title: Deep learning-based reconstruction of highly accelerated 3D MRI

abstract:
  Purpose: To accelerate brain 3D MRI scans by using a deep learning method for
reconstructing images from highly-undersampled multi-coil k-space data
  Methods: DL-Speed, an unrolled optimization architecture with dense
skip-layer connections, was trained on 3D T1-weighted brain scan data to
reconstruct complex-valued images from highly-undersampled k-space data. The
trained model was evaluated on 3D MPRAGE brain scan data
retrospectively-undersampled with a 10-fold acceleration, compared to a
conventional parallel imaging method with a 2-fold acceleration. Scores of SNR,
artifacts, gray/white matter contrast, resolution/sharpness, deep gray-matter,
cerebellar vermis, anterior commissure, and overall quality, on a 5-point
Likert scale, were assessed by experienced radiologists. In addition, the
trained model was tested on retrospectively-undersampled 3D T1-weighted LAVA
(Liver Acquisition with Volume Acceleration) abdominal scan data, and
prospectively-undersampled 3D MPRAGE and LAVA scans in three healthy volunteers
and one, respectively.
  Results: The qualitative scores for DL-Speed with a 10-fold acceleration were
higher than or equal to those for the parallel imaging with 2-fold
acceleration. DL-Speed outperformed a compressed sensing method in quantitative
metrics on retrospectively-undersampled LAVA data. DL-Speed was demonstrated to
perform reasonably well on prospectively-undersampled scan data, realizing a
2-5 times reduction in scan time.
  Conclusion: DL-Speed was shown to accelerate 3D MPRAGE and LAVA with up to a
net 10-fold acceleration, achieving 2-5 times faster scans compared to
conventional parallel imaging and acceleration, while maintaining diagnostic
image quality and real-time reconstruction. The brain scan data-trained
DL-Speed also performed well when reconstructing abdominal LAVA scan data,
demonstrating versatility of the network.


title: Optimizing fog harvesting by biomimicry

abstract:
  Inspired by the stenocara beetle, we study an ideal flat surface composed of
a regular array of hydrophilic circular patches in a hydrophobic matrix on an
incline of tilt $\alpha$ with respect to the horizontal. Based on an exact
solution of the Laplace-Young equation at first order in the Bond number, the
liquid storage capacity of the surface is maximized as function of the patch
radius, for suitable ranges of hydrophilic and hydrophobic contact angles, for
tilt angles such as $45^{\circ}$ or $90^\circ$. It is found that the optimal
radius equally prevents dewetting from the top of the patches and overflow at
the bottom. These theoretical considerations are validated by several
experiments for the glass/octadecyltrichlorosilane (OTS) system involving
different patch sizes and different inclinations. In a simple dynamical model,
taking into account the flux of fog onto the surface or condensation on a
suitably cooled surface, we find that the conditions for maximum harvest agree
with the ones of maximum static storage. The method could be developed for drop
storage and drop transport applications such as water-harvesting systems.


title: SparseChem: Fast and accurate machine learning model for small molecules

abstract:
  SparseChem provides fast and accurate machine learning models for biochemical
applications. Especially, the package supports very high-dimensional sparse
inputs, e.g., millions of features and millions of compounds. It is possible to
train classification, regression and censored regression models, or combination
of them from command line. Additionally, the library can be accessed directly
from Python. Source code and documentation is freely available under MIT
License on GitHub.


title: Resolution of singular fibers of an $S^1$-manifold

abstract:
  In this paper, we present a construction of resolution of discrete singular
fibers of a closed $5$-manifold that admits a locally free $S^1$-action, and
prove its compatibility with the resolution of cyclic surface singularities in
the quotient space by the $S^1$-action.


title: A note on the critical Laplace Equation and Ricci curvature

abstract:
  We study strictly positive solutions to the critical Laplace equation \[ -
\Delta u = n(n-2) u^{\frac{n+2}{n-2}}, \] decaying at most like $d(o,
x)^{-(n-2)/2}$, on complete noncompact manifolds $(M, g)$ with nonnegative
Ricci curvature, of dimension $n \geq 3$. We prove that, under an additional
mild assumption on the volume growth, such a solution does not exist, unless
$(M, g)$ is isometric to $\mathbb{R}^n$ and $u$ is a Talenti function. The
method employs an elementary analysis of a suitable function defined along the
level sets of $u$.


title:
Assessing and mitigating systematic errors in forest attribute maps
  utilizing harvester and airborne laser scanning data

abstract:
  Cut-to-length harvesters collect useful information for modeling
relationships between forest attributes and airborne laser scanning (ALS) data.
However, harvesters operate in mature forests, which may introduce selection
biases and systematic errors in harvester data-based forest attribute maps. We
fitted regression models (harvester models) for volume (V), height (HL), stem
frequency (N), above-ground biomass, basal area, and quadratic mean diameter
(QMD) using harvester and ALS data. The performances of the harvester models
were evaluated using Norwegian national forest inventory plots in an 8.7Mha
study area. We estimated the bias of large-area synthetic estimators and
compared efficiencies of model-assisted (MA) estimators with field data-based
estimators. The harvester models performed better in productive than
unproductive forests, but systematic errors occurred in both. The use of the MA
estimators resulted in efficiency gains that were the largest for HL (relative
efficiency, RE=6.0) and the smallest for QMD (RE=1.5). The bias of the
synthetic estimator was largest for N (39%) and smallest for V (1%). The latter
was due to an overestimation of deciduous and an underestimation of spruce
forest that by chance balanced. We conclude that a probability sample of
reference observations may be required to ensure the unbiasedness of estimators
utilizing harvester data.


title: A database of MMS bow shock crossings compiled using machine learning

abstract:
  Identifying collisionless shock crossings in data sent from spacecraft has so
far been done manually. It is a tedious job that shock physicists have to go
through if they want to conduct case studies or perform statistical studies. We
use a machine learning approach to automatically identify shock crossings from
the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of those
crossings including various spacecraft related and shock related parameters for
each event. Furthermore, we show that the shocks in the database have
properties that are spread out both in real space and parameter space. We also
present a possible science application of the database by looking for
correlations between ion acceleration efficiency at shocks and different shock
parameters such as $\theta_{Bn}$ and $M_A$. Furthermore, we investigate
statistically the ion acceleration efficiency. We find no clear correlation
between the acceleration efficiency and $M_A$ and we find that quasi-parallel
shocks are more efficient at accelerating ions.


title: A fermionic portal to a non-abelian dark sector

abstract:
  We introduce a new class of renormalisable models, consisting of a dark
$SU(2)_D$ gauge sector connected to the Standard Model (SM) through a
Vector-Like (VL) fermion mediator, not requiring a Higgs portal, in which a
massive vector boson is the Dark Matter (DM) candidate. These models are
labelled Fermion Portal Vector Dark Matter (FPVDM). Multiple realisations are
possible, depending on the properties of the VL partner and of the scalar
potential. One example is discussed in detail. FPVDM models have a large number
of applications in collider and non-collider experiments, depending on the
mediator sector.


title:
Reliable IoT Firmware Updates: A Large-scale Mesh Network Performance
  Investigation

abstract:
  Internet of Things (IoT) networks require regular firmware updates to ensure
enhanced security and stability. As we move towards methodologies of codifying
security and policy decisions and exchanging them over IoT large-scale
deployments (security-as-a-code), these demands should be considered a routine
operation. However, rolling out firmware updates to large-scale networks
presents a crucial challenge for constrained wireless environments with large
numbers of IoT devices. This paper initially investigates how the current
state-of-the-art protocols operate in such adverse conditions by measuring
various Quality-of-Service (QoS) Key Performance Indicators (KPIs) of the
shared wireless medium. We later discuss how Concurrent Transmissions (CT) can
extend the scalability of IoT protocols and ensure reliable firmware roll-outs
over large geographical areas. Measuring KPIs such as the mesh join time, the
throughput, and the number of nodes forming a network, we provide great insight
into how an IoT environment will behave under a large-scale firmware roll-out.
Finally, we conducted our performance investigation over the UMBRELLA platform,
a real-world IoT testbed deployed in Bristol, UK. This ensures our findings
represent a realistic IoT scenario and meet the strict QoS requirements of
today's IoT applications.


title:
Signatures of shape phase transitions in krypton isotopes based on the
  relativistic energy density functionals

abstract:
  Spectroscopic properties that characterize the shape phase transitions in
krypton isotopes with the mass $A\approx80$ region are investigated within the
framework of the nuclear density functional theory. Triaxial quadrupole
constrained self-consistent mean-field calculations using relativistic energy
density functionals and a pairing interaction are carried out for the even-even
nuclei $^{76-86}$Kr. The spectroscopic properties are computed by solving the
triaxial quadrupole collective Hamiltonian, the ingredients of which, i.e., the
moments of inertia, mass parameters, and collective potential, are completely
determined by using the SCMF solutions as microscopic inputs. Systematic
behaviours of the SCMF potential energy surfaces, the corresponding low-energy
spectra, electric quadrupole and monopole transition probabilities, and the
fluctuations in the triaxial quadrupole deformations indicate evolution of the
underlying nuclear structure as functions of the neutron number, that is
characterized by a considerable degree of shape mixing. A special attention is
paid to the transitional nucleus $^{82}$Kr, which has been recently identified
experimentally as an empirical realization of the E(5) critical-point symmetry.


title:
Correspondence on ACMG STATEMENT: ACMG SF v3.0 list for reporting of
  secondary findings in clinical exome and genome sequencing: a policy
  statement of the American College of Medical Genetics and Genomics (ACMG) by
  Miller et al

abstract:
  We were interested to read the recent update on recommendations for reporting
of secondary findings in clinical sequencing1, and the accompanying updated
list of genes in which secondary findings should be sought (ACMG SF v3.0)2.
Though the authors discuss challenges around incomplete penetrance in
considerable detail, we are concerned that the recommendations do not fully
convey the degree of uncertainty regarding the penetrance of variants in genes
associated with inherited cardiomyopathies, which make up almost a quarter of
the list. Since penetrance is incomplete and age-related, individuals found to
carry variants will often require surveillance, rather than a one-off
definitive diagnostic assessment. There is a lack of evidence regarding
benefits, harms, and healthcare costs associated with opportunistic screening.


title:
Longitudinal dynamics and particle production in relativistic nuclear
  collisions

abstract:
  This work presents a three-dimensional dynamical initialization model for
relativistic heavy-ion collisions, implementing local energy-momentum
conservation and baryon charge fluctuations at string junctions. Constraining
parameters using experimental data from p+p collisions at various collision
energies, the model provides a very good description of the charged hadron and
net proton rapidity distributions in Au+Au collisions from 7.7 to 200 GeV and
Pb+Pb collisions at 8.77 and 17.3 GeV. We demonstrate the importance of
fluctuations of baryon densities to string junctions for describing net-proton
distributions at collision energies of 62.4 and 200 GeV. Including this
improved baryon stopping description along with the requirement of strangeness
neutrality also yields a good description of identified particle yields as
functions of the collision energy above 7.7 GeV. We further study asymmetric
p+Al and (p, d, $^3$He)+Au collisions at the top RHIC energy and p+Pb, Xe+Xe,
and Pb+Pb collisions at LHC energies. We identify the produced particle
rapidity distributions in asymmetric collision systems as particularly useful
for constraining models of the early-time longitudinal dynamics.


title:
The Cross-evaluation of Machine Learning-based Network Intrusion
  Detection Systems

abstract:
  Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine
Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations
requiring data where benign and malicious samples are clearly labelled. Such
labels demand costly expert knowledge, resulting in a lack of real deployments,
as well as on papers always relying on the same outdated data. The situation
improved recently, as some efforts disclosed their labelled datasets. However,
most past works used such datasets just as a 'yet another' testbed, overlooking
the added potential provided by such availability.
  In contrast, we promote using such existing labelled data to cross-evaluate
ML-NIDS. Such approach received only limited attention and, due to its
complexity, requires a dedicated treatment. We hence propose the first
cross-evaluation model. Our model highlights the broader range of realistic
use-cases that can be assessed via cross-evaluations, allowing the discovery of
still unknown qualities of state-of-the-art ML-NIDS. For instance, their
detection surface can be extended--at no additional labelling cost. However,
conducting such cross-evaluations is challenging. Hence, we propose the first
framework, XeNIDS, for reliable cross-evaluations based on Network Flows. By
using XeNIDS on six well-known datasets, we demonstrate the concealed
potential, but also the risks, of cross-evaluations of ML-NIDS.


title: Optically-induced Umklapp shift currents in striped cuprates

abstract:
  Motivated by recent experiments that observed low-frequency second-order
optical responses in doped striped superconductors, here we investigate the
nonlinear electrodynamics of systems exhibiting a charge density wave (CDW)
order parameter. Due to the Bragg scattering off the CDW order, an incoming
spatially homogeneous electric field in addition to zero momentum current
generates Umklapp currents that are modulated in space at momenta of the
reciprocal CDW lattice. In particular, here we predict and microscopically
evaluate the Umklapp shift current, a finite momentum analog of the regular
shift current which represents the second-order optical process that
downconverts homogeneous AC electric field into low-frequency, zero momentum
current. Specifically, we evaluate real-time response functions within
mean-field theory via the Keldysh technique and use the Peierls substitution to
compute observables at finite momenta in lattice models. We find that systems
with certain lattice symmetries (such as inversion symmetry), where the regular
shift current is disallowed, may give rise to the Umklapp one. We apply our
framework to investigate lattice symmetries in layered materials with
helical-like stripes and show that both types of shift currents provide insight
into the nature of intertwined phases of matter. Finally, we discuss the
relation of our findings to recent experiments in striped superconductors.


title:
Magnetochemical effects on phase stability and vacancy formation in fcc
  Fe-Ni alloys

abstract:
  We investigate phase stability and vacancy formation in fcc Fe-Ni alloys over
a broad composition-temperature range, via a density functional theory
parametrized effective interaction model, which includes explicitly spin and
chemical variables. On-lattice Monte Carlo simulations based on this model are
used to predict the temperature evolution of the magnetochemical phase. The
experimental composition-dependent Curie and chemical order-disorder transition
temperatures are successfully predicted. We point out a significant effect of
chemical and magnetic orders on the magnetic and chemical transitions,
respectively. The resulting phase diagram shows a magnetically driven phase
separation around 10-40% Ni and 570-700 K, between ferromagnetic and
paramagnetic solid solutions, in agreement with experimental observations. We
compute vacancy formation magnetic free energy as a function of temperature and
alloy composition. We identify opposite magnetic and chemical disordering
effects on vacancy formation in the alloys with 50% and 75% Ni. We find that
thermal magnetic effects on vacancy formation are much larger in concentrated
Fe-Ni alloys than in fcc Fe and Ni due to a stronger magnetic interaction.


title: Reevaluating COVID-19 Mandates using Tensor Completion

abstract:
  We propose a new method that uses tensor completion to estimate causal
effects with multivariate longitudinal data -- data in which multiple outcomes
are observed for each unit and time period. Our motivation is to estimate the
number of COVID-19 fatalities prevented by government mandates such as travel
restrictions, mask-wearing directives, and vaccination requirements. In
addition to COVID-19 fatalities, we also observe the number of fatalities from
other diseases and injuries. The proposed method arranges the data as a tensor
with three dimensions -- unit, time, and outcome -- and uses tensor completion
to impute the missing counterfactual outcomes. We use the method to investigate
an empirical paradox in which observational studies estimate the benefit of
mask-wearing directives to be five times higher than a randomized controlled
trial (cf. Talic et al., BMJ, 2021; Abaluck et al., Science, 2021); when
applied to observational data, our estimate is more consistent with the
randomized controlled trial, suggesting mandates prevent fewer fatalities than
reported by the typical observational study. We then provide several
justifications. Most notably, we show that under standard regularity
conditions, combining multiple outcomes improves the accuracy of counterfactual
imputations. Our method can be applied whenever multivariate longitudinal data
are available, but we believe it is particularly timely as governments
increasingly rely on this data to choose between mandates. Governments should
use all available data to choose the mandates that best balance public health
and individual liberties as new waves of COVID-19 variants are expected to
follow delta and omicron.


title: S-1-absorbing primary submodules

abstract:
  In this work, we introduce the notion of $S$-1-absorbing primary submodule as
an extension of 1-absorbing primary submodule. Let $S$ be a multiplicatively
closed subset of a ring $R$ and $M$ be an $R$-module. A submodule $N$ of $M$
with $(N:_{R}M)\cap S=\emptyset$ is said to be $S$-1-absorbing primary if
whenever $abm\in N$ for some non-unit $a,b\in R$ and $m\in M$, then either
$sab\in(N:_{R}M)$ or $sm\in M$-$rad(N)$. We examine several properties of this
concept and provide some characterizations. In addition, $S$-1-absorbing
primary avoidance theorem and $S $-1-absorbing primary property for
idealization and amalgamation are presented.


title:
Doppler shifts of spectral lines formed in the solar transition region
  and corona

abstract:
  Context. Emission lines formed in the transition region and corona show
dominantly redshifts and blueshifts, respectively. Aims. We investigate the
Doppler shifts in a 3D radiation MHD model of the quiet Sun and compare these
to observed properties. We concentrate on Si IV 1394 A originating in the
transition region and examine the Doppler shifts of several other spectral
lines at different formation temperatures. Methods. We construct a radiation
MHD model extending from the upper convection zone to the lower corona using
the MURaM code. In this quiet Sun model the magnetic field is self-consistently
maintained by the action of a small-scale dynamo. We synthesize the profiles of
several optically thin emission lines, formed at temperatures from the
transition region into the corona. We investigate the spatial structure and
coverage of red- and blueshifts and how this changes with line-formation
temperature. Results. The model successfully reproduces the observed change of
average net Doppler shifts from red- to blueshifted from the transition region
into the corona. In particular, the model shows a clear imbalance of area
coverage of red- vs. blueshifts in the transition region of ca. 80% to 20%. We
determine that (at least) four processes generate the systematic Doppler shifts
in our model, including pressure enhancement in the transition region,
transition region brightenings unrelated to coronal emission, boundaries
between cold and hot plasma, and siphon-type flows. Conclusions. We show that
there is not a single process that is responsible for the observed net Doppler
shifts in the transition region and corona. Because current 3D MHD models do
not yet fully capture the evolution of spicules, one of the key ingredients of
the chromosphere, most probably these have still to be added to the list of
processes responsible for the persistent Doppler shifts.


title:
FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation
  and Prediction

abstract:
  Modern scientific research and applications very often encounter "fragmentary
data" which brings big challenges to imputation and prediction. By leveraging
the structure of response patterns, we propose a unified and flexible framework
based on Generative Adversarial Nets (GAN) to deal with fragmentary data
imputation and label prediction at the same time. Unlike most of the other
generative model based imputation methods that either have no theoretical
guarantee or only consider Missing Completed At Random (MCAR), the proposed
FragmGAN has theoretical guarantees for imputation with data Missing At Random
(MAR) while no hint mechanism is needed. FragmGAN trains a predictor with the
generator and discriminator simultaneously. This linkage mechanism shows
significant advantages for predictive performances in extensive experiments.


title: PION: Simulations of Wind-Blown Nebulae

abstract:
  We present an overview of PION, an open-source software project for solving
radiation-magnetohydrodynamics equations on a nested grid, aimed at modelling
asymmetric nebulae around massive stars. A new implementation of hybrid
OpenMP/MPI parallel algorithms is briefly introduced, and improved scaling is
demonstrated compared with the current release version. Three-dimensional
simulations of an expanding nebula around a Wolf-Rayet star are then presented
and analysed, similar to previous 2D simulations in the literature. The
evolution of the emission measure of the gas and the X-ray surface brightness
are calculated as a function of time, and some qualitative comparison with
observations is made.


title:
Align-Deform-Subtract: An Interventional Framework for Explaining Object
  Differences

abstract:
  Given two object images, how can we explain their differences in terms of the
underlying object properties? To address this question, we propose
Align-Deform-Subtract (ADS) -- an interventional framework for explaining
object differences. By leveraging semantic alignments in image-space as
counterfactual interventions on the underlying object properties, ADS
iteratively quantifies and removes differences in object properties. The result
is a set of "disentangled" error measures which explain object differences in
terms of their underlying properties. Experiments on real and synthetic data
illustrate the efficacy of the framework.


title: Structured Multi-task Learning for Molecular Property Prediction

abstract:
  Multi-task learning for molecular property prediction is becoming
increasingly important in drug discovery. However, in contrast to other
domains, the performance of multi-task learning in drug discovery is still not
satisfying as the number of labeled data for each task is too limited, which
calls for additional data to complement the data scarcity. In this paper, we
study multi-task learning for molecular property prediction in a novel setting,
where a relation graph between tasks is available. We first construct a dataset
including around 400 tasks as well as a task relation graph. Then to better
utilize such relation graph, we propose a method called SGNN-EBM to
systematically investigate the structured task modeling from two perspectives.
(1) In the \emph{latent} space, we model the task representations by applying a
state graph neural network (SGNN) on the relation graph. (2) In the
\emph{output} space, we employ structured prediction with the energy-based
model (EBM), which can be efficiently trained through noise-contrastive
estimation (NCE) approach. Empirical results justify the effectiveness of
SGNN-EBM. Code is available on https://github.com/chao1224/SGNN-EBM.


title:
Robust Federated Learning Against Adversarial Attacks for Speech Emotion
  Recognition

abstract:
  Due to the development of machine learning and speech processing, speech
emotion recognition has been a popular research topic in recent years. However,
the speech data cannot be protected when it is uploaded and processed on
servers in the internet-of-things applications of speech emotion recognition.
Furthermore, deep neural networks have proven to be vulnerable to
human-indistinguishable adversarial perturbations. The adversarial attacks
generated from the perturbations may result in deep neural networks wrongly
predicting the emotional states. We propose a novel federated adversarial
learning framework for protecting both data and deep neural networks. The
proposed framework consists of i) federated learning for data privacy, and ii)
adversarial training at the training stage and randomisation at the testing
stage for model robustness. The experiments show that our proposed framework
can effectively protect the speech data locally and improve the model
robustness against a series of adversarial attacks.


title: Non-equilibrium molecular geometries in graph neural networks

abstract:
  Graph neural networks have become a powerful framework for learning complex
structure-property relationships and fast screening of chemical compounds.
Recently proposed methods have demonstrated that using 3D geometry information
of the molecule along with the bonding structure can lead to more accurate
prediction on a wide range of properties. A common practice is to use 3D
geometries computed through density functional theory (DFT) for both training
and testing of models. However, the computational time needed for DFT
calculations can be prohibitively large. Moreover, many of the properties that
we aim to predict can often be obtained with little or no overhead on top of
the DFT calculations used to produce the 3D geometry information, voiding the
need for a predictive model. To be practically useful for high-throughput
chemical screening and drug discovery, it is desirable to work with 3D
geometries obtained using less-accurate but much more efficient non-DFT
methods. In this work we investigate the impact of using non-DFT conformations
in the training and the testing of existing models and propose a data
augmentation method for improving the prediction accuracy of classical
forcefield-derived geometries.


title: Score-Based Generative Models for Molecule Generation

abstract:
  Recent advances in generative models have made exploring design spaces easier
for de novo molecule generation. However, popular generative models like GANs
and normalizing flows face challenges such as training instabilities due to
adversarial training and architectural constraints, respectively. Score-based
generative models sidestep these challenges by modelling the gradient of the
log probability density using a score function approximation, as opposed to
modelling the density function directly, and sampling from it using annealed
Langevin Dynamics. We believe that score-based generative models could open up
new opportunities in molecule generation due to their architectural
flexibility, such as replacing the score function with an SE(3) equivariant
model. In this work, we lay the foundations by testing the efficacy of
score-based models for molecule generation. We train a Transformer-based score
function on Self-Referencing Embedded Strings (SELFIES) representations of 1.5
million samples from the ZINC dataset and use the Moses benchmarking framework
to evaluate the generated samples on a suite of metrics.


title: Gym-saturation: an OpenAI Gym environment for saturation provers

abstract:
  `gym-saturation` is an OpenAI Gym environment for reinforcement learning (RL)
agents capable of proving theorems. Currently, only theorems written in a
formal language of the Thousands of Problems for Theorem Provers (TPTP) library
in clausal normal form (CNF) are supported. `gym-saturation` implements the
'given clause' algorithm (similar to the one used in Vampire and E Prover).
Being written in Python, `gym-saturation` was inspired by PyRes. In contrast to
the monolithic architecture of a typical Automated Theorem Prover (ATP),
`gym-saturation` gives different agents opportunities to select clauses
themselves and train from their experience. Combined with a particular agent,
`gym-saturation` can work as an ATP. Even with a non trained agent based on
heuristics, `gym-saturation` can find refutations for 688 (of 8257) CNF
problems from TPTP v7.5.0.


title:
Multi-robot Cooperative Pursuit via Potential Field-Enhanced
  Reinforcement Learning

abstract:
  It is of great challenge, though promising, to coordinate collective robots
for hunting an evader in a decentralized manner purely in light of local
observations. In this paper, this challenge is addressed by a novel hybrid
cooperative pursuit algorithm that combines reinforcement learning with the
artificial potential field method. In the proposed algorithm, decentralized
deep reinforcement learning is employed to learn cooperative pursuit policies
that are adaptive to dynamic environments. The artificial potential field
method is integrated into the learning process as predefined rules to improve
the data efficiency and generalization ability. It is shown by numerical
simulations that the proposed hybrid design outperforms the pursuit policies
either learned from vanilla reinforcement learning or designed by the potential
field method. Furthermore, experiments are conducted by transferring the
learned pursuit policies into real-world mobile robots. Experimental results
demonstrate the feasibility and potential of the proposed algorithm in learning
multiple cooperative pursuit strategies.


title: Millisecond Time-Scale Measurements of Heat Transfer to Confined He II

abstract:
  We explore transient heat transfer, on the millisecond time-scale, from a
narrow, rectangular stainless steel heater cooled from one side by He II
confined to a channel of 120 $\mu$m depth. The helium is isolated from the
external bath with the exception of two pin-holes of cross section about 10%
that of the channel.
  We measure the temperatures of both the heater strip and the channel helium
during slow-pulse heating that reaches peak power after 9 ms, fast-pulse
heating that reaches peak power after 100 $\mu$s, and step heating that reaches
steady power after 100 $\mu$s.
  Using the steady state Kapitza heat transfer expression at the interface
between heater and helium, and the Gorter-Mellink heat transfer regime in the
helium channel, we obtain excellent agreement between simulation and
measurement during the first 5 ms of slow-pulse tests. Using instead the
measured helium temperature in the Kapitza expression, we obtain excellent
agreement between the simulated and measured heater response during the first
150 ms of slow-pulse tests.
  The same model fails to explain the fast-pulse transient response of the
heater and helium, while it can explain the helium response to a step in
applied power but not the heater response. The steady state Kapitza expression
may therefore not be applicable to heating events that are over within a single
millisecond.


title: ModulE: Module Embedding for Knowledge Graphs

abstract:
  Knowledge graph embedding (KGE) has been shown to be a powerful tool for
predicting missing links of a knowledge graph. However, existing methods mainly
focus on modeling relation patterns, while simply embed entities to vector
spaces, such as real field, complex field and quaternion space. To model the
embedding space from a more rigorous and theoretical perspective, we propose a
novel general group theory-based embedding framework for rotation-based models,
in which both entities and relations are embedded as group elements.
Furthermore, in order to explore more available KGE models, we utilize a more
generic group structure, module, a generalization notion of vector space.
Specifically, under our framework, we introduce a more generic embedding
method, ModulE, which projects entities to a module. Following the method of
ModulE, we build three instantiating models: ModulE$_{\mathbb{R},\mathbb{C}}$,
ModulE$_{\mathbb{R},\mathbb{H}}$ and ModulE$_{\mathbb{H},\mathbb{H}}$, by
adopting different module structures. Experimental results show that
ModulE$_{\mathbb{H},\mathbb{H}}$ which embeds entities to a module over
non-commutative ring, achieves state-of-the-art performance on multiple
benchmark datasets.


title: Language Model-driven Negative Sampling

abstract:
  Knowledge Graph Embeddings (KGEs) encode the entities and relations of a
knowledge graph (KG) into a vector space with a purpose of representation
learning and reasoning for an ultimate downstream task (i.e., link prediction,
question answering). Since KGEs follow closed-world assumption and assume all
the present facts in KGs to be positive (correct), they also require negative
samples as a counterpart for learning process for truthfulness test of existing
triples. Therefore, there are several approaches for creating negative samples
from the existing positive ones through a randomized distribution. This choice
of generating negative sampling affects the performance of the embedding models
as well as their generalization. In this paper, we propose an approach for
generating negative sampling considering the existing rich textual knowledge in
KGs. %The proposed approach is leveraged to cluster other relevant
representations of the entities inside a KG. Particularly, a pre-trained
Language Model (LM) is utilized to obtain the contextual representation of
symbolic entities. Our approach is then capable of generating more meaningful
negative samples in comparison to other state of the art methods. Our
comprehensive evaluations demonstrate the effectiveness of the proposed
approach across several benchmark datasets for like prediction task. In
addition, we show cased our the functionality of our approach on a clustering
task where other methods fall short.


title: Mixed norm spaces and RM(p,q) spaces

abstract:
  In this paper we present the containment relationship between the spaces of
analytic functions with average radial integrability RM(p,q) and a family of
mixed norm spaces.


title: FlexIT: Towards Flexible Semantic Image Translation

abstract:
  Deep generative models, like GANs, have considerably improved the state of
the art in image synthesis, and are able to generate near photo-realistic
images in structured domains such as human faces. Based on this success, recent
work on image editing proceeds by projecting images to the GAN latent space and
manipulating the latent vector. However, these approaches are limited in that
only images from a narrow domain can be transformed, and with only a limited
number of editing operations. We propose FlexIT, a novel method which can take
any input image and a user-defined text instruction for editing. Our method
achieves flexible and natural editing, pushing the limits of semantic image
translation. First, FlexIT combines the input image and text into a single
target point in the CLIP multimodal embedding space. Via the latent space of an
auto-encoder, we iteratively transform the input image toward the target point,
ensuring coherence and quality with a variety of novel regularization terms. We
propose an evaluation protocol for semantic image translation, and thoroughly
evaluate our method on ImageNet. Code will be made publicly available.


title: Data Representativity for Machine Learning and AI Systems

abstract:
  Data representativity is crucial when drawing inference from data through
machine learning models. Scholars have increased focus on unraveling the bias
and fairness in the models, also in relation to inherent biases in the input
data. However, limited work exists on the representativity of samples
(datasets) for appropriate inference in AI systems. This paper analyzes data
representativity in scientific literature related to AI and sampling, and gives
a brief overview of statistical sampling methodology from disciplines like
sampling of physical materials, experimental design, survey analysis, and
observational studies. Different notions of a 'representative sample' exist in
past and present literature. In particular, the contrast between the notion of
a representative sample in the sense of coverage of the input space, versus a
representative sample as a miniature of the target population is of relevance
when building AI systems. Using empirical demonstrations on US Census data, we
demonstrate that the first is useful for providing equality and demographic
parity, and is more robust to distribution shifts, whereas the latter notion is
useful in situations where the purpose is to make historical inference or draw
inference about the underlying population in general, or make better
predictions for the majority in the underlying population. We propose a
framework of questions for creating and documenting data, with data
representativity in mind, as an addition to existing datasheets for datasets.
Finally, we will also like to call for caution of implicit, in addition to
explicit, use of a notion of data representativeness without specific
clarification.


title: Ab-initio cluster approach for high-harmonic generation in liquids

abstract:
  High harmonic generation (HHG) takes place in all phases of matter. In
gaseous atomic and molecular media, it has been extensively studied and is very
well-understood. In solids research is ongoing, but a consensus is forming for
the dominant microscopic HHG mechanisms. In liquids on the other hand, no
established theory yet exit and approaches developed for gases and solids are
generally inapplicable, hindering our current understanding. We develop here a
powerful and reliable ab-initio cluster-based approach for describing the
nonlinear interactions between isotropic bulk liquids and intense laser pulses.
The scheme is based on time-dependent density functional theory and utilizes
several approximations that make it feasible yet accurate in realistic systems.
We demonstrate our approach with HHG calculations in water, ammonia, and
methane liquids, and compare the characteristic response of polar and non-polar
liquids. We identify unique features in the HHG spectra of liquid methane that
could be utilized for ultrafast spectroscopy of its chemical and physical
properties: (i) a structural minima at 15-17eV, and (ii) a well-like shape in
the perturbative region that is reminiscent of a shape resonance. Our results
pave the way to accessible calculations of HHG in liquids and illustrate the
unique nonlinear nature of liquid systems.


title:
A Unified Transformer Framework for Group-based Segmentation:
  Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection

abstract:
  Humans tend to mine objects by learning from a group of images or several
frames of video since we live in a dynamic world. In the computer vision area,
many researches focus on co-segmentation (CoS), co-saliency detection (CoSD)
and video salient object detection (VSOD) to discover the co-occurrent objects.
However, previous approaches design different networks on these similar tasks
separately, and they are difficult to apply to each other, which lowers the
upper bound of the transferability of deep learning frameworks. Besides, they
fail to take full advantage of the cues among inter- and intra-feature within a
group of images. In this paper, we introduce a unified framework to tackle
these issues, term as UFO (Unified Framework for Co-Object Segmentation).
Specifically, we first introduce a transformer block, which views the image
feature as a patch token and then captures their long-range dependencies
through the self-attention mechanism. This can help the network to excavate the
patch structured similarities among the relevant objects. Furthermore, we
propose an intra-MLP learning module to produce self-mask to enhance the
network to avoid partial activation. Extensive experiments on four CoS
benchmarks (PASCAL, iCoseg, Internet and MSRC), three CoSD benchmarks
(Cosal2015, CoSOD3k, and CocA) and four VSOD benchmarks (DAVIS16, FBMS, ViSal
and SegV2) show that our method outperforms other state-of-the-arts on three
different tasks in both accuracy and speed by using the same network
architecture , which can reach 140 FPS in real-time.


title:
Sum Rate Maximization in STAR-RIS Assisted Full-Duplex Communication
  Systems

abstract:
  The sum-rate performance of simultaneous transmitting and reflecting
reconfigurable intelligent surface (STAR-RIS) assisted full-duplex (FD)
communication systems is investigated. The reflection and transmission
coefficients of STAR-RIS elements are optimized for the energy splitting and
mode switching protocols to maximize the weighted sum rate of the system. The
underlying optimization problems are non-convex, and hence, the successive
convex approximation technique has been employed to develop efficient
algorithms to obtain sub-optimal solutions. Thereby, the maximum average
weighted sum rate and corresponding coefficients at the STAR-RIS subject to
predefined threshold rates and unit-modulus constraints are quantified. The
performance of the proposed system design is compared with the conventional
reflecting/transmitting-only RISs and half-duplex counterparts via simulations
where it is observed that STAR-RIS can boost the performance of FD systems.


title:
Efficiently computing excitations of complex systems: linear-scaling
  time-dependent embedded mean-field theory in implicit solvent

abstract:
  Quantum embedding schemes have the potential to significantly reduce the
computational cost of first principles calculations, whilst maintaining
accuracy, particularly for calculations of electronic excitations in complex
systems. In this work, I combine time-dependent embedded mean field theory
(TD-EMFT) with linear-scaling density functional theory and implicit solvation
models, extending previous work within the ONETEP code. This provides a way to
perform multi-level calculations of electronic excitations on very large
systems, where long-range environmental effects, both quantum and classical in
nature, are important. I demonstrate the power of this method by performing
simulations on a variety of systems, including a molecular dimer, a chromophore
in solution, and a doped molecular crystal. This work paves the way for high
accuracy calculations to be performed on large-scale systems that were
previously beyond the reach of quantum embedding schemes.


title: On a linear fused Gromov-Wasserstein distance for graph structured data

abstract:
  We present a framework for embedding graph structured data into a vector
space, taking into account node features and topology of a graph into the
optimal transport (OT) problem. Then we propose a novel distance between two
graphs, named linearFGW, defined as the Euclidean distance between their
embeddings. The advantages of the proposed distance are twofold: 1) it can take
into account node feature and structure of graphs for measuring the similarity
between graphs in a kernel-based framework, 2) it can be much faster for
computing kernel matrix than pairwise OT-based distances, particularly fused
Gromov-Wasserstein, making it possible to deal with large-scale data sets.
After discussing theoretical properties of linearFGW, we demonstrate
experimental results on classification and clustering tasks, showing the
effectiveness of the proposed linearFGW.


title: Entr\'ee-sortie dans le halo d'une courbe lente semi-stable

abstract:
  We consider a slow-fast differential system (SF) in dimension two which
appears in the study of some linear model (LM) with periodic coefficients in
population dynamics. We show existence of "canard solutions" of (SF) along
semi-stable slow curve which explains some stability properties of (LM) when
the period tends to infinity.


title: Defending Black-box Skeleton-based Human Activity Classifiers

abstract:
  Deep learning has been regarded as the `go to' solution for many tasks today,
but its intrinsic vulnerability to malicious attacks has become a major
concern. The vulnerability is affected by a variety of factors including
models, tasks, data, and attackers. Consequently, methods such as Adversarial
Training and Randomized Smoothing have been proposed to tackle the problem in a
wide range of applications. In this paper, we investigate skeleton-based Human
Activity Recognition, which is an important type of time-series data but
under-explored in defense against attacks. Our method is featured by (1) a new
Bayesian Energy-based formulation of robust discriminative classifiers, (2) a
new parameterization of the adversarial sample manifold of actions, and (3) a
new post-train Bayesian treatment on both the adversarial samples and the
classifier. We name our framework Bayesian Energy-based Adversarial Training or
BEAT. BEAT is straightforward but elegant, which turns vulnerable black-box
classifiers into robust ones without sacrificing accuracy. It demonstrates
surprising and universal effectiveness across a wide range of action
classifiers and datasets, under various attacks.


title: Modeling Needs for High Power Target

abstract:
  The next generation of high power targets will use more complex geometries,
novel materials, and new concepts (like flowing granular materials); however,
the current numerical approaches will not be sufficient to converge towards a
reliable target design that satisfies the physical requirements. We will
discuss what can be improved in the next 10 years in target modeling to support
high power (MW class) targets.


title:
Self-consistent dynamical models with a finite extent -- I. The uniform
  density sphere

abstract:
  The standard method to generate dynamical models with a finite extent is to
apply a truncation in binding energy to the distribution function. This
approach has the disadvantages that one cannot choose the density to start
with, that the important dynamical quantities cannot be calculated
analytically, and that a fraction of the possible bound orbits are excluded a
priori. We explore another route and start from a truncation in radius rather
than a truncation in binding energy. We focus on the simplest truncated density
profile, the uniform density sphere. We explore the most common inversion
techniques to generate distribution functions for the uniform density sphere,
corresponding to a large range of possible anisotropy profiles. We find that
the uniform density sphere cannot be supported by the standard isotropic,
constant anisotropy or Osipkov-Merritt models, as all these models are
characterised by negative distribution functions. We generalise the Cuddeford
inversion method to models with a tangential anisotropy and present a
one-parameter family of dynamical models for the uniform density sphere. Each
member of this family is characterised by an anisotropy profile that smoothly
decreases from an arbitrary value $\beta_0\leqslant0$ at the centre to
completely tangential at the outer radius. All models have a positive
distribution function over the entire phase space, and a nonzero occupancy of
all possible bound orbits. This shows that one can generate nontrivial
self-consistent dynamical models based on preset density profile with a finite
extent.


title: Detecting relevant changes in the spatiotemporal mean function

abstract:
  For a spatiotemporal process $\{X_j(s,t) | ~s \in S~,~t \in T \}_{j =1,
\ldots , n} $, where $S$ denotes the set of spatial locations and $T$ the time
domain, we consider the problem of testing for a change in the sequence of mean
functions. In contrast to most of the literature we are not interested in
arbitrarily small changes, but only in changes with a norm exceeding a given
threshold. Asymptotically distribution free tests are proposed, which do not
require the estimation of the long-run spatiotemporal covariance structure. In
particular we consider a fully functional approach and a test based on the
cumulative sum paradigm, investigate the large sample properties of the
corresponding test statistics and study their finite sample properties by means
of simulation study.


title: Index theory of hypoelliptic operators on Carnot manifolds

abstract:
  We study the index theory of hypoelliptic operators on Carnot manifolds --
manifolds whose Lie algebra of vector fields is equipped with a filtration
induced from sub-bundles of the tangent bundle. A Heisenberg pseudodifferential
operator, elliptic in the calculus of van Erp-Yuncken, is hypoelliptic and
Fredholm. Under some geometric conditions, we compute its Fredholm index by
means of operator $K$-theory. These results extend the work of Baum-van Erp
(Acta Mathematica '2014) for co-oriented contact manifolds to a methodology for
solving this index problem geometrically on Carnot manifolds.
  Under the assumption that the Carnot manifold is regular, i.e. has isomorphic
osculating Lie algebras in all fibres, and admits a flat coadjoint orbit, the
methodology derived from Baum-van Erp's work is developed in full detail. In
this case, we develope $K$-theoretical dualities computing the Fredholm index
by means of geometric $K$-homology a la Baum-Douglas. The duality involves a
Hilbert space bundle of flat orbit representations. Explicit solutions to the
index problem for Toeplitz operators and operators of the form
"$\Delta_H+\gamma T$" are computed in geometric $K$-homology, extending results
of Boutet de Monvel and Baum-van Erp, respectively, from co-oriented contact
manifolds to regular polycontact manifolds.


title:
Snowmass White Paper: New ideas for many-body quantum systems from
  string theory and black holes

abstract:
  During the last two decades many new insights into the dynamics of strongly
coupled quantum many-body systems have been obtained using gauge/gravity
duality, with black holes often playing a universal role. In this white paper
we summarize the results obtained and offer some outlook for future
developments, including the ongoing mutually beneficial feedback loop with the
study of more general, not necessarily holographic, quantum many-body systems.

categories: hep-th cond-mat.str-el nlin.CD nucl-th quant-ph


title: Weighted GJMS operators on smooth metric measure spaces

abstract:
  We construct weighted GJMS operators on smooth metric measure spaces, and
prove that they are formally self-adjoint. We also provide factorization
formulas for them in the case of quasi-Einstein spaces and under Gover--Leitner
conditions.


title:
Multi-sublinear operators and their commutators on product generalized
  mixed Morrey spaces

abstract:
  In this paper, we study the boundedness for a large class of multi-sublinear
operators $T_m$ generated by multilinear Calder{\'o}n-Zygmund operators and
their commutators $T^{b}_{m,i}~(i=1,\cdots,m)$ on the product generalized mixed
Morrey spaces $M^{\varphi_1}_{\vec{q_1}}({\Bbb R}^n)\times\cdots\times
M^{\varphi_m}_{\vec{q_m}}({\Bbb R}^n)$. We find the sufficient conditions on
$(\varphi_1,\cdots,\varphi_m,\varphi)$ which ensure the boundedness of the
operator $T_m$ from $M^{\varphi_1}_{\vec{q_1}}({\Bbb R}^n)\times\cdots\times
M^{\varphi_m}_{\vec{q_m}}({\Bbb R}^n)$ to $M^{\varphi}_{\vec{q}}({\Bbb R}^n)$.
Moreover, the sufficient conditions for the boundeness of $T^b_{m,i}$ from
$M^{\varphi_1}_{\vec{q_1}}({\Bbb R}^n)\times\cdots\times
M^{\varphi_m}_{\vec{q_m}}({\Bbb R}^n)$ to $M^{\varphi}_{\vec{q}}({\Bbb R}^n)$
are also studied. As applications, we obtain the boundedness for the
multi-sublinear maximal operator, the multilinear Calder{\'o}n-Zygmund operator
and their commutators on product generalzied mixed Morrey spaces.


title: Spherical Poisson Waves

abstract:
  We introduce a model of Poisson random waves in $\mathbb{S}^{2}$ and we study
Quantitative Central Limit Theorems when both the rate of the Poisson process
and the energy (i.e., frequency) of the waves (eigenfunctions) diverge to
infinity. We consider finite-dimensional distributions, harmonic coefficients
and convergence in law in functional spaces, and we investigate carefully the
interplay between the rates of divergence of eigenvalues and Poisson governing
measures.


title: The shape of sunspots and solar activity cycles

abstract:
  The paper presents the results of the analysis of the geometric
characteristics of sunspots for the period of 19-24 cycles of activity. The
shape of sunspots was studied on the basis of the method of normalization of
images of sunspots to study the average profile of the spot. The deviation of
the shape of sunspots from the axisymmetric configuration is investigated. It
was found that the spots, as a rule, have an ellipsoid shape, and the major
axis of the ellipse has a predominant inclination to the equator, opposite in
the Northern and Southern hemispheres. The angle of inclination of the sunspot
axis corresponds to the angle of inclination of the bipoles in the activity
cycles. The relationship between the shape of sunspots in the current cycle and
the amplitude of the next cycle of activity is found. The greater the
elongation along the longitude of the current cycle of spots, the higher the
next cycle of activity will be.


title: Language Diversity: Visible to Humans, Exploitable by Machines

abstract:
  The Universal Knowledge Core (UKC) is a large multilingual lexical database
with a focus on language diversity and covering over a thousand languages. The
aim of the database, as well as its tools and data catalogue, is to make the
somewhat abstract notion of diversity visually understandable for humans and
formally exploitable by machines. The UKC website lets users explore millions
of individual words and their meanings, but also phenomena of cross-lingual
convergence and divergence, such as shared interlingual meanings, lexicon
similarities, cognate clusters, or lexical gaps. The UKC LiveLanguage
Catalogue, in turn, provides access to the underlying lexical data in a
computer-processable form, ready to be reused in cross-lingual applications.


title:
Ultrahigh Dynamic Range and Low Noise Figure Programmable Integrated
  Microwave Photonic Filter

abstract:
  Microwave photonics (MWP) has adopted a number of important concepts and
technologies over the recent pasts, including photonic integration, versatile
programmability, and techniques for enhancing key radio frequency performance
metrics such as the noise figure and the dynamic range. However, to date, these
aspects have not been achieved simultaneously in a single circuit. Here, we
demonstrate, for the first time, a multi-functional integrated microwave
photonic circuit that enables on-chip programmable filtering functions while
achieving record-high key radio frequency metrics of >120 dB.Hz dynamic range
and 15 dB of noise figure that are previously unreachable. We unlock this
unique feature by versatile complex spectrum tailoring using an all integrated
modulation transformer and a double injection ring resonator as a
multi-function optical filtering component. This work breaks the conventional
and fragmented approach of integration, functionality and performance that
currently prevents the adoption of integrated MWP systems in real applications.


title:
Predicting conversion of mild cognitive impairment to Alzheimer's
  disease

abstract:
  Alzheimer's disease (AD) is the most common age-related dementia. Mild
cognitive impairment (MCI) is the early stage of cognitive decline before AD.
It is crucial to predict the MCI-to-AD conversion for precise management, which
remains challenging due to the diversity of patients. Previous evidence shows
that the brain network generated from diffusion MRI promises to classify
dementia using deep learning. However, the limited availability of diffusion
MRI challenges the model training. In this study, we develop a self-supervised
contrastive learning approach to generate structural brain networks from
routine anatomical MRI under the guidance of diffusion MRI. The generated brain
networks are applied to train a learning framework for predicting the MCI-to-AD
conversion. Instead of directly modelling the AD brain networks, we train a
graph encoder and a variational autoencoder to model the healthy ageing
trajectories from brain networks of healthy controls. To predict the MCI-to-AD
conversion, we further design a recurrent neural networks based approach to
model the longitudinal deviation of patients' brain networks from the healthy
ageing trajectory. Numerical results show that the proposed methods outperform
the benchmarks in the prediction task. We also visualize the model
interpretation to explain the prediction and identify abnormal changes of white
matter tracts.


title:
Ultrafast control of moir\'e pseudo-electromagnetic field in homobilayer
  semiconductors

abstract:
  In long-wavelength moir\'e patterns of homobilayer semiconductors, the layer
pseudospin of electrons is subject to a sizable Zeeman field that is spatially
modulated from the interlayer coupling in moir\'e. By interference of this
spatial modulation with a homogeneous but dynamically tunable component from
out-of-plane electric field, we show that the spatial-temporal profile of the
overall Zeeman field therefore features a topological texture that can be
controlled in an ultrafast timescale by a terahertz field or an interlayer
bias. Such dynamical modulation leads to the emergence of an in-plane electric
field for low energy carriers, which is related to their real space Berry
curvature -- the moir\'e magnetic field -- through the Faraday's law of
induction. These emergent electromagnetic fields, having opposite signs at the
time reversal pair of valleys, can be exploited to manipulate valley and spin
in the moir\'e landscape under the control by a bias pulse or a terahertz
irradiation.


title: Persistent nonlocality in an ultracold-atom environment

abstract:
  We investigate nonlocal quantum correlations arising between multiple
two-level impurity atoms coupled to an ultracold bosonic gas. We find that the
environment-induced dynamics of the impurity subsystem can generate nonlocal
states that are robust against noise and violate a multipartite Bell inequality
when projective spin measurements are made. Genuine multipartite nonlocality is
also observed in a system of three impurities. We find that non-Markovian
effects in the impurity subsystem are crucial for preventing complete loss of
nonlocality and allow for nonlocal correlations to be generated and maintained
for extended periods of time.


title:
Dynamic mode decomposition as an analysis tool for time-dependent
  partial differential equations

abstract:
  The time-dependent fields obtained by solving partial differential equations
in two and more dimensions quickly overwhelm the analytical capabilities of the
human brain. A meaningful insight into the temporal behaviour can be obtained
by using scalar reductions, which, however, come with a loss of spatial detail.
Dynamic Mode Decomposition is a data-driven analysis method that solves this
problem by identifying oscillating spatial structures and their corresponding
frequencies. This paper presents the algorithm and provides a physical
interpretation of the results by applying the decomposition method to a series
of increasingly complex examples.


title:
Pretrained Domain-Specific Language Model for General Information
  Retrieval Tasks in the AEC Domain

abstract:
  As an essential task for the architecture, engineering, and construction
(AEC) industry, information retrieval (IR) from unstructured textual data based
on natural language processing (NLP) is gaining increasing attention. Although
various deep learning (DL) models for IR tasks have been investigated in the
AEC domain, it is still unclear how domain corpora and domain-specific
pretrained DL models can improve performance in various IR tasks. To this end,
this work systematically explores the impacts of domain corpora and various
transfer learning techniques on the performance of DL models for IR tasks and
proposes a pretrained domain-specific language model for the AEC domain. First,
both in-domain and close-domain corpora are developed. Then, two types of
pretrained models, including traditional wording embedding models and
BERT-based models, are pretrained based on various domain corpora and transfer
learning strategies. Finally, several widely used DL models for IR tasks are
further trained and tested based on various configurations and pretrained
models. The result shows that domain corpora have opposite effects on
traditional word embedding models for text classification and named entity
recognition tasks but can further improve the performance of BERT-based models
in all tasks. Meanwhile, BERT-based models dramatically outperform traditional
methods in all IR tasks, with maximum improvements of 5.4% and 10.1% in the F1
score, respectively. This research contributes to the body of knowledge in two
ways: 1) demonstrating the advantages of domain corpora and pretrained DL
models and 2) opening the first domain-specific dataset and pretrained language
model for the AEC domain, to the best of our knowledge. Thus, this work sheds
light on the adoption and application of pretrained models in the AEC domain.


title:
First-principles study of the electronic and optical properties of
  Ho$_{\rm W}$ impurities in tungsten disulfide

abstract:
  The electronic and optical properties of single-layer (SL) tungsten disulfide
(WS$_2$) in the presence of substitutional Holmium impurities (Ho$_{\rm W}$)
are studied. Although Ho is much larger than W, density functional theory (DFT)
including spin-orbit coupling is used to show that Ho:SL WS$_2$ is stable. The
magnetic moment of the Ho impurity is found to be 4.75$\mu_B$ using
spin-dependent DFT. The optical selection rules identified in the optical
spectrum match exactly the optical selection rules derived by means of group
theory. The presence of neutral Ho$_W$ impurities gives rise to localized
impurity states (LIS) with f-orbital character in the band structure. Using the
Kubo-Greenwood formula and Kohn-Sham orbitals we obtain atom-like sharp
transitions in the in-plane and out-of-plane components of the susceptibility
tensor, Im$\chi_{\parallel}$ and Im$\chi_{\perp}$. The optical resonances are
in good agreement with experimental data.


title:
Reachable set for Hamilton-Jacobi equations with non-smooth Hamiltonian
  and scalar conservation laws

abstract:
  We give a full characterization of the range of the operator which
associates, to any initial condition, the viscosity solution at time $T$ of a
Hamilton-Jacobi equation with convex Hamiltonian. Our main motivation is to be
able to treat the case of convex Hamiltonians with no further regularity
assumptions. We give special attention to the case $H(p) = |p|$, for which we
provide a rather geometrical description of the range of the viscosity operator
by means of an interior ball condition on the sublevel sets. From our
characterization of the reachable set, we are able to deduce further results
concerning, for instance, sharp regularity estimates for the reachable
functions, as well as structural properties of the reachable set. The results
are finally adapted to the case of scalar conservation laws in dimension one.


title:
Minisuperspace Quantum Cosmology in Metric and Affine Theories of
  Gravity

abstract:
  Minisuperpace Quantum Cosmology is an approach by which it is possible to
infer initial conditions for dynamical systems which can suitably represent
observable and non-observable universes. Here we discuss theories of gravity
which, from various points of view, extend Einstein's General Relativity.
Specifically, the Hamiltonian formalism for $f(R)$, $f(T)$ and $f(\mathcal{G})$
gravity, with $R$, $T$, and $\mathcal{G}$ being the curvature, torsion and
Gauss--Bonnet scalars, respectively, is developed starting from the
Arnowitt-Deser-Misner approach. The Minisuperspace Quantum Cosmology is derived
for all these models and cosmological solutions are obtained thanks to the
existence of Noether symmetries. The Hartle criterion allows the interpretation
of solutions in view of observable universes.


title:
Parsimonious Bayesian sparse tensor regression using the Tucker
  decomposition

abstract:
  Tensors, or multidimensional data arrays, require dimension reduction in
modeling applications due to their large size. In addition, these structures
typically exhibit inherent sparsity, requiring the use of regularization
methods to properly characterize an association between a covariate and a
response. In this paper, we propose a Bayesian method to parsimoniously model a
scalar response with a tensor-valued covariate using the Tucker tensor
decomposition. This method retains the spatial relationship within a
tensor-valued covariate, while reducing the number of parameters varying within
the model and applying appropriate regularization methods. Simulated data are
analyzed to demonstrate model effectiveness, with comparisons made to both
classical and Bayesian methods. A neuroimaging analysis using data from the
Alzheimer's Data Neuroimaging Initiative is also included.


title:
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a
  Stacked Recurrent Autoencoder Method with Dynamic Thresholding

abstract:
  With substantial recent developments in aviation technologies, Unmanned
Aerial Vehicles (UAVs) are becoming increasingly integrated in commercial and
military operations internationally. Research into the applications of aircraft
data is essential in improving safety, reducing operational costs, and
developing the next frontier of aerial technology. Having an outlier detection
system that can accurately identify anomalous behaviour in aircraft is crucial
for these reasons. This paper proposes a system incorporating a Long Short-Term
Memory (LSTM) Deep Learning Autoencoder based method with a novel dynamic
thresholding algorithm and weighted loss function for anomaly detection of a
UAV dataset, in order to contribute to the ongoing efforts that leverage
innovations in machine learning and data analysis within the aviation industry.
The dynamic thresholding and weighted loss functions showed promising
improvements to the standard static thresholding method, both in
accuracy-related performance metrics and in speed of true fault detection.


title: A Survey on Reinforcement Learning Methods in Character Animation

abstract:
  Reinforcement Learning is an area of Machine Learning focused on how agents
can be trained to make sequential decisions, and achieve a particular goal
within an arbitrary environment. While learning, they repeatedly take actions
based on their observation of the environment, and receive appropriate rewards
which define the objective. This experience is then used to progressively
improve the policy controlling the agent's behavior, typically represented by a
neural network. This trained module can then be reused for similar problems,
which makes this approach promising for the animation of autonomous, yet
reactive characters in simulators, video games or virtual reality environments.
This paper surveys the modern Deep Reinforcement Learning methods and discusses
their possible applications in Character Animation, from skeletal control of a
single, physically-based character to navigation controllers for individual
agents and virtual crowds. It also describes the practical side of training DRL
systems, comparing the different frameworks available to build such agents.


title: Anisotropic expansion, dissipative hydrodynamics from kinetic theory

abstract:
  We consider Kasner space-time describing anisotropic three dimensional
expansion of the fluid and obtain the dissipative evolution equations for shear
stress tensor and energy density from kinetic theory. For this, we use the
iterative solution of relativistic Boltzmann equation with relaxation time
approximation. We show that our results for second and third order evolution
equations reduce to those of one dimensional expansion case under suitable
conditions for the anisotropic parameters in Kasner space-time.


title:
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained
  TinyML Applications

abstract:
  The demand to process vast amounts of data generated from state-of-the-art
high resolution cameras has motivated novel energy-efficient on-device AI
solutions. Visual data in such cameras are usually captured in the form of
analog voltages by a sensor pixel array, and then converted to the digital
domain for subsequent AI processing using analog-to-digital converters (ADC).
Recent research has tried to take advantage of massively parallel low-power
analog/digital computing in the form of near- and in-sensor processing, in
which the AI computation is performed partly in the periphery of the pixel
array and partly in a separate on-board CPU/accelerator. Unfortunately,
high-resolution input images still need to be streamed between the camera and
the AI processing unit, frame by frame, causing energy, bandwidth, and security
bottlenecks. To mitigate this problem, we propose a novel
Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array
by adding support for analog multi-channel, multi-bit convolution, batch
normalization, and ReLU (Rectified Linear Units). Our solution includes a
holistic algorithm-circuit co-design approach and the resulting P2M paradigm
can be used as a drop-in replacement for embedding memory-intensive first few
layers of convolutional neural network (CNN) models within
foundry-manufacturable CMOS image sensor platforms. Our experimental results
indicate that P2M reduces data transfer bandwidth from sensors and analog to
digital conversions by ~21x, and the energy-delay product (EDP) incurred in
processing a MobileNetV2 model on a TinyML use case for visual wake words
dataset (VWW) by up to ~11x compared to standard near-processing or in-sensor
implementations, without any significant drop in test accuracy.


title:
Parallel Training of GRU Networks with a Multi-Grid Solver for Long
  Sequences

abstract:
  Parallelizing Gated Recurrent Unit (GRU) networks is a challenging task, as
the training procedure of GRU is inherently sequential. Prior efforts to
parallelize GRU have largely focused on conventional parallelization strategies
such as data-parallel and model-parallel training algorithms. However, when the
given sequences are very long, existing approaches are still inevitably
performance limited in terms of training time. In this paper, we present a
novel parallel training scheme (called parallel-in-time) for GRU based on a
multigrid reduction in time (MGRIT) solver. MGRIT partitions a sequence into
multiple shorter sub-sequences and trains the sub-sequences on different
processors in parallel. The key to achieving speedup is a hierarchical
correction of the hidden state to accelerate end-to-end communication in both
the forward and backward propagation phases of gradient descent. Experimental
results on the HMDB51 dataset, where each video is an image sequence,
demonstrate that the new parallel training scheme achieves up to 6.5$\times$
speedup over a serial approach. As efficiency of our new parallelization
strategy is associated with the sequence length, our parallel GRU algorithm
achieves significant performance improvement as the sequence length increases.


title: Regularized Deep Signed Distance Fields for Reactive Motion Generation

abstract:
  Autonomous robots should operate in real-world dynamic environments and
collaborate with humans in tight spaces. A key component for allowing robots to
leave structured lab and manufacturing settings is their ability to evaluate
online and real-time collisions with the world around them. Distance-based
constraints are fundamental for enabling robots to plan their actions and act
safely, protecting both humans and their hardware. However, different
applications require different distance resolutions, leading to various
heuristic approaches for measuring distance fields w.r.t. obstacles, which are
computationally expensive and hinder their application in dynamic obstacle
avoidance use-cases. We propose Regularized Deep Signed Distance Fields
(ReDSDF), a single neural implicit function that can compute smooth distance
fields at any scale, with fine-grained resolution over high-dimensional
manifolds and articulated bodies like humans, thanks to our effective data
generation and a simple inductive bias during training. We demonstrate the
effectiveness of our approach in representative simulated tasks for whole-body
control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces.
Finally, we provide proof of concept of a real-world application in a HRI
handover task with a mobile manipulator robot.


title: Trust levels in social networks

abstract:
  In this paper, we study trust levels in social networks from the perspective
of Dunbar's number.


title:
Modeling and Validating Temporal Rules with Semantic Petri-Net for
  Digital Twins

abstract:
  Semantic rule checking on RDFS/OWL data has been widely used in the
construction industry. At present, semantic rule checking is mainly performed
on static models. There are still challenges in integrating temporal models and
semantic models for combined rule checking. In this paper, Semantic Petri-Net
(SPN) is proposed as a novel temporal modeling and validating method, which
implements the states and transitions of the Colored Petri-Net directly based
on RDFS and SPARQL, and realizes two-way sharing of knowledge between domain
semantic webs and temporal models in the runtime. Several cases are provided to
demonstrate the possible applications in digital twins with concurrent state
changes and dependencies.


title:
Enhancing one-dimensional Particle-in-Cell simulations to
  self-consistently resolve instability-induced electron transport in Hall
  thrusters

abstract:
  The advent of high-power Hall thrusters and the increasing interest towards
their use as a primary propulsion system for various missions have given a new
boost to the efforts aiming at self-consistent predictive modeling of this
thruster technology. In this article, we present a novel approach, which allows
enhancing the predictive capability of one-dimensional Particle-in-Cell (PIC)
simulations to self-consistently capture the wave-induced electron transport
due to the azimuthal instabilities in Hall thrusters. The so-called "pseudo-2D"
PIC scheme resulting from this approach is extensively tested in several
operating conditions. The results are compared against a well-established 2D3V
axial-azimuthal reference case in terms of the axial profiles of the
time-averaged plasma properties, the azimuthal electric field fluctuations and
their dispersion features, and the contributions of the force terms in the
electron azimuthal momentum equation to the cross-field mobility. We have
demonstrated that the pseudo-2D PIC provides a prediction of the above aspects
that compares very closely in almost all conditions with those from the full-2D
simulation. In addition, the sensitivity of the pseudo-2D simulation results to
the numerical parameters associated with our approach is assessed in detail.
The outcomes of these analyses have casted light on the next steps to further
improve the approach. The activities being pursued in this direction are
outlined as the future work within the concluding remarks.


title:
Kids Computer Interaction with a Focus on Story Telling and Educational
  Robotics

abstract:
  In the invited talk at KDAH 2018 workshop (CIKM 2018 conference) I have
presented two projects carried out in our HCI lab and target to child: the
first one is related to the evaluation in the wild of of a storytelling app for
kids based on the swipe story approach, the second one is related to Wolly, an
educational robot co-designed with children.


title:
Continuous harmonic functions on a ball that are not in $H^s$ for
  $s>1/2$

abstract:
  We show that there are harmonic functions on a ball ${\mathbb{B}_n}$ of
$\mathbb{R}^n$, $n\ge 2$, that are continuous up to the boundary (and even
H\"older continuous) but not in the Sobolev space $H^s(\mathbb{B}_n)$ for any
$s$ sufficiently big. The idea for the construction of these functions is
inspired by the two-dimensional example of a harmonic continuous function with
infinite energy presented by Hadamard in 1906. To obtain examples in any
dimension $n\ge 2$ we exploit certain series of spherical harmonics. As an
application, we verify that the regularity of the solutions that was proven for
a class of boundary value problems with nonlinear transmission conditions is,
in a sense, optimal.


title: Simple Closed Quasigeodesics on Tetrahedra

abstract:
  Pogorelov proved in 1949 that every every convex polyhedron has at least
three simple closed quasigeodesics. Whereas a geodesic has exactly pi surface
angle to either side at each point, a quasigeodesic has at most pi surface
angle to either side at each point. Pogorelov's existence proof did not suggest
a way to identify the three quasigeodesics, and it is only recently that a
finite algorithm has been proposed.
  Here we identify three simple closed quasigeodesics on any tetrahedron: at
least one through 1 vertex, at least one through 2 vertices, and at least one
through 3 vertices. The only exception is that isosceles tetrahedra have simple
closed geodesics but do not have a 1-vertex quasigeodesic.
  We also identify an infinite class of tetrahedra that each have at least 34
simple closed quasigeodesics.


title:
SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning
  Prediction of Synthetic Characters

abstract:
  This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network
architecture that computes skinning weights from an input mesh and its
associated skeleton, without making any assumptions on shape class and
structure of the provided mesh. Whereas previous methods pre-compute
handcrafted features that relate the mesh and the skeleton or assume a fixed
topology of the skeleton, the proposed method extracts this information in an
end-to-end learnable fashion by jointly learning the best relationship between
mesh vertices and skeleton joints. The proposed method exploits the benefits of
the novel Multi-Aggregator Graph Convolution that combines the results of
different aggregators during the summarizing step of the Message-Passing
scheme, helping the operation to generalize for unseen topologies. Experimental
results demonstrate the effectiveness of the contributions of our novel
architecture, with SkinningNet outperforming current state-of-the-art
alternatives.


title:
Learning Progressive Distributed Compression Strategies from Local
  Channel State Information

abstract:
  This paper proposes a deep learning framework to design distributed
compression strategies in which distributed agents need to compress
high-dimensional observations of a source, then send the compressed bits via
bandwidth limited links to a fusion center for source reconstruction. Further,
we require the compression strategy to be progressive so that it can adapt to
the varying link bandwidths between the agents and the fusion center. Moreover,
to ensure scalability, we investigate strategies that depend only on the local
channel state information (CSI) at each agent. Toward this end, we use a
data-driven approach in which the progressive linear combination and uniform
quantization strategy at each agent are trained as a function of its local CSI.
To deal with the challenges of modeling the quantization operations (which
always produce zero gradients in the training of neural networks), we propose a
novel approach of exploiting the statistics of the batch training data to set
the dynamic ranges of the uniform quantizers. Numerically, we show that the
proposed distributed estimation strategy designed with only local CSI can
significantly reduce the signaling overhead and can achieve a lower
mean-squared error distortion for source reconstruction than state-of-the-art
designs that require global CSI at comparable overall communication cost.


title: SIERRA: A Modular Framework for Research Automation

abstract:
  Modern intelligent systems researchers employ the scientific method: they
form hypotheses about system behavior, and then run experiments using one or
more independent variables to test their hypotheses. We present SIERRA, a novel
framework structured around that idea for accelerating research developments
and improving reproducibility of results. SIERRA makes it easy to quickly
specify the independent variable(s) for an experiment, generate experimental
inputs, automatically run the experiment, and process the results to generate
deliverables such as graphs and videos. SIERRA provides reproducible automation
independent of the execution environment (HPC hardware, real robots, etc.) and
targeted platform (arbitrary simulator or real robots), enabling exact
experiment replication (up to the limit of the execution environment and
platform). It employs a deeply modular approach that allows easy customization
and extension of automation for the needs of individual researchers, thereby
eliminating manual experiment configuration and result processing via
throw-away scripts.


title:
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car
  Following Model

abstract:
  In the coming years and decades, autonomous vehicles (AVs) will become
increasingly prevalent, offering new opportunities for safer and more
convenient travel and potentially smarter traffic control methods exploiting
automation and connectivity. Car following is a prime function in autonomous
driving. Car following based on reinforcement learning has received attention
in recent years with the goal of learning and achieving performance levels
comparable to humans. However, most existing RL methods model car following as
a unilateral problem, sensing only the vehicle ahead. Recent literature,
however, Wang and Horn [16] has shown that bilateral car following that
considers the vehicle ahead and the vehicle behind exhibits better system
stability. In this paper we hypothesize that this bilateral car following can
be learned using RL, while learning other goals such as efficiency
maximisation, jerk minimization, and safety rewards leading to a learned model
that outperforms human driving.
  We propose and introduce a Deep Reinforcement Learning (DRL) framework for
car following control by integrating bilateral information into both state and
reward function based on the bilateral control model (BCM) for car following
control. Furthermore, we use a decentralized multi-agent reinforcement learning
framework to generate the corresponding control action for each agent. Our
simulation results demonstrate that our learned policy is better than the human
driving policy in terms of (a) inter-vehicle headways, (b) average speed, (c)
jerk, (d) Time to Collision (TTC) and (e) string stability.


title:
Using Statistical Models to Detect Occupancy in Buildings through
  Monitoring VOC, CO$_2$, and other Environmental Factors

abstract:
  Dynamic models of occupancy patterns have shown to be effective in optimizing
building-systems operations. Previous research has relied on CO$_2$ sensors and
vision-based techniques to determine occupancy patterns. Vision-based
techniques provide highly accurate information; however, they are very
intrusive. Therefore, motion or CO$_2$ sensors are more widely adopted
worldwide. Volatile Organic Compounds (VOCs) are another pollutant originating
from the occupants. However, a limited number of studies have evaluated the
impact of occupants on the VOC level. In this paper, continuous measurements of
CO$_2$, VOC, light, temperature, and humidity were recorded in a 17,000 sqft
open office space for around four months. Using different statistical models
(e.g., SVM, K-Nearest Neighbors, and Random Forest) we evaluated which
combination of environmental factors provides more accurate insights on
occupant presence. Our preliminary results indicate that VOC is a good
indicator of occupancy detection in some cases. It is also concluded that
proper feature selection and developing appropriate global occupancy detection
models can reduce the cost and energy of data collection without a significant
impact on accuracy.


title: Visibility-Inspired Models of Touch Sensors for Navigation

abstract:
  This paper introduces mathematical models of touch sensors for mobile
robotics based on visibility. Serving a purpose similar to the pinhole camera
model for computer vision, the introduced models are expected to provide a
useful, idealized characterization of task-relevant information that can be
inferred from their outputs or observations. This allows direct comparisons to
be made between traditional depth sensors, highlighting cases in which touch
sensing may be interchangeable with time of flight or vision sensors, and
characterizing unique advantages provided by touch sensing. The models include
contact detection, compression, load bearing, and deflection. The results could
serve as a basic building block for innovative touch sensor designs for mobile
robot sensor fusion systems.


title: Using Human Gaze For Surgical Activity Recognition

abstract:
  Automatically recognizing surgical activities plays an important role in
providing feedback to surgeons, and is a fundamental step towards
computer-aided surgical systems. Human gaze and visual saliency carry important
information about visual attention, and can be used in computer vision systems.
Although state-of-the-art surgical activity recognition models learn spatial
temporal features, none of these models make use of human gaze and visual
saliency. In this study, we propose to use human gaze with a spatial temporal
attention mechanism for activity recognition in surgical videos. Our model
consists of an I3D-based architecture, learns spatio-temporal features using 3D
convolutions, as well as learning an attention map using human gaze. We
evaluated our model on the Suturing task of JIGSAWS which is a publicly
available surgical video understanding dataset. Our evaluations on a subset of
random video segments in this task suggest that our approach achieves promising
results with an accuracy of 86.2%.


title: Permeability and turbulence over perforated plates

abstract:
  We perform direct numerical simulations of turbulent flow at friction
Reynolds number $Re_\tau \approx 500-2000$ grazing over perforates plates with
moderate viscous-scaled orifice diameter $d^+\approx40$--$160$ and analyse the
relation between permeability and added drag. Unlike previous studies of
turbulent flows over permeable surfaces, we find that flow inside the orifices
is dominated by inertial effects, and that the relevant permeability is the
Forchheimer and not the Darcy one. We find evidence of a fully rough regime
where the relevant length scale is the inverse of the Forchheimer coefficient,
which can be regarded as the resistance experienced by the wall-normal flow.
Moreover, we show that, for low porosities, the Forchheimer coefficient can be
estimated with good accuracy using a simple analytical relation.


title: System Cards for AI-Based Decision-Making for Public Policy

abstract:
  Decisions in public policy are increasingly being made or assisted by
automated decision-making algorithms. Many of these algorithms process personal
data for tasks such as predicting recidivism, assisting welfare decisions,
identifying individuals using face recognition, and more. While potentially
improving efficiency and effectiveness, such algorithms are not inherently free
from issues such as bias, opaqueness, lack of explainability, maleficence, and
the like. Given that the outcomes of these algorithms have significant impacts
on individuals and society and are open to analysis and contestation after
deployment, such issues must be accounted for before deployment. Formal audits
are a way towards ensuring algorithms that are used in public policy meet the
appropriate accountability standards. This work, based on an extensive analysis
of the literature, proposes a unifying framework for system accountability
benchmark for formal audits of artificial intelligence-based decision-aiding
systems in public policy as well as system cards that serve as scorecards
presenting the outcomes of such audits. The benchmark consists of 50 criteria
organized within a four by four matrix consisting of the dimensions of (i)
data, (ii) model, (iii) code, (iv) system and (a) development, (b) assessment,
(c) mitigation, (d) assurance. Each criterion is described and discussed
alongside a suggested measurement scale indicating whether the evaluations are
to be performed by humans or computers and whether the evaluation outcomes are
binary or on an ordinal scale. The proposed system accountability benchmark
reflects the state-of-the-art developments for accountable systems, serves as a
checklist for future algorithm audits, and paves the way for sequential work as
future research.


title: Machine Learning Methods in Solving the Boolean Satisfiability Problem

abstract:
  This paper reviews the recent literature on solving the Boolean
satisfiability problem (SAT), an archetypal NP-complete problem, with the help
of machine learning techniques. Despite the great success of modern SAT solvers
to solve large industrial instances, the design of handcrafted heuristics is
time-consuming and empirical. Under the circumstances, the flexible and
expressive machine learning methods provide a proper alternative to solve this
long-standing problem. We examine the evolving ML-SAT solvers from naive
classifiers with handcrafted features to the emerging end-to-end SAT solvers
such as NeuroSAT, as well as recent progress on combinations of existing CDCL
and local search solvers with machine learning methods. Overall, solving SAT
with machine learning is a promising yet challenging research topic. We
conclude the limitations of current works and suggest possible future
directions.


title:
Enhanced Polling and Infiltration for Highly-Efficient Electro-Optic
  Polymer-Based Mach-Zehnder Modulators

abstract:
  An ultra-narrow slot waveguide is fabricated for use in highly-efficient,
electro-optic-polymer-based, integrated-optic modulators. Measurement results
indicate that $V_\pi L$'s below 1.2 V.mm are possible for balanced Mach-Zehnder
modulators using this ultra-narrow slot waveguide on a silicon-organic hybrid
platform. Simulated $V_\pi L$'s of 0.35 V.mm have also been obtained. In
addition to adapting standard recipes, we developed two novel fabrication
processes for achieving miniaturized devices with high modulation sensitivity.
To boost compactness and decrease the overall footprint, we use a fabrication
approach based on air bridge interconnects on thick, thermally-reflowed, MaN
2410 E-beam resist protected by an alumina layer. To overcome the challenges of
high currents and imperfect infiltration of polymers into ultra-narrow slots,
we use a carefully designed, atomically-thin layer of TiO$_2$ as a
carrier-barrier to enhance the polling efficiency of our electro-optic
polymers. Additionally, finite-difference time-domain simulations are employed
to optimize the effect of the thin layer of TiO$_2$. As compared to other,
non-optimized, cases, our peak measured current is reduced by a factor of 3;
scanning electron microscopy images also demonstrate that we achieve almost
perfect infiltration. The anticipated increase in total capacitance due to the
TiO$_2$ layer is shown to be negligible. In fact, applying our TiO$_2$ surface
treatment to our ultra-narrow slot, allows us to obtain an improved phase shift
efficiency ($\partial n / \partial V$) of $\sim$94% for a 10 nm TiO$_2$ layer.


title: Modern Cosmology, an Amuse-Gueule

abstract:
  This essay is a nontechnical primer for a broader audience, in which I paint
a broad-brush picture of modern cosmology. I begin by reviewing the evidence
for the big bang, including the expansion of our Universe, the cosmic microwave
background, and the primordial abundances of the light elements. Next, I
discuss how these and other cosmological observations can be well explained by
means of the concordance model of cosmology, putting a particular emphasis on
the composition of the cosmic energy budget in terms of visible matter, dark
matter, and dark energy. This sets the stage for a short overview of the
history of the Universe from the earliest moments of its existence all the way
to the accelerated expansion at late times and beyond. Finally, I summarize the
current status of the field, including the challenges it is currently facing
such as the Hubble tension, and conclude with an outlook onto the bright future
that awaits us in the coming years and decades. The text is complemented by an
extensive bibliography serving as a guide for readers who wish to delve deeper.


title:
Limiting the Abundance of LIGO/Virgo Black Holes with Microlensing
  Observations of Quasars of Finite Size

abstract:
  We present a simple but general argument that strongly limits the abundance
of Primordial Black Holes (PBHs) (or other unknown population of compact
objects) with masses similar to those determined by LIGO/Virgo from BH binary
mergers. We show that quasar microlensing can be very sensitive to the mass of
the lenses, and that it is able to distinguish between stars and BHs of high
mass, when the finite size of the source is taken into account. A significant
presence of massive BHs would produce frequent high flux magnifications (except
for unrealistically large sources) which have been very rarely observed. On the
contrary, a typical stellar population would induce flux magnifications
consistent with the observations. This result excludes PBHs (or any type of
compact object) in the mass range determined by LIGO/Virgo as the main dark
matter constituents in the lens galaxies.


title: The little robot, black holes, and spaghettification

abstract:
  The tidal forces generated by a black hole can be so powerful that they cause
unlimited stretching, known as spaghettification. A detailed analysis of this
phenomenon requires the use of Einstein's theory of general relativity. The aim
of this paper is to offer an up-to-date and accessible analysis of
spaghettification, in which the complex mathematics of Einstein's theory are
replaced with the simpler and more intuitive concepts of Newtonian gravity. The
article can serve as educational material for undergraduate modern physics or
astronomy courses.


title: Junta threshold for low degree Boolean functions on the slice

abstract:
  We show that a Boolean degree $d$ function on the slice $\binom{[n]}{k}$ is a
junta if $k \geq 2d$, and that this bound is sharp. We prove a similar result
for $A$-valued degree $d$ functions for arbitrary finite $A$, and for functions
on an infinite analog of the slice.


title: PokeRRT: A Kinodynamic Planning Approach for Poking Manipulation

abstract:
  This work introduces PokeRRT, a novel motion planning algorithm that
demonstrates poking as an effective non-prehensile manipulation skill to enable
fast manipulation of objects and increase the size of a robot's reachable
workspace. Our qualitative and quantitative results demonstrate the advantages
of poking over pushing and grasping in planning object trajectories through
uncluttered and cluttered environments.


title: Autonomous soft hand grasping -- Literature review

abstract:
  Autonomous grasping remains challenging as unlike humans, robots do not
possess a sophisticated sensing nor delicate interaction capability with the
real environment. Among other efforts that tried to close the gap between them,
anthropomorphic robotic hands is the most prominent direction. However, exactly
following human hand design might be unnecessary as it will significantly
increase the mechanical complexity and hence make it less economically
feasible. Recently, soft robotic hands, a new trend has emerged, aiming to make
the design adequately complex and affordable while requiring much less effort
to control. In the first part of this article, we will lay out several
prominent designs in this direction and their applications in real world
scenarios. Having a suitable hardware simplified the complexity of software
designing. However, manually controlling the hand for one task requires a
significantly large amount of time and effort and doing it repeatedly is
unsurprisingly tedious. Therefore, in the second part, we will show some recent
techniques for soft hand autonomous control. We start by briefly discussing the
analytic methods that mainly exploit the hand dynamic information. Then,
data-driven approaches will be our main focus. It is the trending research
topic for soft hand grasping in recent years as it has shown a high performance
when dealing with a large number of various objects.