title: On Ramanujan expansions and primes in arithmetic progressions

abstract:
  A celebrated theorem of Delange gives a sufficient condition for an
arithmetic function to be the sum of the associated Ramanujan expansion with
the coefficients provided by a previous result of Wintner. By applying the
Delange theorem to the correlation of the von Mangoldt function with its
incomplete form, we deduce an inequality involving the counting function of the
prime numbers in arithmetic progressions. A remarkable aspect is that such an
inequality is equivalent to the famous conjectural formula by Hardy and
Littlewood for the twin primes.


title: Twisted Magnon Frequency Comb and Penrose Superradiance

abstract:
  Quantization effects of the nonlinear magnon-vortex interaction in
ferromagnetic nanodisks are studied. We show that the circular geometry twists
the spin-wave fields with spiral phase dislocations carrying quantized orbital
angular momentum (OAM). Meanwhile, the confluence and splitting scattering of
twisted magnons off the gyrating vortex core (VC) generates a frequency comb
consisting of discrete and equally spaced spectral lines, dubbed as twisted
magnon frequency comb (tMFC). It is found that the mode spacing of the tMFC is
equal to the gyration frequency of the VC and the OAM quantum numbers between
adjacent spectral lines differ by one. By applying a magnetic field
perpendicular to the plane of a thick nanodisk, we observe a magnonic Penrose
superradiance inside the cone vortex state, which mimics the amplification of
waves scattered from a rotating black hole. It is demonstrated that the
higher-order modes of tMFC are significantly amplified while the lower-order
ones are trapped within the VC gyrating orbit which manifests as the
ergoregion. These results suggest a promising way to generate twisted magnons
with large OAM and to drastically improve the flatness of the magnon comb.


title: Extensively parallelizable chiral fermion

abstract:
  Chiral symmetry is a key to investigating quantum physics, from condensed
matter to particle physics. We propose a novel way of realizing a chiral
fermion, known as the overlap-Dirac operator, without explicitly calculating
the low modes of the Wilson-Dirac operator. We introduce a projection operator
inspired by the Sakurai-Sugiura method and formulate the exact sign function
and overlap-Dirac operator with a contour-integral form. Like the
Sakurai-Sugiura method, the proposing method is multi-scale parallelizable,
which fits the multi-core/multi-GPGPU paradigm. We confirm that the quality of
chiral symmetry realized with the proposed method is sufficient for double
precision. We evaluate the strong scaling of the proposing method.


title:
Synthesizing Attack-Aware Control and Active Sensing Strategies under
  Reactive Sensor Attacks

abstract:
  We consider the probabilistic planning problem for a defender (P1) who can
jointly query the sensors and take control actions to reach a set of goal
states while being aware of possible sensor attacks by an adversary (P2) who
has perfect observations. To synthesize a provably correct, attack-aware
control and active sensing strategy for P1, we construct a stochastic game on
graph where the augmented state includes the actual game state (known by the
attacker), the belief of the defender about the game state (constructed by the
attacker given the attacker's information about the defender's information). We
presented an algorithm to solve a belief-based, randomized strategy for P1 to
ensure satisfying P1's reachability objective with probability one, under the
worst case sensor attacks carried out by an informed P2. The correctness of the
algorithm is proven and illustrated with an example.


title:
Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean
  (Convex) Optimization

abstract:
  In this paper we revisit the problem of differentially private empirical risk
minimization (DP-ERM) and stochastic convex optimization (DP-SCO). We show that
a well-studied continuous time algorithm from statistical physics called
Langevin diffusion (LD) simultaneously provides optimal privacy/utility
tradeoffs for both DP-ERM and DP-SCO under $\epsilon$-DP and
$(\epsilon,\delta)$-DP. Using the uniform stability properties of LD, we
provide the optimal excess population risk guarantee for $\ell_2$-Lipschitz
convex losses under $\epsilon$-DP (even up to $\log n$ factors), thus improving
on Asi et al.
  Along the way we provide various technical tools which can be of independent
interest: i) A new R\'enyi divergence bound for LD when run on loss functions
over two neighboring data sets, ii) Excess empirical risk bounds for
last-iterate LD analogous to that of Shamir and Zhang for noisy stochastic
gradient descent (SGD), and iii) A two phase excess risk analysis of LD, where
the first phase is when the diffusion has not converged in any reasonable sense
to a stationary distribution, and in the second phase when the diffusion has
converged to a variant of Gibbs distribution. Our universality results
crucially rely on the dynamics of LD. When it has converged to a stationary
distribution, we obtain the optimal bounds under $\epsilon$-DP. When it is run
only for a very short time $\propto 1/p$, we obtain the optimal bounds under
$(\epsilon,\delta)$-DP. Here, $p$ is the dimensionality of the model space.
  Our work initiates a systematic study of DP continuous time optimization. We
believe this may have ramifications in the design of discrete time DP
optimization algorithms analogous to that in the non-private setting, where
continuous time dynamical viewpoints have helped in designing new algorithms,
including the celebrated mirror-descent and Polyak's momentum method.


title:
Object Level Depth Reconstruction for Category Level 6D Object Pose
  Estimation From Monocular RGB Image

abstract:
  Recently, RGBD-based category-level 6D object pose estimation has achieved
promising improvement in performance, however, the requirement of depth
information prohibits broader applications. In order to relieve this problem,
this paper proposes a novel approach named Object Level Depth reconstruction
Network (OLD-Net) taking only RGB images as input for category-level 6D object
pose estimation. We propose to directly predict object-level depth from a
monocular RGB image by deforming the category-level shape prior into
object-level depth and the canonical NOCS representation. Two novel modules
named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth
Reconstruction (SDDR) module are introduced to learn high fidelity object-level
depth and delicate shape representations. At last, the 6D object pose is solved
by aligning the predicted canonical representation with the back-projected
object-level depth. Extensive experiments on the challenging CAMERA25 and
REAL275 datasets indicate that our model, though simple, achieves
state-of-the-art performance.


title: FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation

abstract:
  Robust visual recognition under adverse weather conditions is of great
importance in real-world applications. In this context, we propose a new method
for learning semantic segmentation models robust against fog. Its key idea is
to consider the fog condition of an image as its style and close the gap
between images with different fog conditions in neural style spaces of a
segmentation model. In particular, since the neural style of an image is in
general affected by other factors as well as fog, we introduce a fog-pass
filter module that learns to extract a fog-relevant factor from the style.
Optimizing the fog-pass filter and the segmentation model alternately gradually
closes the style gap between different fog conditions and allows to learn
fog-invariant features in consequence. Our method substantially outperforms
previous work on three real foggy image datasets. Moreover, it improves
performance on both foggy and clear weather images, while existing methods
often degrade performance on clear scenes.


title: Exosoul: ethical profiling in the digital world

abstract:
  The development and the spread of increasingly autonomous digital
technologies in our society pose new ethical challenges beyond data protection
and privacy violation. Users are unprotected in their interactions with digital
technologies and at the same time autonomous systems are free to occupy the
space of decisions that is prerogative of each human being. In this context the
multidisciplinary project Exosoul aims at developing a personalized software
exoskeleton which mediates actions in the digital world according to the moral
preferences of the user. The exoskeleton relies on the ethical profiling of a
user, similar in purpose to the privacy profiling proposed in the literature,
but aiming at reflecting and predicting general moral preferences. Our approach
is hybrid, first based on the identification of profiles in a top-down manner,
and then on the refinement of profiles by a personalized data-driven approach.
In this work we report our initial experiment on building such top-down
profiles. We consider the correlations between ethics positions (idealism and
relativism) personality traits (honesty/humility, conscientiousness,
Machiavellianism and narcissism) and worldview (normativism), and then we use a
clustering approach to create ethical profiles predictive of user's digital
behaviors concerning privacy violation, copy-right infringements, caution and
protection. Data were collected by administering a questionnaire to 317 young
individuals. In the paper we discuss two clustering solutions, one data-driven
and one model-driven, in terms of validity and predictive power of digital
behavior.


title: Self-propelled deformable particle model for keratocyte galvanotaxis

abstract:
  During wound healing, fish keratocyte cells undergo galvanotaxis where they
follow a wound-induced electric field. In addition to their stereotypical
persistent motion, keratocytes can develop circular motion without a field or
oscillate around the field direction. We developed a coarse-grained
phenomenological model that captures these keratocyte behaviors. We fit this
model to experimental data on keratocyte response to an electric field being
turned on. A critical element of our model is a tendency for cells to turn
toward their long axis, arising from a coupling between cell shape and
velocity, which gives rise to oscillatory and circular motion. Galvanotaxis is
influenced not only by the field-dependent responses, but also cell speed and
cell shape relaxation rate. When the cell reacts to an electric field being
turned on, our model predicts that stiff, slow cells react slowly but follow
the signal reliably. Cells that polarize and align to the field at a faster
rate react more quickly and follow the signal more reliably. When cells are
exposed to a field that switches direction rapidly, cells follow the average of
field directions, while if the field is switched more slowly, cells follow a
"staircase" pattern.


title:
CWBound: boundary node detection algorithm for complex non-convex mobile
  ad hoc networks

abstract:
  Efficient message forwarding in mobile ad hoc network in disaster scenarios
is challenging because location information on the boundary and interior nodes
is often unavailable. Information related to boundary nodes can be used to
design efficient routing protocols as well as to prolong the battery power of
devices along the boundary of an ad hoc network. In this article, we developed
an algorithm, CWBound, which discovers boundary nodes in a complex non-convex
mobile ad hoc (CNCAH) networks. Experiments show that the CWBound algorithm is
at least three times faster than other state-of-the-art algorithms, and up to
400 times faster than classical force-directed algorithms. The experiments also
confirmed that the CWBound algorithm achieved the highest accuracy (above 97%
for 3 out of the 4 types of CNCAH networks) and sensitivity (90%) among the
algorithms evaluated.


title:
Time-Domain and Frequency-Domain Mappings of Voltage-to-Charge and
  Charge-to-Voltage in Capacitive Devices

abstract:
  In this work, we aim to show that there are generally four possible mapping
functions that can be used to map the time-domain or frequency-domain
representations of an applied voltage input to the resulting time-domain or
frequency-domain electrical charge output; i.e. when the capacitive device is
voltage-charged. Alternatively, there are four more possible combinations when
the device is current-charged. The dual relationship between each pair of
functions for the case of voltage or charge input are provided in terms of
single or double Fourier transforms. All eight system functions coincide with
each other if and only if a constant time- and frequency-independent
capacitance is considered.


title:
Coverage hole detection in WSN with force-directed algorithm and
  transfer learning

abstract:
  Coverage hole detection is an important research problem in wireless sensor
network research community. However, distributed approaches proposed in recent
years for coverage hole detection problem have high computational complexity.
In this paper, we propose a novel approach for coverage hole detection in
wireless sensor networks called FD-TL (Force-directed and Transfer-learning)
which is based on layout generation capability of Force-directed Algorithms and
image recognition power of Convolutional Neural Network with transfer learning.
In contrast to existing approaches, the proposed approach is a pure
topology-based approach since FD-TL can detect both triangular and
non-triangular coverage holes from a wireless sensor network based on the input
network topology without relying on the physical locations of the anchor nodes.
In FD-TL, a Force-directed Algorithm is used to generate a series of possible
layouts from a given input topology. Next, a Convolutional Neural Network is
used to recognize potential coverage holes from the generated layouts. During
the training phase, a transfer learning method is used to aid the recognition
process. Experimental results show that FD-TL method can achieve 90%
sensitivity and 96% specificity for coverage hole detection in wireless sensor
networks.


title:
Optimize Deep Learning Models for Prediction of Gene Mutations Using
  Unsupervised Clustering

abstract:
  Deep learning has become the mainstream methodological choice for analyzing
and interpreting whole-slide digital pathology images (WSIs). It is commonly
assumed that tumor regions carry most predictive information. In this paper, we
proposed an unsupervised clustering-based multiple-instance learning, and apply
our method to develop deep-learning models for prediction of gene mutations
using WSIs from three cancer types in The Cancer Genome Atlas (TCGA) studies
(CRC, LUAD, and HNSCC). We showed that unsupervised clustering of image patches
could help identify predictive patches, exclude patches lack of predictive
information, and therefore improve prediction on gene mutations in all three
different cancer types, compared with the WSI based method without selection of
image patches and models based on only tumor regions. Additionally, our
proposed algorithm outperformed two recently published baseline algorithms
leveraging unsupervised clustering to assist model prediction. The
unsupervised-clustering-based approach for mutation prediction allows
identification of the spatial regions related to mutation of a specific gene
via the resolved probability scores, highlighting the heterogeneity of a
predicted genotype in the tumor microenvironment. Finally, our study also
demonstrated that selection of tumor regions of WSIs is not always the best way
to identify patches for prediction of gene mutations, and other tissue types in
the tumor micro-environment may provide better prediction ability for gene
mutations than tumor tissues.


title: The origin of early-type runaway stars from open clusters

abstract:
  Runaway stars are ejected from their place of birth in the Galactic disk,
with some young B-type runaways found several tens of kiloparsecs from the
plane traveling at speeds beyond the escape velocity. Young open clusters are a
likely place of origin, and ejection may be either through N-body interactions
or in binary supernova explosions. The excellent quality of Gaia astrometry
opens up the path to study the kinematics of young runaway stars to such a high
precision that the place of origin in open stellar clusters can be identified
uniquely. We developed an efficient minimization method to calculate whether
two or more objects may come from the same place, which we tested against
samples of Orion runaways. Our fitting procedure was then used to calculate
trajectories for known runaway stars where we used Gaia data and updated radial
velocities. We found that only half of the sample could be classified as
runaways while the others were walkaway stars. Most of the latter stars turned
out to be binaries. We identified parent clusters for runaways based on their
trajectories and then used cluster age and flight time of the stars to
investigate whether the ejection was likely due to a binary supernova or due to
a dynamical ejection. In particular, we show that the classical runaways AE
Aurigae and $\mu$ Columbae might not have originated together, with $\mu$
Columbae having an earlier ejection from Collinder 69, a cluster near the ONC.
The second sample investigated comprises a set of distant runaway B stars in
the halo which have been studied carefully by quantitative spectral analyses.
We are able to identify candidate parent clusters for at least four stars
including the hyper-runaway candidate HIP 60350. The ejection events had to be
very violent, ejecting stars at velocities as large as 150 to 400 km/s.


title:
Topology of real multi-affine hypersurfaces and a homological stability
  property

abstract:
  Let $\mathrm{R}$ be a real closed field. We prove that the number of
semi-algebraically connected components of a real hypersurface in
$\mathrm{R}^n$ defined by a multi-affine polynomial of degree $d$ is bounded by
$2^{d-1}$. This bound is sharp and is independent of $n$ (as opposed to the
classical bound of $d(2d -1)^{n-1}$ on the Betti numbers of hypersurfaces
defined by arbitrary polynomials of degree $d$ in $\mathrm{R}^n$ due to
Petrovski{\u\i} and Ole{\u\i}nik, Thom and Milnor). Moreover, we show there
exists $c > 1$, such that given a sequence $(B_n)_{n >0}$ where $B_n$ is a
closed ball in $\mathrm{R}^n$ of positive radious, there exist hypersurfaces
$(V_n)_{n_>0}$ defined by symmetric multi-affine polynomials of degree $4$,
such that $\sum_{i \leq 5} b_i(V_n \cap B_n) > c^n$, where $b_i(\cdot)$ denotes
the $i$-th Betti number with rational coeffcients. Finally, as an application
of the main result of the paper we verify a representational stability
conjecture due to Basu and Riener on the cohomology modules of symmetric real
algebraic sets for a new and much larger class of symmetric real algebraic sets
than known before.


title: Time-Frequency Analysis (Lecture Notes)

abstract:
  These lecture notes accompanied the course Time-Frequency Analysis given at
the Faculty of Mathematics of the University of Vienna in the summer term 2021.
The material is suitable for an advanced undergraduate course in mathematics or
a mathematics class for PhD students. Besides standard linear algebra and
calculus only some basics from functional analysis are needed. A course in
Fourier analysis may be of advantage, but is not needed. The course contained 4
academic units per week. The appendices and Section 11 were not presented in
class.


title:
Optimising Energy Efficiency in UAV-Assisted Networks using Deep
  Reinforcement Learning

abstract:
  In this letter, we study the energy efficiency (EE) optimisation of unmanned
aerial vehicles (UAVs) providing wireless coverage to static and mobile ground
users. Recent multi-agent reinforcement learning approaches optimise the
system's EE using a 2D trajectory design, neglecting interference from nearby
UAV cells. We aim to maximise the system's EE by jointly optimising each UAV's
3D trajectory, number of connected users, and the energy consumed, while
accounting for interference. Thus, we propose a cooperative Multi-Agent
Decentralised Double Deep Q-Network (MAD-DDQN) approach. Our approach
outperforms existing baselines in terms of EE by as much as 55 -- 80%.


title:
Long term radio monitoring of the neutron star X-ray binary Swift
  J1858.6-0814

abstract:
  We present the results of our long term radio monitoring campaign at 1.3GHz
(MeerKAT) and 15.5GHz (Arcminute Microkelvin Imager - Large Array, AMI-LA) for
the outburst of the recently discovered neutron star X-ray binary Swift
J1858.6-0814. Throughout the outburst, we observe radio emission consistent
with a quasi-persistent, self-absorbed jet. In addition, we see two flares at
MJD 58427 and 58530. The second flare allows us to place constraints on the
magnetic field and minimum energy of the jet at 0.2G and 5x10^37erg,
respectively. We use the multi-frequency radio data in conjunction with data
from Swift-BAT to place Swift J1858.6-0814 on the radio/X-ray correlation. We
find that the quasi-simultaneous radio and BAT data makes Swift J1858.6-0814
appears to bridge the gap in the radio/X-ray plane between atoll and Z sources.
Furthermore, AMI-LA observations made whilst Swift J1858.6-0814 was in the soft
state have allowed us to show that the radio emission during the soft state is
quenched by at least a factor of four.


title:
DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic
  Segmentation

abstract:
  Deep learning approaches achieve prominent success in 3D semantic
segmentation. However, collecting densely annotated real-world 3D datasets is
extremely time-consuming and expensive. Training models on synthetic data and
generalizing on real-world scenarios becomes an appealing alternative, but
unfortunately suffers from notorious domain shifts. In this work, we propose a
Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and
context gaps caused by different sensing mechanisms and layout placements
across domains. Our DODA encompasses virtual scan simulation to imitate
real-world point cloud patterns and tail-aware cuboid mixing to alleviate the
interior context gap with a cuboid-based intermediate domain. The first
unsupervised sim-to-real adaptation benchmark on 3D indoor semantic
segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 7 popular
Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA
approaches by over 13% on both 3D-FRONT $\rightarrow$ ScanNet and 3D-FRONT
$\rightarrow$ S3DIS. Code will be available.


title:
Interplay of magnetic field and trigonal distortion in honeycomb
  $\Gamma$ model: Occurrence of a spin-flop phase

abstract:
  In candidate Kitaev materials, the off-diagonal $\Gamma$ and $\Gamma'$
interactions are identified to come from the spin-orbit coupling and trigonal
distortion, respectively. They have generated intense research efforts because
of their intimate relation to the reported field-induced magnetically
disordered state in $\alpha$-RuCl$_3$. Theoretically, while a plethora of
field-induced phases have been proposed in the honeycomb lattice, a stable
intermediate phase that can survive in a wide parameter region regardless of
the underlying phases is still lacking. Here we focus on the interplay of an
out-of-plane magnetic field and a symmetry-allowed $\Gamma'$ term due to
trigonal distortion in the dominant antiferromagnetic $\Gamma$ region. By using
multifaceted approaches ranging from classical Monte Carlo and semiclassical
spin-wave theory to density-matrix renormalization group, we identify an
intriguing spin-flop phase in the presence of the magnetic field and
antiferromagnetic $\Gamma'$ interaction, before it eventually enters into a
fully polarized state. As the $\Gamma'$ interaction approaches to the size of
$\Gamma$ one, the $\Gamma$-$\Gamma'$ model maps to the easy-axis XXZ
antiferromagnet, where the spin-flop phase can be understood as a superfluid
phase in the extended Bose-Hubbard model. Our finding thus demonstrates an
exciting path from the honeycomb $\Gamma$ model towards a $U(1)$-symmetric XXZ
antiferromagnet in a magnetic field.


title: Towards Privacy-Preserving and Verifiable Federated Matrix Factorization

abstract:
  Recent years have witnessed the rapid growth of federated learning (FL), an
emerging privacy-aware machine learning paradigm that allows collaborative
learning over isolated datasets distributed across multiple participants. The
salient feature of FL is that the participants can keep their private datasets
local and only share model updates. Very recently, some research efforts have
been initiated to explore the applicability of FL for matrix factorization
(MF), a prevalent method used in modern recommendation systems and services. It
has been shown that sharing the gradient updates in federated MF entails
privacy risks on revealing users' personal ratings, posing a demand for
protecting the shared gradients. Prior art is limited in that they incur
notable accuracy loss, or rely on heavy cryptosystem, with a weak threat model
assumed. In this paper, we propose VPFedMF, a new design aimed at
privacy-preserving and verifiable federated MF. VPFedMF provides for federated
MF guarantees on the confidentiality of individual gradient updates through
lightweight and secure aggregation. Moreover, VPFedMF ambitiously and newly
supports correctness verification of the aggregation results produced by the
coordinating server in federated MF. Experiments on a real-world moving rating
dataset demonstrate the practical performance of VPFedMF in terms of
computation, communication, and accuracy.


title:
Dynamic of Single Molecules in Collective Light-Matter States from First
  Principles

abstract:
  The coherent interaction of a large collection of molecules with a common
photonic mode results in strong light-matter coupling, a feature that proved
highly beneficial for chemistry and termed the research topics polaritonic and
QED chemistry. Considering complex microscopic chemical reactions in
combination with a macroscopic number of molecules renders existing ab initio
approaches inapplicable. In this work, I introduce a simple approach to capture
the collective nature while retaining the full ab initio representation of
single molecules. By embedding the majority of the molecular ensemble into the
dyadic Green tensor, we obtain a computationally cheap and intuitive
description of the dynamic of a single molecule in the ensemble - an approach
that seems ideal for polaritonic chemistry. The introduced embedding
radiation-reaction potential is thoroughly discussed, including prospects,
applications and limitations. A first application demonstrates the linear
response of single molecules that are part of a larger ensembles of molecules.
Then, by virtue of a simple proton-tunneling model, I illustrate that the
influence of collective strong coupling on chemical reactions features a
nontrivial dependence on the number of emitters. Bridging classical
electrodynamics, quantum optical descriptions and the ab initio description of
realistic molecules, this work can serve as guiding light for future
developments and investigations in the quickly growing fields of QED chemistry
and QED material design.

categories: quant-ph cond-mat.mes-hall physics.chem-ph physics.optics


title:
Looking for winning strategies in two-player games on Petri nets with
  partial observability

abstract:
  We define a game on 1-safe Petri nets, where a user plays against an
environment in order to reach a goal on the system. The goal is expressed
through an LTL-X formula, and represents a behaviour of the system that the
user needs to guarantee. The user can try to reach his goal by controlling a
subset of transitions, and by observing a subset of local states. Although we
do not put any requirement on the local states observable by the user, we
assume that he cannot be sure to observe them in the exact moment in which they
become marked. For this reason, we define a notion of stability of the
observation. We propose a method to determine whether the user has a winning
strategy, i.e. if he can win every play by taking some decisions based on the
information available for him.


title: Discovery of new TESS pulsating hot subdwarfs

abstract:
  This work is dedicated to a search for new pulsating hot subdwarfs in TESS
photometric data which could have been missed in previous searches. By matching
catalogues of hot subdwarfs with TESS targets and using luminosities from Gaia
parallaxes, a list of 1389 candidate hot subdwarfs observed by TESS was
created. The periodograms of these stars were inspected, and the stars were
classified according to variability type. An updated catalogue of all known
pulsating hot subdwarfs is presented. A number of probable pulsating binaries
have been identified, which might prove useful for verifying the asteroseismic
masses. The mean masses of p- and g-mode pulsators are estimated from the
stellar parameters. A list of 63 previously unknown pulsating hot subdwarfs
observed by TESS is presented. More than half of the stars previously
identified as pure p-mode pulsators are found to have frequencies in the g-mode
region as well. As a result, hybrid p- and g-mode pulsators occur over the
whole instability strip.


title:
Phonon trapping states as a witness for generation of phonon blockade in
  a hybrid micromaser system

abstract:
  In a hybrid micromaser system consisting of an optical cavity with a moving
mirror connected to a low temperature thermal bath, we demonstrate, both
analytically and numerically, that for certain interaction times between a
random atomic flux and the optomechanical cavity, vacuum phonon trapping states
are generated. Furthermore, under the approach of the master equation with
independent phonon and photon thermal baths, we show that the trapping of the
phonons and photons is achieved for the same interaction times. The results
also indicate that by increasing the cavity-oscillator coupling one may
generate a coherent phonon state aside from the trapping states. Within the
same hybrid system, but now connected to the squeezed phonon reservoir, a
phonon blockade effect can be engineered. Moreover, we identify an
interconnection between the trapping and blockade effects, particularly if one
approaches the vacuum trapping state, strong phonon blockade can be achieved
when the system is connected with a weakly squeezed phonon reservoir.


title: Magnetic impurity in a one-dimensional few-fermion system

abstract:
  We present a numerical analysis of spin-$\frac{1}{2}$ fermions in a
one-dimensional harmonic potential in the presence of a magnetic point-like
impurity at the center of the trap. The model represents a few-body analogue of
a magnetic impurity in the vicinity of an $s$-wave superconductor. Already for
a few particles we find a ground-state level crossing between sectors with
different fermion parities. We interpret this crossing as a few-body precursor
of a quantum phase transition, which occurs when the impurity `breaks' a Cooper
pair. This picture is further corroborated by analyzing density-density
correlations in momentum space. Finally, we discuss how the system may be
realized with existing cold-atoms platforms.


title: Modern Views of Machine Learning for Precision Psychiatry

abstract:
  In light of the NIMH's Research Domain Criteria (RDoC), the advent of
functional neuroimaging, novel technologies and methods provide new
opportunities to develop precise and personalized prognosis and diagnosis of
mental disorders. Machine learning (ML) and artificial intelligence (AI)
technologies are playing an increasingly critical role in the new era of
precision psychiatry. Combining ML/AI with neuromodulation technologies can
potentially provide explainable solutions in clinical practice and effective
therapeutic treatment. Advanced wearable and mobile technologies also call for
the new role of ML/AI for digital phenotyping in mobile mental health. In this
review, we provide a comprehensive review of the ML methodologies and
applications by combining neuroimaging, neuromodulation, and advanced mobile
technologies in psychiatry practice. Additionally, we review the role of ML in
molecular phenotyping and cross-species biomarker identification in precision
psychiatry. We further discuss explainable AI (XAI) and causality testing in a
closed-human-in-the-loop manner, and highlight the ML potential in multimedia
information extraction and multimodal data fusion. Finally, we discuss
conceptual and practical challenges in precision psychiatry and highlight ML
opportunities in future research.


title:
Impedance-based Root-cause Analysis: Comparative Study of Impedance
  Models and Calculation of Eigenvalue Sensitivity

abstract:
  Impedance models of power systems are useful when state-space models of
apparatus such as inverter-based resources (IBRs) have not been made available
and instead only black-box impedance models are available. For tracing the root
causes of poor damping and tuning modes of the system, the sensitivity of the
modes to components and parameters are needed. The so-called critical
admittance-eigenvalue sensitivity based on nodal admittance model has provided
a partial solution but omits meaningful directional information. The
alternative whole-system impedance model yields participation factors of
shunt-connected apparatus with directional information that allows separate
tuning for damping and frequency, yet do not cover series-connected components.
This paper formalises the relationships between the two forms of impedance
models and between the two forms of root-cause analysis. The calculation of
system eigenvalue sensitivity in impedance models is further developed, which
fills the gaps of previous research and establishes a complete theory of
impedance-based root-cause analysis. The theoretical relationships and the
tuning of parameters have been illustrated with a three-node passive network, a
modified IEEE 14-bus network and a modified NETS-NYPS 68-bus network, showing
that tools can be developed for tuning of IBR-rich power systems where only
black-box impedance models are available.


title:
Classification of anomalous diffusion in animal movement data using
  power spectral analysis

abstract:
  The field of movement ecology has seen a rapid increase in high-resolution
data in recent years, leading to the development of numerous statistical and
numerical methods to analyse relocation trajectories. Data are often collected
at the level of the individual and for long periods that may encompass a range
of behaviours. Here, we use the power spectral density (PSD) to characterise
the random movement patterns of a black-winged kite (Elanus caeruleus) and a
white stork (Ciconia ciconia). The tracks are first segmented and clustered
into different behaviours (movement modes), and for each mode we measure the
PSD and the ageing properties of the process. For the foraging kite we find
$1/f$ noise, previously reported in ecological systems mainly in the context of
population dynamics, but not for movement data. We further suggest plausible
models for each of the behavioural modes by comparing both the measured PSD
exponents and the distribution of the single-trajectory PSD to known
theoretical results and simulations.

categories: cond-mat.stat-mech physics.bio-ph physics.data-an q-bio.PE


title: The Secretary Problem with multiple items at each rank

abstract:
  For $k\ge2$, consider the following variation of the classical secretary
problem. There are $k$ items at each of $n$ ranks. The $kn$ items are revealed,
one by one in a uniformly random order, to an observer whose objective is to
select an item with the highest rank. At each stage the observer only knows the
relative ranks of the items that have arrived thus far, and must either select
the current item, or reject it and continue to the next item. Let
$\mathcal{S}(n,k;M)$ denote the strategy whereby one allows the first $M$ items
to pass, and then chooses the first later arriving item whose rank is either
equal to or greater than the highest rank of the first $M$ items (if such an
item exists). Let $P_{n,k}(\mathcal{S}(n,k;M))$ denote the probability of
selecting an item of highest rank using strategy $\mathcal{S}(n,k;M)$. We
obtain a formula for $P_{n,k}(\mathcal{S}(n,k;M))$, and a formula for
$\lim_{n\to\infty}P_{n,k}(\mathcal{S}(n,k,M_n))$, when $M_n\sim ckn$, with
$c\in(0,1)$. In the classical secretary problem, the asymptotically optimal
strategy $M_n\sim cn$ occurs with $c=\frac1e\approx0.368$, and the
corresponding asymptotic probability of success is $\frac1e\approx 0.368$. For
$k=2$, the asymptotically optimal strategy $M_n\sim ckn$ occurs with $c\approx
0.368$ -- almost exactly the same $c$ as in the classical case, and the
corresponding asymptotic probability of success jumps dramatically to 0.701.
For $k=10$, the optimal probability is above 0.999. The optimal value of $c$
stabilizes around 0.486 for $n\ge20$. We also consider the strategies
$\mathcal{S}^+(n,k;M)$, whereby one allows the first $M$ items to pass, and
then chooses the first later arriving item whose rank is strictly greater than
the highest rank of the first $M$ items (if such an item exists). These
strategies turn the problem into one that is essentially equivalent to the
classical secretary problem.


title: A Machine With Human-Like Memory Systems

abstract:
  Inspired by the cognitive science theory, we explicitly model an agent with
both semantic and episodic memory systems, and show that it is better than
having just one of the two memory systems. In order to show this, we have
designed and released our own challenging environment, "the Room", compatible
with OpenAI Gym, where an agent has to properly learn how to encode, store, and
retrieve memories to maximize its rewards. The Room environment allows for a
hybrid intelligence setup where machines and humans can collaborate. We show
that two agents collaborating with each other results in better performance
than one agent acting alone. We have open-sourced our code and models at
https://github.com/tae898/explicit-memory.


title:
Neural Estimation of the Rate-Distortion Function With Applications to
  Operational Source Coding

abstract:
  A fundamental question in designing lossy data compression schemes is how
well one can do in comparison with the rate-distortion function, which
describes the known theoretical limits of lossy compression. Motivated by the
empirical success of deep neural network (DNN) compressors on large, real-world
data, we investigate methods to estimate the rate-distortion function on such
data, which would allow comparison of DNN compressors with optimality. While
one could use the empirical distribution of the data and apply the
Blahut-Arimoto algorithm, this approach presents several computational
challenges and inaccuracies when the datasets are large and high-dimensional,
such as the case of modern image datasets. Instead, we re-formulate the
rate-distortion objective, and solve the resulting functional optimization
problem using neural networks. We apply the resulting rate-distortion
estimator, called NERD, on popular image datasets, and provide evidence that
NERD can accurately estimate the rate-distortion function. Using our estimate,
we show that the rate-distortion achievable by DNN compressors are within
several bits of the rate-distortion function for real-world datasets.
Additionally, NERD provides access to the rate-distortion achieving channel, as
well as samples from its output marginal. Therefore, using recent results in
reverse channel coding, we describe how NERD can be used to construct an
operational one-shot lossy compression scheme with guarantees on the achievable
rate and distortion. Experimental results demonstrate competitive performance
with DNN compressors.


title:
SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity
  Limits of One-shot Graph Generators

abstract:
  We approach the graph generation problem from a spectral perspective by first
generating the dominant parts of the graph Laplacian spectrum and then building
a graph matching these eigenvalues and eigenvectors. Spectral conditioning
allows for direct modeling of the global and local graph structure and helps to
overcome the expressivity and mode collapse issues of one-shot graph
generators. Our novel GAN, called SPECTRE, enables the one-shot generation of
much larger graphs than previously possible with one-shot models. SPECTRE
outperforms state-of-the-art deep autoregressive generators in terms of
modeling fidelity, while also avoiding expensive sequential generation and
dependence on node ordering. A case in point, in sizable synthetic and
real-world graphs SPECTRE achieves a 4-to-170 fold improvement over the best
competitor that does not overfit and is 23-to-30 times faster than
autoregressive generators.


title:
MetaAID: A Flexible Framework for Developing Metaverse Applications via
  AI Technology and Human Editing

abstract:
  Achieving the expansion of domestic demand and the economic internal
circulation requires balanced and coordinated support from multiple industries
(domains) such as consumption, education, entertainment, engineering
infrastructure, etc., which is indispensable for maintaining economic
development. Metaverse applications may help with this task and can make many
industries more interesting, more efficient, and provide a better user
experience. The first challenge is that metaverse application development
inevitably requires the support of various artificial intelligence (AI)
technologies such as natural language processing (NLP), knowledge graph (KG),
computer vision (CV), and machine learning (ML), etc. However, existing
metaverse application development lacks a lightweight AI technology framework.
This paper proposes a flexible metaverse AI technology framework metaAID that
aims to support language and semantic technologies in the development of
digital twins and virtual humans. The second challenge is that the development
process of metaverse applications involves both technical development tasks and
manual editing work, and often becomes a heavyweight multi-team collaboration
project, not to mention the development of metaverse applications in multiple
industries. Our framework summarizes common AI technologies and application
development templates with common functional modules and interfaces. Based on
this framework, we have designed 5 applications for 3 industries around the
expansion of domestic demand and economic internal circulation. Experimental
results show that our framework can support AI technologies when developing
metaverse applications in different industries.


title:
The Prospects of Finding Tidal Disruption Events with 2.5-Metre
  Wide-Field Survey Telescope (WFST) Based on Mock Observations

abstract:
  Optical time-domain survey has been the dominant means of hunting for rare
tidal disruption events (TDEs) in the past decade and remarkably advanced the
TDE study. Particularly, the Zwicky Transient Facility (ZTF) has opened the era
of population studies and the upcoming Large Synoptic Survey Telescope (LSST)
at the Vera Rubin Observatory (VRO) is believed to further revolutionize the
field soon. Here we present the prospects of finding TDEs with another powerful
survey to be performed by 2.5-metre Wide-Field Survey Telescope (WFST). The
WFST, located in western China, will be the most advanced facility dedicated to
optical time-domain surveys in the northern hemisphere once commissioning. We
choose to assess its TDE detectability on the basis of mock observations, which
is hitherto closest to reality by taking into consideration of site conditions,
telescope parameters, survey strategy and transient searching pipeline. Our
mock observations on 440 deg$^2$ field (CosmoDC2 catalogue) show that $29\pm6$
TDEs can be robustly found per year if observed at $u, g, r, i$ bands with
30-second exposure every 10 days, in which a discovery is defined as $\geq$10
epochal detections in at least two filters. If the WFST survey is fully
optimized for discovering TDE, we would expect to identify $392\pm74$ of TDEs
every year, with the redshift up to $z\sim0.8$, which poses a huge challenge to
follow-up resources.


title:
Room-temperature quantification of $^{14}$CO$_{2}$ below the natural
  abundance with two-color cavity ring-down spectroscopy

abstract:
  Radiocarbon's natural production, radiative decay, and isotopic rarity make
it a unique tool to probe carbonaceous systems in the life and earth sciences.
However, the difficulty of current radiocarbon ($^{14}$C) detection methods
limits scientific adoption. Here, two-color cavity ring-down spectroscopy
detects $^{14}$CO$_{2}$ in room-temperature samples with an accuracy of 8% of
the natural abundance in 3 minutes. The intra-cavity pump-probe measurement
uses two cavity-enhanced lasers to cancel out cavity ring-down rate
fluctuations and strong one-photon absorption interference (>10,000 1/s) from
hot-band transitions of CO$_{2}$ isotopologues. Selective, room-temperature
detection of small $^{14}$CO$_{2}$ absorption signals (<1 1/s) reduces the
technical and operational burdens for cavity-enhanced measurements of
radiocarbon, which can benefit a wide range of applications like biomedical
research and field-detection of combusted fossil fuels.


title: Far-field Perfect Imaging with Time Modulated Gratings

abstract:
  We study the capabilities of time-modulated diffraction gratings as imaging
devices. It is shown that a time-dependent but transversally homogeneous slab
can be used to make a perfect image of an object in the far-field, since all
the evanescent modes couple to propagative time-diffracted orders. It is found
that, if the image to be obtained is axially symmetric, it can be recovered by
measuring the time-signal at a single point, without the need of performing a
spatial scan, so that time gratings can act as well as single-pixel imaging
devices. In the case of having an object without axial symmetry, the
time-grating can be combined with a spatial grating, and then the full image
can be recovered again with a measurement at a single point. We apply the
theory of compressive sensing to optimize the recovery method and numerical
examples are provided. We show therefore that time-modulated gratings can be
used to perfectly recover the image of an object in the far field and after
measuring at a single point in the space, being therefore a promising approach
to superesoloution and ultra-fast imaging.


title:
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph
  Neural Networks for Traffic Forecasting

abstract:
  Deep-learning-based data-driven forecasting methods have produced impressive
results for traffic forecasting. A major limitation of these methods, however,
is that they provide forecasts without estimates of uncertainty, which are
critical for real-time deployments. We focus on a diffusion convolutional
recurrent neural network (DCRNN), a state-of-the-art method for short-term
traffic forecasting. We develop a scalable deep ensemble approach to quantify
uncertainties for DCRNN. Our approach uses a scalable Bayesian optimization
method to perform hyperparameter optimization, selects a set of high-performing
configurations, fits a generative model to capture the joint distributions of
the hyperparameter configurations, and trains an ensemble of models by sampling
a new set of hyperparameter configurations from the generative model. We
demonstrate the efficacy of the proposed methods by comparing them with other
uncertainty estimation techniques. We show that our generic and scalable
approach outperforms the current state-of-the-art Bayesian and a number of
other commonly used frequentist techniques.


title:
A Performance Evaluation of Nomon: A Flexible Interface for Noisy
  Single-Switch Users

abstract:
  Some individuals with motor impairments communicate using a single switch --
such as a button click, air puff, or blink. Row-column scanning provides a
method for choosing items arranged in a grid using a single switch. An
alternative, Nomon, allows potential selections to be arranged arbitrarily
rather than requiring a grid (as desired for gaming, drawing, etc.) -- and
provides an alternative probabilistic selection method. While past results
suggest that Nomon may be faster and easier to use than row-column scanning, no
work has yet quantified performance of the two methods over longer time periods
or in tasks beyond writing. In this paper, we also develop and validate a
webcam-based switch that allows a user without a motor impairment to
approximate the response times of a motor-impaired single switch user; although
the approximation is not a replacement for testing with single-switch users, it
allows us to better initialize, calibrate, and evaluate our method. Over 10
sessions with the webcam switch, we found users typed faster and more easily
with Nomon than with row-column scanning. The benefits of Nomon were even more
pronounced in a picture-selection task. Evaluation and feedback from a
motor-impaired switch user further supports the promise of Nomon.


title:
Towards Deep Industrial Transfer Learning: Clustering for Transfer Case
  Selection

abstract:
  Industrial transfer learning increases the adaptability of deep learning
algorithms towards heterogenous and dynamic industrial use cases without high
manual efforts. The appropriate selection of what to transfer can vastly
improve a transfer's results. In this paper, a transfer case selection based
upon clustering is presented. Founded on a survey of clustering algorithms, the
BIRCH algorithm is selected for this purpose. It is evaluated on an industrial
time series dataset from a discrete manufacturing scenario. Results underline
the approaches' applicability caused by its results' reproducibility and
practical indifference to sequence, size and dimensionality of (sub-)datasets
to be clustered sequentially.


title:
$^{57}$Zn $\beta$-delayed proton emission establishes the $^{56}$Ni
  $rp$-process waiting point bypass

abstract:
  We measured the $^{57}$Zn $\beta$-delayed proton ($\beta$p) and $\gamma$
emission at the National Superconducting Cyclotron Laboratory. We find a
$^{57}$Zn half-life of 43.6 $\pm$ 0.2 ms, $\beta$p branching ratio of (84.7
$\pm$ 1.4)%, and identify four transitions corresponding to the exotic
$\beta$-$\gamma$-$p$ decay mode, the second such identification in the $f
p$-shell. The $p/\gamma$ ratio was used to correct for isospin mixing while
determining the $^{57}$Zn mass via the isobaric multiplet mass equation.
Previously, it was uncertain as to whether the rp-process flow could bypass the
textbook waiting point $^{56}$Ni for astrophysical conditions relevant to
Type-I X-ray bursts. Our results definitively establish the existence of the
$^{56}$Ni bypass, with 14-17% of the $rp$-process flow taking this route.


title:
Exploring the potential of FCC-hh to search for particles from $B$
  mesons

abstract:
  The Future Circular Collider (FCC-hh) is a proposed successor of the Large
Hadron Collider (LHC). FCC-hh would push both the energy and intensity
frontiers of searches for new physics particles. In particular, due to higher
energy and luminosity than at the LHC, at FCC-hh there would be produced around
$\simeq\!30$ times larger amount of $B$ mesons and $\simeq 120$ times of $W$
bosons, which then may decay into feebly interacting particles. In this paper
we demonstrate the potential of FCC-hh by studying its sensitivity to heavy
neutral leptons (HNLs) with masses $m_{N}<m_{B}$. We consider various locations
of a displaced decay volume embedded in the planned infrastructure of FCC-hh.
We demonstrate that FCC-hh may substantially improve the reach of the parameter
space of HNLs as compared to the searches proposed at the LHC.


title:
More Efficient Identifiability Verification in ODE Models by Reducing
  Non-Identifiability

abstract:
  Structural global parameter identifiability indicates whether one can
determine a parameter's value from given inputs and outputs in the absence of
noise. If a given model has parameters for which there may be infinitely many
values, such parameters are called non-identifiable. We present a procedure for
accelerating a global identifiability query by eliminating algebraically
independent non-identifiable parameters. Our proposed approach significantly
improves performance across different computer algebra frameworks.


title:
Local and global heights on weighted projective varieties and Vojta's
  conjecture

abstract:
  In this paper we extend results on weighted heights by extending the
definition of weighted heights and introducing the Weil machinery for weighted
projective spaces via Cartier and Weil divisors. We define such heights for
weighted projective varieties and their closed subvarieties. Moreover, we state
Vojta's conjecture for weighted projective varieties in terms of weighted
heights and generalized weighted greatest common divisors..


title:
Tomography of Ultra-relativistic Nuclei with Polarized Photon-gluon
  Collisions

abstract:
  A linearly polarized photon can be quantized from the Lorentz-boosted
electromagnetic field of a nucleus traveling at ultra-relativistic speed. When
two relativistic heavy nuclei pass one another at a distance of a few nuclear
radii, the photon from one nucleus may interact through a virtual
quark-antiquark pair with gluons from the other nucleus forming a short-lived
vector meson (e.g. ${\rho^0}$). In this experiment, the polarization was
utilized in diffractive photoproduction to observe a unique spin interference
pattern in the angular distribution of ${\rho^0\rightarrow\pi^+\pi^-}$ decays.
The observed interference is a result of an overlap of two wave functions at a
distance an order of magnitude larger than the ${\rho^0}$ travel distance
within its lifetime. The strong-interaction nuclear radii were extracted from
these diffractive interactions, and found to be $6.53\pm 0.06$ fm ($^{197} {\rm
Au }$) and $7.29\pm 0.08$ fm ($^{238} {\rm U}$), larger than the nuclear charge
radii. The observable is demonstrated to be sensitive to the nuclear geometry
and quantum interference of non-identical particles.


abstract:
  Real-life industrial use cases for machine learning oftentimes involve
heterogeneous and dynamic assets, processes and data, resulting in a need to
continuously adapt the learning algorithm accordingly. Industrial transfer
learning offers to lower the effort of such adaptation by allowing the
utilization of previously acquired knowledge in solving new (variants of)
tasks. Being data-driven methods, the development of industrial transfer
learning algorithms naturally requires appropriate datasets for training.
However, open-source datasets suitable for transfer learning training, i.e.
spanning different assets, processes and data (variants), are rare. With the
Stuttgart Open Relay Degradation Dataset (SOReDD) we want to offer such a
dataset. It provides data on the degradation of different electromechanical
relays under different operating conditions, allowing for a large number of
different transfer scenarios. Although such relays themselves are usually
inexpensive standard components, their failure often leads to the failure of a
machine as a whole due to their role as the central power switching element of
a machine. The main cost factor in the event of a relay defect is therefore not
the relay itself, but the reduced machine availability. It is therefore
desirable to predict relay degradation as accurately as possible for specific
applications in order to be able to replace relays in good time and avoid
unplanned machine downtimes. Nevertheless, data-driven failure prediction for
electromechanical relays faces the challenge that relay degradation behavior is
highly dependent on the operating conditions, high-resolution measurement data
on relay degradation behavior is only collected in rare cases, and such data
can then only cover a fraction of the possible operating environments. Relays
are thus representative of many other central standard components in automation
technology.


title:
Thermodynamic Uncertainty Relation in the interlinked cascade of
  RabGTPases

abstract:
  We model the well-known interlinked cascade of Rab GTPases found in
eukaryotic cells by using a network of Markov states to investigate if the
universal Thermodynamic Uncertainty Relation (TUR) is obeyed in such
non-equilibrium system. First, we prove numerically the TUR in both single
species model and double species interlinked model. Moreover, our TUR results
show that when two Rab GTPase proteins are interlinked, the thermodynamic cost
and hence precision is greatly enhanced as compared to single species
switching. This implies that at far from equilibrium, the proteins tries to
optimise the precision of their performance of biological processes by forming
interlinks in the cascade. Again,our TUR results imply that the interlinked
cascade (or oscillator) can achieve a range of tunable rate constants (or
frequencies) which suggests a means of maintaining its robustness. Lastly, we
highlight a close relation between thermodynamic cost-precision, triangular
motifs and cancer biology.


title: On the stability of the periodic waves for the Benney system

abstract:
  We analyze the Benney model for interaction of short and long waves in
resonant water wave interactions. Our particular interest is in the periodic
traveling waves, which we construct and study in detail. The main results are
that, for all natural values of the parameters, the periodic dnoidal waves are
spectrally stable with respect to perturbations of the same period. For another
natural set of parameters, we construct the snoidal waves, which exhibit
instabilities, in the same setup.
  Our results are the first instability results in this context. On the other
hand, the spectral stability established herein improves significantly upon the
work Angulo-Corcho-Hakkaev, which established stability of the dnoidal waves,
on a subset of parameter space, by relying on the Grillakis-Shatah theory. Our
approach, which turns out to give definite answer for the entire domain of
parameters, relies on the instability index theory. Interestingly, end even
though the linearized operators are explicit, our spectral analysis requires
subtle and detailed analysis of matrix Schr\"odinger operators in the periodic
context, which support some interesting features.


title:
Generation of twisted magnons via spin-to-orbital angular momentum
  conversion

abstract:
  Twisted magnons (TMs) carrying orbital angular momentum (OAM) have attracted
much attention from the magnonic community. The fabrication of such novel
magnon state however is still challenging. Here we present a simple method to
generate TMs with arbitrary radial and azimuthal quantum numbers through the
spin-to-orbital angular momentum conversion. The conversion rate from
plane-wave magnons to twisted ones is shown to be insensitive to the quantum
index. The spectrum of TMs in thin nanodisks is solved analytically, showing a
good agreement with micromagnetic simulations. Moreover, we numerically study
the propagation of TMs in magnetic nanodisk arrays and obtain the quantitative
dependence of the decay length on quantum indexes. Our results are helpful for
realizing TMs with large OAMs that are indispensable for future high-capacity
magnonic communications and computings.


title:
Strong convergence rates of a fully discrete scheme for the
  Cahn-Hilliard-Cook equation

abstract:
  The first aim of this paper is to examine existence, uniqueness and
regularity for the Cahn-Hilliard-Cook (CHC) equation in space dimension $d\leq
3$. By applying a spectral Galerkin method to the infinite dimensional
equation, we elaborate the well-posedness and regularity of the finite
dimensional approximate problem. The key idea lies in transforming the
stochastic problem {\color{black}{with additive noise}} into an equivalent
random equation. The regularity of the solution to the equivalent random
equation is obtained, in one dimension, with the aid of the Gagliardo-Nirenberg
inequality and done in two and three dimensions, by the energy argument.
Further, the approximate solution is shown to be strongly convergent to the
unique mild solution of the original CHC equation, whose spatio-temporal
regularity can be attained by similar arguments. In addition, a fully discrete
approximation of such problem is investigated, performed by the spectral
Galerkin method in space and the backward Euler method in time. The previously
obtained regularity results of the problem help us to identify strong
convergence rates of the fully discrete scheme.


title:
Ion specificity of confined ion-water structuring and nanoscale surface
  forces in clays

abstract:
  Ion specificity and related Hofmeister effects, ubiquitous in aqueous
systems, can have spectacular consequences in hydrated clays, where
ion-specific nanoscale surface forces can determine large scale cohesive,
swelling and shrinkage behaviors of soil and sediments. We have used a
semi-atomistic computational approach and examined sodium, calcium and aluminum
counterions confined with water between charged surfaces representative of clay
materials, to show that ion-water structuring in nanoscale confinement is at
the origin of surface forces between clay particles which are intrinsically
ion-specific. When charged surfaces strongly confine ions and water, the
amplitude and oscillations of the net pressure naturally emerge from the
interplay of electrostatics and steric effects, which can not be captured by
existing theories. Increasing confinement and surface charge densities promote
ion-water structures that increasingly deviate from the ions' bulk hydration
shells, being strongly anisotropic and persistent, and self-organizing into
optimized, nearly solid-like assemblies where hardly any free water is left. In
these conditions, strongly attractive interactions can prevail between charged
surfaces, due to the dramatically reduced dielectric screening of water and the
highly organized water-ion structures. By unravelling the ion-specific nature
of these nanoscale interactions, we provide evidence that ion-specific
solvation structures determined by confinement are at the origin of ion
specificity in clays and potentially a broader range of confined aqueous
systems.


title: Semantic Similarity Metrics for Evaluating Source Code Summarization

abstract:
  Source code summarization involves creating brief descriptions of source code
in natural language. These descriptions are a key component of software
documentation such as JavaDocs. Automatic code summarization is a prized target
of software engineering research, due to the high value summaries have to
programmers and the simultaneously high cost of writing and maintaining
documentation by hand. Current work is almost all based on machine models
trained via big data input. Large datasets of examples of code and summaries of
that code are used to train an e.g. encoder-decoder neural model. Then the
output predictions of the model are evaluated against a set of reference
summaries. The input is code not seen by the model, and the prediction is
compared to a reference. The means by which a prediction is compared to a
reference is essentially word overlap, calculated via a metric such as BLEU or
ROUGE. The problem with using word overlap is that not all words in a sentence
have the same importance, and many words have synonyms. The result is that
calculated similarity may not match the perceived similarity by human readers.
In this paper, we conduct an experiment to measure the degree to which various
word overlap metrics correlate to human-rated similarity of predicted and
reference summaries. We evaluate alternatives based on current work in semantic
similarity metrics and propose recommendations for evaluation of source code
summarization.


title: Estimating Social Influence from Observational Data

abstract:
  We consider the problem of estimating social influence, the effect that a
person's behavior has on the future behavior of their peers. The key challenge
is that shared behavior between friends could be equally explained by influence
or by two other confounding factors: 1) latent traits that caused people to
both become friends and engage in the behavior, and 2) latent preferences for
the behavior. This paper addresses the challenges of estimating social
influence with three contributions. First, we formalize social influence as a
causal effect, one which requires inferences about hypothetical interventions.
Second, we develop Poisson Influence Factorization (PIF), a method for
estimating social influence from observational data. PIF fits probabilistic
factor models to networks and behavior data to infer variables that serve as
substitutes for the confounding latent traits. Third, we develop assumptions
under which PIF recovers estimates of social influence. We empirically study
PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity
analysis. We find that PIF estimates social influence most accurately compared
to related methods and remains robust under some violations of its assumptions.


title: Commutative graded monads

abstract:
  It is well-known that the category of Kleisli algebras for a monoidal monad
carries a canonical monoidal structure. We define the notion of a commutative
graded monad and present a strictly two-categorical proof that Kleisli algebras
for such monads equally carry a canonical monoidal structure which reduce to
the monoidal monad case when the commutative graded monad in question is graded
over the trivial monoidal category.


title:
Exceptional fracture toughness of CrCoNi-based medium- and high-entropy
  alloys close to liquid helium temperatures

abstract:
  Medium- and high-entropy alloys based on the CrCoNi-system have been shown to
display outstanding strength, tensile ductility and fracture toughness
(damage-tolerance properties), especially at cryogenic temperatures. Here we
examine the JIc and (back-calculated) KJIc fracture toughness values of the
face-centered cubic, equiatomic CrCoNi and CrMnFeCoNi alloys at 20 K. At flow
stress values of ~1.5 GPa, crack-initiation KJIc toughnesses were found to be
exceptionally high, respectively 235 and 415 MPa(square-root)m for CrMnFeCoNi
and CrCoNi, with the latter displaying a crack-growth toughness Kss exceeding
540 MPa(square-root)m after 2.25 mm of stable cracking, which to our knowledge
is the highest such value ever reported. Characterization of the crack-tip
regions in CrCoNi by scanning electron and transmission electron microscopy
reveal deformation structures at 20 K that are quite distinct from those at
higher temperatures and involve heterogeneous nucleation, but restricted
growth, of stacking faults and fine nano-twins, together with transformation to
the hexagonal closed-packed phase. The coherent interfaces of these features
can promote both the arrest and transmission of dislocations to generate
respectively strength and ductility which strongly contributes to sustained
strain hardening. Indeed, we believe that these nominally single-phase,
concentrated solid-solution alloys develop their fracture resistance through a
progressive synergy of deformation mechanisms, including dislocation glide,
stacking-fault formation, nano-twinning and eventually in situ phase
transformation, all of which serve to extend continuous strain hardening which
simultaneously elevates strength and ductility (by delaying plastic
instability), leading to truly exceptional resistance to fracture.


title: Evidence of Noncollinear Spin Texture in Magnetic Moir\'e Superlattices

abstract:
  Moir\'e magnetism, parallel with moir\'e electronics that has led to novel
correlated and topological electronic states, emerges as a new venue to design
and control exotic magnetic phases in twisted magnetic two-dimensional(2D)
crystals. Here, we report direct evidence of noncollinear spin texture in 2D
twisted double bilayer (tDB) magnet chromium triiodide (CrI$_3$). Using
magneto-optical spectroscopy in tDB CrI$_3$, we revealed the presence of a net
magnetization, unexpected from the composing antiferromagnetic bilayers with
compensated magnetizations, and the emergence of noncollinear spins, originated
from the moir\'e exchange coupling-induced spin frustrations. Exploring the
twist angle dependence, we demonstrated that both features are present in tDB
CrI$_3$ with twist angles from 0.5$^o$ to 5$^o$, but are most prominent in the
1.1$^o$ tDB CrI$_3$. Focusing on the temperature dependence of the 1.1$^o$ tDB
CrI$_3$, we resolved the dramatic suppression in the net magnetization onset
temperature and the significant softening of noncollinear spins, as a result of
the moir\'e induced frustration. Our results demonstrate the power of moir\'e
superlattices in introducing novel magnetic phenomena that are absent in
natural 2D magnets.


title:
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly
  Detection?

abstract:
  Anomaly detection in time series is a complex task that has been widely
studied. In recent years, the ability of unsupervised anomaly detection
algorithms has received much attention. This trend has led researchers to
compare only learning-based methods in their articles, abandoning some more
conventional approaches. As a result, the community in this field has been
encouraged to propose increasingly complex learning-based models mainly based
on deep neural networks. To our knowledge, there are no comparative studies
between conventional, machine learning-based and, deep neural network methods
for the detection of anomalies in multivariate time series. In this work, we
study the anomaly detection performance of sixteen conventional, machine
learning-based and, deep neural network approaches on five real-world open
datasets. By analyzing and comparing the performance of each of the sixteen
methods, we show that no family of methods outperforms the others. Therefore,
we encourage the community to reincorporate the three categories of methods in
the anomaly detection in multivariate time series benchmarks.


title:
Reliable Editions from Unreliable Components: Estimating Ebooks from
  Print Editions Using Profile Hidden Markov Models

abstract:
  A profile hidden Markov model, a popular model in biological sequence
analysis, can be used to model related sequences of characters transcribed from
books, magazines, and other printed materials. This paper documents one
application of a profile HMM: automatically producing an ebook edition from
distinct print editions. The resulting ebook has virtually all the desired
properties found in a publisher-prepared ebook, including accurate
transcription and an absence of print artifacts such as end-of-line hyphenation
and running headers. The technique, which has particular benefits for readers
and libraries that require books in an accessible format, is demonstrated using
seven copies of a 19th century novel.


title:
Shear Viscosity of hadronic matter at finite temperature and magnetic
  field

abstract:
  We calculate the transport coefficient of hadronic matter in the presence of
temperature and magnetic field using the linear sigma model. In the relaxation
time approximation, we estimate the shear viscosity over entropy density
$\eta/s$. The point-like interaction rates of hadrons are evaluated through the
$S$-matrix approach in the presence of a magnetic field to obtain the
temperature and magnetic field-dependent relaxation time. We observe that the
transport coefficients are anisotropic in the presence of the magnetic field.
We calculate the temperature and magnetic field-dependent anisotropic shear
viscosity coefficients by incorporating the estimated relaxation time. The
value of viscosity over entropy density is lower in the presence of a magnetic
field than the value of it in a thermal medium. The behavior of the
perpendicular components of the shear viscosity coefficient is also discussed.
We consider the temperature-dependent hadron masses from mean-field effects in
this work.


title: APP: Anytime Progressive Pruning

abstract:
  With the latest advances in deep learning, there has been a lot of focus on
the online learning paradigm due to its relevance in practical settings.
Although many methods have been investigated for optimal learning settings in
scenarios where the data stream is continuous over time, sparse networks
training in such settings have often been overlooked. In this paper, we explore
the problem of training a neural network with a target sparsity in a particular
case of online learning: the anytime learning at macroscale paradigm (ALMA). We
propose a novel way of progressive pruning, referred to as \textit{Anytime
Progressive Pruning} (APP); the proposed approach significantly outperforms the
baseline dense and Anytime OSP models across multiple architectures and
datasets under short, moderate, and long-sequence training. Our method, for
example, shows an improvement in accuracy of $\approx 7\%$ and a reduction in
the generalization gap by $\approx 22\%$, while being $\approx 1/3$ rd the size
of the dense baseline model in few-shot restricted imagenet training. We
further observe interesting nonmonotonic transitions in the generalization gap
in the high number of megabatches-based ALMA. The code and experiment
dashboards can be accessed at
\url{https://github.com/landskape-ai/Progressive-Pruning} and
\url{https://wandb.ai/landskape/APP}, respectively.


title:
Biohybrid membrane formation by directed insertion of Aquaporin into a
  solid-state nanopore

abstract:
  Technical challenges in molecule sensing and chemical detection have created
an increasing demand for transformative materials with high sensitivity and
specificity. Biohybrid nanopores have attracted growing interest as they can
ideally combine the durability of solid-state nanopores with the precise
structure of biological nanopores. Particular care must be taken to control how
biological nanopores adapt to their surroundings once in contact with the
solid-state nanopore. Two major challenges are to precisely control this
adaptability under dynamic conditions and provide predesigned functionalities
that can be manipulated for engineering applications. Here, we report on the
computational design of a distinctive class of biohybrid active membrane layer,
built from the directed insertion of an aquaporin-incorporated lipid shell into
a silica nanopore. First, we describe in detail the mechanisms at play in the
insertion of the biological membrane into the solid-state nanopore. Then we
analyze the structural stability of the system and demonstrate that its water
permeability is comparable to the one measured in the biological environment.
Finally, we discuss how the technology implemented could be applicable to
environmental and biomedical applications, such as water desalination and drug
discovery, where targeting and controlled permeation of small molecules must be
efficiently addressed.


title: Highlights of pion and kaon structure from continuum analyses

abstract:
  One of the biggest challenges in contemporary physics is understanding the
origin and dynamics of the internal structure of hadrons which, at a
fundamental level, is described by quantum chromodynamics (QCD). Taking great
prominence amongst hadrons are pions and kaons which, despite being the
lightest hadrons in nature, their very existence is intimately connected to
those mechanisms responsible for almost all of the mass of the visible matter.
In this manuscript we discuss many aspects of the pion and kaon structure via
light front wave functions and generalized parton distributions, and a
collection of other distributions and structural properties that are inferred
therefrom.


title:
Characterizing Parametric and Convergence Stability in Nonconvex and
  Nonsmooth Optimizations: A Geometric Approach

abstract:
  We consider stability issues in minimizing a continuous (probably
parameterized, nonconvex and nonsmooth) real-valued function $f$. We call a
point stationary if all its possible directional derivatives are nonnegative.
In this work, we focus on two notions of stability on stationary points of $f$:
parametric stability and convergence stability. Parametric considerations are
widely studied in various fields, including smoothed analysis, numerical
stability, condition numbers and sensitivity analysis for linear programming.
Parametric stability asks whether minor perturbations on parameters lead to
dramatic changes in the position and $f$ value of a stationary point.
Meanwhile, convergence stability indicates a non-escapable solution: Any point
sequence iteratively produced by an optimization algorithm cannot escape from a
neighborhood of a stationary point but gets close to it in the sense that such
stationary points are stable to the precision parameter and algorithmic
numerical errors. It turns out that these notions have deep connections to
geometry theory. We show that parametric stability is linked to deformations of
graphs of functions. On the other hand, convergence stability is concerned with
area partitioning of the function domain. Utilizing these connections, we prove
quite tight conditions of these two stability notions for a wide range of
functions and optimization algorithms with small enough step sizes and
precision parameters. These conditions are subtle in the sense that a slightly
weaker function requirement goes to the opposite of primitive intuitions and
leads to wrong conclusions. We present three applications of this theory. These
applications reveal some understanding on Nash equilibrium computation,
nonconvex and nonsmooth optimization, as well as the new optimization
methodology of deep neural networks.


title:
Mutual information in changing environments: non-linear interactions,
  out-of-equilibrium systems, and continuously-varying diffusivities

abstract:
  Biochemistry, ecology, and neuroscience are examples of prominent fields
aiming at describing interacting systems that exhibit non-trivial couplings to
complex, ever-changing environments. We have recently shown that linear
interactions and a switching environment are encoded separately in the mutual
information of the overall system. Here, we first generalize these findings to
a broad class of non-linear interacting models. We find that a new term in the
mutual information appears, quantifying the interplay between non-linear
interactions and environmental changes, and leading to either constructive or
destructive information interference. Furthermore, we show that a higher mutual
information emerges in out-of-equilibrium environments with respect to an
equilibrium scenario. Finally, we generalize our framework to the case of
continuously varying environments. We find that environmental changes can be
mapped exactly into an effective spatially-varying diffusion coefficient,
shedding light on modeling and information structure of biophysical systems in
inhomogeneous media.


title:
Three-dimensional Microstructural Image Synthesis from 2D Backscattered
  Electron Image of Cement Paste

abstract:
  The microstructure is significant for exploring the physical properties of
hardened cement paste. In general, the microstructures of hardened cement paste
are obtained by microscopy. As a popular method, scanning electron microscopy
(SEM) can acquire high-quality 2D images but fails to obtain 3D
microstructures.Although several methods, such as microtomography (Micro-CT)
and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM), can acquire 3D
microstructures, these fail to obtain high-quality 3D images or consume
considerable cost. To address these issues, a method based on solid texture
synthesis is proposed, synthesizing high-quality 3D microstructural image of
hardened cement paste. This method includes 2D backscattered electron (BSE)
image acquisition and 3D microstructure synthesis phases. In the approach, the
synthesis model is based on solid texture synthesis, capturing microstructure
information of the acquired 2D BSE image and generating high-quality 3D
microstructures. In experiments, the method is verified on actual 3D Micro-CT
images and 2D BSE images. Finally, qualitative experiments demonstrate that the
3D microstructures generated by our method have similar visual characteristics
to the given 2D example. Furthermore, quantitative experiments prove that the
synthetic 3D results are consistent with the actual instance in terms of
porosity, particle size distribution, and grey scale co-occurrence matrix.


title:
A PRticle filter algorithm for nonparametric estimation of multivariate
  mixing distributions

abstract:
  Predictive recursion (PR) is a fast, recursive algorithm that gives a smooth
estimate of the mixing distribution under the general mixture model. However,
the PR algorithm requires evaluation of a normalizing constant at each
iteration. When the support of the mixing distribution is of relatively low
dimension, this is not a problem since quadrature methods can be used and are
very efficient. But when the support is of higher dimension, quadrature methods
are inefficient and there is no obvious Monte Carlo-based alternative. In this
paper, we propose a new strategy, which we refer to as PRticle filter, wherein
we augment the basic PR algorithm with a filtering mechanism that adaptively
reweights an initial set of particles along the updating sequence which are
used to obtain Monte Carlo approximations of the normalizing constants.
Convergence properties of the PRticle filter approximation are established and
its empirical accuracy is demonstrated with simulation studies and a marked
spatial point process data analysis.


title:
Discovery of cyclotron and narrow Fe K$_{\alpha}$ lines in HMXB GRO
  J1750-27

abstract:
  We report on timing and spectral analysis of transient Be X-ray pulsar GRO
J1750-27 by using the Nuclear Spectroscopic Telescope Array (NuSTAR)
observation from September 2021. This is the fourth outburst of the system
since 1995. The NuSTAR observation was performed during the rising phase of the
outburst. Pulsations at a period of 4.450710(1) s were observed in the 3-60 keV
energy range. The average pulse profile comprised of a broad peak with a weak
secondary peak which evolved with energy. We did not find any appreciable
variation in the X-ray emission during this observation. The broad-band
phase-averaged spectrum is described by a blackbody, a powerlaw or
Comptonization component. We report discovery of Fe K$_{\alpha}$ line at 6.4
keV along with presence of two cyclotron resonant scattering features around 36
and 42 keV. These lines indicate a magnetic field strength of
$3.7_{-0.3}^{+0.1} \times 10^{12}$ and $4.4 \pm 0.1 \times 10^{12}$ G for the
neutron star. We have estimated a source distance of $\sim$ 13.6-16.4 kpc based
on the accretion-disc torque models.


title:
Ground state and nodal solutions for fractional Orlicz problems with
  lack of regularity and without the Ambrosetti-Rabinowitz condition

abstract:
  We consider a non-local Shr\"odinger problem driven by the fractional Orlicz
g-Laplace operator as follows \begin{equation}\label{PP}
(-\triangle_{g})^{\alpha}u+g(u)=K(x)f(x,u),\ \ \text{in}\
\mathbb{R}^{d},\tag{P} \end{equation} where $d\geq 3,\
(-\triangle_{g})^{\alpha}$ is the fractional Orlicz g-Laplace operator,
$f:\mathbb{R}^d\times\mathbb{R}\rightarrow \mathbb{R}$ is a measurable function
and $K$ is a positive continuous function. Employing the Nehari manifold method
and without assuming the well-known Ambrosetti-Rabinowitz and differentiability
conditions on the non-linear term $f$, we prove that the problem \eqref{PP} has
a ground state of fixed sign and a nodal (or sign-changing) solutions.


title:
Cosmic star formation history with tomographic CIB-galaxy
  cross-correlation

abstract:
  In this work, we probe the star formation history of the Universe using
tomographic cross-correlation between the cosmic infrared background (CIB) and
galaxy samples. The galaxy samples are from the Kilo-Degree Survey (KiDS),
while the CIB maps are made from \planck\, sky maps. We measure the
cross-correlation in harmonic space with a significance of 43$\sigma$. We model
the cross-correlation with a halo model, which links CIB anisotropies to star
formation rates (SFR) and galaxy abundance. We assume that SFR has a lognormal
dependence on halo mass, while galaxy abundance follows the halo occupation
distribution (HOD) model. The cross-correlations give a best-fit maximum star
formation efficiency of $\eta_{\mathrm{max}}= 0.41^{+0.09}_{-0.14}$ at a halo
mass $\log_{10}(M_{\mathrm{peak}}/M_{\odot})= {12.14\pm 0.36}$. The derived
star formation rate density (SFRD) is well constrained up to $z\sim 1.5$. The
constraining power at high redshift is mainly limited by the KiDS survey depth.
A combination with external SFRD measurements from previous studies gives
$\log_{10}(M_{\mathrm{peak}}/M_{\odot})=12.42^{+0.35}_{-0.19}$. This tightens
the SFRD constraint up to $z=4$, yielding a peak SFRD of
$0.09_{-0.004}^{+0.003}\,M_{\odot} \mathrm { year }^{-1} \mathrm{Mpc}^{-3}$ at
$z=1.74^{+0.06}_{-0.02}$, corresponding to a lookback time of
$10.05^{+0.12}_{-0.03}$ Gyr. Both constraints are consistent, and the derived
SFRD agrees with previous studies and simulations. Additionally, we estimate
the galaxy bias $b$ of KiDS galaxies from the constrained HOD parameters and
yield an increasing bias from $b=1.1_{-0.31}^{+0.17}$ at $z=0$ to
$b=1.96_{-0.64}^{+0.18}$ at $z=1.5$. Finally, we provide a forecast for future
galaxy surveys and conclude that, due to their considerable depth, future
surveys will yield a much tighter constraint on the evolution of the SFRD.


title: Yoneda algebras of the triplet vertex operator algebra

abstract:
  Given a vertex operator algebra $V$, one can construct two associative
algebras, the Zhu algebra $A(V)$ and the $C_2$-algebra $R(V)$. This gives rise
to two abelian categories $A(V)-\text{Mod}$ and $R(V)-\text{Mod}$, in addition
to the category of admissible modules of $V$. In case $V$ is rational and
$C_2$-cofinite, the category of admissible $V$-modules and the category of all
$A(V)$-modules are equivalent. However, when $V$ is not rational, the
connection between these two categories is unclear. The goal of this paper is
to study the triplet vertex operator algebra $\mathcal{W}(p)$, as an example to
compare these three categories, in terms of abelian categories. For each of
these three abelian categories, we will determine the associated Ext-quiver,
the Morita equivalent basic algebra, i.e., the algebra $ \text{End}
(\oplus_{L\in \text{Irr}} P_L)^{op}$, and the Yoneda algebra
$\text{Ext}^{*}(\oplus_{L\in \text{Irr}}L, \oplus_{L\in \text{Irr}}L)$. As a
consequence, the category of admissible log-modules for the triplet VOA $
\mathcal W(p)$ has infinite global dimension, as do the Zhu algebra $A(\mathcal
W(p))$, and the associated graded algebra $\text{gr}A(\mathcal W(p))$ which is
isomorphic to $R(\mathcal W(p))$.


title: Improved bounds on number fields of small degree

abstract:
  We study the number of degree $n$ number fields with discriminant bounded by
$X$. In this article, we improve an upper bound due to Schmidt on the number of
such fields that was previously the best known upper bound for $6 \leq n \leq
94$.


title: Dyson-Schwinger equations and the muon g-2

abstract:
  We present a brief introduction to the Dyson-Schwinger equations (DSEs)
approach to hadron and high-energy physics. In particular, how this formalism
is applied to calculate the electromagnetic form factors $\gamma^* \gamma^* \to
\textbf{P}^0$ and $\gamma^* \textbf{P}^\pm \to \textbf{P}^\pm$ (with
$\textbf{P}^\pm$ and $\textbf{P}^0$ charged and neutral ground-state
pseudoscalar mesons, respectively) is discussed. Subsequently, the
corresponding contributions of those form factors to the muon anomalous
magnetic moment ($g-2$) are estimated. We look forward to promoting the DSE
approach to address theoretical aspects of the muon $g-2$, highlighting some
calculations that could be carried out in the future.


title: Randomized Block Adaptive Linear System Solvers

abstract:
  Randomized linear solvers leverage randomization to structure-blindly
compress and solve a linear system to produce an inexpensive solution. While
such a property is highly desirable, randomized linear solvers often suffer
when it comes to performance as either (1) problem structure is not being
exploited, and (2) hardware is inefficiently used. Thus, randomized adaptive
solvers are starting to appear that use the benefits of randomness while
attempting to still exploit problem structure and reduce hardware
inefficiencies. Unfortunately, such randomized adaptive solvers are likely to
be without a theoretical foundation to show that they will work (i.e., find a
solution). Accordingly, here, we distill three general criteria for randomized
block adaptive solvers, which, as we show, will guarantee convergence of the
randomized adaptive solver and supply a worst-case rate of convergence. We will
demonstrate that these results apply to existing randomized block adaptive
solvers, and to several that we devise for demonstrative purposes.


title: Distributed MPC with ALADIN -- A Tutorial

abstract:
  This paper consists of a tutorial on the Augmented Lagrangian based
Alternating Direction Inexact Newton method (ALADIN) and its application to
distributed model predictive control (MPC). The focus is - for simplicity of
presentation - on convex quadratic programming (QP) formulations of MPC. It is
explained how ALADIN can be used to synthesize sparse QP solvers for
large-scale linear-quadratic optimal control by combining ideas from augmented
Lagrangian methods, sequential quadratic programming, as well as barrier or
interior point methods. The highlight of this tutorial is a real-time ALADIN
variant that can be implemented with a few lines of code yet arriving at a
sparse QP solver that can compete with mature open-source and commercial QP
solvers in terms of both run-time as well as numerical accuracy. It is
discussed why this observation could have far reaching consequences on the
future of algorithm and software development in the field of large-scale
optimization and MPC.


title:
Classical Yang Mills equations with sources: consequences of specific
  scalar potentials

abstract:
  Some well known gauge scalar potential very often considered or used in the
literature are investigated by means of the classical Yang Mills equations for
the $SU(2)$ subgroups of $N_c=3$. By fixing a particular shape for the scalar
potential, the resulting vector potentials and the corresponding color-charges
sources are found. By adopting the spherical coordinate system, it is shown
that spherically symmetric solutions, only dependent on the radial coordinate,
are only possible for the Abelian limit, otherwise, there must have
angle-dependent component(s). The following solutions for the scalar potential
are investigated: the Coulomb potential and a non-spherically symmetric
generalization, a linear potential $A_0 (\vec{r}) \sim (\kappa r)$, a
Yukawa-type potential $A_0 (\vec{r}) \sim (C e^{-r/r_0}/r)$ and finite spatial
regions in which the scalar potential assumes constant values. The
corresponding chromo-electric and chromo-magnetic fields, as well as the
color-charge densities, are found to have strong deviations from the spherical
symmetric configurations. We speculate these types of non-spherically symmetric
configurations may contribute (or favor) for the (anisotropic) confinement
mechanism since they should favor color charge-anti-charge (or
three-color-charge) bound states that are intrinsically non spherically
symmetric with (asymmetric) confinement of fluxes. Specific conditions and
relations between the parameters of the solutions are also presented.


title:
An English translation of A. Wiman's "On the algebraic curves of genus
  $p=4$, $5$ and $6$, which posses unambiguous transformations into themselves"

abstract:
  This is an English translation of the paper "Ueber die algebraischen Curven
von den Geschlechtern $p = 4$, $5$ und $6$, welche eindeutigen Transformationen
in sich besitzen" by Anders Wiman, Bihang till Kongl. Svenska
vetenskaps-akademiens handlingar 21 (1895): 1-41. The article, originally
written in German, classifies non-hyperelliptic curves which have non-trivial
automorphism groups via classical methods in algebraic geometry.


title:
Coherent interaction-free detection of microwave pulses with a
  superconducting circuit

abstract:
  We show that it is possible to ascertain the presence of a microwave pulse
resonant with the second transition of a superconducting transmon circuit,
while at the same time avoiding to excite the device onto the third level. In
contrast to standard interaction-free measurement setups, where the dynamics
involves a series of projection operations, our protocol employs a fully
coherent evolution, which results, surprisingly, in a higher efficiency.
Experimentally, this is done by using a series of Ramsey microwave pulses
coupled into the first transition and monitoring the ground-state population.


title: Kaon, Nucleon and $\Delta^*$ Resonances with Hidden Charm

abstract:
  In this presentation we discuss the generation of hadrons with an exotic
quark content and hidden charm emerging from three-body interactions. To be
more specific, we have studied the $KD\bar{D}^*/K\bar D D^*$ and
$ND\bar{D}^*/N\bar{D}D^*$ systems and predicted states generated from the
respective three-body dynamics. In the case of the $KD\bar{D}^*/K\bar D D^*$
system we predict the formation of a meson state with mass around $4307$ MeV
and quantum numbers $I(J^P) = 1/2(1^-)$, $K^*(4307)$, and from the study of the
$ND\bar{D}^*/N\bar{D}D^*$ system we found $N^*$ states with masses in the range
$4400\sim 4600$ MeV, width of $2\sim 20$ MeV and positive parity.


title: Notes on $n$-point Witten diagrams in AdS${}_2$

abstract:
  Witten diagrams provide a perturbative framework for calculations in
Anti-de-Sitter space, and play an essential role in a variety of holographic
computations. In the case of this study in AdS$_2$, the one-dimensional
boundary allows for a simple setup, in which we obtain perturbative analytic
results for correlators with the residue theorem. This elementary method is
used to find all scalar $n$-point contact Witten diagrams for external
operators of conformal dimension $\Delta=1$ and $\Delta=2$, and to determine
topological correlators of Yang-Mills in AdS$_2$. Another established method is
applied to explicitly compute exchange diagrams and give an example of a
Polyakov block in $d=1$. We also check perturbatively a recently proposed
multipoint Ward identity with the strong coupling expansion of the six-point
function of operators inserted on the 1/2 BPS Wilson line in $\mathcal{N}$=4
SYM.


title: NICER monitoring of supersoft X-ray sources

abstract:
  We monitored four supersoft sources - two persistent ones, CAL 83 and MR Vel,
and the recent novae YZ Ret (Nova Ret 2020) and V1674 Her (Nova Her 2021) -
with NICER. The two persistent SSS were observed with unvaried X-ray flux level
and spectrum, respectively, 13 and 20 years after the last observations. Short
period modulations of the supersoft X-ray source (SSS) appear where the
spectrum of the luminous central source was fully visibl (in CAL 83 and V1674
Her) and were absent in YZ Ret and MR Vel, in which the flux originated in
photoionized or shocked plasma, while the white dwarf (WD) was not observable.
We thus suggest that the pulsations occur on, or very close to, the WD surface.
The pulsations of CAL 83 were almost unvaried after 15 years, including an
irregular drift of the $\simeq$67 s period by 2.1 s. Simulations, including
previous XMM-Newton data, indicate actual variations in period length within
hours, rather than an artifact of the variable amplitude of the pulsations.
Large amplitude pulsations with a period of 501.53$\pm$0.30 s were always
detected in V1674 Her, as long as the SSS was observable. This period seems to
be due to rotation of a highly magnetized WD.We cannot confirm the maximum
effective temperature of ($\simeq$145,000 K) previously inferred for this nova,
and discuss the difficulty in interpreting its spectrum. The WD appears to
present two surface zones, one of which does not emit SSS flux.


title: Towards black hole entropy in chiral loop quantum supergravity

abstract:
  Recently, many geometric aspects of $\mathcal{N}$-extended AdS supergravity
in chiral variables have been encountered and clarified. In particular, if the
theory is supposed to be invariant under SUSY transformations also on
boundaries, the boundary term has to be the action of a
$\mathrm{OSp}(\mathcal{N}|2)_{\mathbb{C}}$ super Chern-Simons theory, and
particular boundary conditions must be met. Based on this, we propose a way to
calculate an entropy $S$ for surfaces, presumably including black hole
horizons, in the supersymmetric version of loop quantum gravity for the minimal
case $\mathcal{N}=1$. It proceeds in analogy to the non-supersymmetric theory,
by calculating dimensions of quantum state spaces of the super Chern-Simons
theory with punctures, for fixed quantum (super) area of the surface. We find
$S = a_H/4$ for large areas and determine the subleading correction. Due to the
non-compactness of $\mathrm{OSp}(1|2)_{\mathbb{C}}$ and the corresponding
difficulties with the Chern-Simons quantum theory, we use analytic continuation
from the Verlinde formula for a compact real form, $\mathrm{UOSp}(1|2)$, in
analogy to work by Noui et al. This also entails studying some properties of
$\mathrm{OSp}(1|2)_{\mathbb{C}}$ representations that we have not found
elsewhere in the literature.


title: Evolving Neural Selection with Adaptive Regularization

abstract:
  Over-parameterization is one of the inherent characteristics of modern deep
neural networks, which can often be overcome by leveraging regularization
methods, such as Dropout. Usually, these methods are applied globally and all
the input cases are treated equally. However, given the natural variation of
the input space for real-world tasks such as image recognition and natural
language understanding, it is unlikely that a fixed regularization pattern will
have the same effectiveness for all the input cases. In this work, we
demonstrate a method in which the selection of neurons in deep neural networks
evolves, adapting to the difficulty of prediction. We propose the Adaptive
Neural Selection (ANS) framework, which evolves to weigh neurons in a layer to
form network variants that are suitable to handle different input cases.
Experimental results show that the proposed method can significantly improve
the performance of commonly-used neural network architectures on standard image
recognition benchmarks. Ablation studies also validate the effectiveness and
contribution of each component in the proposed framework.