title: Enhanced sampling methods for molecular dynamics simulations

abstract:
  Enhanced sampling algorithms have emerged as powerful methods to extend the
potential of molecular dynamics simulations and allow the sampling of larger
portions of the configuration space of complex systems. This review aims to
present the unifying principles and differences of several computational
methods for enhanced sampling in molecular simulations of biomolecules, soft
matter and molecular crystals. Indeed, despite the apparent abundance and
divergence of such methods, the principles at their core can be boiled down to
a few statistical and physical principles. To enable comparisons, the various
methods are introduced using the same terminology and notation. We then
illustrate in which ways many different methods combine principles from a
smaller class of enhanced sampling concepts. This review is intended for
scientists with an understanding of the basics of molecular dynamics
simulations and statistical physics who want a deeper understanding of the
ideas that underlie various enhanced sampling methods and the relationships
between them. We expect this to help scientists make informed decisions about
which method to use. This "living" review is intended to be updated to continue
to reflect the wealth of sampling methods as they emerge in the literature.


title:
Instantaneous and limiting behavior of an n-node blockchain under cyber
  attacks from a single hacker

abstract:
  We investigate the instantaneous and limiting behavior of an n-node
blockchain which is under continuous monitoring of the IT department of a
company but faces non-stop cyber attacks from a single hacker. The blockchain
is functional as far as no data stored on it has been changed, deleted, or
locked. Once the IT department detects the attack from the hacker, it will
immediately re-set the blockchain, rendering all previous efforts of the hacker
in vain. The hacker will not stop until the blockchain is dysfunctional. For
arbitrary distributions of the hacking times and detecting times, we derive the
limiting functional probability, instantaneous functional probability, and mean
functional time of the blockchain. We also show that all these quantities are
increasing functions of the number of nodes, substantiating the intuition that
the more nodes a blockchain has, the harder it is for a hacker to succeed in a
cyber attack.


title:
The Lifecycle of a Statistical Model: Model Failure Detection,
  Identification, and Refitting

abstract:
  The statistical machine learning community has demonstrated considerable
resourcefulness over the years in developing highly expressive tools for
estimation, prediction, and inference. The bedrock assumptions underlying these
developments are that the data comes from a fixed population and displays
little heterogeneity. But reality is significantly more complex: statistical
models now routinely fail when released into real-world systems and scientific
applications, where such assumptions rarely hold. Consequently, we pursue a
different path in this paper vis-a-vis the well-worn trail of developing new
methodology for estimation and prediction. In this paper, we develop tools and
theory for detecting and identifying regions of the covariate space
(subpopulations) where model performance has begun to degrade, and study
intervening to fix these failures through refitting. We present empirical
results with three real-world data sets -- including a time series involving
forecasting the incidence of COVID-19 -- showing that our methodology generates
interpretable results, is useful for tracking model performance, and can boost
model performance through refitting. We complement these empirical results with
theory proving that our methodology is minimax optimal for recovering anomalous
subpopulations as well as refitting to improve accuracy in a structured normal
means setting.


title: Understanding the bias-variance tradeoff of Bregman divergences

abstract:
  This paper builds upon the work of Pfau (2013), which generalized the bias
variance tradeoff to any Bregman divergence loss function. Pfau (2013) showed
that for Bregman divergences, the bias and variances are defined with respect
to a central label, defined as the mean of the label variable, and a central
prediction, of a more complex form. We show that, similarly to the label, the
central prediction can be interpreted as the mean of a random variable, where
the mean operates in a dual space defined by the loss function itself. Viewing
the bias-variance tradeoff through operations taken in dual space, we
subsequently derive several results of interest. In particular, (a) the
variance terms satisfy a generalized law of total variance; (b) if a source of
randomness cannot be controlled, its contribution to the bias and variance has
a closed form; (c) there exist natural ensembling operations in the label and
prediction spaces which reduce the variance and do not affect the bias.


title: Hopf bifurcations in the full SKT model and where to find them

abstract:
  In this paper, we consider the Shigesada-Kawasaki-Teramoto (SKT) model, which
presents cross-diffusion terms describing competition pressure effects. Even
though the reaction part does not present the activator-inhibitor structure,
cross-diffusion can destabilise the homogeneous equilibrium. However, in the
full cross-diffusion system and weak competition regime, the cross-diffusion
terms have an opposite effect and the bifurcation structure of the system
modifies increasing the interspecific competition pressure. The major changes
in the bifurcation structure, the type of pitchfork bifurcations on the
homogeneous branch, as well as the presence of Hopf bifurcation points are here
investigated. Through weakly nonlinear analysis, we can predict the type of
pitchfork bifurcation. Increasing the additional cross-diffusion coefficients,
the first two pitchfork bifurcation points from super-critical become
sub-critical, leading to the appearance of a multi-stability region. The
interspecific competition pressure also influences the possible appearance of
stable time-period spatial patterns appearing through a Hopf bifurcation point.


title:
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure
  Aggregation for Federated Learning with Worst-Case Security Guarantees

abstract:
  We propose SwiftAgg, a novel secure aggregation protocol for federated
learning systems, where a central server aggregates local models of $N$
distributed users, each of size $L$, trained on their local data, in a
privacy-preserving manner. Compared with state-of-the-art secure aggregation
protocols, SwiftAgg significantly reduces the communication overheads without
any compromise on security. Specifically, in presence of at most $D$ dropout
users, SwiftAgg achieves a users-to-server communication load of $(T+1)L$ and a
users-to-users communication load of up to $(N-1)(T+D+1)L$, with a worst-case
information-theoretic security guarantee, against any subset of up to $T$
semi-honest users who may also collude with the curious server. The key idea of
SwiftAgg is to partition the users into groups of size $D+T+1$, then in the
first phase, secret sharing and aggregation of the individual models are
performed within each group, and then in the second phase, model aggregation is
performed on $D+T+1$ sequences of users across the groups. If a user in a
sequence drops out in the second phase, the rest of the sequence remain silent.
This design allows only a subset of users to communicate with each other, and
only the users in a single group to directly communicate with the server,
eliminating the requirements of 1) all-to-all communication network across
users; and 2) all users communicating with the server, for other secure
aggregation protocols. This helps to substantially slash the communication
costs of the system.


title: A proof of the fermionic Theta coinvariant conjecture

abstract:
  Let $(x_1, \dots, x_n, y_1, \dots, y_n)$ be a list of $2n$ commuting
variables, $(\theta_1, \dots, \theta_n, \xi_1, \dots, \xi_n)$ be a list of $2n$
anticommuting variables, and $\mathbb{C}[X_n, Y_n] \otimes \wedge \{\Theta_n,
\Xi_n\}$ be the algebra generated by these variables. D'Adderio, Iraci, and
Vanden Wyngaerd introduced the {\em Theta operators} on the ring of symmetric
functions and used them to conjecture a formula for the quadruply-graded
$\mathfrak{S}_n$-isomorphism type of $\mathbb{C}[X_n,Y_n] \otimes \wedge
\{\Theta_n, \Xi_n\}/I$ where $I$ is the ideal generated by
$\mathfrak{S}_n$-invariants with vanishing constant term. We prove their
conjecture in the `purely fermionic setting' obtained by setting the commuting
variables equal $x_i, y_i$ equal to zero.


title:
Inferring Strategies from Observations in Long Iterated Prisoner's
  Dilemma Experiments

abstract:
  While many theoretical studies have revealed the strategies that could lead
to and maintain cooperation in the Iterated Prisoner's Dilemma, less is known
about what human participants actually do in this game and how strategies
change when being confronted with anonymous partners in each round. Previous
attempts used short experiments, made different assumptions of possible
strategies, and led to very different conclusions. We present here two long
treatments that differ in the partner matching strategy used, i.e. fixed or
shuffled partners. Here we use unsupervised methods to cluster the players
based on their actions and then Hidden Markov Model to infer what are those
strategies in each cluster. Analysis of the inferred strategies reveals that
fixed partner interaction leads to a behavioral self-organization. Shuffled
partners generate subgroups of strategies that remain entangled, apparently
blocking the self-selection process that leads to fully cooperating
participants in the fixed partner treatment. Analyzing the latter in more
detail shows that AllC, AllD, TFT- and WSLS-like behavior can be observed. This
study also reveals that long treatments are needed as experiments less than 25
rounds capture mostly the learning phase participants go through in these kinds
of experiments.


title:
A Speech Intelligibility Enhancement Model based on Canonical
  Correlation and Deep Learning for Hearing-Assistive Technologies

abstract:
  Current deep learning (DL) based approaches to speech intelligibility
enhancement in noisy environments are generally trained to minimise the
distance between clean and enhanced speech features. These often result in
improved speech quality however they suffer from a lack of generalisation and
may not deliver the required speech intelligibility in everyday noisy
situations. In an attempt to address these challenges, researchers have
explored intelligibility-oriented (I-O) loss functions to train DL approaches
for robust speech enhancement (SE). In this paper, we formulate a novel
canonical correlation-based I-O loss function to more effectively train DL
algorithms. Specifically, we present a fully convolutional SE model that uses a
modified canonical-correlation based short-time objective intelligibility
(CC-STOI) metric as a training cost function. To the best of our knowledge,
this is the first work that exploits the integration of canonical correlation
in an I-O based loss function for SE. Comparative experimental results
demonstrate that our proposed CC-STOI based SE framework outperforms DL models
trained with conventional STOI and distance-based loss functions, in terms of
both standard objective and subjective evaluation measures when dealing with
unseen speakers and noises.


title:
Exploring the Limits of Domain-Adaptive Training for Detoxifying
  Large-Scale Language Models

abstract:
  Pre-trained language models (LMs) are shown to easily generate toxic
language. In this work, we systematically explore domain-adaptive training to
reduce the toxicity of language models. We conduct this study on three
dimensions: training corpus, model size, and parameter efficiency. For the
training corpus, we propose to leverage the generative power of LMs and
generate nontoxic datasets for domain-adaptive training, which mitigates the
exposure bias and is shown to be more data-efficient than using a curated
pre-training corpus. We demonstrate that the self-generation method
consistently outperforms the existing baselines across various model sizes on
both automatic and human evaluations, even when it uses a 1/3 smaller training
corpus. We then comprehensively study detoxifying LMs with parameter sizes
ranging from 126M up to 530B (3x larger than GPT-3), a scale that has never
been studied before. We find that i) large LMs have similar toxicity levels as
smaller ones given the same pre-training corpus, and ii) large LMs require more
endeavor to detoxify. We also explore parameter-efficient training methods for
detoxification. We demonstrate that adding and training adapter-only layers in
LMs not only saves a lot of parameters but also achieves a better trade-off
between toxicity and perplexity than whole model adaptation for the large-scale
models.


title:
Behavioral epidemiology: An economic model to evaluate optimal policy in
  the midst of a pandemic

abstract:
  This paper combines a canonical epidemiology model of disease dynamics with
government policy of lockdown and testing, and agents' decision to social
distance in order to avoid getting infected. The model is calibrated with data
on deaths and testing outcomes in the Unites States. It is shown that an
intermediate but prolonged lockdown is socially optimal when both mortality and
GDP are taken into account. This is because the government wants the economy to
keep producing some output and the slack in reducing infection is picked up by
social distancing agents. Social distancing best responds to the optimal
government policy to keep the effective reproductive number at one and avoid
multiple waves through the pandemic. Calibration shows testing to have been
effective, but it could have been even more instrumental if it had been
aggressively pursued from the beginning of the pandemic. Not having any
lockdown or shutting down social distancing would have had extreme
consequences. Greater centralized control on social activities would have
mitigated further the spread of the pandemic.


title: Federated Learning of Generative Image Priors for MRI Reconstruction

abstract:
  Multi-institutional efforts can facilitate training of deep MRI
reconstruction models, albeit privacy risks arise during cross-site sharing of
imaging data. Federated learning (FL) has recently been introduced to address
privacy concerns by enabling distributed training without transfer of imaging
data. Existing FL methods for MRI reconstruction employ conditional models to
map from undersampled to fully-sampled acquisitions via explicit knowledge of
the imaging operator. Since conditional models generalize poorly across
different acceleration rates or sampling densities, imaging operators must be
fixed between training and testing, and they are typically matched across
sites. To improve generalization and flexibility in multi-institutional
collaborations, here we introduce a novel method for MRI reconstruction based
on Federated learning of Generative IMage Priors (FedGIMP). FedGIMP leverages a
two-stage approach: cross-site learning of a generative MRI prior, and
subject-specific injection of the imaging operator. The global MRI prior is
learned via an unconditional adversarial model that synthesizes high-quality MR
images based on latent variables. Specificity in the prior is preserved via a
mapper subnetwork that produces site-specific latents. During inference, the
prior is combined with subject-specific imaging operators to enable
reconstruction, and further adapted to individual test samples by minimizing
data-consistency loss. Comprehensive experiments on multi-institutional
datasets clearly demonstrate enhanced generalization performance of FedGIMP
against site-specific and federated methods based on conditional models, as
well as traditional reconstruction methods.


title:
Police Text Analysis: Topic Modeling and Spatial Relative Density
  Estimation

abstract:
  We analyze a large corpus of police incident narrative documents in
understanding the spatial distribution of the topics. The motivation for doing
this is that police narratives in each incident report contains very
fine-grained information that is richer than the category that is manually
assigned by the police. Our approach is to split the corpus into topics using
two different unsupervised machine learning algorithms - Latent Dirichlet
Allocation and Non-negative Matrix Factorization. We validate the performance
of each learned topic model using model coherence. Then, using a k-nearest
neighbors density ratio estimation (kNN-DRE) approach that we propose, we
estimate the spatial density ratio per topic and use this for data discovery
and analysis of each topic, allowing for insights into the described incidents
at scale. We provide a qualitative assessment of each topic and highlight some
key benefits for using our kNN-DRE model for estimating spatial trends.


title: Plethora of tunable Weyl fermions in kagome magnet Fe3Sn2 thin films

abstract:
  Interplay of magnetism and electronic band topology in unconventional magnets
enables the creation and fine control of novel electronic phenomena. In this
work, we reveal an unusually large number of clusters of low-energy Weyl
fermions in thin films of a prototypical kagome magnet Fe3Sn2. Using scanning
tunneling microscopy and spectroscopy, we image the formation of the Weyl nodes
and the topological Fermi arc surface states as densely-spaced spectroscopic
features straddling the Fermi level. By measuring their response as a function
of magnetic field, we discover a pronounced evolution in energy tied to the
magnetization direction. Electron scattering and interference imaging further
demonstrates the tunable nature of related surface states. Our experiments
provide the first direct visualization of how in-situ spin reorientation drives
changes in the Weyl fermion band structure. Combined with previous reports of
massive Dirac fermions, flat bands and electronic nematicity, our work
establishes Fe3Sn2 as a unique platform that harbors an extraordinarily wide
array of topological and correlated electron phenomena.


title:
VAEL: Bridging Variational Autoencoders and Probabilistic Logic
  Programming

abstract:
  We present VAEL, a neuro-symbolic generative model integrating variational
autoencoders (VAE) with the reasoning capabilities of probabilistic logic (L)
programming. Besides standard latent subsymbolic variables, our model exploits
a probabilistic logic program to define a further structured representation,
which is used for logical reasoning. The entire process is end-to-end
differentiable. Once trained, VAEL can solve new unseen generation tasks by (i)
leveraging the previously acquired knowledge encoded in the neural component
and (ii) exploiting new logical programs on the structured latent space. Our
experiments provide support on the benefits of this neuro-symbolic integration
both in terms of task generalization and data efficiency. To the best of our
knowledge, this work is the first to propose a general-purpose end-to-end
framework integrating probabilistic logic programming into a deep generative
model.


title:
A Framework for the Time- and Frequency-Domain Assessment of High-Order
  Interactions in Brain and Physiological Networks

abstract:
  While the standard network description of complex systems is based on
quantifying links between pairs of system units, higher-order interactions
(HOIs) involving three or more units play a major role in governing the
collective network behavior. This work introduces an approach to quantify
pairwise and HOIs for multivariate rhythmic processes interacting across
multiple time scales. We define the so-called O-information rate (OIR) as a new
metric to assess HOIs for multivariate time series, and propose a framework to
decompose it into measures quantifying Granger-causal and instantaneous
influences, as well as to expand it in the frequency domain. The framework
exploits the spectral representation of vector autoregressive and state-space
models to assess synergistic and redundant interactions among groups of
processes, both in specific bands and in the time domain after whole-band
integration. Validation on simulated networks illustrates how the spectral OIR
can highlight redundant and synergistic HOIs emerging at specific frequencies
but not using time-domain measures. The application to physiological networks
described by heart period, arterial pressure and respiration measured in
healthy subjects during paced breathing, and to brain networks described by
ECoG signals acquired in an animal experiment during anesthesia, document the
capability of our approach to identify informational circuits relevant to
well-defined cardiovascular oscillations and brain rhythms and related to
specific physiological mechanisms of autonomic control and altered
consciousness. The proposed framework allows a hierarchically-organized
evaluation of time- and frequency-domain interactions in networks mapped by
multivariate time series, and its high flexibility and scalability make it
suitable to investigate networks beyond pairwise interactions in neuroscience,
physiology and other fields.


title: Photo- and Electrocouplings of Nucleon Resonances

abstract:
  Advances in the exploration of the spectrum and structure of the excited
states of the nucleon from experiments with electromagnetic probes on proton
targets are presented. Impressive progress has been achieved in the studies of
exclusive meson photoproduction in experiments with continuous electron beams
and with detectors of almost $4\pi$ acceptance. The high-quality data, coupled
with the advances in the amplitude analyses of exclusive photo- and
hadroproduction data, allow for the observation of several long-awaited new
baryon states known previously as the ``missing" resonances. Studies of
exclusive meson electroproduction in the resonance region with the CLAS
detector at JLab have provided the dominant part of the available world
information on exclusive meson electroproduction observables. These data offer
unique information on the structure of most well-established excited nucleon
states in the mass region up to 1.8~GeV in terms of the evolution of their
electroexcitation amplitudes with momentum transfer of the virtual photon. We
discuss the impact of these results on the insight into the strong interaction
dynamics that underlie the generation of the full spectrum of nucleon
resonances of distinctively different structure. These results shed light on
the emergence of hadron mass, which is one of the most important and still open
problems in the Standard Model. The extension of the nucleon resonance studies
in the experiments of the 12-GeV era at JLab with the CLAS12 detector are
outlined.


title:
TransformNet: Self-supervised representation learning through predicting
  geometric transformations

abstract:
  Deep neural networks need a big amount of training data, while in the real
world there is a scarcity of data available for training purposes. To resolve
this issue unsupervised methods are used for training with limited data. In
this report, we describe the unsupervised semantic feature learning approach
for recognition of the geometric transformation applied to the input data. The
basic concept of our approach is that if someone is unaware of the objects in
the images, he/she would not be able to quantitatively predict the geometric
transformation that was applied to them. This self supervised scheme is based
on pretext task and the downstream task. The pretext classification task to
quantify the geometric transformations should force the CNN to learn high-level
salient features of objects useful for image classification. In our baseline
model, we define image rotations by multiples of 90 degrees. The CNN trained on
this pretext task will be used for the classification of images in the CIFAR-10
dataset as a downstream task. we run the baseline method using various models,
including ResNet, DenseNet, VGG-16, and NIN with a varied number of rotations
in feature extracting and fine-tuning settings. In extension of this baseline
model we experiment with transformations other than rotation in pretext task.
We compare performance of selected models in various settings with different
transformations applied to images,various data augmentation techniques as well
as using different optimizers. This series of different type of experiments
will help us demonstrate the recognition accuracy of our self-supervised model
when applied to a downstream task of classification.


title:
Optically excited spin dynamics of thermally metastable skyrmions in
  Fe$_{0.75}$Co$_{0.25}$Si

abstract:
  We investigate the microwave spin excitations of the cubic chiral magnet
Fe$_{0.75}$Co$_{0.25}$Si as driven by the thermal modulation of magnetic
interactions via laser heating and probed by time-resolved measurements of the
magneto-optical Kerr effect. Focusing on the topologically nontrivial skyrmion
lattice state, the dynamic properties in thermodynamic equilibrium are compared
with those of a metastable state prepared by means of rapid field cooling. In
both cases, we find precessional and exponential contributions to the dynamic
response, characteristic of a breathing mode and energy dissipation,
respectively. When taking into account the universal scaling as a function of
temperature, the precession frequencies in the equilibrium and metastable
skyrmion state are in excellent quantitative agreement. This finding highlights
that skyrmion states far from thermal equilibrium promise great flexibility,
for instance with respect to temperature and field scales, both for possible
microwave applications and the study of fundamental properties.


title:
Generation mechanism of solar-like differential rotation in
  high-resolution simulations

abstract:
  We analyze the simulation result shown in Hotta & Kusano, 2021 in which the
solar-like differential rotation is reproduced. The Sun is rotating
differentially with the fast equator and the slow pole. It is widely thought
that the thermal convection maintains the differential rotation, but recent
high-resolution simulations tend to fail to reproduce the fast equator. This
fact is an aspect of one of the biggest problems in solar physics called the
convective conundrum. Hotta & Kusano, 2021 succeed in reproducing the
solar-like differential rotation without using any manipulation with
unprecedentedly high-resolution simulation. In this study, we analyze the
simulation data to understand the maintenance mechanism of the fast equator.
Our analyses lead to conclusions that are summarized as follows. 1.
Superequipatition magnetic field is generated by the compression, which can
indirectly convert the massive internal energy to magnetic energy. 2. Extended
subadiabatic region around the base of the convection zone and the efficient
small-scale energy transport suppresses large-scale convection energy. 3.
Non-Taylor--Proudman differential rotation is maintained by the entropy
gradient caused by the anisotropic latitudinal energy transport enhanced by the
magnetic field. 4. The fast equator is maintained by the meridional flow mainly
caused by the Maxwell stress. The Maxwell stress itself also has a role in the
angular momentum transport for fast near-surface equator (we call it the
Punching ball effect). This study newly finds the role of the magnetic field in
the maintenance of differential rotation


title: $\mathbb{Q}$-Fano threefolds and Laurent inversion

abstract:
  We construct families of non-toric $\mathbb{Q}$-factorial terminal Fano
($\mathbb{Q}$-Fano) threefolds of codimension $\geq 20$ corresponding to 54
mutation classes of rigid maximally mutable Laurent polynomials. From the point
of view of mirror symmetry, they are the highest codimension (non-toric)
$\mathbb{Q}$-Fano varieties for which we can currently establish the
Fano/Landau-Ginzburg correspondence. We construct 46 additional
$\mathbb{Q}$-Fano threefolds with codimensions of new examples ranging between
19 and 10. Some of these varieties will be presented as toric complete
intersections, and others as Pfaffian varieties.


title: OSM-tree: A Sortedness-Aware Index

abstract:
  Indexes facilitate efficient querying when the selection predicate is on an
indexed key. As a result, when loading data, if we anticipate future selective
(point or range) queries, we typically maintain an index that is gradually
populated as new data is ingested. In that respect, indexing can be perceived
as the process of adding structure to an incoming, otherwise unsorted, data
collection. The process of adding structure comes at a cost, as instead of
simply appending incoming data, every new entry is inserted into the index. If
the data ingestion order matches the indexed attribute order, the ingestion
cost is entirely redundant and can be avoided (e.g., via bulk loading in a
B+-tree). However, state-of-the-art index designs do not benefit when data is
ingested in an order that is close to being sorted but not fully sorted. In
this paper, we study how indexes can benefit from partial data sortedness or
near-sortedness, and we propose an ensemble of techniques that combine bulk
loading, index appends, variable node fill/split factor, and buffering, to
optimize the ingestion cost of a tree index in presence of partial data
sortedness. We further augment the proposed design with necessary metadata
structures to ensure competitive read performance. We apply the proposed design
paradigm on a state-of-the-art B+-tree, and we propose the Ordered Sort-Merge
tree (OSM-tree). OSM-tree outperforms the state of the art by up to 8.8x in
ingestion performance in the presence of sortedness, while falling back to a
B+-tree's ingestion performance when data is scrambled. OSM-tree offers
competitive query performance, leading to performance benefits between 28% and
5x for mixed read/write workloads.


title: Precession of entangled spin and pseudospin in double quantum dots

abstract:
  Quantum dot spin valves are characterized by exchange fields which induce
spin precession and generate current spin resonances even in absence of spin
splitting. Analogous effects have been studied in double quantum dots, in which
the orbital degree of freedom, the pseudospin, replaces the spin in the valve
configuration. We generalize, now, this setup to allow for arbitrary spin and
orbital polarization of the leads, thus obtaining an even richer variety of
current resonances, stemming from the precession dynamics of entangled spin and
pseudospin. We observe for both vectors a delicate interplay of decoherence,
pumping and precession which can only be understood by also considering the
dynamics of the spin-pseudospin correlators. The numerical results are obtained
in the framework of a generalized master equation within the cotunneling
approximation and are complemented by the analytics of a coherent sequential
tunneling model.


title: FMP: Toward Fair Graph Message Passing against Topology Bias

abstract:
  Despite recent advances in achieving fair representations and predictions
through regularization, adversarial debiasing, and contrastive learning in
graph neural networks (GNNs), the working mechanism (i.e., message passing)
behind GNNs inducing unfairness issue remains unknown. In this work, we
theoretically and experimentally demonstrate that representative aggregation in
message-passing schemes accumulates bias in node representation due to topology
bias induced by graph topology. Thus, a \textsf{F}air \textsf{M}essage
\textsf{P}assing (FMP) scheme is proposed to aggregate useful information from
neighbors but minimize the effect of topology bias in a unified framework
considering graph smoothness and fairness objectives. The proposed FMP is
effective, transparent, and compatible with back-propagation training. An
acceleration approach on gradient calculation is also adopted to improve
algorithm efficiency. Experiments on node classification tasks demonstrate that
the proposed FMP outperforms the state-of-the-art baselines in effectively and
efficiently mitigating bias on three real-world datasets.


title:
On the traversability of wormhole solutions in asymptotically safe
  gravity

abstract:
  In this paper we study the traversability of a wormhole in the context of
Asymptotically Safe Gravity (ASG) for two different cases, namely, the
spherical case and the pseudospherical case. We carried out an analysis in the
throat of the wormhole with an specific choice of the cutoff function $f=\xi
R$. Recently, this study has been performed in Ref \cite{Moti:2020whf}, however
the authors consider only a classical source with constant state parameter
$\omega$. Here we have generalized their study in two ways: a) with a classical
state parameter which is dependent of the position $\omega(r)$ and b) with an
ASG improvement of the source. We have showed that it is possible to have
traversability with exotic and non-exotic matter, but the kind of matter is
highly dependent on the wormhole's parameters.


title: Half-space theorems for $1$-surfaces of $\mathbb{H}^3$

abstract:
  In this paper we investigate the intersection problem for $1$-surfaces
immersed in a complete Riemannian three-manifold $P$ with Ricci curvature
bounded from below by $-2$. We first prove a Frankel's type theorem for
$1$-surfaces with bounded curvature immersed in $P$ when $\text{\rm Ric}_{P} >
-2$. In this setting we also give a criterion for deciding whether a complete
$1$-surface is proper. A splitting result is established when the distance
between the $1$-surfaces is realized, even if $\text{\rm Ric}_{P} \geq -2$. In
the hyperbolic space $\mathbb{H}^3$ we show strong half-space theorems for the
classes of complete $1$-surfaces with bounded curvature, parabolic
$1$-surfaces, and stochastically complete $H$-surfaces with $H<1$. As a
by-product of our techniques a Maximum Principle at Infinity is given for
$1$-surfaces in $\mathbb{H}^3.$


title:
Uniform stabilization of Navier-Stokes equations in critical $L^q$-based
  Sobolev and Besov spaces by finite dimensional interior localized feedback
  controls

abstract:
  We consider 2- or 3-dimensional incompressible Navier-Stokes equations
defined on a bounded domain $\Omega$, with no-slip boundary conditions and
subject to an external force, assumed to cause instability. We then seek to
uniformly stabilize such N-S system, in the vicinity of an unstable equilibrium
solution, in critical $L^q$-based Sobolev and Besov spaces, by finite
dimensional feedback controls. These spaces are `close' to $L^3(\Omega)$ for
$d=3$. This functional setting is significant. In fact, in the case of the
uncontrolled N-S dynamics, extensive research efforts have recently lead to the
space $L^3(\mathbb{R}^3)$ as being a critical space for the issue of
well-posedness in the full space. Thus, our present work manages to solve the
stated uniform stabilization problem for the controlled N-S dynamics in a
correspondingly related function space setting. In this paper, the feedback
controls are localized on an arbitrarily small open interior subdomain $\omega$
of $\Omega$. In addition to providing a solution of the uniform stabilization
problem in such critical function space setting, this paper manages also to
much improve and simplify, at both the conceptual and computational level, the
solution given in the more restrictive Hilbert space setting in the literature.
Moreover, such treatment sets the foundation for the authors' final goal in a
subsequent paper. Based critically on said low functional level where
compatibility conditions are not recognized, the subsequent paper solves in the
affirmative a presently open problem: whether uniform stabilization by
localized tangential boundary feedback controls, which-in addition-are finite
dimensional, is also possible in dim $\Omega = 3$.


title:
Robust preconditioning for a mixed formulation of phase-field fracture
  problems

abstract:
  In this work, we consider fracture propagation in nearly incompressible and
(fully) incompressible materials using a phase-field formulation. We use a
mixed form of the elasticity equation to overcome volume locking effects and
develop a robust, nonlinear and linear solver scheme and preconditioner for the
resulting system. The coupled variational inequality system, which is solved
monolithically, consists of three unknowns: displacements, pressure, and
phase-field. Nonlinearities due to coupling, constitutive laws, and crack
irreversibility are solved using a combined Newton algorithm for the
nonlinearities in the partial differential equation and employing a primal-dual
active set strategy for the crack irreverrsibility constraint. The linear
system in each Newton step is solved iteratively with a flexible generalized
minimal residual method (GMRES). The key contribution of this work is the
development of a problem-specific preconditioner that leverages the
saddle-point structure of the displacement and pressure variable. Four
numerical examples in pure solids and pressure-driven fractures are conducted
on uniformly and locally refined meshes to investigate the robustness of the
solver concerning the Poisson ratio as well as the discretization and
regularization parameters.


title: An Executable Formal Model of the VHDL in Isabelle/HOL

abstract:
  In the hardware design process, hardware components are usually described in
a hardware description language. Most of the hardware description languages,
such as Verilog and VHDL, do not have mathematical foundation and hence are not
fit for formal reasoning about the design. To enable formal reasoning in one of
the most commonly used description language VHDL, we define a formal model of
the VHDL language in Isabelle/HOL. Our model targets the functional part of
VHDL designs used in industry, specifically the design of the LEON3 processor's
integer unit. We cover a wide range of features in the VHDL language that are
usually not modelled in the literature and define a novel operational semantics
for it. Furthermore, our model can be exported to OCaml code for execution,
turning the formal model into a VHDL simulator. We have tested our simulator
against simple designs used in the literature, as well as the div32 module in
the LEON3 design. The Isabelle/HOL code is publicly available:
https://zhehou.github.io/apps/VHDLModel.zip


title:
Data-Driven Chance Constrained Control using Kernel Distribution
  Embeddings

abstract:
  We present a data-driven algorithm for efficiently computing stochastic
control policies for general joint chance constrained optimal control problems.
Our approach leverages the theory of kernel distribution embeddings, which
allows representing expectation operators as inner products in a reproducing
kernel Hilbert space. This framework enables approximately reformulating the
original problem using a dataset of observed trajectories from the system
without imposing prior assumptions on the parameterization of the system
dynamics or the structure of the uncertainty. By optimizing over a finite
subset of stochastic open-loop control trajectories, we relax the original
problem to a linear program over the control parameters that can be efficiently
solved using standard convex optimization techniques. We demonstrate our
proposed approach in simulation on a system with nonlinear non-Markovian
dynamics navigating in a cluttered environment.


title: A second-order Magnus-type integrator for evolution equations with delay

abstract:
  We rewrite abstract delay equations to nonautonomous abstract Cauchy problems
allowing us to introduce a Magnus-type integrator for the former. We prove the
second-order convergence of the obtained Magnus-type integrator. We also show
that if the differential operators involved admit a common invariant set for
their generated semigroups, then the Magnus-type integrator will respect this
invariant set as well, allowing for much weaker assumptions to obtain the
desired convergence. As an illustrative example we consider a space-dependent
epidemic model with latent period and diffusion.


title:
Kuznetsov's Fano threefold conjecture via K3 categories and enhanced
  group actions

abstract:
  We settle the last open case of Kuznetsov's conjecture on the derived
categories of Fano threefolds. Contrary to the original conjecture, we prove
the Kuznetsov components of quartic double solids and Gushel-Mukai threefolds
are never equivalent, as recently shown independently by Zhang. On the other
hand, we prove the modified conjecture asserting their deformation equivalence.
Our proof of nonequivalence combines a categorical Enriques-K3 correspondence
with the Hodge theory of categories, and gives as a byproduct a simple proof of
a theorem of Debarre and Kuznetsov about the fibers of the period map for
Gushel-Mukai varieties. Our proof of deformation equivalence relies on results
of independent interest about obstructions to enhancing group actions on
categories.


title:
Coarse-grained modeling of crystals by the amplitude expansion of the
  phase-field crystal model: an overview

abstract:
  Comprehensive investigations of crystalline systems often require methods
bridging atomistic and continuum scales. In this context, coarse-grained
mesoscale approaches are of particular interest as they allow the examination
of large systems and time scales while retaining some microscopic details. The
so-called Phase-Field Crystal (PFC) model conveniently describes crystals at
diffusive time scales through a continuous periodic field which varies on
atomic scales and is related to the atomic number density. To go beyond the
restrictive atomic length scales of the PFC model, a complex amplitude
formulation was first developed by Goldenfeld et al. [Phys. Rev. E 72, 020601
(2005)]. While focusing on length scales larger than the lattice parameter,
this approach can describe crystalline defects, interfaces, and lattice
deformations. It has been used to examine many phenomena including liquid/solid
fronts, grain boundary energies, and strained films. This topical review
focuses on this amplitude expansion of the PFC model and its developments. An
overview of the derivation, connection to the continuum limit, representative
applications, and extensions is presented. A few practical aspects, such as
suitable numerical methods and examples, are illustrated as well. Finally, the
capabilities and bounds of the model, current challenges, and future
perspectives are addressed.

categories: cond-mat.mtrl-sci cond-mat.mes-hall cond-mat.other physics.comp-ph


title:
Differential entropy per particle as a probe of van Hove singularities
  and flat bands

abstract:
  In the present paper we derive the general expressions for the differential
entropy per particle near van Hove singularities (vHs) in the density of
states. The dependence of entropy per particle on chemical potential and
temperature demonstrates different behavior depending on the type of vHs, and
distinguishes high-order vHs with different divergence exponents and flat
bands. In addition, it allows one to test the "flatness" of the band in
experiment. We compare the analytic predictions with the numerical calculation
of the differential entropy for tight-binding models of graphene, Lieb lattice
and square-octagon lattice. Our results show that the obtained analytic
expressions capture the main features of the differential entropy, thus serving
as a good probe for details of the density of states structure.


title:
Multilayer Adjusted Cluster Point Process Model: Application to
  Microbial Biofilm Image Data Analysis

abstract:
  A common problem in spatial statistics tackles spatial distributions of
clusters of objects. Such clusters of similar or dissimilar objects are
encountered in many fields, including field ecology, astronomy, and biomedical
imaging. Still challenging is to quantify spatial clustering when one or more
entities clusters around a different entity in multiple layers. Such
multi-entity and multi-layered structures are observed, for example, in human
dental plaque biofilm images, which exhibit multi-species structures in
corncob-like arrangements. We propose a novel, fully Bayesian, multivariate
spatial point process model to quantify corncob-like arrangements with
"parent-offspring" statistical approaches. The proposed multilayer adjusted
cluster point process (MACPP) model departs from commonly used approaches in
that it exploits the locations of the central "parent" object in clusters and
accounts for multilayered multivariate parent-offspring clustering. In
simulated datasets, the MACPP outperforms the classical Neyman-Scott process
model, a univariate model for modeling spatially clustered processes, by
producing decisively more accurate and precise parameter estimates. We analyzed
data from a human dental plaque biofilm image in which \emph{Streptococcus} and
\emph{Porphyromonas} simultaneously cluster around \emph{Corynebacterium} and
\emph{Pasteurellaceae} clusters around \emph{Streptococcus}. The proposed MACPP
model successfully captured the parent-offspring structure for all the taxa
involved.


title:
A population synthesis fitting of the $Gaia$ resolved white dwarf binary
  population within 100 pc

abstract:
  The $Gaia$ mission has provided an unprecedented wealth of information about
the white dwarf population of our Galaxy. In particular, our studies show that
the sample up to 100\,pc from the Sun can be considered as practically
complete. This fact allows us to estimate a precise fraction of
double-degenerate ($1.18\pm 0.10$\%) and white dwarf plus main-sequence stars
($6.31\pm0.23$\%) among all white dwarfs through comoving pairs identification.
With the aid of a detailed population synthesis code we are able to reproduce
synthetic white dwarf populations with nearly identical fractions as the
observed ones, thus obtaining valuable information about the binary fraction,
$f_{\rm b}$, initial mass ratio distribution, $n(q)$, and initial separation
distribution, $f(a)$, among other parameters. Our best-fit model is achieved
within a $1\sigma$ confidence level for $f(a)\propto a^{-1}$, $n(q)\propto
q^{n_q}$, with $n_q=-1.13^{+0.12}_{-0.10}$ and $f_{\rm b}=0.32\pm 0.02$. The
fraction of white dwarf mergers generated by this model is $9\sim16\%$,
depending on the common-envelope treatment. As sub-products of our modelling we
find that around $1\sim3\%$ of the white dwarf population are unresolved
double-degenerates and that only $\sim1\%$ of all white dwarfs contain a
He-core. Finally, only a mild kick during white dwarf formation seems to be
necessary for fitting the observed sky separation of double-degenerate systems.


title: MaskGIT: Masked Generative Image Transformer

abstract:
  Generative transformers have experienced rapid popularity growth in the
computer vision community in synthesizing high-fidelity and high-resolution
images. The best generative transformer models so far, however, still treat an
image naively as a sequence of tokens, and decode an image sequentially
following the raster scan ordering (i.e. line-by-line). We find this strategy
neither optimal nor efficient. This paper proposes a novel image synthesis
paradigm using a bidirectional transformer decoder, which we term MaskGIT.
During training, MaskGIT learns to predict randomly masked tokens by attending
to tokens in all directions. At inference time, the model begins with
generating all tokens of an image simultaneously, and then refines the image
iteratively conditioned on the previous generation. Our experiments demonstrate
that MaskGIT significantly outperforms the state-of-the-art transformer model
on the ImageNet dataset, and accelerates autoregressive decoding by up to 64x.
Besides, we illustrate that MaskGIT can be easily extended to various image
editing tasks, such as inpainting, extrapolation, and image manipulation.


title: Efficiency with(out) intermediation in repeated bilateral trade

abstract:
  This paper analyzes repeated version of the bilateral trade model where the
independent payoff relevant private information of the buyer and the seller is
correlated across time. Using this setup it makes the following five
contributions. First, it derives necessary and sufficient conditions on the
primitives of the model as to when efficiency can be attained under ex post
budget balance and participation constraints. Second, in doing so, it
introduces an intermediate notion of budget balance called interim budget
balance that allows for the extension of liquidity but with participation
constraints for the issuing authority interpreted here as an intermediary.
Third, it pins down the class of all possible mechanisms that can implement the
efficient allocation with and without an intermediary. Fourth, it provides a
foundation for the role of an intermediary in a dynamic mechanism design model
under informational constraints. And, fifth, it argues for a careful
interpretation of the "folk proposition" that less information is better for
efficiency in dynamic mechanisms under ex post budget balance and observability
of transfers.


title:
RECOVER: sequential model optimization platform for combination drug
  repurposing identifies novel synergistic compounds in vitro

abstract:
  Selecting optimal drug repurposing combinations for further preclinical
development is a challenging technical feat. Due to the toxicity of many
therapeutic agents (e.g., chemotherapy), practitioners have favoured selection
of synergistic compounds whereby lower doses can be used whilst maintaining
high efficacy. For a fixed small molecule library, an exhaustive combinatorial
chemical screen becomes infeasible to perform for academic and industry
laboratories alike. Deep learning models have achieved state-of-the-art results
in silico for the prediction of synergy scores. However, databases of drug
combinations are highly biased towards synergistic agents and these results do
not necessarily generalise out of distribution. We employ a sequential model
optimization search applied to a deep learning model to quickly discover highly
synergistic drug combinations active against a cancer cell line, while
requiring substantially less screening than an exhaustive evaluation. Through
iteratively adapting the model to newly acquired data, after only 3 rounds of
ML-guided experimentation (including a calibration round), we find that the set
of combinations queried by our model is enriched for highly synergistic
combinations. Remarkably, we rediscovered a synergistic drug combination that
was later confirmed to be under study within clinical trials.


title:
Quantum mechanical rules for observed observers and the consistency of
  quantum theory

abstract:
  I argue that the usual rules of quantum mechanics on measurement outcomes
have to be complemented with (or rather, understood to include) the condition
that observers who are themselves going to be subject to measurements in a
linear combination of macroscopic states cannot make reliable predictions on
the results of experiments performed after such measurements. Quantum
mechanical rules completed with this condition lift the paradox identified by
Frauchiger and Renner and become fully consistent.


title:
The Absurdity of Death Estimates Based on the Vaccine Adverse Event
  Reporting System

abstract:
  We demonstrate from first principles a core fallacy employed by a coterie of
authors who claim that data from the Vaccine Adverse Reporting System (VAERS)
show that hundreds of thousands of U.S. deaths are attributable to COVID
vaccination.


title:
Quantum limits to resolution and discrimination of spontaneous emission
  lifetimes

abstract:
  In this work we investigate the quantum information theoretical limits to
several tasks related to lifetime estimation and discrimination of a two-level
spontaneous optical emitter. We focus in particular on the model problem of
resolving two mutually incoherent exponential decays with highly overlapping
temporal probability profiles. Mirroring recent work on quantum-inspired
super-resolution of point emitters, we find that direct lifetime measurement
suffers from an analogue of "Rayleigh's Curse" when the time constants of the
two decay channels approach one another. We propose alternative measurement
schemes that circumvent this limit, and also demonstrate superiority to direct
measurement for a related binary hypothesis test. Our findings add to a growing
list of examples in which a quantum analysis uncovers significant information
gains for certain tasks in opto-molecular metrology that do not rely on
multiphoton interference, but evidently do benefit from a more thorough
exploitation of the coherence properties of single photons.


title:
Covariate-informed Representation Learning with Samplewise Optimal
  Identifiable Variational Autoencoders

abstract:
  Recently proposed identifiable variational autoencoder (iVAE, Khemakhem et
al. (2020)) framework provides a promising approach for learning latent
independent components of the data. Although the identifiability is appealing,
the objective function of iVAE does not enforce the inverse relation between
encoders and decoders. Without the inverse relation, representations from the
encoder in iVAE may not reconstruct observations,i.e., representations lose
information in observations. To overcome this limitation, we develop a new
approach, covariate-informed identifiable VAE (CI-iVAE). Different from
previous iVAE implementations, our method critically leverages the posterior
distribution of latent variables conditioned only on observations. In doing so,
the objective function enforces the inverse relation, and learned
representation contains more information of observations. Furthermore, CI-iVAE
extends the original iVAE objective function to a larger class and finds the
optimal one among them, thus providing a better fit to the data. Theoretically,
our method has tighter evidence lower bounds (ELBOs) than the original iVAE. We
demonstrate that our approach can more reliably learn features of various
synthetic datasets, two benchmark image datasets (EMNIST and Fashion MNIST),
and a large-scale brain imaging dataset for adolescent mental health research.


title: Phenomenology of the hadronic structure at small-$x$

abstract:
  We present exploratory studies of the proton structure via two distinct kinds
of gluon densities: the transverse-momentum dependent functions, whose
evolution is determined by the CSS equation, and the unintegrated gluon
distribution, whose definition is given in the BFKL formalism. Our analyses are
relevant for the exploration of the intrinsic motion of gluons inside protons
in the small-$x$ regime, and for a comprehensive tomographic imaging of the
proton at new-generation colliding facilities.


title: Evaluating Causal Inference Methods

abstract:
  The fundamental challenge of drawing causal inference is that counterfactual
outcomes are not fully observed for any unit. Furthermore, in observational
studies, treatment assignment is likely to be confounded. Many statistical
methods have emerged for causal inference under unconfoundedness conditions
given pre-treatment covariates, including propensity score-based methods,
prognostic score-based methods, and doubly robust methods. Unfortunately for
applied researchers, there is no `one-size-fits-all' causal method that can
perform optimally universally. In practice, causal methods are primarily
evaluated quantitatively on handcrafted simulated data. Such data-generative
procedures can be of limited value because they are typically stylized models
of reality. They are simplified for tractability and lack the complexities of
real-world data. For applied researchers, it is critical to understand how well
a method performs for the data at hand. Our work introduces a deep generative
model-based framework, Credence, to validate causal inference methods. The
framework's novelty stems from its ability to generate synthetic data anchored
at the empirical distribution for the observed sample, and therefore virtually
indistinguishable from the latter. The approach allows the user to specify
ground truth for the form and magnitude of causal effects and confounding bias
as functions of covariates. Thus simulated data sets are used to evaluate the
potential performance of various causal estimation methods when applied to data
similar to the observed sample. We demonstrate Credence's ability to accurately
assess the relative performance of causal estimation techniques in an extensive
simulation study and two real-world data applications from Lalonde and Project
STAR studies.


title:
Memristor Compact Model with Oxygen-Vacancy Concentration as State
  Variable

abstract:
  We present a unique compact model for oxide memristors, based upon the
concentration of oxygen vacancies as state variables. In this model, the
increase (decrease) in oxygen vacancy concentration is similar in effect to the
reduction (expansion) of the tunnel gap used as a state variable in existing
compact models, providing a mechanism for the electronic current to increase
(decrease) based upon the polarity of the applied voltage. Rate equations
defining the dynamics of state variables are obtained from simplifications of a
recent manuscript in which electronic processes (i.e., electron
capture/emission) were combined with atomic processes (i.e., Frenkel pair
generation/recombination, diffusion) stemming from the thermochemical model of
dielectric breakdown. Central to the proposed model is the effect of the
electron occupancy of oxygen vacancy traps on resistive switching dynamics. The
electronic current is calculated considering Ohmic, band-to-band, and
bound-to-band contributions. The model includes uniform self-heating with
Joule-heating and conductive loss terms. The model is calibrated using
experimental current-voltage characteristics for HfO2 memristors with different
electrode materials. Though a general model is presented, a delta-shaped
density of states profile for oxygen vacancies is found capable of accurately
representing experimental data while providing a minimal description of bound
to band transitions. The model is implemented in Verilog-A and tested using
read/write operations in a 4x4 1T1R nonvolatile memory array to evaluate its
ability to perform circuit simulations of practical interest. A particular
benefit is that the model does not make strong assumptions regarding filament
geometry of which scant experimental-evidence exists to support.


title: Dimer model on the square lattice with interface

abstract:
  In this exposition, we consider the dimer problem on an infinite square
lattice with partially non-periodic edge weights, which we refer to as the
square lattice with interface. In particular, we compute an exact integral form
of the inverse Kasteleyn operator and study its asymptotics behavior in
different regions of the lattice to gain a general understanding of the local
statistics of the model.


title:
$L^{p}$-$L^{q}$ boundedness of Fourier multipliers on Fundamental
  domains of Lattices in $\mathbb{R}^d$

abstract:
  In this paper we study the $L^{p}$-$L^{q}$ boundedness of Fourier multipliers
on the fundamental domain of a lattice in $\mathbb{R}^{d}$ for $1 < p,q <
\infty$ under the classical H\"ormander condition. First, we introduce Fourier
analysis on lattices and have a look at possible generalisations. We then prove
the Hausdorff-Young inequality, Paley's inequality and the
Hausdorff-Young-Paley inequality in the context of lattices. This amounts to a
quantitative version of the $L^{p}$-$L^{q}$ boundedness of Fourier multipliers.
Moreover, the Paley inequality allows us to prove the Hardy-Littlewood
inequality.


title:
Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid
  Framework for Rotating Machinery

abstract:
  Fault diagnosis plays an essential role in reducing the maintenance costs of
rotating machinery manufacturing systems. In many real applications of fault
detection and diagnosis, data tend to be imbalanced, meaning that the number of
samples for some fault classes is much less than the normal data samples. At
the same time, in an industrial condition, accelerometers encounter high levels
of disruptive signals and the collected samples turn out to be heavily noisy.
As a consequence, many traditional Fault Detection and Diagnosis (FDD)
frameworks get poor classification performances when dealing with real-world
circumstances. Three main solutions have been proposed in the literature to
cope with this problem: (1) the implementation of generative algorithms to
increase the amount of under-represented input samples, (2) the employment of a
classifier being powerful to learn from imbalanced and noisy data, (3) the
development of an efficient data pre-processing including feature extraction
and data augmentation. This paper proposes a hybrid framework which uses the
three aforementioned components to achieve an effective signal-based FDD system
for imbalanced conditions. Specifically, it first extracts the fault features,
using Fourier and wavelet transforms to make full use of the signals. Then, it
employs Wasserstein Generative Adversarial Networks (WGAN) to generate
synthetic samples to populate the rare fault class and enhance the training
set. Moreover, to achieve a higher performance a novel combination of
Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning
Machine (WELM) is proposed. To verify the effectiveness of the developed
framework, different datasets settings on different imbalance severities and
noise degrees were used. The comparative results demonstrate that in different
scenarios GAN-CLSTM-ELM outperforms the other state-of-the-art FDD frameworks.


title: Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation

abstract:
  Estimation of a dynamical system's latent state subject to sensor noise and
model inaccuracies remains a critical yet difficult problem in robotics. While
Kalman filters provide the optimal solution in the least squared sense for
linear and Gaussian noise problems, the general nonlinear and non-Gaussian
noise case is significantly more complicated, typically relying on sampling
strategies that are limited to low-dimensional state spaces. In this paper we
devise a general inference procedure for filtering of nonlinear, non-Gaussian
dynamical systems that exploits the differentiability of both the update and
prediction models to scale to higher dimensional spaces. Our method, Stein
particle filter, can be seen as a deterministic flow of particles, embedded in
a reproducing kernel Hilbert space, from an initial state to the desirable
posterior. The particles evolve jointly to conform to a posterior approximation
while interacting with each other through a repulsive force. We evaluate the
method in simulation and in complex localization tasks while comparing it to
sequential Monte Carlo solutions.


title:
Aspects of the gauge boson-gaugino mixing in a supersymmetric scenario
  with Lorentz-symmetry violation

abstract:
  We write down an $\mathcal{N}=1$ supersymmetric extension for non-Abelian
gauge theories in (1+3) dimensions with a Lorentz- and CPT-violating term of
the Carroll-Field-Jackiw type. By including effects of the background
(supersymmetric) fermion bilinears that accompany Lorentz-symmetry violation in
a Carroll-Field-Jackiw scenario, we investigate both the gauge boson and
gaugino dispersion relations in order to compute their respective masses in
terms of the background structures. Such results open up a potential path
towards a possible mechanism for gaugino-gauge boson conversion, an analogue of
the Primakoff effect, induced here not by an external magnetic field, but
instead by the (Majorana) fermionic sector of the supersymmetry multiplet in
the backstage of the Lorentz-symmetry violation.


title: QAC: Quantum-computing Aided Composition

abstract:
  In this chapter I will discuss the role of quantum computing in computer
music and how it can be integrated to better serve the creative artists. I will
start by considering different approaches in current computer music and quantum
computing tools, as well as reviewing some previous attempts to integrate them.
Then, I will reflect on the meaning of this integration and present what I
coined as QAC (Quantum-computing Aided Composition) as well as an early attempt
at realizing it. This chapter will also introduce The QAC Toolkit Max package,
analyze its performance, and explore some examples of what it can offer to
realtime creative practice. Lastly, I will present a real case scenario of QAC
in the creative work Disklavier Prelude #3.


title:
Magnetic Switchback Occurrence Rates in the Inner Heliosphere: Parker
  Solar Probe and 1 au

abstract:
  The subject of switchbacks, defined either as large angular deflections or
polarity reversals of the magnetic field, has generated substantial interest in
the space physics community since the launch of Parker Solar Probe (PSP) in
2018. Previous studies have characterized switchbacks in several different
ways, and have been restricted to data available from the first few orbits.
Here, we analyze the frequency of occurrence of switchbacks per unit distance
for the first full eight orbits of PSP. In this work, are considered switchback
only the events that reverse the sign of magnetic field relative to a regional
average. A significant finding is that the rate of occurrence falls off sharply
approaching the sun near 0.2 au (40 $R_\odot$), and rises gently from 0.2 au
outward. The analysis is varied for different magnetic field cadences and for
different local averages of the ambient field, confirming the robustness of the
results. We discuss implications for the mechanisms of switchback generation. A
publicly available database has been created with the identified reversals.


title: On the algebraic solutions of the Painleve-III (D7) equation

abstract:
  The D7 degeneration of the Painleve-III equation has solutions that are
rational functions of $x^{1/3}$ for certain parameter values. We apply the
isomonodromy method to obtain a Riemann-Hilbert representation of these
solutions. We demonstrate the utility of this representation by analyzing
rigorously the behavior of the solutions in the large parameter limit.


title:
Managers versus Machines: Do Algorithms Replicate Human Intuition in
  Credit Ratings?

abstract:
  We use machine learning techniques to investigate whether it is possible to
replicate the behavior of bank managers who assess the risk of commercial loans
made by a large commercial US bank. Even though a typical bank already relies
on an algorithmic scorecard process to evaluate risk, bank managers are given
significant latitude in adjusting the risk score in order to account for other
holistic factors based on their intuition and experience. We show that it is
possible to find machine learning algorithms that can replicate the behavior of
the bank managers. The input to the algorithms consists of a combination of
standard financials and soft information available to bank managers as part of
the typical loan review process. We also document the presence of significant
heterogeneity in the adjustment process that can be traced to differences
across managers and industries. Our results highlight the effectiveness of
machine learning based analytic approaches to banking and the potential
challenges to high-skill jobs in the financial sector.


title:
Improving Computational Complexity in Statistical Models with
  Second-Order Information

abstract:
  It is known that when the statistical models are singular, i.e., the Fisher
information matrix at the true parameter is degenerate, the fixed step-size
gradient descent algorithm takes polynomial number of steps in terms of the
sample size $n$ to converge to a final statistical radius around the true
parameter, which can be unsatisfactory for the application. To further improve
that computational complexity, we consider the utilization of the second-order
information in the design of optimization algorithms. Specifically, we study
the normalized gradient descent (NormGD) algorithm for solving parameter
estimation in parametric statistical models, which is a variant of gradient
descent algorithm whose step size is scaled by the maximum eigenvalue of the
Hessian matrix of the empirical loss function of statistical models. When the
population loss function, i.e., the limit of the empirical loss function when
$n$ goes to infinity, is homogeneous in all directions, we demonstrate that the
NormGD iterates reach a final statistical radius around the true parameter
after a logarithmic number of iterations in terms of $n$. Therefore, for fixed
dimension $d$, the NormGD algorithm achieves the optimal overall computational
complexity $\mathcal{O}(n)$ to reach the final statistical radius. This
computational complexity is cheaper than that of the fixed step-size gradient
descent algorithm, which is of the order $\mathcal{O}(n^{\tau})$ for some $\tau
> 1$, to reach the same statistical radius. We illustrate our general theory
under two statistical models: generalized linear models and mixture models, and
experimental results support our prediction with general theory.


title: Optimal annuitization post-retirement with labor income

abstract:
  Evidence shows that the labor participation rate of retirement age cohorts is
non-negligible, and it is a widespread phenomenon globally. In the United
States, the labor force participation rate for workers age 75 and older is
projected to be over 10 percent by 2026 as reported by the Bureau of Labor
Statistics. The prevalence of post-retirement work changes existing
considerations of optimal annuitization, a research question further
complicated by novel factors such as post-retirement labor rates, wage rates,
and capacity or willingness to work. To our knowledge, this poses a practical
and theoretical problem not previously investigated in actuarial literature. In
this paper, we study the problem of post-retirement annuitization with extra
labor income in the framework of stochastic control, optimal stopping, and
expected utility maximization. The utility functions are of the Cobb-Douglas
type. The martingale methodology and duality techniques are employed to obtain
closed-form solutions for the dual and primal problems. The effect of labor
income is investigated by exploiting the explicit solutions and Monte-Carlo
simulation. The latter reveals that the optimal annuitization time is strongly
linear with respect to the initial wealth, with or without labor income. When
it comes to optimal annuitization, we find that the wage and labor rates may
play opposite roles. However, their impact is mediated by the leverage ratio.


title: Gravitational waves in models with multicritical-point principle

abstract:
  The multicritical-point principle (MPP) provides a natural explanation of the
large hierarchy between the Planck and electroweak scales. We consider a
scenario in which MPP is applied to the Standard Model extended by two real
singlet scalar fields $\phi$ and $S$, and a dimensional transmutation occurs by
the vacuum expectation value of $\phi$. In this paper, we focus on the critical
points that possess a $\mathbb Z_2$ symmetry $S\rightarrow -S$ and all the
other fields are left invariant. Then $S$ becomes a natural dark matter (DM)
candidate. Further, we concentrate on the critical points where $\phi$ does not
possess further $\mathbb Z_2$ symmetry so that there is no cosmological
domain-wall problem. Among such critical points, we focus on maximally critical
one called CP-1234 that fix all the superrenormalizable parameters. We show
that there remains a parameter region that satisfies the DM relic abundance, DM
direct-detection bound and the current LHC constraints. In this region, we find
a first-order phase transition in the early universe around the TeV-scale
temperature. The resultant gravitational waves are predicted with a peak
amplitude of ${\cal O}(10^{-12})$ at a frequency of $10^{-2}$-$10^{-1}$ Hz,
which can be tested with future space-based instruments such as DECIGO and BBO.


title: Type-II two-Higgs-doublet model in noncommutative geometry

abstract:
  In noncommutative geometry (NCG) the spectral action principle predicts the
standard model (SM) particle masses by constraining the scalar and Yukawa
couplings at some heavy scale, but gives an inconsistent value for the Higgs
mass. Nevertheless, the scalar sector in the NCG approach to the standard
model, is in general composed of two Higgs doublets and its phenomenology
remains unexplored. In this work, we present a type-II two-Higgs-doublet model
in NCG, with a SM-like Higgs mass compatible with the 125~GeV experimental
value and extra scalars within the alignment limit without decoupling with
masses from 350 GeV.


title:
Longitudinal structure function $F_L$ at low $Q^2$ and low $x$ with
  model for higher twist: an update

abstract:
  A reanalysis of the model for the longitudinal structure function $F_L
(x,Q^2)$ at low $x$ and low $Q^2$ was undertaken, in view of the advent of the
EIC. The model is based on the photon-gluon fusion mechanism suitably
extrapolated to the region of low $Q^2$. It includes the kinematic constraint
$F_L\sim Q^4$ as $Q^2\rightarrow$ 0 and higher twist contribution which
vanishes as $Q^2 \rightarrow \infty$. Revised model was critically updated and
compared to the presently available data.


title: Intelligent Autonomous Intersection Management

abstract:
  Connected Autonomous Vehicles will make autonomous intersection management a
reality replacing traditional traffic signal control. Autonomous intersection
management requires time and speed adjustment of vehicles arriving at an
intersection for collision-free passing through the intersection. Due to its
computational complexity, this problem has been studied only when vehicle
arrival times towards the vicinity of the intersection are known beforehand,
which limits the applicability of these solutions for real-time deployment. To
solve the real-time autonomous traffic intersection management problem, we
propose a reinforcement learning (RL) based multiagent architecture and a novel
RL algorithm coined multi-discount Q-learning. In multi-discount Q-learning, we
introduce a simple yet effective way to solve a Markov Decision Process by
preserving both short-term and long-term goals, which is crucial for
collision-free speed control. Our empirical results show that our RL-based
multiagent solution can achieve near-optimal performance efficiently when
minimizing the travel time through an intersection.


title:
Kinetically-controlled epitaxial growth of Fe$_3$GeTe$_2$ van der Waals
  ferromagnetic films

abstract:
  We demonstrate that kinetics play an important role in the epitaxial growth
of Fe$_3$GeTe$_2$ (FGT) van der Waals (vdW) ferromagnetic films by molecular
beam epitaxy. By varying the deposition rate, we control the formation or
suppression of an initial tellurium-deficient non-van der Waals phase
(Fe$_3$Ge$_2$) prior to realizing epitaxial growth of the vdW FGT phase. Using
cross-sectional scanning transmission electron microscopy and scanning
tunneling microscopy, we optimize the FGT films to have atomically smooth
surfaces and abrupt interfaces with the Ge(111) substrate. The magnetic
properties of our high quality material are confirmed through magneto-optic,
magnetotransport, and spin-polarized STM studies. Importantly, this
demonstrates how the interplay of energetics and kinetics can help tune the
re-evaporation rate of chalcogen atoms and interdiffusion from the underlayer,
which paves the way for future studies of van der Waals epitaxy.


title:
First-principles calculations for transient absorption of laser-excited
  magnetic materials

abstract:
  We investigate the modification in the optical properties of laser-excited
bulk cobalt and nickel using the time-dependent density functional theory at a
finite electron temperature. As a result of the first-principles simulation, a
complex change in the photoabsorption of the magnetic materials is observed
around the $M_{2,3}$ absorption edge. Based on the microscopic analysis, we
clarify that this complex absorption change consists of the two following
components: (i) the decrease in the photoabsorption in a narrow energy range
around the $M_{2,3}$ edge, which reflects the blue shift of the absorption edge
due to the light-induced demagnetization, and (ii) the increase in the
photoabsorption in a wider range around the $M_{2,3}$ edge, which reflects the
modification in the local-field effect due to the light-induced electron
localization. The relation between the transient optical and magnetic
properties may open a way to monitor ultrafast (de)magnetization and spin
dynamics in magnetic materials via transient absorption spectroscopy.


title:
Feedback stabilization of the resonant frequency in tunable microwave
  cavities with single-photon occupancy

abstract:
  We successfully demonstrate low-frequency noise suppression in the resonant
frequency fluctuations of a cavity-embedded Cooper pair transistor (cCPT)
driven at single-photon occupancy. In particular, we report a reduction in the
resonant frequency fluctuations caused by the internal charge noise over a
bandwidth of $\sim$1.4 kHz when the cavity is driven at an average photon
number $n=10$, and a bandwidth of 11 Hz for average $n=1$. The gate-dependent
tunability of the cCPT allows us to implement a feedback-scheme, derived from
the Pound-Drever-Hall locking technique. This reduces fluctuations due to
intrinsic charge-noise, which otherwise interferes with the cCPT's operation as
a near quantum-limited electrometer. We believe our technique can be
generalized to achieve frequency stabilization in tunable microwave resonators
that play a vital role in today's quantum computing architecture, thereby
moderating the limitations in detection caused by the intrinsic $1/f$-noise on
such circuit devices. The work discusses the various aspects relating to the
operation of a fully functional feedback loop down to the single-photon level.


title: Anthropic constraint on transient variations of fundamental constants

abstract:
  The anthropic principle implies that life can emerge and be sustained only in
a narrow range of values of fundamental constants (FCs). We extend the
anthropic arguments to a regime of transient variations of FCs. Such regime is
characteristic of clumpy dark matter models where inside the clumps FCs can
reach values vastly different from their everyday values. We show that the
passage of such a macroscopic clump through Earth would make Earth
uninhabitable. The periodic table of elements is truncated and water fails to
serve as a universal solvent. Anthropic principle enables us to improve
existing astrophysical bounds on certain dark matter model couplings by five
orders of magnitude.

categories: physics.atom-ph astro-ph.CO hep-ph physics.chem-ph physics.pop-ph


title:
Matter radii and skins of $^{6,8}$He from reaction cross section of
  proton+$^{6,8}$He scattering based on the Love-Franey $t$-matrix model

abstract:
  For $^{4,6,8}$He, Tanihata et al. determined matter radii $r_{m}(\sigma_{\rm
I})=1.57(4), 2.48(3), 2.52(3)$~fm from interaction cross sections $\sigma_{\rm
I}$ for $^{4,6,8}$He scattering on Be, C Al targets at 790~MeV/nucleon. Lu et
al. measured the atomic isotope shifts (AIS) for $^{4,6,8}$He and determined
proton radii $r_{p}({\rm AIS})$ for $^{4,6,8}$He. As for p+$^{4,6,8}$He
scattering, reaction cross sections $\sigma_{\rm R}({\rm exp})$ are available
at 700~MeV with high accuracy. Our aim is to determine matter radii $r_{m}$ and
skins $r_{\rm skin}$ for $^{6,8}$He from the $\sigma_{\rm R}({\rm exp})$ and
the $r_{p}({\rm AIS})$. {\bf Method:} Our model is the Love-Franey $t$-matrix
folding model, since the model is better than the optical limit of Glauber
model. Our results for $^{6,8}$He are $r_{m}({\rm exp})=2.48(3), 2.53(2)$~fm
and $r_{\rm skin}=$0.78(3), 0.82(2)~fm. For $^{6,8}$He, our results
$r_{m}(\sigma_{\rm R})$ agree with those of Tanihata {\it et al.}. For
$^{8}$He, the distance between $^{4}$He and the center of mass of valence four
neutrons is 2.367~fm.


title:
$N$-body interactions between trapped ion qubits via spin-dependent
  squeezing

abstract:
  We describe a simple protocol for the single-step generation of $N$-body
entangling interactions between trapped atomic ion qubits. We show that qubit
state-dependent squeezing operations and displacement forces on the collective
atomic motion can generate full $N$-body interactions. Similar to the
M{\o}lmer-S{\o}rensen two-body Ising interaction at the core of most trapped
ion quantum computers and simulators, the proposed operation is relatively
insensitive to the state of motion. We show how this $N$-body gate operation
allows the single-step implementation of a family of $N$-bit gate operations
such as the powerful $N$-Toffoli gate, which flips a single qubit if and only
if all other $N$-$1$ qubits are in a particular state.


title: Real-Time Event-Based Tracking and Detection for Maritime Environments

abstract:
  Event cameras are ideal for object tracking applications due to their ability
to capture fast-moving objects while mitigating latency and data redundancy.
Existing event-based clustering and feature tracking approaches for
surveillance and object detection work well in the majority of cases, but fall
short in a maritime environment. Our application of maritime vessel detection
and tracking requires a process that can identify features and output a
confidence score representing the likelihood that the feature was produced by a
vessel, which may trigger a subsequent alert or activate a classification
system. However, the maritime environment presents unique challenges such as
the tendency of waves to produce the majority of events, demanding the majority
of computational processing and producing false positive detections. By
filtering redundant events and analyzing the movement of each event cluster, we
can identify and track vessels while ignoring shorter lived and erratic
features such as those produced by waves.


title:
Amplitude analysis and branching-fraction measurement of $D_{s}^{+} \to
  \pi^{+}\pi^{0}\eta^{\prime}$

abstract:
  Using data collected with the BESIII detector in $e^+e^-$ collisions at
center-of-mass energies between 4.178 and 4.226 GeV and corresponding to
6.32~fb$^{-1}$ of integrated luminosity, we report the amplitude analysis and
branching-fraction measurement of the $D^+_s \to \pi^+ \pi^0 \eta^{\prime}$
decay. We find that the dominant intermediate process is $D^+_s \to\rho^+
\eta^{\prime}$ and the significances of other resonant and nonresonant
processes are all less than $3\sigma$. The upper limits on the branching
fractions of $S$-wave and $P$-wave nonresonant components are set to $0.10\%$
and $0.74\%$ at the $90\%$ confidence level, respectively. In addition, the
branching fraction of the $D^+_s \to \pi^+ \pi^0 \eta^{\prime}$ decay is
measured to be $(6.15\pm0.25(\rm stat.)\pm0.18(\rm syst.))\%$, which receives
significant contribution only from $D_s^+\to \rho^+\eta^{\prime}$ according to
the amplitude analysis.


title: Deep Neural Networks to Correct Sub-Precision Errors in CFD

abstract:
  Loss of information in numerical simulations can arise from various sources
while solving discretized partial differential equations. In particular,
precision-related errors can accumulate in the quantities of interest when the
simulations are performed using low-precision 16-bit floating-point arithmetic
compared to an equivalent 64-bit simulation. Here, low-precision computation
requires much lower resources than high-precision computation. Several machine
learning (ML) techniques proposed recently have been successful in correcting
the errors arising from spatial discretization. In this work, we extend these
techniques to improve Computational Fluid Dynamics (CFD) simulations performed
using low numerical precision. We first quantify the precision related errors
accumulated in a Kolmogorov forced turbulence test case. Subsequently, we
employ a Convolutional Neural Network together with a fully differentiable
numerical solver performing 16-bit arithmetic to learn a tightly-coupled ML-CFD
hybrid solver. Compared to the 16-bit solver, we demonstrate the efficacy of
the ML-CFD hybrid solver towards reducing the error accumulation in the
velocity field and improving the kinetic energy spectrum at higher frequencies.

categories: physics.flu-dyn cs.LG cs.NA math.NA physics.comp-ph


title: Gamma conjecture I for blowing up $\mathbb{P}^n$ along $\mathbb{P}^r$

abstract:
  Consider the Fano manifold $X$ formed by blowing up $\mathbb{P}^n$ along its
linear subspace $\mathbb{P}^r$, we check the conifold conditions [3, 1] for its
mirror Laurent polynomial $f$, which can imply that $X$ satisfies both
Conjecture $\mathcal{O}$ and Gamma conjecture I by Galkin-Golyshev-Iritani [2].


title:
Towards Compositional Adversarial Robustness: Generalizing Adversarial
  Training to Composite Semantic Perturbations

abstract:
  Model robustness against adversarial examples of single perturbation type
such as the $\ell_{p}$-norm has been widely studied, yet its generalization to
more realistic scenarios involving multiple semantic perturbations and their
composition remains largely unexplored. In this paper, we firstly propose a
novel method for generating composite adversarial examples. By utilizing
component-wise projected gradient descent and automatic attack-order
scheduling, our method can find the optimal attack composition. We then propose
\textbf{generalized adversarial training} (\textbf{GAT}) to extend model
robustness from $\ell_{p}$-norm to composite semantic perturbations, such as
the combination of Hue, Saturation, Brightness, Contrast, and Rotation. The
results on ImageNet and CIFAR-10 datasets show that GAT can be robust not only
to any single attack but also to any combination of multiple attacks. GAT also
outperforms baseline $\ell_{\infty}$-norm bounded adversarial training
approaches by a significant margin.


title: Data-Driven Online Interactive Bidding Strategy for Demand Response

abstract:
  Demand response (DR), as one of the important energy resources in the
future's grid, provides the services of peak shaving, enhancing the efficiency
of renewable energy utilization with a short response period, and low cost.
Various categories of DR are established, e.g. automated DR, incentive DR,
emergency DR, and demand bidding. However, with the practical issue of the
unawareness of residential and commercial consumers' utility models, the
researches about demand bidding aggregator involved in the electricity market
are just at the beginning stage. For this issue, the bidding price and bidding
quantity are two required decision variables while considering the
uncertainties due to the market and participants. In this paper, we determine
the bidding and purchasing strategy simultaneously employing the smart meter
data and functions. A two-agent deep deterministic policy gradient method is
developed to optimize the decisions through learning historical bidding
experiences. The online learning further utilizes the daily newest bidding
experience attained to ensure trend tracing and self-adaptation. Two
environment simulators are adopted for testifying the robustness of the model.
The results prove that when facing diverse situations the proposed model can
earn the optimal profit via off/online learning the bidding rules and robustly
making the proper bid.


title:
Learning Robust Convolutional Neural Networks with Relevant Feature
  Focusing via Explanations

abstract:
  Existing image recognition techniques based on convolutional neural networks
(CNNs) basically assume that the training and test datasets are sampled from
i.i.d distributions. However, this assumption is easily broken in the real
world because of the distribution shift that occurs when the co-occurrence
relations between objects and backgrounds in input images change. Under this
type of distribution shift, CNNs learn to focus on features that are not
task-relevant, such as backgrounds from the training data, and degrade their
accuracy on the test data. To tackle this problem, we propose relevant feature
focusing (ReFF). ReFF detects task-relevant features and regularizes CNNs via
explanation outputs (e.g., Grad-CAM). Since ReFF is composed of post-hoc
explanation modules, it can be easily applied to off-the-shelf CNNs.
Furthermore, ReFF requires no additional inference cost at test time because it
is only used for regularization while training. We demonstrate that CNNs
trained with ReFF focus on features relevant to the target task and that ReFF
improves the test-time accuracy.


title: Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network

abstract:
  t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data
visualization method in classical machine learning. It maps the data from the
high-dimensional space into a low-dimensional space, especially a
two-dimensional plane, while maintaining the relationship, or similarities,
between the surrounding points. In t-SNE, the initial position of the
low-dimensional data is randomly determined, and the visualization is achieved
by moving the low-dimensional data to minimize a cost function. Its variant
called parametric t-SNE uses neural networks for this mapping. In this paper,
we propose to use quantum neural networks for parametric t-SNE to reflect the
characteristics of high-dimensional quantum data on low-dimensional data. We
use fidelity-based metrics instead of Euclidean distance in calculating
high-dimensional data similarity. We visualize both classical (Iris dataset)
and quantum (time-depending Hamiltonian dynamics) data for classification
tasks. Since this method allows us to represent a quantum dataset in a higher
dimensional Hilbert space by a quantum dataset in a lower dimension while
keeping their similarity, the proposed method can also be used to compress
quantum data for further quantum machine learning.


title:
A multiscale spatiotemporal approach for smallholder irrigation
  detection

abstract:
  In presenting an irrigation detection methodology that leverages multiscale
satellite imagery of vegetation abundance, this paper introduces a process to
supplement limited ground-collected labels and ensure classifier applicability
in an area of interest. Spatiotemporal analysis of MODIS 250m Enhanced
Vegetation Index (EVI) timeseries characterizes native vegetation phenologies
at regional scale to provide the basis for a continuous phenology map that
guides supplementary label collection over irrigated and non-irrigated
agriculture. Subsequently, validated dry season greening and senescence cycles
observed in 10m Sentinel-2 imagery are used to train a suite of classifiers for
automated detection of potential smallholder irrigation. Strategies to improve
model robustness are demonstrated, including a method of data augmentation that
randomly shifts training samples; and an assessment of classifier types that
produce the best performance in withheld target regions. The methodology is
applied to detect smallholder irrigation in two states in the Ethiopian
highlands, Tigray and Amhara. Results show that a transformer-based neural
network architecture allows for the most robust prediction performance in
withheld regions, followed closely by a CatBoost random forest model. Over
withheld ground-collection survey labels, the transformer-based model achieves
96.7% accuracy over non-irrigated samples and 95.9% accuracy over irrigated
samples. Over a larger set of samples independently collected via the
introduced method of label supplementation, non-irrigated and irrigated labels
are predicted with 98.3% and 95.5% accuracy, respectively. The detection model
is then deployed over Tigray and Amhara, revealing crop rotation patterns and
year-over-year irrigated area change. Predictions suggest that irrigated area
in these two states has decreased by approximately 40% from 2020 to 2021.


title: Remarks on Exact G$_{2}$-Structures on Compact Manifolds

abstract:
  An important open question in G$_{2}$ geometry concerns whether or not a
compact seven-manifold can support an exact G$_{2}$-Structure. Given the
significance of this question we initiate a study of exact G$_{2}$-Structures
on compact manifolds. We focus on exact G$_2$-Structures subject to no
additional constraints but we also consider the relationship between the exact
condition and other conditions for closed G$_{2}$-Structures such as the
Extremally Ricci-Pinched and Laplacian Soliton conditions.


title:
Distillation with Contrast is All You Need for Self-Supervised Point
  Cloud Representation Learning

abstract:
  In this paper, we propose a simple and general framework for self-supervised
point cloud representation learning. Human beings understand the 3D world by
extracting two levels of information and establishing the relationship between
them. One is the global shape of an object, and the other is the local
structures of it. However, few existing studies in point cloud representation
learning explored how to learn both global shapes and local-to-global
relationships without a specified network architecture. Inspired by how human
beings understand the world, we utilize knowledge distillation to learn both
global shape information and the relationship between global shape and local
structures. At the same time, we combine contrastive learning with knowledge
distillation to make the teacher network be better updated. Our method achieves
the state-of-the-art performance on linear classification and multiple other
downstream tasks. Especially, we develop a variant of ViT for 3D point cloud
feature extraction, which also achieves comparable results with existing
backbones when combined with our framework, and visualization of the attention
maps show that our model does understand the point cloud by combining the
global shape information and multiple local structural information, which is
consistent with the inspiration of our representation learning method. Our code
will be released soon.


title:
Finding the stable structures of 2D hexagonal materials with Bayesian
  optimization: Beyond the structural relationship with 3D crystals in
  weakly-bonded binary systems

abstract:
  The graphene-graphite relationship in structural geometry is a basic
principle to predict novel two-dimensional (2D) materials. Here, we demonstrate
that this is not the case in binary metallic systems. We use the Bayesian
optimization framework combined with the density-functional theory approach to
determine the stable configuration of atomic species on a hexagonal plane. We
show that the optimized structure of 2D Cu-Au exhibits the hexagonal lattice of
a hexagonal ring of Cu atoms containing one Au atom, where the number of the Cu
atoms is larger than that of the Au atoms in the unit cell, which is difficult
to speculate from the atomic distribution of CuAu in the L1$_0$ structure. We
also show that 2D Cu-$X$ with $X=$ Be, Zn, and Pd have hexagonal or elongated
rings containing different atoms in the unit cell. Based on the binary
Lennard-Jones model, we propose that such structures can appear for
weakly-bonded systems located in between the phase-separated and
strongly-bonded systems with the interatomic interaction energy between
different species.


title: Motion-Aware Transformer For Occluded Person Re-identification

abstract:
  Recently, occluded person re-identification(Re-ID) remains a challenging task
that people are frequently obscured by other people or obstacles, especially in
a crowd massing situation. In this paper, we propose a self-supervised deep
learning method to improve the location performance for human parts through
occluded person Re-ID. Unlike previous works, we find that motion information
derived from the photos of various human postures can help identify major human
body components. Firstly, a motion-aware transformer encoder-decoder
architecture is designed to obtain keypoints heatmaps and part-segmentation
maps. Secondly, an affine transformation module is utilized to acquire motion
information from the keypoint detection branch. Then the motion information
will support the segmentation branch to achieve refined human part segmentation
maps, and effectively divide the human body into reasonable groups. Finally,
several cases demonstrate the efficiency of the proposed model in
distinguishing different representative parts of the human body, which can
avoid the background and occlusion disturbs. Our method consistently achieves
state-of-the-art results on several popular datasets, including occluded,
partial, and holistic.


title: K3 surfaces with Picard number two

abstract:
  It is known that an automorphism group of a K3 surface with Picard number two
is either infinite cyclic group or infinite dihedral group if it is infinite.
In this paper, we study the generators of an automorphism group. We use the
eigenvector corresponding to the spectral radius of an automorphism of infinite
order to determine the generators. We also determine whether an automorphism
group is the infinite cyclic group or the infinite dihedral group with several
examples.


title: Regulatory Instruments for Fair Personalized Pricing

abstract:
  Personalized pricing is a business strategy to charge different prices to
individual consumers based on their characteristics and behaviors. It has
become common practice in many industries nowadays due to the availability of a
growing amount of high granular consumer data. The discriminatory nature of
personalized pricing has triggered heated debates among policymakers and
academics on how to design regulation policies to balance market efficiency and
equity. In this paper, we propose two sound policy instruments, i.e., capping
the range of the personalized prices or their ratios. We investigate the
optimal pricing strategy of a profit-maximizing monopoly under both regulatory
constraints and the impact of imposing them on consumer surplus, producer
surplus, and social welfare. We theoretically prove that both proposed
constraints can help balance consumer surplus and producer surplus at the
expense of total surplus for common demand distributions, such as uniform,
logistic, and exponential distributions. Experiments on both simulation and
real-world datasets demonstrate the correctness of these theoretical results.
Our findings and insights shed light on regulatory policy design for the
increasingly monopolized business in the digital era.