidal crystalline clusters on periodically corrugated
surfaces in the presence of magnetically exerted torques. We demonstrate that
the traversing of locally commensurate areas of the moir\'e pattern through the
edges of clusters, which is hindered by potential barriers during cluster
rotation, controls its rotational depinning. The experimentally measured
depinning thresholds as a function of cluster size strikingly collapse onto a
universal theoretical curve which predicts the possibility of a
superlow-static-torque state for large clusters. We further reveal a
cluster-size-independent rotation-translation depinning transition when
lattice-matched clusters are driven jointly by a torque and a force. Our work
provides guidelines to the design of nanomechanical devices that involve
rotational motions on atomic surfaces.


title:
Gamma/hadron discrimination at high energies through the azimuthal
  fluctuations of the particle distributions at ground

abstract:
  This work introduces a novel gamma/hadron discriminating variable, $LCm$,
which quantifies the azimuthal non-uniformity of the particle distributions at
the ground. This non-uniformity, due to the presence of hadronic sub-sh owers,
is higher in proton showers than in gamma showers. The discrimination power of
this new variable is then discussed, as a function of the array fill factor, in
the energy range $10\,$TeV to $1\,$PeV, and compared to the classical
gamma/hadron discriminator based on the measurement of the number of muons at
the ground. The results obtained are extremely encouraging, paving the way for
the use of the proposed quantity in present and future large ground-array
gamma-ray observatories.


title: Generating Topological Structure of Floorplans from Room Attributes

abstract:
  Analysis of indoor spaces requires topological information. In this paper, we
propose to extract topological information from room attributes using what we
call Iterative and adaptive graph Topology Learning (ITL). ITL progressively
predicts multiple relations between rooms; at each iteration, it improves node
embeddings, which in turn facilitates generation of a better topological graph
structure. This notion of iterative improvement of node embeddings and
topological graph structure is in the same spirit as \cite{chen2020iterative}.
However, while \cite{chen2020iterative} computes the adjacency matrix based on
node similarity, we learn the graph metric using a relational decoder to
extract room correlations. Experiments using a new challenging indoor dataset
validate our proposed method. Qualitative and quantitative evaluation for
layout topology prediction and floorplan generation applications also
demonstrate the effectiveness of ITL.


title:
Electric-field-controlled cold dipolar collisions between trapped
  CH$_3$F molecules

abstract:
  Reaching high densities is a key step towards cold-collision experiments with
polyatomic molecules. We use a cryofuge to load up to 2$\times10^7$ CH$_3$F
molecules into a box-like electric trap, achieving densities up to
10$^7$/cm$^3$ at temperatures around 350 mK where the elastic dipolar
cross-section exceeds 7$\times$10$^{-12}$cm$^2$. We measure inelastic rate
constants below 4$\times$10$^{-8}$cm$^3$/s and control these by tuning a
homogeneous electric field that covers a large fraction of the trap volume.
Comparison to ab-initio calculations gives excellent agreement with dipolar
relaxation. Our techniques and findings are generic and immediately relevant
for other cold-molecule collision experiments.


title: Lattices Without a Big Constant and With Noise

abstract:
  We show how Frieze's analysis of subset sum solving using lattices can be
done with out any large constants and without flipping. We apply the variant
without the large constant to inputs with noise.


title: A short review on clustering dark energy

abstract:
  We review dark energy models which can present non-negligible fluctuations on
scales smaller than Hubble radius. Both linear and nonlinear evolutions of dark
energy fluctuations are discussed. The linear evolution has a well-established
framework, based on linear perturbation theory in General Relativity, and is
well studied and implemented in numerical codes. We highlight the main results
from linear theory to explain how dark energy perturbations become important on
the scales of interest for structure formation. Next, we review some attempts
to understand the impact of clustering dark energy models in the nonlinear
regime, usually based on generalizations of the Spherical Collapse Model. We
critically discuss the proposed generalizations of the Spherical Collapse Model
that can treat clustering dark energy models and their shortcomings. Proposed
implementations of clustering dark energy models in halo mass functions are
reviewed. We also discuss some recent numerical simulations capable of treating
dark energy fluctuations. Finally, we make an overview of the observational
predictions based on these models.


title:
Some aspects of Microscopic Mechanism of Superconductivity in
  conventional and non-conventional systems

abstract:
  A broad review of theoretical research work involving different types of
microscopic mechanism in various classes of superconductors, carried out in our
research group over a decade or so, is presented. These mechanisms include both
conventional as well as exotic ones. Special emphasis is placed on the possible
applications to the experimental situations. Moreover, comparison of our works
with various theoretical proposals made by various other researchers, with
regard to high temperature superconductivity in particular, is made. The
crucial importance and special significance of our results are highlighted.


title: Ultrafast pseudospin quantum beats in multilayer WSe$_2$ and MoSe$_2$

abstract:
  Layered van-der-Waals materials with hexagonal symmetry offer an extra degree
of freedom to their electrons, the so called valley index or valley pseudospin.
This quantity behaves conceptually like the electron spin and the term
valleytronics has been coined. In this context, the group of semiconducting
transition-metal dichalcogenides (TMDC) are particularly appealing, due to
large spin-orbit interactions and a direct bandgap at the K points of the
hexagonal Brillouin zone. In this work, we present investigations of excitonic
transitions in mono- and multilayer WSe$_2$ and MoSe$_2$ materials by
time-resolved Faraday ellipticity (TRFE) with in-plane magnetic fields,
$B_{\parallel}$, of up to 9 T. In monolayer samples, the measured TRFE time
traces are almost independent of $B_{\parallel}$, which confirms a close to
zero in-plane exciton $g$ factor $g_\parallel$, consistent with
first-principles calculations. In stark contrast, we observe pronounced
temporal oscillations in multilayer samples for $B_{\parallel}>0$. Remarkably,
the extracted in-plane $g_\parallel$ are very close to reported out-of-plane
exciton $g$ factors of the materials, namely $|g_{\parallel 1s}|=3.1\pm 0.2$
and $2.5\pm0.2$ for the 1s A excitons in WSe$_2$ and MoSe$_2$ multilayers,
respectively. Our first-principles calculations nicely confirm the presence of
a non-zero $g_{\parallel}$ for the multilayer samples. We propose that the
oscillatory TRFE signal in the multilayer samples is caused by pseudospin
quantum beats of excitons, which is a manifestation of spin- and pseudospin
layer locking in the multilayer samples. Our results demonstrate ultrafast
pseudospin rotations in the GHz- to THz frequency range, which pave the way
towards ultrafast pseudospin manipulation in multilayer TMDC samples.


title: REDCHO: Robust Exact Dynamic Consensus of High Order

abstract:
  This article addresses the problem of average consensus in a multi-agent
system when the desired consensus quantity is a time varying signal. Recently,
the EDCHO protocol leveraged high order sliding modes to achieve exact
consensus under a constrained set of initial conditions, limiting its
applicability to static networks. In this work, we propose REDCHO, an extension
of the previous protocol which is robust to mismatch in the initial conditions,
making it suitable to use cases in which connection and disconnection of agents
is possible. The convergence properties of the protocol are formally explored.
Finally, the effectiveness and advantages of our proposal are shown with
concrete simulation examples showing the benefits of REDCHO against other
methods in the literature.


title:
The Diophantine equation $f(x)=g(y)$ for polynomials with simple
  rational roots

abstract:
  In this paper we consider Diophantine equations of the form $f(x)=g(y)$ where
$f$ has simple rational roots and $g$ has rational coefficients. We give strict
conditions for the cases where the equation has infinitely many solutions in
rationals with a bounded denominator. We give examples illustrating that the
given conditions are necessary. It turns out that such equations with
infinitely many solutions are strongly related to Prouhet-Tarry-Escott tuples.
In the special, but important case when $g$ has only simple rational roots as
well, we can give a simpler statement. Also we provide an application to equal
products with terms belonging to blocks of consecutive integers of bounded
length. The latter theorem is related to problems and results of Erd\H{o}s and
Turk, and of Erd\H{o}s and Graham.


title:
On automatic calibration of the SIRD epidemiological model for COVID-19
  data in Poland

abstract:
  We propose a novel methodology for estimating the epidemiological parameters
of a modified SIRD model (acronym of Susceptible, Infected, Recovered and
Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus
spread. We mainly focus on forecasting number of deceased. The procedure was
tested on reported data for Poland. For some short-time intervals we performed
numerical test investigating stability of parameter estimates in the proposed
approach. Numerical experiments confirm the effectiveness of short-term
forecasts (up to 2 weeks) and stability of the method. To improve their
performance (i.e. computation time) GPU architecture was used in computations.


title: Restricted Black-box Adversarial Attack Against DeepFake Face Swapping

abstract:
  DeepFake face swapping presents a significant threat to online security and
social media, which can replace the source face in an arbitrary photo/video
with the target face of an entirely different person. In order to prevent this
fraud, some researchers have begun to study the adversarial methods against
DeepFake or face manipulation. However, existing works focus on the white-box
setting or the black-box setting driven by abundant queries, which severely
limits the practical application of these methods. To tackle this problem, we
introduce a practical adversarial attack that does not require any queries to
the facial image forgery model. Our method is built on a substitute model
persuing for face reconstruction and then transfers adversarial examples from
the substitute model directly to inaccessible black-box DeepFake models.
Specially, we propose the Transferable Cycle Adversary Generative Adversarial
Network (TCA-GAN) to construct the adversarial perturbation for disrupting
unknown DeepFake systems. We also present a novel post-regularization module
for enhancing the transferability of generated adversarial examples. To
comprehensively measure the effectiveness of our approaches, we construct a
challenging benchmark of DeepFake adversarial attacks for future development.
Extensive experiments impressively show that the proposed adversarial attack
method makes the visual quality of DeepFake face images plummet so that they
are easier to be detected by humans and algorithms. Moreover, we demonstrate
that the proposed algorithm can be generalized to offer face image protection
against various face translation methods.