title:
Electromagnetic Follow-ups of Binary Neutron Star Mergers with Early
  Warnings from Decihertz Gravitational-wave Observatories

abstract:
  We investigate the prospects of electromagnetic follow-ups for binary neutron
star (BNS) mergers, with the help of early warnings from decihertz
gravitational-wave (GW) observatories, B-DECIGO and DO-Optimal. Extending our
previous work in Liu et al. (2022), we not only give quick assessments of joint
short $\gamma$-ray burst (sGRB) detection rates for different $\gamma$-ray
satellites and BNS population models, but also elaborate on the analyses and
results on multi-band kilonova detections for survey telescopes with different
limiting magnitudes. During an assumed 4-year mission time for decihertz GW
observatories, we find that for the goals of electromagnetic follow-ups,
DO-Optimal performs better than B-DECIGO as a whole on the detection rate, and
has a larger detectable distance for joint sGRB/kilonova searches. Taking the
log-normal population model for BNS mergers and a $\text{one-day}$
early-warning time as an example, we discuss the accuracy in localization and
timing, as well as the redshift distributions for various synergy observations
with electromagnetic facilities and decihertz GW detectors. Based on our
analyses, we propose a feasible "wait-for" pattern as a novel detecting mode
for future multi-messenger astrophysics.


title:
Neuroevolutionary Multi-objective approaches to Trajectory Prediction in
  Autonomous Vehicles

abstract:
  The incentive for using Evolutionary Algorithms (EAs) for the automated
optimization and training of deep neural networks (DNNs), a process referred to
as neuroevolution, has gained momentum in recent years. The configuration and
training of these networks can be posed as optimization problems. Indeed, most
of the recent works on neuroevolution have focused their attention on
single-objective optimization. Moreover, from the little research that has been
done at the intersection of neuroevolution and evolutionary multi-objective
optimization (EMO), all the research that has been carried out has focused
predominantly on the use of one type of DNN: convolutional neural networks
(CNNs), using well-established standard benchmark problems such as MNIST. In
this work, we make a leap in the understanding of these two areas
(neuroevolution and EMO), regarded in this work as neuroevolutionary
multi-objective, by using and studying a rich DNN composed of a CNN and
Long-short Term Memory network. Moreover, we use a robust and challenging
vehicle trajectory prediction problem. By using the well-known Non-dominated
Sorting Genetic Algorithm-II, we study the effects of five different
objectives, tested in categories of three, allowing us to show how these
objectives have either a positive or detrimental effect in neuroevolution for
trajectory prediction in autonomous vehicles.


title:
Babel: A Framework for Developing Performant and Dependable Distributed
  Protocols

abstract:
  Prototyping and implementing distributed algorithms, particularly those that
address challenges related with fault-tolerance and dependability, is a time
consuming task. This is, in part, due to the need of addressing low level
aspects such as management of communication channels, controlling timeouts or
periodic tasks, and dealing with concurrency issues. This has a significant
impact for researchers that want to build prototypes for conducting
experimental evaluation; practitioners that want to compare different design
alternatives/solutions; and even for practical teaching activities on
distributed algorithms courses.
  In this paper we present Babel, a novel framework to develop, implement, and
execute distributed protocols and systems. Babel promotes an event driven
programming and execution model that simplifies the task of translating typical
specifications or descriptions of algorithms into performant prototypes, while
allowing the programmer to focus on the relevant challenges of these algorithms
by transparently handling time consuming low level aspects. Furthermore, Babel
provides, and allows the definition of, networking components that can capture
different network capabilities (e.g., P2P, Client/Server, phi-accrual Failure
Detector), making the code mostly independent from the underlying communication
aspects. Babel was built to be generic and can be used to implement a wide
variety of different classes of distributed protocols.
  We conduct our experimental work with two relevant case studies, a
Peer-to-Peer application and a State Machine Replication application, that show
the generality and ease of use of Babel and present competitive performance
when compared with significantly more complex implementations.


title:
Prediction of fish location by combining fisheries data and sea bottom
  temperature forecasting

abstract:
  This paper combines fisheries dependent data and environmental data to be
used in a machine learning pipeline to predict the spatio-temporal abundance of
two species (plaice and sole) commonly caught by the Belgian fishery in the
North Sea. By combining fisheries related features with environmental data, sea
bottom temperature derived from remote sensing, a higher accuracy can be
achieved. In a forecast setting, the predictive accuracy is further improved by
predicting, using a recurrent deep neural network, the sea bottom temperature
up to four days in advance instead of relying on the last previous temperature
measurement.


title:
Using Deep Reinforcement Learning to solve Optimal Power Flow problem
  with generator failures

abstract:
  Deep Reinforcement Learning (DRL) is being used in many domains. One of the
biggest advantages of DRL is that it enables the continuous improvement of a
learning agent. Secondly, the DRL framework is robust and flexible enough to be
applicable to problems of varying nature and domain. Presented work is evidence
of using the DRL technique to solve an Optimal Power Flow (OPF) problem. Two
classical algorithms have been presented to solve the OPF problem. The
drawbacks of the vanilla DRL application are discussed, and an algorithm is
suggested to improve the performance. Secondly, a reward function for the OPF
problem is presented that enables the solution of inherent issues in DRL.
Reasons for divergence and degeneration in DRL are discussed, and the correct
strategy to deal with them with respect to OPF is presented.


title: The van Est Map on Geometric Stacks

abstract:
  We generalize the van Est map and isomorphism theorem in three ways and
discuss conjectured connections with homotopy theory. First, we generalize the
van Est map from a comparison map between Lie groupoid cohomology and Lie
algebroid cohomology to a (more conceptual) comparison map between the
cohomology of a stack $\mathcal{G}$ and the foliated cohomology of a stack
$\mathcal{H}\to\mathcal{G}$ mapping into it. In the case of a wide subgroupoid
$H\to G\,,$ this foliation can be thought of as equipping the normal bundle of
$H$ with the structure of an LA-groupoid. This generalization allows us to
derive results that could not be obtained with the usual van Est map for Lie
groupoids. Secondly, we generalize the functions that we can take cohomology of
in the context of the van Est map. Instead of using functions valued in
representations, we can use functions valued in modules, for example we can use
$S^1$-valued functions and $\mathbb{Z}$-valued functions. Thirdly, everything
we do works in the holomorphic category, in addition to the smooth category. At
the end of this thesis we give a conjectural definition of Morita equivalences
of Lie algebroids and generalized morphisms, and we prove a no-go theorem
regarding any possible notion of differentiability of generalized morphisms of
groupoids. We explore future directions (involving higher structures) and
conjecture a link between Lie's second theorem and the van Est theorem. This
involves describing higher cohomology classes (eg. gerbes) as generalized
morphisms in a higher category, similar to how principal bundles define
generalized morphisms in a (2,1)-category. We conjecture the existence of a
smooth version of Grothendieck's homotopy hypothesis and explore its
implications.


title:
Vehicle Noise: Comparison of Loudness Ratings in the Field and the
  Laboratory

abstract:
  Objective: Distorted loudness perception is one of the main complaints of
hearing aid users. Being able to measure loudness perception correctly in the
clinic is essential for fitting hearing aids. For this, experiments in the
clinic should be able to reflect and capture loudness perception as in
everyday-life situations. Little research has been done comparing loudness
perception in the field and in the laboratory. Design: Participants rated the
loudness in the field and in the laboratory of 36 driving actions done by four
different vehicles. The field measurements were done in a restricted street and
recorded with a 360deg camera and a tetrahedral microphone. The recorded
stimuli, which are openly accessible, were presented in three different
conditions in the laboratory: 360deg video recordings with a head-mounted
display, video recordings with a desktop monitor, and audio-only. Sample:
Thirteen normal-hearing participants and 18 hearing-impaired participants
participated in the study. Results: The driving actions were rated
significantly louder in the laboratory than in the field for the audio-only
condition. These loudness rating differences were bigger for louder sounds in
two laboratory conditions, i.e., the higher the sound level of a driving action
was the more likely it was to be rated louder in the laboratory. There were no
significant differences in the loudness ratings between the three laboratory
conditions and between groups. Conclusions: The results of this experiment
further remark the importance of increasing the realism and immersion when
measuring loudness in the clinic.


title:
Improved Orientation Estimation and Detection with Hybrid Object
  Detection Networks for Automotive Radar

abstract:
  This paper presents novel hybrid architectures that combine grid- and
point-based processing to improve the detection performance and orientation
estimation of radar-based object detection networks. Purely grid-based
detection models operate on a bird's-eye-view (BEV) projection of the input
point cloud. These approaches suffer from a loss of detailed information
through the discrete grid resolution. This applies in particular to radar
object detection, where relatively coarse grid resolutions are commonly used to
account for the sparsity of radar point clouds. In contrast, point-based models
are not affected by this problem as they continuously process point clouds.
However, they generally exhibit worse detection performances than grid-based
methods.
  We show that a point-based model can extract neighborhood features,
leveraging the exact relative positions of points, before grid rendering. This
has significant benefits for a following convolutional detection backbone. In
experiments on the public nuScenes dataset our hybrid architecture achieves
improvements in terms of detection performance and orientation estimates over
networks from previous literature.


title: Combining Reciprocity and CSI Feedback in MIMO Systems

abstract:
  Reciprocity-based time-division duplex (TDD) Massive MIMO (multiple-input
multiple-output) systems utilize channel estimates obtained in the uplink to
perform precoding in the downlink. However, this method has been criticized of
breaking down, in the sense that the channel estimates are not good enough to
spatially separate multiple user terminals, at low uplink reference signal
signal-to-noise ratios, due to insufficient channel estimation quality.
Instead, codebook-based downlink precoding has been advocated for as an
alternative solution in order to bypass this problem. We analyze this problem
by considering a "grid-of-beams world" with a finite number of possible
downlink channel realizations. Assuming that the terminal accurately can detect
the downlink channel, we show that in the case where reciprocity holds,
carefully designing a mapping between the downlink channel and the uplink
reference signals will perform better than both the conventional TDD Massive
MIMO and frequency-division duplex (FDD) Massive MIMO approach. We derive
elegant metrics for designing this mapping, and further, we propose algorithms
that find good sequence mappings.


title:
Predicting vacant parking space availability zone-wisely: a graph based
  spatio-temporal prediction approach

abstract:
  Vacant parking space (VPS) prediction is one of the key issues of intelligent
parking guidance systems. Accurately predicting VPS information plays a crucial
role in intelligent parking guidance systems, which can help drivers find
parking space quickly, reducing unnecessary waste of time and excessive
environmental pollution. Through the simple analysis of historical data, we
found that there not only exists a obvious temporal correlation in each parking
lot, but also a clear spatial correlation between different parking lots. In
view of this, this paper proposed a graph data-based model ST-GBGRU
(Spatial-Temporal Graph Based Gated Recurrent Unit), the number of VPSs can be
predicted both in short-term (i.e., within 30 min) and in long-term (i.e., over
30min). On the one hand, the temporal correlation of historical VPS data is
extracted by GRU, on the other hand, the spatial correlation of historical VPS
data is extracted by GCN inside GRU. Two prediction methods, namely direct
prediction and iterative prediction, are combined with the proposed model.
Finally, the prediction model is applied to predict the number VPSs of 8 public
parking lots in Santa Monica. The results show that in the short-term and
long-term prediction tasks, ST-GBGRU model can achieve high accuracy and have
good application prospects.


title:
Putting the micro into the macro: A molecularly-augmented hydrodynamic
  model of dynamic wetting applied to flow instabilities during forced
  dewetting

abstract:
  We report a molecularly-augmented continuum-based computational model of
dynamic wetting and apply it to the displacement of an externally-driven liquid
plug between two partially-wetted parallel plates. The results closely follow
those obtained in a recent molecular-dynamics (MD) study of the same problem
Toledano (2021) which we use as a benchmark. We are able to interpret the
maximum speed of dewetting $U^*_{\mathrm{crit}}$ as a fold bifurcation in the
steady phase diagram and show that its dependence on the true contact angle
$\theta_{\mathrm{cl}}$ is quantitatively similar to that found using MD. A key
feature of the model is that the contact angle is dependent on the speed of the
contact line, with $\theta_{\mathrm{cl}}$ emerging as part of the solution. The
model enables us to study the formation of a thin film at dewetting speeds
$U^*>U^*_{\mathrm{crit}}$ across a range of length scales, including those that
are computationally prohibitive to MD simulations. We show that the thickness
of the film scales linearly with the channel width and is only weakly dependent
on the capillary number. This work provides a link between matched asymptotic
techniques (valid for larger geometries) and MD simulations (valid for smaller
geometries). In addition, we find that the apparent angle, the experimentally
visible contact angle at the fold bifurcation, is not zero. This is in contrast
to the prediction of conventional treatments based on the lubrication model of
flow near the contact line, but consistent with experiment.


title:
Axonal Delay As a Short-Term Memory for Feed Forward Deep Spiking Neural
  Networks

abstract:
  The information of spiking neural networks (SNNs) are propagated between the
adjacent biological neuron by spikes, which provides a computing paradigm with
the promise of simulating the human brain. Recent studies have found that the
time delay of neurons plays an important role in the learning process.
Therefore, configuring the precise timing of the spike is a promising direction
for understanding and improving the transmission process of temporal
information in SNNs. However, most of the existing learning methods for spiking
neurons are focusing on the adjustment of synaptic weight, while very few
research has been working on axonal delay. In this paper, we verify the
effectiveness of integrating time delay into supervised learning and propose a
module that modulates the axonal delay through short-term memory. To this end,
a rectified axonal delay (RAD) module is integrated with the spiking model to
align the spike timing and thus improve the characterization learning ability
of temporal features. Experiments on three neuromorphic benchmark datasets :
NMNIST, DVS Gesture and N-TIDIGITS18 show that the proposed method achieves the
state-of-the-art performance while using the fewest parameters.


title: Optimizing One-pixel Black-box Adversarial Attacks

abstract:
  The output of Deep Neural Networks (DNN) can be altered by a small
perturbation of the input in a black box setting by making multiple calls to
the DNN. However, the high computation and time required makes the existing
approaches unusable. This work seeks to improve the One-pixel (few-pixel)
black-box adversarial attacks to reduce the number of calls to the network
under attack. The One-pixel attack uses a non-gradient optimization algorithm
to find pixel-level perturbations under the constraint of a fixed number of
pixels, which causes the network to predict the wrong label for a given image.
We show through experimental results how the choice of the optimization
algorithm and initial positions to search can reduce function calls and
increase attack success significantly, making the attack more practical in
real-world settings.


title:
ATDD: Fine-Grained Assured Time-Sensitive Data Deletion Scheme in Cloud
  Storage

abstract:
  With the rapid development of general cloud services, more and more
individuals or collectives use cloud platforms to store data. Assured data
deletion deserves investigation in cloud storage. In time-sensitive data
storage scenarios, it is necessary for cloud platforms to automatically destroy
data after the data owner-specified expiration time. Therefore, assured
timesensitive data deletion should be sought. In this paper, a finegrained
assured time-sensitive data deletion (ATDD) scheme in cloud storage is proposed
by embedding the time trapdoor in Ciphertext-Policy Attribute-Based Encryption
(CP-ABE). Timesensitive data is self-destructed after the data owner-specified
expiration time so that the authorized users cannot get access to the related
data. In addition, a credential is returned to the data owner for data deletion
verification. This proposed scheme provides solutions for fine-grained access
control and verifiable data self-destruction. Detailed security and performance
analysis demonstrate the security and the practicability of the proposed
scheme.


title:
The effect of time delay for synchronisation suppression in neuronal
  networks

abstract:
  We study the time delay in the synaptic conductance for suppression of spike
synchronisation in a random network of Hodgkin Huxley neurons coupled by means
of chemical synapses. In the first part, we examine in detail how the time
delay acts over the network during the synchronised and desynchronised neuronal
activities. We observe a relation between the neuronal dynamics and the syaptic
conductance distributions. We find parameter values in which the time delay has
high effectiveness in promoting the suppression of spike synchronisation. In
the second part, we analyse how the delayed neuronal networks react when pulsed
inputs with different profiles (periodic, random, and mixed) are applied on the
neurons. We show the main parameters responsible for inducing or not
synchronous neuronal oscillations in delayed networks.


title: Processing Network Controls via Deep Reinforcement Learning

abstract:
  Novel advanced policy gradient (APG) algorithms, such as proximal policy
optimization (PPO), trust region policy optimization, and their variations,
have become the dominant reinforcement learning (RL) algorithms because of
their ease of implementation and good practical performance. This dissertation
is concerned with theoretical justification and practical application of the
APG algorithms for solving processing network control optimization problems.
Processing network control problems are typically formulated as Markov decision
process (MDP) or semi-Markov decision process (SMDP) problems that have several
unconventional for RL features: infinite state spaces, unbounded costs,
long-run average cost objectives. Policy improvement bounds play a crucial role
in the theoretical justification of the APG algorithms. In this thesis we
refine existing bounds for MDPs with finite state spaces and prove novel policy
improvement bounds for classes of MDPs and SMDPs used to model processing
network operations. We consider two examples of processing network control
problems and customize the PPO algorithm to solve them. First, we consider
parallel-server and multiclass queueing networks controls. Second, we consider
the drivers repositioning problem in a ride-hailing service system. For both
examples the PPO algorithm with auxiliary modifications consistently generates
control policies that outperform state-of-art heuristics.


title: The transverse K\"ahler-Ricci flow on Vaisman manifolds

abstract:
  We define the transverse K\"ahler-Ricci equation on the canonical foliation
of a closed Vaisman manifold, and investigate the existence and the convergence
of the solution. Using the transverse K\"ahler-Ricci flow, in a particular
case, we deform the Vaisman metric into another Vaisman metric with a
transverse K\"ahler-Einstein structure. In the final part of the article we
discuss an example.


title: Accelerating phase-field-based simulation via machine learning

abstract:
  Phase-field-based models have become common in material science, mechanics,
physics, biology, chemistry, and engineering for the simulation of
microstructure evolution. Yet, they suffer from the drawback of being
computationally very costly when applied to large, complex systems. To reduce
such computational costs, a Unet-based artificial neural network is developed
as a surrogate model in the current work. Training input for this network is
obtained from the results of the numerical solution of initial-boundary-value
problems (IBVPs) based on the Fan-Chen model for grain microstructure
evolution. In particular, about 250 different simulations with varying initial
order parameters are carried out and 200 frames of the time evolution of the
phase fields are stored for each simulation. The network is trained with 90% of
this data, taking the $i$-th frame of a simulation, i.e. order parameter field,
as input, and producing the $(i+1)$-th frame as the output. Evaluation of the
network is carried out with a test dataset consisting of 2200 microstructures
based on different configurations than originally used for training. The
trained network is applied recursively on initial order parameters to calculate
the time evolution of the phase fields. The results are compared to the ones
obtained from the conventional numerical solution in terms of the errors in
order parameters and the system's free energy. The resulting order parameter
error averaged over all points and all simulation cases is 0.005 and the
relative error in the total free energy in all simulation boxes does not exceed
1%.


title: Probing Phase Structure of Black Holes with Lyapunov Exponents

abstract:
  We conjecture that there exists a relationship between Lyapunov exponents and
black hole phase transitions. To support our conjecture, Lyapunov exponents of
the motion of particles and ring strings are calculated for
Reissner-Nordstr\"{o}m-AdS black holes. When a phase transition occurs, the
Lyapunov exponents become multivalued, and branches of the Lyapunov exponents
coincide with black hole phases. Moreover, the discontinuous change in the
Lyapunov exponents can be treated as an order parameter, and has a critical
exponent of $1/2$ near the critical point. Our findings reveal that Lyapunov
exponents can be an efficient tool to study phase structure of black holes.


title: Eine empirische Analyse der Skalierung von Value-at-Risk Schaetzungen

abstract:
  In practice, the value-at-risk (VaR) for a longer holding period is often
scaled using the 'square root of time rule'. The VaR is determined for a
shorter holding period and then scaled up according to the desired holding
period. For example, the Basel rules allow banks to scale up the 1-day VaR by
the square root of ten to determine the 10-day VaR. It can be seen from the
results of this thesis that scaling can also provide good and accurate
estimates of VaR. However, it is probably much more important to consider that,
depending on the methods or data set involved, there may also be significant
consequences for risk provisioning. Particularly, since scaling does not always
avoid the occurrence of losses that exceed the VaR estimate on a frequent basis
over a period of time. Overall, the permission to use the square root of time
rule in the regulatory framework should be reconsidered.


title:
Combinatorial games on Galton-Watson trees involving
  several-generation-jump moves

abstract:
  We study the $k$-jump normal and $k$-jump mis\`{e}re games on rooted
Galton-Watson trees, expressing the probabilities of various outcomes of these
games as specific fixed points of certain functions that depend on $k$ and the
offspring distribution. We discuss results on phase transitions pertaining to
draw probabilities when the offspring distribution is Poisson$(\lambda)$ (i.e.
for which values of $\lambda$, the draw probability is strictly positive). We
compare the probabilities of the various outcomes of the $2$-jump normal game
with those of the $2$-jump mis\`{e}re game, and a similar comparison is drawn
between the $2$-jump normal game and the $1$-jump normal game, under the
Poisson regime. We describe the rate of decay of the probability that the first
player loses the $2$-jump normal game as $\lambda \rightarrow \infty$. Finally,
we discuss a sufficient condition for the average duration of the $k$-jump
normal game to be finite.


title:
Engineering deep learning methods on automatic detection of damage in
  infrastructure due to extreme events

abstract:
  This paper presents a few comprehensive experimental studies for automated
Structural Damage Detection (SDD) in extreme events using deep learning methods
for processing 2D images. In the first study, a 152-layer Residual network
(ResNet) is utilized to classify multiple classes in eight SDD tasks, which
include identification of scene levels, damage levels, material types, etc. The
proposed ResNet achieved high accuracy for each task while the positions of the
damage are not identifiable. In the second study, the existing ResNet and a
segmentation network (U-Net) are combined into a new pipeline, cascaded
networks, for categorizing and locating structural damage. The results show
that the accuracy of damage detection is significantly improved compared to
only using a segmentation network. In the third and fourth studies, end-to-end
networks are developed and tested as a new solution to directly detect cracks
and spalling in the image collections of recent large earthquakes. One of the
proposed networks can achieve an accuracy above 67.6% for all tested images at
various scales and resolutions, and shows its robustness for these human-free
detection tasks. As a preliminary field study, we applied the proposed method
to detect damage in a concrete structure that was tested to study its
progressive collapse performance. The experiments indicate that these solutions
for automatic detection of structural damage using deep learning methods are
feasible and promising. The training datasets and codes will be made available
for the public upon the publication of this paper.


title: Magnetised Winds in Transition Discs I: 2.5D Global Simulations

abstract:
  Protoplanetary discs (PPDs) are cold, dense and weakly ionised environments
that witness the planetary formation. Among these discs, transition discs (TDs)
are characterised by a wide cavity in the dust and gas distribution. Despite
this lack of material, many TDs strongly accrete onto their central star,
possibly indicating that a mechanism is driving fast accretion in TDs cavities.
The presence of radially extended 'dead zones' in PPDs recently revived the
interest in magnetised disc winds (MDWs), where accretion is driven by a large
magnetic field. We propose that TDs could be subject to similar winds,
explaining their fast-accreting and long-lived cavities. We present the results
of the first 2.5D global numerical simulations of TDs harbouring MDWs using the
PLUTO code. We impose a cavity in the gas distribution and consider a power law
distribution for the large-scale magnetic field strength. We assume weakly
ionised discs subject to ambipolar diffusion, as expected in this range of
densities and temperatures. Our simulated TDs reach a steady state with an
inner cavity and an outer 'standard' disc. The accretion rates remain
approximately constant through the entire discs, reaching
$10^{-7}~M_\odot/\text{yrs}^{-1}$ for typical surface density values. The MDW
launched from the cavity is more magnetised with a much larger lever arm than
the MDW launched from the outer disc. The material in the cavity is accreted at
sonic velocities, and the cavity itself is rotating at 70% of the Keplerian
velocity due to the efficient magnetic braking imposed by the MDW. Overall, our
cavity matches the dynamical properties of an inner jet emitting disc (JED) and
of magnetically arrested discs (MADs) in black hole physics. Finally, kinematic
diagnostics (wind speeds, orbital and accretion velocities) could disentangle
classical photo-evaporation from MDW models.


title: Using Sums-of-Squares to Prove Gaussian Product Inequalities

abstract:
  The long-standing Gaussian product inequality (GPI) conjecture states that $E
[\prod_{j=1}^{n}X_j^{2m_j}]\geq\prod_{j=1}^{n}E[X_j^{2m_j}]$ for any centered
Gaussian random vector $(X_1,\dots,X_n)$ and $m_1,\dots,m_n\in\mathbb{N}$. In
this note, we describe a computational method involving sums-of-squares
representations of multivariate polynomials that can be used to resolve the GPI
conjecture. To exhibit the power of this novel method, we apply it to prove two
special GPIs: $E[X_1^{8}X_2^{6}X_3^{4}]\ge E[X_1^{8}]E[X_2^{6}]E[X_3^{4}]$ and
$E[X_1^{4}X_2^{2}X_3^{2}X_4^{2}]\ge E[X_1^{4}]E[X_2^{2}]E[X_3^{2}]E[X_4^{2}]$.


title: Rate of convergence of the smoothed empirical Wasserstein distance

abstract:
  Consider an empirical measure $\mathbb{P}_n$ induced by $n$ iid samples from
a $d$-dimensional $K$-subgaussian distribution $\mathbb{P}$ and let $\gamma =
\mathcal{N}(0,\sigma^2 I_d)$ be the isotropic Gaussian measure. We study the
speed of convergence of the smoothed Wasserstein distance $W_2(\mathbb{P}_n *
\gamma, \mathbb{P}*\gamma) = n^{-\alpha + o(1)}$ with $*$ being the convolution
of measures. For $K<\sigma$ and in any dimension $d\ge 1$ we show that $\alpha
= {1\over2}$. For $K>\sigma$ in dimension $d=1$ we show that the rate is slower
and is given by $\alpha = {(\sigma^2 + K^2)^2\over 4 (\sigma^4 + K^4)} < 1/2$.
This resolves several open problems in \cite{goldfeld2020convergence}, and in
particular precisely identifies the amount of smoothing $\sigma$ needed to
obtain a parametric rate. In addition, we also establish that
$D_{KL}(\mathbb{P}_n * \gamma \|\mathbb{P}*\gamma)$ has rate $O(1/n)$ for
$K<\sigma$ but only slows down to $O({(\log n)^{d+1}\over n})$ for $K>\sigma$.
The surprising difference of the behavior of $W_2^2$ and KL implies the failure
of $T_{2}$-transportation inequality when $\sigma < K$. Consequently, the
requirement $K<\sigma$ is necessary for validity of the log-Sobolev inequality
(LSI) for the Gaussian mixture $\mathbb{P} * \mathcal{N}(0, \sigma^{2})$,
closing an open problem in \cite{wang2016functional}, who established the LSI
under precisely this condition.


title:
Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset
  Evaluation for Text Classification

abstract:
  In this paper, we ask the research question of whether all the datasets in
the benchmark are necessary. We approach this by first characterizing the
distinguishability of datasets when comparing different systems. Experiments on
9 datasets and 36 systems show that several existing benchmark datasets
contribute little to discriminating top-scoring systems, while those less used
datasets exhibit impressive discriminative power. We further, taking the text
classification task as a case study, investigate the possibility of predicting
dataset discrimination based on its properties (e.g., average sentence length).
Our preliminary experiments promisingly show that given a sufficient number of
training experimental records, a meaningful predictor can be learned to
estimate dataset discrimination over unseen datasets. We released all datasets
with features explored in this work on DataLab:
\url{https://datalab.nlpedia.ai}.


title: The Limits of Word Level Differential Privacy

abstract:
  As the issues of privacy and trust are receiving increasing attention within
the research community, various attempts have been made to anonymize textual
data. A significant subset of these approaches incorporate differentially
private mechanisms to perturb word embeddings, thus replacing individual words
in a sentence. While these methods represent very important contributions, have
various advantages over other techniques and do show anonymization
capabilities, they have several shortcomings. In this paper, we investigate
these weaknesses and demonstrate significant mathematical constraints
diminishing the theoretical privacy guarantee as well as major practical
shortcomings with regard to the protection against deanonymization attacks, the
preservation of content of the original sentences as well as the quality of the
language output. Finally, we propose a new method for text anonymization based
on transformer based language models fine-tuned for paraphrasing that
circumvents most of the identified weaknesses and also offers a formal privacy
guarantee. We evaluate the performance of our method via thorough
experimentation and demonstrate superior performance over the discussed
mechanisms.


title:
Domino Saliency Metrics: Improving Existing Channel Saliency Metrics
  with Structural Information

abstract:
  Channel pruning is used to reduce the number of weights in a Convolutional
Neural Network (CNN). Channel pruning removes slices of the weight tensor so
that the convolution layer remains dense. The removal of these weight slices
from a single layer causes mismatching number of feature maps between layers of
the network. A simple solution is to force the number of feature map between
layers to match through the removal of weight slices from subsequent layers.
This additional constraint becomes more apparent in DNNs with branches where
multiple channels need to be pruned together to keep the network dense. Popular
pruning saliency metrics do not factor in the structural dependencies that
arise in DNNs with branches. We propose Domino metrics (built on existing
channel saliency metrics) to reflect these structural constraints. We test
Domino saliency metrics against the baseline channel saliency metrics on
multiple networks with branches. Domino saliency metrics improved pruning rates
in most tested networks and up to 25% in AlexNet on CIFAR-10.


title:
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias
  in Emotion-Cause Pair Extraction

abstract:
  The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and
causes as pairs from documents. We observe that the relative distance
distribution of emotions and causes is extremely imbalanced in the typical ECPE
dataset. Existing methods have set a fixed size window to capture relations
between neighboring clauses. However, they neglect the effective semantic
connections between distant clauses, leading to poor generalization ability
towards position-insensitive data. To alleviate the problem, we propose a novel
\textbf{M}ulti-\textbf{G}ranularity \textbf{S}emantic \textbf{A}ware
\textbf{G}raph model (MGSAG) to incorporate fine-grained and coarse-grained
semantic features jointly, without regard to distance limitation. In
particular, we first explore semantic dependencies between clauses and keywords
extracted from the document that convey fine-grained semantic features,
obtaining keywords enhanced clause representations. Besides, a clause graph is
also established to model coarse-grained semantic relations between clauses.
Experimental results indicate that MGSAG surpasses the existing
state-of-the-art ECPE models. Especially, MGSAG outperforms other models
significantly in the condition of position-insensitive data.


title: Inverse formula for distance matrices of gear graphs

abstract:
  Distance matrices of some star like graphs are investigated in \cite{JAK}.
These graphs are trees which are stars, wheel graphs, helm graphs and gear
graphs. Except for gear graphs in the above list of star like graphs, there are
precise formulas available in the literature to compute the
inverse/Moore-Penrose inverse of their distance matrices. These formulas tell
that if $D$ is the distance matrix of $G$, then $D^\dagger =
-\frac{1}{2}L+uu'$, where $L$ is a Laplacian-like matrix which is positive
semidefinite and all row sums equal to zero. The matrix $L$ and the vector $u$
depend only on the degree and number of vertices in $G$ and hence, can be
written directly from $G$. The earliest formula obtained is for distance
matrices of trees in Graham and Lov\'{a}sz \cite{GL}. In this paper, we obtain
an elegant formula of this kind to compute the Moore-Penrose inverse of the
distance matrix of a gear graph.


title:
Hodge Decomposition and General Laplacian Solvers for Embedded
  Simplicial Complexes

abstract:
  We describe a nearly-linear time algorithm to solve the linear system $L_1x =
b$ parameterized by the first Betti number of the complex, where $L_1$ is the
1-Laplacian of a simplicial complex $K$ that is a subcomplex of a collapsible
complex $X$ linearly embedded in $\mathbb{R}^{3}$. Our algorithm generalizes
the work of Black et al.~[SODA2022] that solved the same problem but required
that $K$ have trivial first homology. Our algorithm works for complexes $K$
with arbitrary first homology with running time that is nearly-linear with
respect to the size of the complex and polynomial with respect to the first
Betti number. The key to our solver is a new algorithm for computing the Hodge
decomposition of 1-chains of $K$ in nearly-linear time. Additionally, our
algorithm implies a nearly quadratic solver and nearly quadratic Hodge
decomposition for the 1-Laplacian of any simplicial complex $K$ embedded in
$\mathbb{R}^{3}$, as $K$ can always be expanded to a collapsible embedded
complex of quadratic complexity.


title: Human-to-Human Strokes Recordings for Tactile Apparent Motion

abstract:
  The main objective of this study is to investigate whether one can use
recordings of human-to-human touch, such as a caress, to improve tactile
apparent motion interfaces to make them feel more natural. We report here
preliminary recordings of natural and continuous human-to-human caresses. To do
this, six accelerometers were positioned on the receiving hand next to the
stimulated area while a finger gently stroked the skin. The results suggest
that we are able to capture signals from real human caresses that can be
compared to signals produced by apparent motion stimuli. This is encouraging
for our plan to continue the study in the second stage, which consists of
tuning vibrotactile actuators to reproduce a similar pattern of vibrational
responses in the accelerometers. In this way, the actuators mimic human
behavior.


title: Non-perturbative aspects of the self-dual double copy

abstract:
  The double copy is by now a firmly-established correspondence between
amplitudes and classical solutions in biadjoint scalar, gauge and gravity
theories. To date, no strongly coupled examples of the double copy in four
dimensions have been found, and previous attempts based on exact non-linear
solutions of biadjoint theory in Lorentzian signature have failed. In this
paper, we instead look for biadjoint solutions in Euclidean signature, which
may be relatable to Yang-Mills or gravitational instantons. We show that
spherically symmetric power-like Euclidean solutions do not exist in precisely
four spacetime dimensions. The explanation for why this is the case turns out
to involve the Eguchi-Hanson instanton, whose single copy structure is found to
be more complicated (and interesting) than previously thought. We provide a
more general prescription for double-copying instantons, and explain how our
results provide a higher-dimensional complement to a recently presented
non-perturbative double copy of exact solutions in two spacetime dimensions. In
doing so, we demonstrate how the replacement of colour by kinematic Lie
algebras operates at the level of exact classical solutions.


title:
Spatial search by continuous-time quantum walks on renormalized Internet
  networks

abstract:
  We study spatial search with continuous-time quantum walks on real complex
networks. We use smaller replicas of the Internet network obtained with a
recent geometric renormalization method introduced by Garc\'ia-P\'erez et al.
(2018). This allows us to infer for the first time the behavior of a quantum
spatial search algorithm on a real complex network. By simulating numerically
the dynamics and optimizing the coupling parameter, we study the optimality of
the algorithm and its scaling with the size of the network, showing that on
average it is considerably better than the classical scaling $O(N)$, but it
does not reach the ideal quadratic speedup $O(\sqrt{N})$ that can be achieved,
e.g. in complete graphs. However, the performance of the search algorithm
strongly depends on the degree of the nodes and, in fact, the scaling is found
to be very close to optimal when we consider the nodes below the $90$th
percentile ordered according to the degree.


title:
Ballistic Dynamics of Flexural Thermal Movements in a Nano-membrane
  Revealed with Subatomic Resolution

abstract:
  Flexural oscillations of free-standing films, nano-membranes and nano-wires
are attracting growing attention for their importance to the thermal,
electrical and mechanical properties of 2D materials. Here we report on the
observation of short-timescale ballistic motion in the flexural mode of a
nano-membrane cantilever, driven by thermal fluctuation of flexural phonons,
including measurements of ballistic velocities and displacements performed with
sub-atomic resolution, using a new free electron edge-scattering technique.
Within intervals <10 {\mu}s, the membrane moves ballistically at a constant
velocity, typically ~300 {\mu}m/s, while Brownian-like dynamics emerge for
longer observation periods. Access to the ballistic regime provides
verification of the equipartition theorem and Maxwell-Boltzmann statistics for
flexural modes, and can be used in fast thermometry and mass sensing during
atomic absorption/desorption processes on the membrane. We argue that the
ballistic regime should be accounted for in understanding the electrical,
optical, thermal and mechanical properties of 2D materials.


title:
Logarithmic negativity in out-of-equilibrium open free-fermion chains:
  An exactly solvable case

abstract:
  We derive the quasiparticle picture for the fermionic logarithmic negativity
in a tight-binding chain subject to gain and loss dissipation. We focus on the
dynamics after the quantum quench from the fermionic N\'eel state. We consider
the negativity between both adjacent and disjoint intervals embedded in an
infinite chain. Our result holds in the standard hydrodynamic limit of large
subsystems and long times, with their ratio fixed. Additionally, we consider
the weakly-dissipative limit, in which the dissipation rates are inversely
proportional to the size of the intervals. We show that the negativity is
proportional to the number of entangled pairs of quasiparticles that are shared
between the two intervals, as is the case for the mutual information.
Crucially, in contrast with the unitary case, the negativity content of
quasiparticles is not given by the R\'enyi entropy with R\'enyi index 1/2, and
it is in general not easily related to thermodynamic quantities.

categories: cond-mat.stat-mech cond-mat.quant-gas cond-mat.str-el hep-th quant-ph


title:
Snowmass White Paper: prospects for the measurement of top-quark
  couplings

abstract:
  In this contribution to the 2021 Snowmass community planning exercise that
informs the American strategy for particle physics, we present the prospects
for measurements of the top-quark couplings at future colliders. Projections
are presented for the high luminosity phase of the Large Hadron Collider and a
future Higgs/electroweak/top factory electron-positron collider. Results are
presented for the expected bounds on Wilson coefficients of the relevant SMEFT
operators from a global fit to the top physics sector.


title: RecipeSnap -- a lightweight image-to-recipe model

abstract:
  In this paper we want to address the problem of automation for recognition of
photographed cooking dishes and generating the corresponding food recipes.
Current image-to-recipe models are computation expensive and require powerful
GPUs for model training and implementation. High computational cost prevents
those existing models from being deployed on portable devices, like smart
phones. To solve this issue we introduce a lightweight image-to-recipe
prediction model, RecipeSnap, that reduces memory cost and computational cost
by more than 90% while still achieving 2.0 MedR, which is in line with the
state-of-the-art model. A pre-trained recipe encoder was used to compute recipe
embeddings. Recipes from Recipe1M dataset and corresponding recipe embeddings
are collected as a recipe library, which are used for image encoder training
and image query later. We use MobileNet-V2 as image encoder backbone, which
makes our model suitable to portable devices. This model can be further
developed into an application for smart phones with a few effort. A comparison
of the performance between this lightweight model to other heavy models are
presented in this paper. Code, data and models are publicly accessible on
github.


title: Semimodules and the (syntactically-)linear lambda calculus

abstract:
  In a recent paper, the $\mathcal L^{\mathcal S}$-calculus has been defined.
It is a proof-language for a significant fragment of intuitionistic linear
logic. Its main feature is that the linearity properties can be expressed in
its syntax, since it has interstitial logical rules whose proof-terms are a sum
and a multiplication by scalar.
  The calculus is parametrized on the structure $\mathcal S$. This structure
was originally identified with the field of complex numbers, since the calculus
is designed as a quantum lambda calculus. However, in this paper we show that a
semiring is enough, and we provide a categorical semantics for this calculus in
the category of cancellative semimodules over the given semiring. We prove the
semantics to be sound and adequate.


title:
Estimating Complier Average Causal Effects for Clustered RCTs When the
  Treatment Effects the Service Population

abstract:
  RCTs sometimes test interventions that aim to improve existing services
targeted to a subset of individuals identified after randomization.
Accordingly, the treatment could affect the composition of service recipients
and the offered services. With such bias, intention-to-treat estimates using
data on service recipients and nonrecipients may be difficult to interpret.
This article develops causal estimands and inverse probability weighting (IPW)
estimators for complier populations in these settings, using a generalized
estimating equation approach that adjusts the standard errors for estimation
error in the IPW weights. While our focus is on more general clustered RCTs,
the methods also apply (reduce) to non-clustered RCTs. We present simulation
results and an empirical application using data from a large-scale RCT testing
the effects of early childhood services on children's cognitive development
scores.


title:
On the utility of flux rope models for CME magnetic structure below
  30$R_{\odot}$

abstract:
  We present a comprehensive analysis of the three-dimensional magnetic flux
rope structure generated during the Lynch et al. (2019) magnetohydrodynamic
(MHD) simulation of a global-scale, 360 degree-wide streamer blowout coronal
mass ejection (CME) eruption. We create both fixed and moving synthetic
spacecraft to generate time series of the MHD variables through different
regions of the flux rope CME. Our moving spacecraft trajectories are derived
from the spatial coordinates of Parker Solar Probe's past encounters 7 and 9
and future encounter 23. Each synthetic time series through the simulation flux
rope ejecta is fit with three different in-situ flux rope models commonly used
to characterize the large-scale, coherent magnetic field rotations observed in
a significant fraction of interplanetary CMEs (ICMEs). We present each of the
in-situ flux rope model fits to the simulation data and discuss the
similarities and differences between the model fits and the MHD simulation's
flux rope spatial orientations, field strengths and rotations, expansion
profiles, and magnetic flux content. We compare in-situ model properties to
those calculated with the MHD data for both classic bipolar and unipolar ICME
flux rope configurations as well as more problematic profiles such as those
with a significant radial component to the flux rope axis orientation or
profiles obtained with large impact parameters. We find general agreement among
the in-situ flux rope fitting results for the classic profiles and much more
variation among results for the problematic profiles. We also examine the
force-free assumption for a subset of the flux rope models and quantify
properties of the Lorentz force within MHD ejecta intervals. We conclude that
the in-situ flux rope models are generally a decent approximation to the field
structure, but all the caveats associated with in-situ flux rope models will
still apply...


title:
D-finiteness, rationality, and height II: lower bounds over a set of
  positive density

abstract:
  We consider D-finite power series $f(z)=\sum a_n z^n$ with coefficients in a
number field $K$. We show that there is a dichotomy governing the behaviour of
$h(a_n)$ as a function of $n$, where $h$ is the absolute logarithmic Weil
height. As an immediate consequence of our results, we have that either $f(z)$
is rational or $h(a_n)>[K:\mathbb{Q}]^{-1}\cdot \log(n)+O(1)$ for $n$ in a set
of positive upper density and this is best possible when $K=\mathbb{Q}$.


title: An Analysis of Generative Methods for Multiple Image Inpainting

abstract:
  Image inpainting refers to the restoration of an image with missing regions
in a way that is not detectable by the observer. The inpainting regions can be
of any size and shape. This is an ill-posed inverse problem that does not have
a unique solution. In this work, we focus on learning-based image completion
methods for multiple and diverse inpainting which goal is to provide a set of
distinct solutions for a given damaged image. These methods capitalize on the
probabilistic nature of certain generative models to sample various solutions
that coherently restore the missing content. Along the chapter, we will analyze
the underlying theory and analyze the recent proposals for multiple inpainting.
To investigate the pros and cons of each method, we present quantitative and
qualitative comparisons, on common datasets, regarding both the quality and the
diversity of the set of inpainted solutions. Our analysis allows us to identify
the most successful generative strategies in both inpainting quality and
inpainting diversity. This task is closely related to the learning of an
accurate probability distribution of images. Depending on the dataset in use,
the challenges that entail the training of such a model will be discussed
through the analysis.


title: A novel modular neutron long counter for ({\alpha},n) reactions

abstract:
  In this work, we present the design of a new modular and transportable
neutron detector for ({\alpha},n) reactions. The detector is based on the use
of several 3He-filled proportional counters embedded in high density
polyethylene. In order to provide the detector with a response independent of
the neutron energy, a flat response, an innovative design methodology has been
applied. The method is based on the optimization of the counters contribution
to the total efficiency by using thermal neutron absorbers. This allows to
obtain flat responses up to 10 MeV. The characterization of a proof-of-concept
prototype detector is presented. The perspectives for using this new detector
for ({\alpha},n) reactions measurements are discussed.


title:
Study of water Cherenkov detector design for ground-based gamma-ray
  experiments

abstract:
  In the framework of the development of the SWGO experiment we have performed
a detailed study of the single unit of an extensive air shower observatory
based on an array of water Cherenkov detectors. Indeed, one of the possible
water Cherenkov detector unit configurations for SWGO consists of tanks, and to
reach a high detection efficiency and discrimination capability between
gamma-ray and hadronic air showers, different tank designs are under
investigation. In this study, we considered double-layer tanks with several
sizes, shapes and number of photo-multiplier tubes (PMTs). Muons, electrons,
and gamma-rays with energies typical of secondary particles in extensive air
showers have been simulated entering the tanks with zenith angles from 0 to 60
degrees. The tank response was evaluated considering the number of
photoelectrons produced by the PMTs, the detection efficiency, and the time
resolution of the measurement of the first photon. This analysis allowed to
compare the performance of tanks with different size, configuration of PMTs,
and with circular, hexagonal and square geometry. The method used and the
results will be discussed in this paper.


title:
Degree-restricted strength decompositions and algebraic branching
  programs

abstract:
  We analyze Kumar's recent quadratic algebraic branching program size lower
bound proof method (CCC 2017). We provide a refinement of this method and show
examples in which the refined method gives a better lower bound than the
original one.
  The lower bound relies on Noether-Lefschetz type conditions on the
hypersurface defined by the homogeneous polynomial. In the explicit example
that we provide, the lower bound is proved resorting to classical intersection
theory.
  Further, we use similar methods to improve the known lower bound methods for
slice rank of polynomials. We give a sequence of polynomials for which the
improved lower bound matches the known upper bound.


title:
COVID-19 epidemiology as emergent behavior on a dynamic transmission
  forest

abstract:
  In this paper we create a compartmental, stochastic process model of
SARS-CoV-2 transmission, where the process's mean and variance have distinct
dynamics. The model is fit to time series data from Washington from January
2020 to March 2021 using a deterministic, biologically-motivated signal
processing approach, and we show that the model's hidden states, like
population prevalence, agree with survey and other estimates. Then, in the
paper's second half, we demonstrate that the same model can be reframed as a
branching process with a dynamic degree distribution. This perspective allows
us to generate approximate transmission trees and estimate some higher order
statistics, like the clustering of cases as outbreaks, which we find to be
consistent with related observations from contact tracing and phylogenetics.


title:
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and
  Object Re-Identification

abstract:
  Recently, self-attention mechanisms have shown impressive performance in
various NLP and CV tasks, which can help capture sequential characteristics and
derive global information. In this work, we explore how to extend
self-attention modules to better learn subtle feature embeddings for
recognizing fine-grained objects, e.g., different bird species or person
identities. To this end, we propose a dual cross-attention learning (DCAL)
algorithm to coordinate with self-attention learning. First, we propose
global-local cross-attention (GLCA) to enhance the interactions between global
images and local high-response regions, which can help reinforce the
spatial-wise discriminative clues for recognition. Second, we propose pair-wise
cross-attention (PWCA) to establish the interactions between image pairs. PWCA
can regularize the attention learning of an image by treating another image as
distractor and will be removed during inference. We observe that DCAL can
reduce misleading attentions and diffuse the attention response to discover
more complementary parts for recognition. We conduct extensive evaluations on
fine-grained visual categorization and object re-identification. Experiments
demonstrate that DCAL performs on par with state-of-the-art methods and
consistently improves multiple self-attention baselines, e.g., surpassing
DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively.


title:
Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2
  segmentation models

abstract:
  Recent studies indicate that detecting radiographic patterns on CT scans can
yield high sensitivity and specificity for COVID-19 localization. In this
paper, we investigate the appropriateness of deep learning models
transferability, for semantic segmentation of pneumonia-infected areas in CT
images. Transfer learning allows for the fast initialization/ reutilization of
detection models, given that large volumes of training are not available. Our
work explores the efficacy of using pre-trained U-Net architectures, on a
specific CT data set, for identifying Covid-19 side-effects over images from
different datasets. Experimental results indicate improvement in the
segmentation accuracy of identifying COVID-19 infected regions.


title:
The Hunt for Light Primordial Black Hole Dark Matter with
  Ultra-High-Frequency Gravitational Waves

abstract:
  Light primordial black holes may comprise a dominant fraction of the dark
matter in our Universe. This paper critically assesses whether planned and
future gravitational wave detectors in the ultra-high-frequency band could
constrain the fraction of dark matter composed of sub-solar primordial black
holes. Adopting the state-of-the-art description of primordial black hole
merger rates, we compare various signals with currently operating and planned
detectors. As already noted in the literature, our findings confirm that
detecting individual primordial black hole mergers with currently existing and
operating proposals remains difficult. Current proposals involving
gravitational wave to electromagnetic wave conversion in a static magnetic
field and microwave cavities feature a technology gap with respect to the
loudest gravitational wave signals from primordial black holes of various
orders of magnitude. However, we point out that one recent proposal involving
resonant LC circuits represents the best option in terms of individual merger
detection prospects in the range $(1\div 100) \, \text{MHz}$. In the same
frequency range, we note that alternative setups involving resonant cavities,
whose concept is currently under development, might represent a promising
technology to detect individual merger events. We also show that a detection of
the stochastic gravitational wave background produced by unresolved binaries is
possible only if the theoretical sensitivity of the proposed Gaussian beam
detector is achieved. Such a detector, whose feasibility is subject to various
caveats, may be able to rule-out some scenarios for asteroidal mass primordial
black hole dark matter. We conclude that pursuing dedicated studies and
developments of gravitational wave detectors in the ultra-high-frequency band
remains motivated and may lead to novel probes on the existence of light
primordial black holes.


title:
First results from the ENTOTO neutron monitor: Quantifying the waiting
  time distribution

abstract:
  We discuss a newly established neutron monitor station installed at the
ENTOTO Observatory Research Center outside of Addis Ababa, Ethiopia. This is a
version of a mini-neutron monitor, recently upgraded to detect individual
neutrons and able to calculate the waiting time distribution between neutron
pulses down to $\sim 1 $ $\mu$s. From the waiting time distribution we define
and calculate a new quantity, the correlation ratio, as the ratio of correlated
to uncorrelated neutrons measured inside the monitor. We propose that this
quantity can, in future, be used as a proxy for spectral index of atmospheric
particles incident on the monitor and show that this quantity has a weak
pressure dependence. We believe that future measurements from the ENTOTO
mini-neutron monitor will contribute towards the understanding of cosmic ray
acceleration and transport in the heliosphere.


title: Effective Mass of $\alpha$-Cluster in $^{12}$C Nucleus

abstract:
  Based on the effective-mass concept, we perform the Faddeev calculations for
a low-lying spectrum of 3$\alpha$ states in $^{12}$C nucleus. A three-body
potential is used to describe the known breaking of the 3$\alpha$-cluster
structure in the nucleus. We show that the contribution of the three-body
potential to the Hamiltonian can be compensated by increasing/decreasing the
$\alpha$-particle free mass. The effective-mass values are adjusted so that to
reproduce the experimental data for the $^{12}$C nucleus. The energy dependence
of the effective mass and the correlation to a three-body potential are
discussed. We show that the coupling between the $0^+$ ($2^+$) levels forms a
specific picture of anti-crossing on the energy/effective-mass plane.


title:
Approximations of dispersive PDEs in the presence of low-regularity
  randomness

abstract:
  We introduce a new class of numerical schemes which allow for low regularity
approximations to the expectation $ \mathbb{E}(|u_{k}(\tau, v^{\eta})|^2)$,
where $u_k$ denotes the $k$-th Fourier coefficient of the solution $u$ of the
dispersive equation and $ v^{\eta}(x) $ the associated random initial data.
This quantity plays an important role in physics, in particular in the study of
wave turbulence where one needs to adopt a statistical approach in order to
obtain deep insight into the generic long-time behaviour of solutions to
dispersive equations. Our new class of schemes is based on Wick's theorem and
Feynman diagrams together with a resonance based discretisation (see
arXiv:2005.01649) set in a more general context: we introduce a novel
combinatorial structure called paired decorated forests which are two decorated
trees whose decorations on the leaves come in pair. The character of the scheme
draws its inspiration from the treatment of singular stochastic partial
differential equations via Regularity Structures. In contrast to classical
approaches, we do not discretize the PDE itself, but rather its expectation.
This allows us to heavily exploit the optimal resonance structure and
underlying gain in regularity on the finite dimensional (discrete) level.


title: Arrangement for $K_2^*$ meson family

abstract:
  In this work, the mass spectrum of the $K_2^*$ meson family is analyzed by
the modified Godfrey-Isgur model with a color screening effect approximating
the kaon as a heavy-light meson system, two-body Okubo-Zweig-Iizuka allowed
strong decay partial widths are calculated via the $^3P_0$ model. This analysis
gives us the structures and possible assignments of the observed $K_2^*$
candidates especially $K_2^* (1980)$. %which can be tested by comparing the
theoretical results of their two-body strong decays with the experimental data.
Additionally, prediction of some partial decay widths is made on the high
excitations of $K_2^*$ family. This study is crucial to establishing and
searching for their higher excitations in the future.


title: On the geometry of a weakened $f$-structure

abstract:
  An $f$-structure, introduced by K. Yano in 1963 and subsequently studied by a
number of geometers, is a higher dimensional analog of almost complex and
almost contact structures. We recently introduced the weakened $f$-structure
and its subclasses of weak $K$-, ${\cal S}$-, and ${\cal C}$- structures on
Riemannian manifolds with totally geodesic foliations, which allow us to take a
fresh look at the classical theory. We demonstrate this by generalizing several
known results on framed $f$-manifolds. First, we express the covariant
derivative of $f$ using a new tensor on a weak metric $f$-structure, then we
prove that on a weak $K$-manifold the characteristic vector fields are Killing
and $\ker f$ defines a totally geodesic foliation, an ${\cal S}$-structure is
rigid, i.e., our weak ${\cal S}$-structure is an ${\cal S}$-structure, and a
weak $f$-structure with parallel tensor $f$ reduces to a weak ${\cal
C}$-structure.


title:
Sobolev Differentiability Properties of Logarithmic Modulus of Real
  Analytic Functions

abstract:
  Let $f$ be a real analytic function at a point $p$ in $\mathbb{R}^n $ for $n
\geq 2$, and suppose the zero set of $f$ has codimension at least $2$ at $p$.
We show that $\log |f|$ is $W^{1,1}_{\operatorname{loc}}$ at $p$. In
particular, this implies the differential inequality $|\nabla f |\leq V |f|$,
where $V \in L^1_{\operatorname{loc}}$. As an application, we derive an
inequality relating the {\L}ojasiewicz exponent and the singularity exponent
for such functions.


title: Making SGD Parameter-Free

abstract:
  We develop an algorithm for parameter-free stochastic convex optimization
(SCO) whose rate of convergence is only a double-logarithmic factor larger than
the optimal rate for the corresponding known-parameter setting. In contrast,
the best previously known rates for parameter-free SCO are based on online
parameter-free regret bounds, which contain unavoidable excess logarithmic
terms compared to their known-parameter counterparts. Our algorithm is
conceptually simple, has high-probability guarantees, and is also partially
adaptive to unknown gradient norms, smoothness, and strong convexity. At the
heart of our results is a novel parameter-free certificate for SGD step size
choice, and a time-uniform concentration result that assumes no a-priori bounds
on SGD iterates.


title: Is the Algorithmic Kadison-Singer Problem Hard?

abstract:
  We study the following $\mathsf{KS}_2(c)$ problem: let $c \in\mathbb{R}^+$ be
some constant, and $v_1,\ldots, v_m\in\mathbb{R}^d$ be vectors such that
$\|v_i\|^2\leq \alpha$ for any $i\in[m]$ and $\sum_{i=1}^m \langle v_i,
x\rangle^2 =1$ for any $x\in\mathbb{R}^d$ with $\|x\|=1$. The
$\mathsf{KS}_2(c)$ problem asks to find some $S\subset [m]$, such that it holds
for all $x \in \mathbb{R}^d$ with $\|x\| = 1$ that \[ \left|\sum_{i \in S}
\langle v_i, x\rangle^2 - \frac{1}{2}\right| \leq c\cdot\sqrt{\alpha},\] or
report no if such $S$ doesn't exist. Based on the work of Marcus et al. and
Weaver, the $\mathsf{KS}_2(c)$ problem can be seen as the algorithmic
Kadison-Singer problem with parameter $c\in\mathbb{R}^+$.
  Our first result is a randomised algorithm with one-sided error for the
$\mathsf{KS}_2(c)$ problem such that (1) our algorithm finds a valid set $S
\subset [m]$ with probability at least $1-2/d$, if such $S$ exists, or (2)
reports no with probability $1$, if no valid sets exist. The algorithm has
running time \[ O\left(\binom{m}{n}\cdot \mathrm{poly}(m, d)\right)~\mbox{ for
}~n = O\left(\frac{d}{\epsilon^2} \log(d)
\log\left(\frac{1}{c\sqrt{\alpha}}\right)\right). \] This presents the first
algorithm for the Kadison-Singer problem whose running time is quasi-polynomial
in $m$, although having exponential dependency on $d$. Moreover, it shows that
the algorithmic Kadison-Singer problem is easier to solve in low dimensions.
  Our second result is on the computational complexity of the
$\mathsf{KS}_2(c)$ problem. We show that the $\mathsf{KS}_2(1/(4\sqrt{2}))$
problem is $\mathsf{FNP}$-hard for general values of $d$, and solving the
$\mathsf{KS}_2(1/(4\sqrt{2}))$ problem is as hard as solving the
$\mathsf{NAE\mbox{-}3SAT}$ problem.


title: UnrealNAS: Can We Search Neural Architectures with Unreal Data?

abstract:
  Neural architecture search (NAS) has shown great success in the automatic
design of deep neural networks (DNNs). However, the best way to use data to
search network architectures is still unclear and under exploration. Previous
work [19, 46] has analyzed the necessity of having ground-truth labels in NAS
and inspired broad interest. In this work, we take a further step to question
whether real data is necessary for NAS to be effective. The answer to this
question is important for applications with limited amount of accessible data,
and can help people improve NAS by leveraging the extra flexibility of data
generation. To explore if NAS needs real data, we construct three types of
unreal datasets using: 1) randomly labeled real images; 2) generated images and
labels; and 3) generated Gaussian noise with random labels. These datasets
facilitate to analyze the generalization and expressivity of the searched
architectures. We study the performance of architectures searched on these
constructed datasets using popular differentiable NAS methods. Extensive
experiments on CIFAR, ImageNet and CheXpert [12] show that the searched
architectures can achieve promising results compared with those derived from
the conventional NAS pipeline with real labeled data, suggesting the
feasibility of performing NAS with unreal data.


title: Lattice Points Close to the Heisenberg Spheres

abstract:
  We study a lattice point counting problem for spheres arising from the
Heisenberg groups. In particular, we prove an upper bound on the number of
points on and near large dilates of the unit spheres generated by the
anisotropic norms $\|(z,t)\|_\alpha = ( |z|^\alpha +
|t|^{\alpha/2})^{1/\alpha}$ for $\alpha\geq 2$. As a first step, we reduce our
counting problem to one of bounding an energy integral. The primary new
challenges that arise are the presence of vanishing curvature and uneven
dilations. In the process, we establish bounds on the Fourier transform of the
surface measures arising from these norms. Further, we utilize the techniques
developed here to estimate the number of lattice points in the intersection of
two such surfaces.


title: The Policy Implications of Economic Complexity

abstract:
  In recent years economic complexity has grown into an active field of
fundamental and applied research. Yet, despite important advances, the policy
implications of economic complexity remain unclear. Here I organize the policy
implications of economic complexity in a framework grounded on 4 Ws: "what"
approaches, focused on identifying target activities and/or locations; "when"
approaches, focused on when to time support for developing related and
unrelated activities; "where" approaches, focused on the geographic diffusion
of knowledge; and "who" approaches, focused on the role played by agents of
structural change. The goal of this framework is to clarify the policy
implications of recent work in economic complexity and to facilitate its
continued use in regional and international development efforts.

categories: econ.GN cond-mat.stat-mech cs.CY q-fin.EC


title: On the physical size of the Milky Way globular cluster NGC 7089 (M 2)

abstract:
  We study the outer regions of the Milky Way globular cluster NGC 7089 based
on new Dark Energy Camera (DECam) observations. The resulting background
cleaned stellar density profile reveals the existence of an extended envelope.
We confirm previous results that cluster stars are found out to ~ 1deg from the
cluster's centre, which is nearly three times the value of the most robust
tidal radii estimations. We also used results from direct N-body simulations in
order to compare with the observations. We found a fairly good agreement
between the observed and numerically generated stellar density profiles.
Because of the existence of gaps and substructures along globular cluster tidal
tails, we closely examined the structure of the outer cluster region beyond the
Jacobi radius. We extended the analysis to a sample of 35 globular clusters, 20
of them with observed tidal tails. We found that if the stellar density profile
follows a power law ~ r**(-alpha), the alpha slope correlates with the globular
cluster present mass, in the sense that, the more massive the globular cluster
the smaller the alpha value. This trend is not found in globular clusters
without observed tidal tails. The origin of such a phenomenon could be related,
among other reasons, to the proposed so-called potential escapers or to the
formation of globular clusters within dark matter mini-haloes.


title:
Fate of the Quasi-condensed State for Bias-driven Hard-Core Bosons in
  one Dimension

abstract:
  Bosons in one dimension display a phenomenon called quasi-condensation, where
correlations decay in a powerlaw fashion. We study the fate of
quasi-condensation in the non-equilibrium steady-state of a chain of hard-core
bosons coupled to macroscopic leads which are held at different chemical
potentials. It is found that a finite bias destroys the quasi-condensed state
and the critical scaling function of the quasi-condensed fraction, near the
zero bias transition, is determined. Associated critical exponents are
determined and numerically verified. Away from equilibrium, the system exhibits
exponentially decaying correlations that are characterized by a bias-dependent
correlation length that diverges in equilibrium. In addition, power-law
corrections are found, which are characterized by an exponent that depends on
the chain-leads coupling and is non-analytic at zero bias. This
exactly-solvable nonequilibrium strongly-interacting system has the remarkable
property that, the near-equilibrium state at infinitesimal bias, cannot be
obtained within linear response. These results aid in unraveling the intricate
properties spawned by strong interactions once liberated from equilibrium
constraints.


title: Knowledge is non-fungible

abstract:
  What would you do if you were asked to "add" knowledge? Would you say that
"one plus one knowledge" is two "knowledges"? Less than that? More? Or
something in between? Adding knowledge sounds strange, but it brings to the
forefront questions that are as fundamental as they are eclectic. These are
questions about the nature of knowledge and about the use of mathematics to
model reality. In this chapter, I explore the mathematics of adding knowledge
starting from what I believe is an overlooked but key observation: the idea
that knowledge is non-fungible.


title: Separations in Proof Complexity and TFNP

abstract:
  It is well-known that Resolution proofs can be efficiently simulated by
Sherali--Adams~(SA) proofs. We show, however, that any such simulation needs to
exploit huge coefficients: Resolution cannot be efficiently simulated by SA
when the coefficients are written in unary. We also show that \emph{Reversible
Resolution} (a variant of MaxSAT Resolution) cannot be efficiently simulated by
Nullstellensatz (NS).
  These results can be interpreted in the language of total $\textsf{NP}$
search problems. We show that $\textsf{PPADS}$, $\textsf{PPAD}$,
$\textsf{SOPL}$ are captured by unary-SA, unary-NS, and Reversible Resolution,
respectively. Consequently, relative to an oracle,
$\textsf{PLS}\not\subseteq\textsf{PPADS}$ and
$\textsf{SOPL}\not\subseteq\textsf{PPA}$.


title:
Compound virtual screening by learning-to-rank with gradient boosting
  decision tree and enrichment-based cumulative gain

abstract:
  Learning-to-rank, a machine learning technique widely used in information
retrieval, has recently been applied to the problem of ligand-based virtual
screening, to accelerate the early stages of new drug development. Ranking
prediction models learn based on ordinal relationships, making them suitable
for integrating assay data from various environments. Existing studies of rank
prediction in compound screening have generally used a learning-to-rank method
called RankSVM. However, they have not been compared with or validated against
the gradient boosting decision tree (GBDT)-based learning-to-rank methods that
have gained popularity recently. Furthermore, although the ranking metric
called Normalized Discounted Cumulative Gain (NDCG) is widely used in
information retrieval, it only determines whether the predictions are better
than those of other models. In other words, NDCG is incapable of recognizing
when a prediction model produces worse than random results. Nevertheless, NDCG
is still used in the performance evaluation of compound screening using
learning-to-rank. This study used the GBDT model with ranking loss functions,
called lambdarank and lambdaloss, for ligand-based virtual screening; results
were compared with existing RankSVM methods and GBDT models using regression.
We also proposed a new ranking metric, Normalized Enrichment Discounted
Cumulative Gain (NEDCG), which aims to properly evaluate the goodness of
ranking predictions. Results showed that the GBDT model with learning-to-rank
outperformed existing regression methods using GBDT and RankSVM on diverse
datasets. Moreover, NEDCG showed that predictions by regression were comparable
to random predictions in multi-assay, multi-family datasets, demonstrating its
usefulness for a more direct assessment of compound screening performance.


title: Efficient Few-Shot Fine-Tuning for Opinion Summarization

abstract:
  Abstractive summarization models are typically pre-trained on large amounts
of generic texts, then fine-tuned on tens or hundreds of thousands of annotated
samples. However, in opinion summarization, large annotated datasets of reviews
paired with reference summaries are not available and would be expensive to
create. This calls for fine-tuning methods robust to overfitting on small
datasets. In addition, generically pre-trained models are often not accustomed
to the specifics of customer reviews and, after fine-tuning, yield summaries
with disfluencies and semantic mistakes. To address these problems, we utilize
an efficient few-shot method based on adapters which, as we show, can easily
store in-domain knowledge. Instead of fine-tuning the entire model, we add
adapters and pre-train them in a task-specific way on a large corpus of
unannotated customer reviews, using held-out reviews as pseudo summaries. Then,
fine-tune the adapters on the small available human-annotated dataset. We show
that this self-supervised adapter pre-training improves summary quality over
standard fine-tuning by 2.0 and 1.3 ROUGE-L points on the Amazon and Yelp
datasets, respectively. Finally, for summary personalization, we condition on
aspect keyword queries, automatically created from generic datasets. In the
same vein, we pre-train the adapters in a query-based manner on customer
reviews and then fine-tune them on annotated datasets. This results in
better-organized summary content reflected in improved coherence and fewer
redundancies.


title:
Characterizing the optical nature of the blazar S5 1803+784 during its
  2020 flare

abstract:
  We report the results from our study of the blazar S5 1803+784 carried out
using the quasi-simultaneous $B$, $V$, $R$, and $I$ observations from May 2020
to July 2021 on 122 nights. Our observing campaign detected the historically
bright optical flare during MJD 59063.5$-$MJD 59120.5. We also found the source
in its brightest ($R_{mag}$= 13.617) and faintest ($R_{mag}$= 15.888) states
till date. On 13 nights, covering both flaring and non-flaring periods, we
searched for the intraday variability using the power-enhanced $F-$test and the
nested ANOVA test. We found significant variability in 2 out of these 13
nights. However, no such variability was detected during the flaring period.
From the correlation analysis, we observed that the emission in all optical
bands were strongly correlated with a time lag of $\sim$ 0 days. To get
insights into its dominant emission mechanisms, we generated the optical
spectral energy distributions of the source on 79 nights and estimated the
spectral indices by fitting the simple power law. Spectral index varied from
1.392 to 1.911 and showed significant variations with time and $R-$band
magnitude. We have detected a mild bluer-when-brighter trend (BWB) during the
whole monitoring period while a much stronger BWB trend during the flare. We
also carried out a periodicity search using four different methods and found no
significant periodicity during our observation duration. Based on the analysis
during the flaring state of the source one can say that the emissions most
likely originate from the jet rather than the accretion disk.


title:
Using virtual edges to extract keywords from texts modeled as complex
  networks

abstract:
  Detecting keywords in texts is important for many text mining applications.
Graph-based methods have been commonly used to automatically find the key
concepts in texts, however, relevant information provided by embeddings has not
been widely used to enrich the graph structure. Here we modeled texts
co-occurrence networks, where nodes are words and edges are established either
by contextual or semantical similarity. We compared two embedding approaches --
Word2vec and BERT -- to check whether edges created via word embeddings can
improve the quality of the keyword extraction method. We found that, in fact,
the use of virtual edges can improve the discriminability of co-occurrence
networks. The best performance was obtained when we considered low percentages
of addition of virtual (embedding) edges. A comparative analysis of structural
and dynamical network metrics revealed the degree, PageRank, and accessibility
are the metrics displaying the best performance in the model enriched with
virtual edges.


title: Observation of biradical spin coupling through hydrogen bonds

abstract:
  Investigation of intermolecular electron spin interaction is of fundamental
importance in both science and technology.Here, radical pairs of all-trans
retinoic acid molecules on Au(111) are created using an ultra-low temperature
scanning tunneling microscope. Antiferromagnetic coupling between two radicals
is identified by magnetic-field dependent spectroscopy.The measured exchange
energies are from 0.1 to 1.0 meV. The biradical spin coupling is mediated
through O-H$\cdots$O hydrogen bonds, as elucidated from analysis combining
density functional theory calculation and a modern version of valence bond
theory.


title: Reticulation functor and the transfer properties

abstract:
  It is known that by using the commutator operation, for each congruence
modular algebra $A$ one can define a notion of prime congruence. The set
$Spec(A)$ of prime congruences of $A$ is endowed with a Zariski style topology.
The reticulation of the algebra $A$ is a bounded distributive lattice $L(A)$
whose prime spectrum $Spec(L(A))$ (with the Stone topology) is homemorphic to
$Spec(A)$.
  In a recent paper, C. Mure\c{s}an and the author have proven the existence of
reticulation for a semidegenerate congruence modular algebra $A$.
  The present paper aims to give an answer to two types of problems:
  $(I)$ how some properties of the algebra $A$ can be transferred to the
lattice $L(A)$ and viceversa, how some properties of $L(A)$ can be transferred
to $A$;
  $(II)$ how the transfer properties from $(I)$ can be used to prove some old
and new characterizations of some remarkable classes of algebras.
  We study the transfer properties related to Boolean centers, annihilators,
patch and flat topologies of spectra, Pierce spectrum, pure and $w$ - pure
congruences, the operators $Ker(\cdot)$ and $O(\cdot)$,etc.
  By using these transfer properties, we obtain characterization theorems for
several types of algebras : hyperarchimedean algebras, congruence normal and
congruence $B$ - normal algebras, $mp$ - algebras, $PF$ - algebras, congruence
purified algebras and $PP$ - algebras.


title:
Band structure of molybdenum disulfide: from first principle to
  analytical band model

abstract:
  A simple band model such as the effective mass approximation (EMA) can be
used to quickly obtain the lower-energy region for the band structure of
monolayer molybdenum disulfide. But the EMA band model cannot give the correct
description for the band structure in the higher-energy region. To address this
major issue, we propose an analytical band calculation (ABC) model to study
monolayer molybdenum disulfide. Important parameters of the ABC model are
obtained by fitting the three-direction band structure of monolayer molybdenum
disulfide obtained from the first-principles (FP) method. The proposed ABC
model fits well with the FP band structure calculation result for monolayer
molybdenum disulfide. We also use the ABC model to calculate physical
quantities used in carrier transport such as density of states and group
velocity. Our ABC model can be extended and further utilized for calculating
the key physical quantities of ballistic transport of 2D semiconductor
materials.


title:
Stability, uniqueness and existence of solutions to McKean-Vlasov SDEs
  in arbitrary moments

abstract:
  We deduce stability and pathwise uniqueness for a McKean-Vlasov equation with
random coefficients and a multidimensional Brownian motion as driver. Our
analysis focuses on a non-Lipschitz drift coefficient and includes moment
estimates for random It\^o processes that are of independent interest. For
deterministic coefficients we provide unique strong solutions, even if the
drift fails to be of affine growth. The theory that we develop rests on It\^o's
formula and leads to $p$-th moment and pathwise $\alpha$-exponential stability
for $p\geq 2$ and $\alpha > 0$ with explicit Lyapunov exponents, regardless of
whether a Lyapunov function exists.


title: Tangle 2.0 Leaderless Nakamoto Consensus on the Heaviest DAG

abstract:
  We introduce the theoretical foundations of the Tangle 2.0, a probabilistic
leaderless consensus protocol based on a directed acyclic graph (DAG) called
the Tangle. The Tangle naturally succeeds the blockchain as its next
evolutionary step as it offers features suited to establish more efficient and
scalable distributed ledger solutions.
  Consensus is no longer found in the longest chain but on the heaviest DAG,
where PoW is replaced by a stake- or reputation-based weight function. The DAG
structure and the underlying Reality-based UTXO Ledger allow parallel
validation of transactions without the need for total ordering. Moreover, it
enables the removal of the intermediary of miners and validators, allowing a
pure two-step process that follows the \emph{propose-vote} paradigm at the node
level and not at the validator level.
  We propose a framework to analyse liveness and safety under different
communication and adversary models. This allows providing impossibility results
in some edge cases and in the asynchronous communication model. We provide
formal proof of the security of the protocol assuming a common random coin.


title: Existence of the $det^{S^2}$ map

abstract:
  In this paper we show that for a vector space $V_d$ of dimension $d$ there
exists a linear map $det^{S^2}:V_d^{\otimes d(2d-1)}\to k$ with the property
that $det^{S^2}(\otimes_{1\leq i<j\leq 2d}(v_{i,j}))=0$ if there exists $1\leq
x<y<z\leq 2d$ such that $v_{x,y}=v_{x,z}=v_{y,z}$. The existence of such a map
was conjectured in [4]. We present two applications of the map $det^{S^2}$ to
geometry and combinatorics.


title:
Measurements and Numerical Calculations of Thermal Conductivity to
  Evaluate the Quality of \beta-Gallium Oxide Thin Films Grown on Sapphire and
  Silicon Carbide by Molecular Beam Epitaxy

abstract:
  We report a method to obtain insights into lower thermal conductivity of
\beta-Ga2O3 thin films grown by molecular beam epitaxy (MBE) on c-plane
sapphire and 4H-SiC substrates. We compare experimental values against the
numerical predictions to decipher the effect of boundary scattering and defects
in thin-films. We used time domain thermoreflectance (TDTR) to perform the
experiments, density functional theory and the Boltzmann transport equation for
thermal conductivity calculations, and the diffuse mismatch model for TBC
predictions. The experimental thermal conductivities were approximately 3 times
smaller than those calculated for perfect Ga2O3 crystals of similar size. When
considering the presence of grain boundaries, gallium and oxygen vacancies, and
stacking faults in the calculations, the crystals that present around 1% of
gallium vacancies and a density of stacking faults of 106 faults/cm were the
ones whose thermal conductivities were closer to the experimental results. Our
analysis suggests the level of different types of defects present in the Ga2O3
crystal that could be used to improve the quality of MBE-grown samples by
reducing these defects and thereby produce materials with higher thermal
conductivities.


title:
Radio Spectral Energy Distributions for Single Massive Star Winds with
  Free-Free and Synchrotron Emission

abstract:
  The mass-loss rates from single massive stars are high enough to form radio
photospheres at large distances from the stellar surface where the wind is
optically thick to (thermal) free-free opacity. Here we calculate the
far-infrared, millimeter, and radio band spectral energy distributions (SEDs)
that can result from the combination of free-free processes and synchrotron
emission, to explore the conditions for non-thermal SEDs. Simplifying
assumptions are adopted in terms of scaling relations for the magnetic field
strength and the spatial distribution of relativistic electrons. The wind is
assumed to be spherically symmetric, and we consider the effect of Razin
suppression on the synchrotron emission. Under these conditions,
long-wavelength SEDs with synchrotron emission can be either more steep or more
shallow than the canonical asymptotic power-law SED from a non-magnetic wind.
When non-thermal emission is present, the resultant SED shape is generally not
a power-law; however, the variation in slope can change slowly with wavelength.
Consequently, over a limited range of wavelengths, the SED can masquerade as
approximately a power law. While most observed non-thermal long-wavelength
spectra are associated with binarity, synchroton emission can have only a mild
influence on single-star SEDs, requiring finer levels of wavelength sampling
for detection of the effect.


title: Observation of Robust Zero Energy Extended States

abstract:
  Topological edge states arise at the interface of two topologically-distinct
structures and have two distinct features: they are localized and robust
against symmetry protecting disorder. On the other hand, conventional transport
in one dimension is associated with extended states, which typically do not
have topological robustness. In this paper, using lossy coupled resonators in
one dimension, we demonstrate both theoretically and experimentally the
existence of robust states residing in the bulk. We show that they are
unusually robust against disorders in coupling between adjacent sites and
losses. Our work paves the way to a new form of robust transport that is not
limited to boundary phenomena and can be accessed more easily from far field.


title:
Reproducibility Beyond the Research Community: Experience from NLP
  Beginners

abstract:
  As NLP research attracts public attention and excitement, it becomes
increasingly important for it to be accessible to a broad audience. As the
research community works to democratize NLP, it remains unclear whether
beginners to the field can easily apply the latest developments. To understand
their needs, we conducted a study with 93 students in an introductory NLP
course, where students reproduced results of recent NLP papers. Surprisingly,
our results suggest that their technical skill (i.e., programming experience)
has limited impact on their effort spent completing the exercise. Instead, we
find accessibility efforts by research authors to be key to a successful
experience, including thorough documentation and easy access to required models
and datasets.


title: Conditional Probability Matrix and the $S^2$-rank

abstract:
  Using the $det^{S^2}$ map from [5], we introduce the notion of $S^2$-rank of
a matrix of type $d\times \frac{s(s-1)}{2}$. As an application, we show that
the conditional probability matrix associated to two random variables has the
$S^2$-rank equal to $1$. Under suitable conditions we prove that the converse
of this result also holds.


title:
Incorporating tunability into a universal scaling framework for shear
  thickening

abstract:
  Recently, we proposed a universal scaling framework that shows shear
thickening in dense suspensions is governed by the crossover between two
critical points: one associated with frictionless isotropic jamming and a
second corresponding to frictional shear jamming. Here, we show that orthogonal
perturbations to the flows, an effective method for tuning shear thickening,
can also be folded into this universal scaling framework. Specifically, we show
that the effect of adding in orthogonal shear perturbations (OSP) can be
incorporated by simply altering the scaling variable to include a
multiplicative term that decreases with the normalized OSP strain rate. These
results demonstrate the broad applicability of our scaling framework, and
illustrate how it can be modified to incorporate other complex flow fields.


title:
Hole Mobility Calculation for Monolayer Molybdenum Tungsten Alloy
  Disulfide

abstract:
  A simple band model using higher order non-parabolic effect was adopted for
single layer molybdenum tungsten alloy disulfide (i.e.,
$\mathrm{Mo}_{1-x}\mathrm{W}_x\mathrm{S}_2$). The first-principles method
considering $2\times2$ supercell was used to study band structure of single
layer alloy $\mathrm{Mo}_{1-x}\mathrm{W}_x\mathrm{S}_2$ and a simple band
(i.e., effective mass approximation model, EMA) model with higher order
non-parabolic effect was used to fit the first-principle band structures in
order to calculate corresponding the hole mobility. In addition, we investigate
the alloy scattering effect on the hole mobility of
$\mathrm{Mo}_{1-x}\mathrm{W}_x\mathrm{S}_2$.


title:
The quasi-periodically oscillating precursor of a long gamma-ray burst
  from a binary neutron star merger

abstract:
  The milestone of GW 170817-GRB 170817A-AT 2017gfo has shown that
gravitational wave (GW) is produced during the merger of neutron star-neutron
star/black hole and that in electromagnetic (EM) wave a gamma-ray burst (GRB)
and a kilonovae (KN) are generated in sequence after the merger.
Observationally, however, EM property during a merger is still unclear. Here we
report a peculiar precursor in a KN-associated long GRB 211211A. The duration
of the precursor is $\sim$ 0.2 s, and the waiting time between the precursor
and the main emission (ME) of the burst is $\sim$ 1 s, which is about the same
as the time interval between GW 170817 and GRB 170817A. Quasi-Periodic
Oscillations (QPO) with frequency $\sim$22 Hz (at $>5\sigma$ significance) are
found throughout the precursor, the first detection of periodic signals from
any {\it bona fide} GRBs. This indicates most likely that a magnetar
participated in the merger, and the precursor might be produced due to a
catastrophic flare accompanying with torsional or crustal oscillations of the
magnetar. The strong seed magnetic field of $\sim 10^{14-15}$ G at the surface
of the magnetar may also account for the prolonged duration of GRB 211211A.
However, it is a challenge to reconcile the rather short lifetime of a magnetar
\cite{kaspi2017magnetars} with the rather long spiraling time of a binary
neutron star system only by gravitational wave radiation before merger.


title: Nonlinear System Level Synthesis for Polynomial Dynamical Systems

abstract:
  This work introduces a controller synthesis method via system level synthesis
for nonlinear systems characterized by polynomial dynamics. The resulting
framework yields finite impulse response, time-invariant, closed-loop transfer
functions with guaranteed disturbance cancellation. Our method generalizes
feedback linearization to enable partial feedback linearization, where the
cancellation of the nonlinearity is spread across a finite-time horizon. This
provides flexibility to use the system dynamics to attenuate disturbances
before cancellation via control, reducing the cost of control compared with
feedback linearization while maintaining guarantees about disturbance
rejection. This approach is illustrated on a benchmark example and on a common
model for fluid flow control.


title: Higher Berry Phase of Fermions and Index Theorem

abstract:
  When a quantum field theory is trivially gapped, its infrared fixed point is
an invertible field theory. The partition function of the invertible field
theory records the response to various background fields in the long-distance
limit. The set of background fields can include spacetime-dependent coupling
constants, in which case we call the corresponding invertible theory a
parameterized invertible field theory. We study such parameterized invertible
field theories arising from free Dirac fermions with spacetime-dependent mass
parameters using the Atiyah-Patodi-Singer index theorem for superconnections.
In particular, the response to an infinitesimal modulation of the mass is
encoded into a higher analog of the Berry curvature, for which we provide a
general formula. When the Berry curvature vanishes, the invertible theory can
still be nontrivial if there is a remaining torsional Berry phase, for which we
list some computable examples.