Abstract:
  In this work we propose 3D-FFS, a novel approach to make sensor fusion based
3D object detection networks significantly faster using a class of
computationally inexpensive heuristics. Existing sensor fusion based networks
generate 3D region proposals by leveraging inferences from 2D object detectors.
However, as images have no depth information, these networks rely on extracting
semantic features of points from the entire scene to locate the object. By
leveraging aggregated intrinsic properties (e.g. point density) of point cloud
data, 3D-FFS can substantially constrain the 3D search space and thereby
significantly reduce training time, inference time and memory consumption
without sacrificing accuracy. To demonstrate the efficacy of 3D-FFS, we have
integrated it with Frustum ConvNet (F-ConvNet), a prominent sensor fusion based
3D object detection model. We assess the performance of 3D-FFS on the KITTI
dataset. Compared to F-ConvNet, we achieve improvements in training and
inference times by up to 62.80% and 58.96%, respectively, while reducing the
memory usage by up to 58.53%. Additionally, we achieve 0.36%, 0.59% and 2.19%
improvements in accuracy for the Car, Pedestrian and Cyclist classes,
respectively. 3D-FFS shows a lot of promise in domains with limited computing
power, such as autonomous vehicles, drones and robotics where LiDAR-Camera
based sensor fusion perception systems are widely used.


Abstract:
  The peridynamic stress tensor proposed by Lehoucq and Silling (2008) is
cumbersome to implement in numerical computations. In this note, we show that
the peridynamic stress tensor has the mathematical expression of a weighted
static Virial stress derived by Irving and Kirkwood (1950), which offers a
simple and clear expression for numerical calculations of peridynamic stress
tensor.


Abstract:
  Globular clusters contain a finite number of stars. As a result, they
inevitably undergo secular evolution (`relaxation') causing their mean
distribution function (DF) to evolve on long timescales. On one hand, this
long-term evolution may be interpreted as driven by the accumulation of local
deflections along each star's mean field trajectory -- so-called `non-resonant
relaxation'. On the other hand, it can be thought of as driven by non-local,
collectively dressed and resonant couplings between stellar orbits, a process
termed `resonant relaxation'. In this paper we consider a model globular
cluster represented by a spherical, isotropic isochrone DF, and compare in
detail the predictions of both resonant and non-resonant relaxation theories
against tailored direct $N$-body simulations. In the space of orbital actions
(namely the radial action and total angular momentum), we find that both
resonant and non-resonant theories predict the correct morphology for the
secular evolution of the cluster's DF, although non-resonant theory
over-estimates the amplitude of the relaxation rate by a factor ${\sim 2}$. We
conclude that the secular relaxation of hot isotropic spherical clusters is not
dominated by collectively amplified large-scale potential fluctuations, despite
the existence of a strong ${\ell = 1}$ damped mode. Instead, collective
amplification affects relaxation only marginally even on the largest scales.
The predicted contributions to relaxation from smaller scale fluctuations are
essentially the same from resonant and non-resonant theories.


Abstract:
  There are many open questions surrounding the characterisation of groups with
context-sensitive word problem. Only in 2018 was it shown that all finitely
generated virtually Abelian groups have multiple context-free word problems,
and it is a long-standing open question as to where to place the word problems
of hyperbolic groups in the formal language hierarchy. In this paper, we
introduce a new language class called the parallel hyperedge replacement string
languages, show that it contains all multiple context-free and ET0L languages,
and lay down the foundations for future work that may be able to place the word
problems of many hyperbolic groups in this class.


Abstract:
  To encourage and guide decarbonization efforts, better tools are needed to
monitor real-time CO2 and criteria air pollutant emissions from electricity
consumption, production, imports, and exports. Using real-time data from the
electricity system is especially challenging for quantitative applications
requiring high quality and physically consistent data. Until now,
time-intensive, ad-hoc and manual data verification and cleaning strategies
have been used to prepare the data for quantitative analysis. As an alternative
to existing techniques, here we provide a physics-informed framework to greatly
accelerate and automate data processing to enable internally consistent
electric system consumption, production, import, and export data in near
real-time. A key component of this framework is an optimization program to
minimize the data adjustments required to satisfy energy conservation
equations. The effectiveness of this method is demonstrated by applying it to
the continental United States electricity network. The resulting
publicly-available data set, which provides in near-real time, hourly updates
on electricity generation, consumption, imports, exports and associated
emissions, is the first of this nature.


Abstract:
  Reinforcement learning (RL) is typically concerned with estimating stationary
policies or single-step models, leveraging the Markov property to factorize
problems in time. However, we can also view RL as a generic sequence modeling
problem, with the goal being to produce a sequence of actions that leads to a
sequence of high rewards. Viewed in this way, it is tempting to consider
whether high-capacity sequence prediction models that work well in other
domains, such as natural-language processing, can also provide effective
solutions to the RL problem. To this end, we explore how RL can be tackled with
the tools of sequence modeling, using a Transformer architecture to model
distributions over trajectories and repurposing beam search as a planning
algorithm. Framing RL as sequence modeling problem simplifies a range of design
decisions, allowing us to dispense with many of the components common in
offline RL algorithms. We demonstrate the flexibility of this approach across
long-horizon dynamics prediction, imitation learning, goal-conditioned RL, and
offline RL. Further, we show that this approach can be combined with existing
model-free algorithms to yield a state-of-the-art planner in sparse-reward,
long-horizon tasks.


Abstract:
  We prove that the critical point and the point $1$ have dense orbits for
Lebesgue-a.e., parameter pairs in the two-parameter skew-tent family and
generalised $\beta$-transformations. As an application, we show that for the
generalised $\beta$-transformation with the tribonacci number as slope, there
is matching (i.e., $T^n(0)=T^n(1)$ for some $n \geq 1$) for Lebesgue-a.e.
translation parameter.


Abstract:
  With recent advances in data collection from multiple sources, multi-view
data has received significant attention. In multi-view data, each view
represents a different perspective of data. Since label information is often
expensive to acquire, multi-view clustering has gained growing interest, which
aims to obtain better clustering solution by exploiting complementary and
consistent information across all views rather than only using an individual
view. Due to inevitable sensor failures, data in each view may contain error.
Error often exhibits as noise or feature-specific corruptions or outliers.
Multi-view data may contain any or combination of these error types. Blindly
clustering multi-view data i.e., without considering possible error in view(s)
could significantly degrade the performance. The goal of error-robust
multi-view clustering is to obtain useful outcome even if the multi-view data
is corrupted. Existing error-robust multi-view clustering approaches with
explicit error removal formulation can be structured into five broad research
categories - sparsity norm based approaches, graph based methods, subspace
based learning approaches, deep learning based methods and hybrid approaches,
this survey summarizes and reviews recent advances in error-robust clustering
for multi-view data. Finally, we highlight the challenges and provide future
research opportunities.


Abstract:
  The $k$-measure of an integer partition was recently introduced by Andrews,
Bhattacharjee and Dastidar. In this paper, we establish trivariate generating
function identities counting both the length and the $k$-measure for partitions
and distinct partitions, respectively. The $2$-measure case for partitions
extends a result of Andrews, Bhattacharjee and Dastidar.


Abstract:
  In this note, we first recall the nonconvex problem setting and introduce the
optimal PAGE algorithm (Li et al., ICML'21). Then we provide a simple and clean
convergence analysis of PAGE for achieving optimal convergence rates. Moreover,
PAGE and its analysis can be easily adopted and generalized to other works. We
hope that this note provides the insights and is helpful for future works.


Abstract:
  When implementing a non-continuous controller for a cyber-physical system, it
may happen that the evolution of the closed-loop system is not anymore
piecewise differentiable along the trajectory, mainly due to conditional
statements inside the controller. This may lead to some unwanted chattering
effects than may damage the system. This behavior is difficult to observe even
in simulation. In this paper, we propose an interval approach to characterize
the sliding surface which corresponds to the set of all states such that the
state trajectory may jump indefinitely between two distinct behaviors. We show
that the recent notion of thick sets will allows us to compute efficiently an
outer approximation of the sliding surface of a given class of hybrid system
taking into account all set-membership uncertainties. An application to the
verification of the controller of a child swing is considered to illustrate the
principle of the approach.


Abstract:
  Underneath the ear skin there are richly branching vascular and neural
networks that ultimately connecting to our heart and brain. Hence, the
three-dimensional (3D) mapping of auricular electrophysiological signals could
provide a new perspective for biomedical studies such as diagnosis of
cardiovascular diseases and neurological disorders. However, it is still
extremely challenging for current sensing techniques to cover the entire
ultra-curved auricle. Here, we report a graphene-based ear-conformable sensing
device with embedded and distributed 3D electrodes which enable full-auricle
physiological monitoring. The sensing device, which incorporates programable 3D
electrode thread array and personalized auricular mold, has 3D-conformable
sensing interfaces with curved auricular skin, and was developed using one-step
multi-material 3D-printing process. As a proof-of-concept, spatiotemporal
auricular electrical skin resistance (AESR) mapping was demonstrated. For the
first time, 3D AESR contours were generated and human subject-specific AESR
distributions among a population were observed. From the data of 17 volunteers,
the auricular region-specific AESR changes after cycling exercise were observed
in 98% of the tests and were validated via machine learning techniques.
Correlations of AESR with heart rate and blood pressure were also studied using
statistical analysis. This 3D electronic platform and AESR-based new
biometrical findings show promising biomedical applications.


Abstract:
  We consider the classical problem of optimal portfolio construction with the
constraint that no short position is allowed, or equivalently the valid
equilibria of multispecies Lotka-Volterra equations with self-regulation in the
special case where the interaction matrix is of unit rank, corresponding to
species competing for a common resource. We compute the average number of
solutions and show that its logarithm grows as $N^\alpha$, where $N$ is the
number of assets or species and $\alpha \leq 2/3$ depends on the interaction
matrix distribution. We conjecture that the most likely number of solutions is
much smaller and related to the typical sparsity $m(N)$ of the solutions, which
we compute explicitly. We also find that the solution landscape is similar to
that of spin-glasses, i.e. very different configurations are quasi-degenerate.
Correspondingly, "disorder chaos" is also present in our problem. We discuss
the consequence of such a property for portfolio construction and ecologies,
and question the meaning of rational decisions when there is a very large
number "satisficing" solutions.


Abstract:
  In this study, we report on face-centered cubic structured CoCrFeNi
high-entropy alloy thin films with finely dispersed nano-oxide particles which
are formed by internal oxidation. Analytical scanning transmission electron
microscopy imaging found that the particles are Cr2O3. The oxide particles
contribute to the hardening of the film increasing its hardness by 14% compared
to that of the film without precipitates, through the Orowan-type strengthening
mechanism. Our novel approach paves the way to design medium- and high-entropy
alloys with high strength by making use of oxide phases.


Abstract:
  Intelligent reflecting surface (IRS) is deemed as a promising and
revolutionizing technology for future wireless communication systems owing to
its capability to intelligently change the propagation environment and
introduce a new dimension into wireless communication optimization. Most
existing studies on IRS are based on an ideal reflection model. However, it is
difficult to implement an IRS which can simultaneously realize any adjustable
phase shift for the signals with different frequencies. Therefore, the
practical phase shift model, which can describe the difference of IRS phase
shift responses for the signals with different frequencies, should be utilized
in the IRS optimization for wideband and multi-band systems. In this paper, we
consider an IRS-assisted multi-cell multi-band system, in which different base
stations (BSs) operate at different frequency bands. We aim to jointly design
the transmit beamforming of BSs and the reflection beamforming of the IRS to
minimize the total transmit power subject to signal to interference-plus-noise
ratio (SINR) constraints of individual user and the practical IRS reflection
model. With the aid of the practical phase shift model, the influence between
the signals with different frequencies is taken into account during the design
of IRS. Simulation results illustrate the importance of considering the
practical communication scenario on the IRS designs and validate the
effectiveness of our proposed algorithm.


Abstract:
  In recent years, Neural Machine Translation (NMT) has achieved notable
results in various translation tasks. However, the word-by-word generation
manner determined by the autoregressive mechanism leads to high translation
latency of the NMT and restricts its low-latency applications.
Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive
mechanism and achieves significant decoding speedup through generating target
words independently and simultaneously. Nevertheless, NAT still takes the
word-level cross-entropy loss as the training objective, which is not optimal
because the output of NAT cannot be properly evaluated due to the multimodality
problem. In this article, we propose using sequence-level training objectives
to train NAT models, which evaluate the NAT outputs as a whole and correlates
well with the real translation quality. Firstly, we propose training NAT models
to optimize sequence-level evaluation metrics (e.g., BLEU) based on several
novel reinforcement algorithms customized for NAT, which outperforms the
conventional method by reducing the variance of gradient estimation. Secondly,
we introduce a novel training objective for NAT models, which aims to minimize
the Bag-of-Ngrams (BoN) difference between the model output and the reference
sentence. The BoN training objective is differentiable and can be calculated
efficiently without doing any approximations. Finally, we apply a three-stage
training strategy to combine these two methods to train the NAT model. We
validate our approach on four translation tasks (WMT14 En$\leftrightarrow$De,
WMT16 En$\leftrightarrow$Ro), which shows that our approach largely outperforms
NAT baselines and achieves remarkable performance on all translation tasks. The
source code is available at https://github.com/ictnlp/Seq-NAT.


Abstract:
  In nominal mission scenarios, geostationary satellites perform end-of-life
orbit maneuvers to reach suitable disposal orbits, where they do not interfere
with operational satellites. This research investigates the long-term orbit
evolution of decommissioned geostationary satellite under the assumption that
the disposal maneuver does not occur and the orbit evolves with no control. The
dynamical model accounts for all the relevant harmonics of the gravity field at
the altitude of geostationary orbits, as well as solar radiation pressure and
third-body perturbations caused by the Moon and the Sun. Orbit propagations are
performed using two algorithms based on different equations of motion and
numerical integration methods: (i) Gauss planetary equations for modified
equinoctial elements with a Runge-Kutta numerical integration scheme based on
8-7th-order Dorman and Prince formulas; (ii) Cartesian state equations of
motion in an Earth-fixed frame with a Runge-Kutta Fehlberg 7/8 integration
scheme. The numerical results exhibit excellent agreement over integration
times of decades. Some well-known phenomena emerge, such as the longitudinal
drift due to the resonance between the orbital motion and Earth's rotation,
attributable to the J22 term of the geopotential. In addition, the third-body
perturbation due to Sun and Moon causes two major effects: (a) a precession of
the orbital plane, and (b) complex longitudinal dynamics. This study proposes
an analytical approach for the prediction of the precessional motion and shows
its agreement with the orbit evolution obtained numerically. Moreover,
long-term orbit propagations show that the above mentioned complex longitudinal
dynamics persists over time scales of several decades. Frequent and
unpredictable migrations toward different longitude regions occur, in contrast
with the known effects due only to the J22 perturbation.


Abstract:
  We consider a stochastic bandit problem with countably many arms that belong
to a finite set of types, each characterized by a unique mean reward. In
addition, there is a fixed distribution over types which sets the proportion of
each type in the population of arms. The decision maker is oblivious to the
type of any arm and to the aforementioned distribution over types, but
perfectly knows the total number of types occurring in the population of arms.
We propose a fully adaptive online learning algorithm that achieves O(log n)
distribution-dependent expected cumulative regret after any number of plays n,
and show that this order of regret is best possible. The analysis of our
algorithm relies on newly discovered concentration and convergence properties
of optimism-based policies like UCB in finite-armed bandit problems with "zero
gap," which may be of independent interest.


Abstract:
  Educational datamining involves the application of datamining techniques to
student activity. However, in the context of computer programming, many
datamining techniques can not be applied because they expect vector-shaped
input whereas computer programs have the form of syntax trees. In this paper,
we present ast2vec, a neural network that maps Python syntax trees to vectors
and back, thereby facilitating datamining on computer programs as well as the
interpretation of datamining results. Ast2vec has been trained on almost half a
million programs of novice programmers and is designed to be applied across
learning tasks without re-training, meaning that users can apply it without any
need for (additional) deep learning. We demonstrate the generality of ast2vec
in three settings: First, we provide example analyses using ast2vec on a
classroom-sized dataset, involving visualization, student motion analysis,
clustering, and outlier detection, including two novel analyses, namely a
progress-variance-projection and a dynamical systems analysis. Second, we
consider the ability of ast2vec to recover the original syntax tree from its
vector representation on the training data and two further large-scale
programming datasets. Finally, we evaluate the predictive capability of a
simple linear regression on top of ast2vec, obtaining similar results to
techniques that work directly on syntax trees. We hope ast2vec can augment the
educational datamining toolbelt by making analyses of computer programs easier,
richer, and more efficient.


Abstract:
  Cooling down a trapped ion into its motional ground state is a central step
for trapped ions based quantum information processing. State of the art cooling
schemes often work under a set of optimal cooling conditions derived
analytically using a perturbative approach, in which the sideband coupling is
assumed to be the weakest of all the relevant transitions. As a result the
cooling rate is severely limited. Here we propose to use quantum control
technique powered with automatic differentiation to speed up the classical
cooling schemes. We demonstrate the efficacy of our approach by applying it to
find the optimal cooling conditions for classical sideband cooling and
electromagnetically induced transparency cooling schemes, which are in general
beyond the weak sideband coupling regime. Based on those numerically found
optimal cooling conditions, we show that faster cooling can be achieved while
at the same time a low average phonon occupation can be retained.


Abstract:
  Let $f$ be an operator convex function on $(0,\infty)$, and $\Phi$ be a
unital positive linear maps on $B(H)$.
  we give a complementary inequality to
  Davis-Choi-Jensen's inequality as follows \begin{equation*} f(\Phi(A))\geq
\frac{4R(A,B)}{(1+R(A,B))^2}\Phi(f(A)), \end{equation*} where $R(A,
B)=\max\{r(A^{-1}B) ,r(B^{-1}A)\}$ and $r(A)$ is the spectral radius of $A$. We
investigate the complementary inequalities related to the operator power means
and the Karcher means via unital positive linear maps,
  and obtain the following result: If $A_{1}$, $A_{2}$,\dots, $A_{n}$, are
positive definite operators in $B(H)$, and $0<m_i\leq A_i\leq M_i$, then
\begin{equation*} \Lambda( \omega;\Phi(\mathbb{A}))\geq\Phi(\Lambda( \omega;
\mathbb{A}))\geq \frac{4\hbar}{(1+\hbar)^2}~\Lambda( \omega;\Phi(\mathbb{A})),
\end{equation*} where $\hbar= \max\limits_{1\leq i\leq n} \frac{M_i}{m_i}$.
Finally, we prove that if $G(A_1,\dots,A_n)$ is the generalized geometric mean
defined by Ando-Li-Mathias for $n$ positive definite operators, then
  \begin{align*}
\Phi(G(A_1,\dots,A_n))\geq\left(\frac{2h^\frac{1}{2}}{1+h}\right)^{n-1}G(\Phi(A_1),\dots,\Phi(A_n)),
\end{align*} where $h=\max\limits_{1\leq i,j\leq n} R(A_i, A_j)$.


Abstract:
  Differential privacy is a formal, mathematical definition of data privacy
that has gained traction in academia, industry, and government. The task of
correctly constructing differentially private algorithms is non-trivial, and
mistakes have been made in foundational algorithms. Currently, there is no
automated support for converting an existing, non-private program into a
differentially private version. In this paper, we propose a technique for
automatically learning an accurate and differentially private version of a
given non-private program. We show how to solve this difficult program
synthesis problem via a combination of techniques: carefully picking
representative example inputs, reducing the problem to continuous optimization,
and mapping the results back to symbolic expressions. We demonstrate that our
approach is able to learn foundational algorithms from the differential privacy
literature and significantly outperforms natural program synthesis baselines.


Abstract:
  We address the issue of the suboptimality in the p-version discontinuous
Galerkin (dG) methods for first order hyperbolic problems. The convergence rate
is derived for the upwind dG scheme on tensor product meshes in any dimension.
The standard proof in seminal work [14] leads to suboptimal convergence in
terms of the polynomial degree by 3/2 order for general convection fields, with
the exception of piecewise multi-linear convection fields, which rather yield
optimal convergence. Such suboptimality is not observed numerically. Thus, it
might be caused by a limitation of the analysis, which we partially overcome:
for a special class of convection fields, we shall show that the dG method has
a p-convergence rate suboptimal by 1/2 order only.


Abstract:
  A new proposal is given for designing a non-volatile, completely spin logic
device, that can be reprogrammed for different functional classical logical
operations. We use the concept of bias driven spin dependent circular current
and current induced magnetic field in a quantum ring under asymmetric
ring-to-electrode interface configuration to implement all the Boolean
operations. We extend our idea to build two kinds of parallel computing
architectures for getting parallelized operations, all at a particular time.
For one case, different kinds of parallel operations are performed in a single
device, whereas in the other type all the possible inputs of a logic gate are
processed in parallel and all the outputs are read simultaneously. The
performance and reliability are investigated in terms of power, delay and
power-delay-product and finally the system temperature. We find that both the
individual and simultaneous logic operations studied here are much superior
compared to the operations performed in different conventional logic families
like complementary metal oxide semiconductor logic, transistor-transistor
logic, etc. The key advantage is that we can perform several logic operations,
as many as we wish, repeating the same or different logic gates using a single
setup, which indeed reduces wiring in the circuits and hence consumes much less
power. Our analysis can be utilized to design optimized logic circuits at
nano-scale level.


Abstract:
  We introduce a new causal inference framework for time series data aimed at
assessing the effectiveness of heat alerts in reducing mortality and
hospitalization risks. We are interested in addressing the following question:
how many deaths and hospitalizations could be averted if we were to increase
the frequency of issuing heat alerts in a given location? In the context of
time series data, the overlap assumption - each unit must have a positive
probability of receiving the treatment - is often violated. This is because, in
a given location, issuing a heat alert is a rare event on an average
temperature day as heat alerts are almost always issued on extremely hot days.
To overcome this challenge, first we introduce a new class of causal estimands
under a stochastic intervention (i.e., increasing the odds of issuing a heat
alert) for a single time series corresponding to a given location. We develop
the theory to show that these causal estimands can be identified and estimated
under a weaker version of the overlap assumption. Second, we propose
nonparametric estimators based on time-varying propensity scores, and derive
point-wise confidence bands for these estimators. Third, we extend this
framework to multiple time series corresponding to multiple locations. Via
simulations, we show that the proposed estimator has good performance with
respect to bias and root mean squared error. We apply our proposed method to
estimate the causal effects of increasing the odds of issuing heat alerts in
reducing deaths and hospitalizations among Medicare enrollees in 2817 U.S.
counties. We found weak evidence of a causal link between increasing the odds
of issuing heat alerts during the warm seasons of 2006-2016 and a reduction in
deaths and cause-specific hospitalizations across the 2817 counties.


Abstract:
  Consider the Langevin process, described by a vector (position,momentum) in
$\mathbb{R}^{d}\times\mathbb{R}^d$. Let $\mathcal O$ be a $\mathcal{C}^2$ open
bounded and connected set of $\mathbb{R}^d$. We prove the compactness of the
semigroup of the Langevin process absorbed at the boundary of the domain
$D:=\mathcal{O}\times\mathbb{R}^d$. We then obtain the existence of a unique
quasi-stationary distribution (QSD) for the Langevin process on $D$. We also
provide a spectral interpretation of this QSD and obtain an exponential
convergence of the Langevin process conditioned on non-absorption towards the
QSD.


Abstract:
  Spectroscopic studies of Galactic O and B stars show that many stars with
masses above 8 M$_{\odot}$ are observed in the HR diagram just beyond the
Main-Sequence (MS) band predicted by stellar models computed with a moderate
overshooting. This may be an indication that the convective core sizes in stars
in the upper part of the HR diagram are larger than predicted by these models.
Combining stellar evolution models and spectroscopic parameters derived for a
large sample of Galactic O and B stars, including brand new information about
their projected rotational velocities, we reexamine the question of the
convective core size in MS massive stars. We confirm that for stars more
massive than about 8 M$_{\odot}$, the convective core size at the end of the MS
phase increases more rapidly with the mass than in models computed with a
constant step overshoot chosen to reproduce the main sequence width in the low
mass range (around 2 M$_{\odot}$). This conclusion is valid for both the cases
of non-rotating models and rotating models either with a moderate or a strong
angular momentum transport. The increase of the convective core mass with the
mass obtained from the TAMS position is, however, larger than the one deduced
from the surface velocity drop for masses above about 15 M$_{\odot}$. Although
observations available at the moment cannot decide what is the best choice
between the core sizes given by the TAMS and the velocity drop, we discuss
different methods to get out of this dilemma. At the moment, comparisons with
eclipsing binaries seem to favor the solution given by the velocity drop. While
we confirm the need for larger convective cores at higher masses, we find
tensions in-between different methods for stars more massive than 15
M$_{\odot}$. The use of single-aged stellar populations (non-interacting
binaries or stellar clusters) would be a great asset to resolve this tension.


Abstract:
  I present a 3D advancing-front mesh refinement algorithm that generates a
constrained Delaunay mesh for any piecewise linear complex (PLC) and extend
this algorithm to produce truly Delaunay meshes for any PLC. First, as in my
recently published 2D algorithm, I split the input line segments such that the
length of the subsegments is asymptotically proportional to the local feature
size (LFS). For each facet, I refine the mesh such that the edge lengths and
the radius of the circumcircle of every triangular element are asymptotically
proportional to the LFS. Finally, I refine the volume mesh to produce a
constrained Delaunay mesh whose tetrahedral elements are well graded and have a
radius-edge ratio less than some $\omega^* > 2/\sqrt{3}$ (except ``near'' small
input angles). I extend this algorithm to generate truly Delaunay meshes by
ensuring that every triangular element on a facet satisfies Gabriel's
condition, i.e., its diametral sphere is empty. On an ``apex'' vertex where
multiple facets intersect, Gabriel's condition is satisfied by a modified
split-on-a-sphere (SOS) technique. On a line where multiple facets intersect,
Gabriel's condition is satisfied by mirroring meshes near the line of
intersection. The SOS technique ensures that the triangles on a facet near the
apex vertex have angles that are proportional to the angular feature size
(AFS), a term I define in the paper. All tetrahedra (except ``near'' small
input angles) are well graded and have a radius-edge ratio less than $\omega^*
> \sqrt{2}$ for a truly Delaunay mesh. The upper bounds for the radius-edge
ratio are an improvement by a factor of $\sqrt{2}$ over current
state-of-the-art algorithms.


Abstract:
  Observations that span a broad range of wavelengths are used to examine
asymmetries in the disk of the nearby late-type spiral galaxy NGC 247. The
northern spiral arm is over-luminous at all wavelengths when compared with
other parts of the galaxy at similar galactocentric radii, while the density of
very luminous red stars in the void that is immediately south of this arm
matches that in other parts of the disk at the same galactocentric radius. Two
bubbles with spatial extents of many kpc are identified in the disk, and many
of the young stars in the southern disk of NGC 247 are located in the walls of
one of these structures. Dynamical age estimates of these bubbles coincide with
the last large-scale star formation event in the nucleus, suggesting that there
was large-scale star formation throughout the disk of NGC 247 a few hundred Myr
in the past. Morphological similarities are seen with the classical lop-sided
galaxy NGC 4027, and it is concluded that NGC 247 is a significantly lop-sided
spiral galaxy. The void in the northern disk is then the area between the main
body of the disk and the northern arm viewed in projection. The implications of
a lop-sided morphology for NGC 247 in the context of interactions with its
nearby starburst galaxy companion NGC 253 are discussed.


Abstract:
  Traceability and auditability are key structures that are vital in supply
chain management and construction. However, trust is the most important aspect
of customers in these systems. Also, we have to rely on third parties to trade
in centralized systems. Although current exist frameworks for these solutions
in the supply chain, these have work poor traceability and lack of real-time
information, and especially lack of privacy-preserving. In this paper, we
propose a privacy-preserving framework for supply chain traceability that using
smart contracts. Development processes, model implementation, and smart
contracts are presented in detail and we show cryptographic techniques in these
technologies to address the aforementioned. Finally, thanks to traceability and
auditability, customers and other parties can view with a single product ID and
also verified with digital signature the claims of the actors in the system.


Abstract:
  In this paper, we show that the finite subalgebra
$\mathcal{A}^{\mathbb{R}}(1)$, generated by $\mathrm{Sq}^1$ and
$\mathrm{Sq}^2$, of the $\mathbb{R}$-motivic Steenrod algebra
$\mathcal{A}^{\mathbb{R}}$ can be given $128$ different
$\mathcal{A}^{\mathbb{R}}$-module structures. We also show that all of these
$\mathcal{A}^{\mathbb{R}}$-modules can be realized as the cohomology of a
$2$-local finite $\mathbb{R}$-motivic spectrum. The realization results are
obtained using an $\mathbb{R}$ -motivic analogue of the Toda realization
theorem. We notice that each realization of $\mathcal{A}^{\mathbb{R}}(1)$ can
be expressed as a cofiber of an $\mathbb{R}$-motivic $v_1$-self-map. The
$\mathrm{C}_2$-equivariant analogue of the above results then follows because
of the Betti realization functor. We identify a relationship between the
$\mathrm{RO}(\mathrm{C}_2)$-graded Steenrod operations on a
$\mathrm{C}_2$-equivariant space and the classical Steenrod operations on both
its underlying space and its fixed-points. This technique is then used to
identify the geometric fixed-point spectra of the $\mathrm{C}_2$-equivariant
realizations of $\mathcal{A}^{\mathrm{C}_2}(1)$. We find another application of
the $\mathbb{R}$-motivic Toda realization theorem: we produce an
$\mathbb{R}$-motivic, and consequently a $\mathrm{C}_2$-equivariant, analogue
of the Bhattacharya-Egger spectrum $\mathcal{Z}$, which could be of independent
interest.


Abstract:
  We study synchronization dynamics in binary mixtures of locally coupled
Kuramoto oscillators which perform Brownian motion in a two-dimensional box. We
introduce two models, where in model $\cal I$ there are two type of
oscillators, say $\cal A$ and $\cal B$, and any two similar oscillators tend to
synchronize their phases, while any two dissimilar ones tend to be out of
phase. In model $\cal J$, in contrast, the oscillators in subpopulation $\cal
A$ behave as in model $\cal I$, while the oscillators in subpopulation $\cal B$
tend to be out of phase with all the others. In the real space all the
oscillators in both models interact via a soft-core repulsive potential. Both
subpopulations of model $\cal I$ and subpopulation $\cal A$ of model $\cal J$,
by their own, exhibit a phase coherent attractor in a certain region of model
parameters. The approach to the attractor, after an initial transient regime,
is exponential with some characteristic synchronization time scale $\tau$.
Numerical analysis reveals that the attractors of the two subpopulations
survive within model $\cal I$, regardless of the composition of the mixture
$\phi$ and the strength of the cross-population negative coupling constant $H$,
and that $\tau$ sensitively depends on $\phi$, $H$ and the packing fraction. In
particular, the ability of the oscillators to move and exchange neighbours can
significantly decrease $\tau$. In contrast, model $\cal J$ predicts suppression
of the synchronized state in subpopulation $\cal A$ and emergence of the
coherent attractor in the "contrarians" subpopulation $\cal B$ for strong and
weak cross-population coupling, respectively.


Abstract:
  This paper considers a networked aggregative game (NAG) where the players are
distributed over a communication network. By only communicating with a subset
of players, the goal of each player in the NAG is to minimize an individual
cost function that depends on its own action and the aggregate of all the
players' actions. To this end, we design a novel distributed algorithm that
jointly exploits the ideas of the consensus algorithm and the conditional
projection descent. Under strongly monotone assumption on the pseudo-gradient
mapping, the proposed algorithm with fixed step-sizes is proved to converge
linearly to the unique Nash equilibrium of the NAG. Then the theoretical
results are validated by numerical experiments.


Abstract:
  At the end of Silicon roadmap, keeping the leakage power in tolerable limit
and bridging the bandwidth gap between processor and memory have become some of
the biggest challenges. Several promising Non-Volatile Memories (NVMs) such as,
Spin-Transfer Torque RAM (STTRAM), Magnetic RAM (MRAM), Phase Change Memory
(PCM), Resistive RAM (RRAM) and Ferroelectric RAM (FeRAM) are being
investigated to address the above issues since they offer high density and
consumes zero leakage power. On one hand, the desirable properties of emerging
NVMs make them suitable candidates for several applications including
replacement of conventional memories. On the other hand, their unique
characteristics such as, high and asymmetric read/write current and persistence
bring new threats to data security and privacy. Some of these memories are
already deployed in full systems and as discrete chips and are believed to
become ubiquitous in future computing devices. Therefore, it is of utmost
important to investigate their security and privacy issues. Note that these
NVMs can be considered for cache, main memory or storage application. They are
also suitable to implement in-memory computation which increases system
throughput and eliminates Von-Neumann Bottleneck. Compute-capable NVMs impose
new security and privacy challenges that are fundamentally different than their
storage counterpart. This work identifies NVM vulnerabilities, attack vectors
originating from device level all the way to circuits and systems considering
both storage and compute applications. We also summarize the circuit/system
level countermeasures to make the NVMs robust against security and privacy
issues.


Abstract:
  In this paper we discuss a hybridised method for FEM-BEM coupling. The
coupling from both sides use a Nitsche type approach to couple to the trace
variable. This leads to a formulation that is robust and flexible with respect
to approximation spaces and can easily be combined as a building block with
other hybridised methods. Energy error norm estimates and the convergence of
Jacobi iterations are proved and the performance of the method is illustrated
on some computational examples.


Abstract:
  We have developed the dynamical model of a clumpy torus in an active galactic
nucleus (AGN) and compared to recent ALMA observations. We present $N$-body
simulations of a torus in the field of a supermassive black hole (SMBH), made
of up to $N=10^5$ gravitationally interacting clouds. As initial conditions, we
choose random distributions of the orbital elements of the clouds with a
cut-off in the inclination to mimic the presence of wind cones produced at the
early AGN stage. When the torus reaches an equilibrium, it has a doughnut
shape. We discuss the presence of box orbits. We have then constructed the
velocity and velocity dispersion maps using the resulting distributions of the
clouds at equilibrium. The effects of torus inclination and cloud sizes are
duly analyzed. We discuss the obscuration effects of the clouds using a ray
tracing simulation matching the model maps to ALMA resolution. By comparing the
model with the observational maps of NGC 1068 we find that the SMBH mass is
$M_\text{smbh}=5\times 10^6 M_\odot$ for the range of the torus inclination
angles $45^\circ - 60^\circ$. We also construct the velocity dispersion maps
for NGC 1326 and NGC 1672. They show that the peaks in the ALMA dispersion maps
are related to the emission of the torus throat. Finally, we obtain the
temperature distribution maps with parameters that correspond to our model
velocity maps for NGC 1068. They show stratification in temperature
distribution with the shape of the high temperature region as in the VLTI/MIDI
map.


Abstract:
  Intelligent reflecting surface (IRS) is an emerging technology to enhance the
spectral and energy efficiency of wireless communications cost-effectively.
This letter considers a new multi-IRS aided wireless network where a cascaded
line-of-sight (LoS) link is established between the base station (BS) and a
remote user by leveraging the multi-hop signal reflection of selected IRSs. As
compared to the conventional single-/double-hop IRS system, multi-hop IRS
system provides more pronounced path diversity and cooperative passive
beamforming gains, especially in the environment with dense obstacles. However,
a more challenging joint active/passive beamforming and multi-hop beam routing
problem also arises for maximizing the end-to-end channel gain. Furthermore,
the number of IRS-associated channel coefficients increases drastically with
the number of IRS hops. To tackle the above issues, in this letter we propose a
new and efficient beam training based solution by considering the use of
practical codebook-based BS/IRS active/passive beamforming without the need of
explicit channel estimation. Instead of exhaustively or sequentially searching
over all combinations of active and passive beam patterns for each beam route,
a distributed beam training scheme is proposed to reduce the complexity, by
exploiting the (nearly) time-invariant BS-IRS and inter-IRS channels and the
cooperative training among the BS and IRSs' controllers. Simulation results
show that our proposed design achieves the end-to-end channel gain close to
that of the sequential beam search, but at a much lower training overhead and
complexity.


Abstract:
  Anomaly detection systems need to consider a lot of information when scanning
for anomalies. One example is the context of the process in which an anomaly
might occur, because anomalies for one process might not be anomalies for a
different one. Therefore data -- such as system events -- need to be assigned
to the program they originate from. This paper investigates whether it is
possible to infer from a list of system events the program whose behavior
caused the occurrence of these system events. To that end, we model transition
probabilities between non-equivalent events and apply the $k$-nearest neighbors
algorithm. This system is evaluated on non-malicious, real-world data using
four different evaluation scores. Our results suggest that the approach
proposed in this paper is capable of correctly inferring program names from
system events.


Abstract:
  In an idealized infinite crystal, the material properties are constrained by
the symmetries of its unit cell. Naturally, the point-group symmetry is broken
by the sample shape of any finite crystal, yet this is commonly unobservable in
macroscopic metals. To sense the shape-induced symmetry lowering in such
metals, long-lived bulk states originating from anisotropic Fermi surfaces are
needed. Here we show how strongly facetted Fermi surfaces and long
quasiparticle mean free paths present in microstructures of PdCoO2 yield an
in-plane resistivity anisotropy that is forbidden by symmetry on an infinite
hexagonal lattice. Bar shaped transport devices narrower than the mean free
path are carved from single crystals using focused ion beam (FIB) milling, such
that the ballistic charge carriers at low temperatures frequently collide with
both sidewalls defining a channel. Two symmetry-forbidden transport signatures
appear: the in-plane resistivity anisotropy exceeds a factor of 2, and
transverse voltages appear in zero magnetic field. We robustly identify the
channel direction as the source of symmetry breaking via ballistic Monte- Carlo
simulations and numerical solution of the Boltzmann equation.


Abstract:
  Deep neural networks with applications from computer vision and image
processing to medical diagnosis are commonly implemented using clock-based
processors, where computation speed is limited by the clock frequency and the
memory access time. Advances in photonic integrated circuits have enabled
research in photonic computation, where, despite excellent features such as
fast linear computation, no integrated photonic deep network has been
demonstrated to date due to the lack of scalable nonlinear functionality and
the loss of photonic devices, making scalability to a large number of layers
challenging. Here we report the first integrated end-to-end photonic deep
neural network (PDNN) that performs instantaneous image classification through
direct processing of optical waves. Images are formed on the input pixels and
optical waves are coupled into nanophotonic waveguides and processed as the
light propagates through layers of neurons on-chip. Each neuron generates an
optical output from input optical signals, where linear computation is
performed optically and the nonlinear activation function is realised
opto-electronically. The output of a laser coupled into the chip is uniformly
distributed among all neurons within the network providing the same per-neuron
supply light. Thus, all neurons have the same optical output range enabling
scalability to deep networks with large number of layers. The PDNN chip is used
for 2- and 4-class classification of handwritten letters achieving accuracies
of higher than 93.7% and 90.3%, respectively, with a computation time less than
one clock cycle of state-of-the-art digital computation platforms. Direct
clock-less processing of optical data eliminates photo-detection, A/D
conversion, and the requirement for a large memory module, enabling
significantly faster and more energy-efficient neural networks for the next
generations of deep learning systems.


Abstract:
  Swing amplification is a model of spiral arm formation in disk galaxies.
Previous $N$-body simulations show that the epicycle phases of stars in spiral
arms are synchronized. However, the elementary process of the phase
synchronization is not well understood. In order to investigate phase
synchronization, we investigate the orbital evolution of stars due to
gravitational scattering by a perturber under the epicycle approximation and
its dependence on orbital elements and a disk parameter. We find that
gravitational scattering by the perturber can cause phase synchronization of
stellar orbits. The epicycle phases are better synchronized for smaller initial
epicycle amplitudes of stars and larger shear rates of galactic disks. The
vertical motion of stars does not affect the phase synchronization. The phase
synchronization forms trailing dense regions, which may correspond to spiral
arms.


Abstract:
  Causal structure discovery (CSD) models are making inroads into several
domains, including Earth system sciences. Their widespread adaptation is
however hampered by the fact that the resulting models often do not take into
account the domain knowledge of the experts and that it is often necessary to
modify the resulting models iteratively. We present a workflow that is required
to take this knowledge into account and to apply CSD algorithms in Earth system
sciences. At the same time, we describe open research questions that still need
to be addressed. We present a way to interactively modify the outputs of the
CSD algorithms and argue that the user interaction can be modelled as a greedy
finding of the local maximum-a-posteriori solution of the likelihood function,
which is composed of the likelihood of the causal model and the prior
distribution representing the knowledge of the expert user. We use a real-world
data set for examples constructed in collaboration with our co-authors, who are
the domain area experts. We show that finding maximally usable causal models in
the Earth system sciences or other similar domains is a difficult task which
contains many interesting open research questions. We argue that taking the
domain knowledge into account has a substantial effect on the final causal
models discovered.


Abstract:
  An open source software package for performing dynamic RMS simulation of
small to medium-sized power systems is presented, written entirely in the
Python programming language. The main objective is to facilitate fast
prototyping of new wide area monitoring, control and protection applications
for the future power system by enabling seamless integration with other tools
available for Python in the open source community, e.g. for signal processing,
artificial intelligence, communication protocols etc. The focus is thus
transparency and expandability rather than computational efficiency and
performance. The main purpose of this paper, besides presenting the code and
some results, is to share interesting experiences with the power system
community, and thus stimulate wider use and further development. Two
interesting conclusions at the current stage of development are as follows:
First, the simulation code is fast enough to emulate real-time simulation for
small and medium-size grids with a time step of 5 ms, and allows for
interactive feedback from the user during the simulation. Second, the
simulation code can be uploaded to an online Python interpreter, edited, run
and shared with anyone with a compatible internet browser. Based on this, we
believe that the presented simulation code could be a valuable tool, both for
researchers in early stages of prototyping real-time applications, and in the
educational setting, for students developing intuition for concepts and
phenomena through real-time interaction with a running power system model.


Abstract:
  Global quantum networks will benefit from the reliable creation and control
of high-performance solid-state telecom photon-spin interfaces. T radiation
damage centres in silicon provide a promising photon-spin interface due to
their narrow O-band optical transition near 1326 nm and long-lived electron and
nuclear spin lifetimes. To date, these defect centres have only been studied as
ensembles in bulk silicon. Here, we demonstrate the reliable creation of high
concentration T centre ensembles in the 220 nm device layer of
silicon-on-insulator (SOI) wafers by ion implantation and subsequent annealing.
We then develop a method that uses spin-dependent optical transitions to
benchmark the characteristic optical spectral diffusion within these T centre
ensembles. Using this new technique, we show that with minimal optimization to
the fabrication process high densities of implanted T centres localized
$\lesssim$100 nm from an interface display ~1 GHz characteristic levels of
total spectral diffusion.


Abstract:
  Existing global convergence guarantees of (stochastic) gradient descent do
not apply to practical deep networks in the practical regime of deep learning
beyond the neural tangent kernel (NTK) regime. This paper proposes an
algorithm, which is ensured to have global convergence guarantees in the
practical regime beyond the NTK regime, under a verifiable condition called the
expressivity condition. The expressivity condition is defined to be both
data-dependent and architecture-dependent, which is the key property that makes
our results applicable for practical settings beyond the NTK regime. On the one
hand, the expressivity condition is theoretically proven to hold
data-independently for fully-connected deep neural networks with narrow hidden
layers and a single wide layer. On the other hand, the expressivity condition
is numerically shown to hold data-dependently for deep (convolutional) ResNet
with batch normalization with various standard image datasets. We also show
that the proposed algorithm has generalization performances comparable with
those of the heuristic algorithm, with the same hyper-parameters and total
number of iterations. Therefore, the proposed algorithm can be viewed as a step
towards providing theoretical guarantees for deep learning in the practical
regime.


Abstract:
  In the study of closed many-body quantum systems one is often interested in
the evolution of a subset of degrees of freedom. On many occasions it is
possible to approach the problem by performing an appropriate decomposition
into a bath and a system. In the simplest case the evolution of the reduced
state of the system is governed by a quantum master equation with a
time-independent, i.e. Markovian, generator. Such evolution is typically
emerging under the assumption of a weak coupling between the system and an
infinitely large bath. Here, we are interested in understanding to which extent
a neural network function approximator can predict open quantum dynamics -
described by time-local generators - from an underlying unitary dynamics. We
investigate this question using a class of spin models, which is inspired by
recent experimental setups. We find that indeed time-local generators can be
learned. In certain situations they are even time-independent and allow to
extrapolate the dynamics to unseen times. This might be useful for situations
in which experiments or numerical simulations do not allow to capture long-time
dynamics and for exploring thermalization occurring in closed quantum systems.


Abstract:
  This paper revisits the concept of damping torque in a multimachine power
system and its relation to the dissipation of oscillation energy in synchronous
machine windings. As a multimachine extension of an existing result on a
single-machine-infinite-bus (SMIB) system, we show that the total damping power
for a mode stemming from the interaction of electromagnetic torques and rotor
speeds is equal to the sum of average power dissipations in the generator
windings corresponding to the modal oscillation. Further, counter-intuitive to
the SMIB result, we demonstrate that, although the equality holds on an
aggregate, such is not the case for individual machines in an interconnected
system. To that end, distribution factors are derived for expressing the
average damping power of each generator as a linear combination of average
powers of modal energy dissipation in the windings of all machines in the
system. These factors represent the distribution of damping power in a
multimachine system. The results are validated on IEEE 4-machine and 16-machine
test systems.


Abstract:
  The multi-terminal Josephson effect allows DC supercurrent to flow at finite
commensurate voltages. Existing proposals to realize this effect rely on
nonlocal Andreev processes in superconductor-normal-superconductor junctions.
However, this approach requires precise control over microscopic states and is
obscured by dissipative current. We show that standard tunnel Josephson
circuits also support multiplet supercurrent mediated only by local tunneling
processes. Furtheremore, we observe that the supercurrents persist even in the
high charging energy regime in which only sequential Cooper transfers are
allowed. Finally, we demonstrate that the multiplet supercurrent in these
circuits has a quantum geometric component that is distinguinshable from the
well-known adiabatic contribution.


Abstract:
  Physical property of a single quantum object is governed by its precise
atomic arrangement. The direct correlation of localized physical properties
with the atomic structures has been therefore strongly desired but still
limited in the theoretical studies. Here, we have successfully examined the
localized electronic properties of individual carbon nanotubes by means of
high-resolution electron energy-loss spectroscopy combined with high-resolution
transmission electron microscopy. Well-separated sharp peaks at the carbon
K(1s) absorption edge and in the valence-loss spectra are obtained from a
single freestanding carbon nanotube with the local chiral index and
unambiguously identified as the transitions between the van Hove singularities.
The spectra features clearly vary upon the different areas even in the
individual carbon nanotube. Variations in interband transitions, plasmonic
behaviors and unoccupied electronic structures are clearly attributed to the
local irregular atomic arrangement such as topological defect and/or elastic
bond stretching.


Abstract:
  Over the last few decades, Lung Ultrasound (LUS) has been increasingly used
to diagnose and monitor different lung diseases in neonates. It is a non
invasive tool that allows a fast bedside examination while minimally handling
the neonate. Acquiring a LUS scan is easy, but understanding the artifacts
concerned with each respiratory disease is challenging. Mixed artifact patterns
found in different respiratory diseases may limit LUS readability by the
operator. While machine learning (ML), especially deep learning can assist in
automated analysis, simply feeding the ultrasound images to an ML model for
diagnosis is not enough to earn the trust of medical professionals. The
algorithm should output LUS features that are familiar to the operator instead.
Therefore, in this paper we present a unique approach for extracting seven
meaningful LUS features that can be easily associated with a specific
pathological lung condition: Normal pleura, irregular pleura, thick pleura,
Alines, Coalescent B-lines, Separate B-lines and Consolidations. These
artifacts can lead to early prediction of infants developing later respiratory
distress symptoms. A single multi-class region proposal-based object detection
model faster-RCNN (fRCNN) was trained on lower posterior lung ultrasound videos
to detect these LUS features which are further linked to four common neonatal
diseases. Our results show that fRCNN surpasses single stage models such as
RetinaNet and can successfully detect the aforementioned LUS features with a
mean average precision of 86.4%. Instead of a fully automatic diagnosis from
images without any interpretability, detection of such LUS features leave the
ultimate control of diagnosis to the clinician, which can result in a more
trustworthy intelligent system.


Abstract:
  We prove that the topological complexity $\mathrm{TC}(\pi)$ equals
$\mathrm{cd}(\pi\times\pi)$ for certain toral relatively hyperbolic groups
$\pi$.


Abstract:
  In this manuscript, we develop an efficient algorithm to evaluate the
azimuthal Fourier components of the Green's function for the Helmholtz equation
in cylindrical coordinates. A computationally efficient algorithm for this
modal Green's function is essential for solvers for electromagnetic scattering
from bodies of revolution (e.g., radar cross sections, antennas). Current
algorithms to evaluate this modal Green's function become computationally
intractable when the source and target are close or when the wavenumber is
large. Furthermore, most state of the art methods cannot be easily
parallelized. In this manuscript, we present an algorithm for evaluating the
modal Green's function that has performance independent of both
source-to-target proximity and wavenumber, and whose cost grows as $O(m)$,
where $m$ is the Fourier mode. Furthermore, our algorithm is embarrassingly
parallelizable.


Abstract:
  In order for off-Earth top surface structures built from regolith to protect
astronauts from radiation, they need to be several meters thick. Technical
University Delft (TUD) proposes to excavate into the ground to create
subsurface habitats. By excavating not only natural protection from radiation
can be achieved but also thermal insulation because the temperature is more
stable underground. At the same time through excavation valuable resources can
be mined for through in situ resource utilization (ISRU). The idea is that a
swarm of autonomous mobile robots excavate the ground in a sloped downwards
spiral movement. The excavated regolith will be mixed with cement, which can be
reproduced on Mars through ISRU, in order to create concrete. The concrete is
3D printed/sprayed on the excavated tunnel to reinforce it. As soon as the
tunnels are reinforced, the material in-between the tunnels can be removed in
order to create a larger cavity that can be used for inhabitation. Proposed
approach relies on Design-to-Robotic-Production (D2RP) technology developed at
TUD1 for on-Earth applications. The rhizomatic 3D printed structure is a
structurally optimized porous shell structure with increased insulation
properties. In order to regulate the indoor pressurised environment an
inflatable structure is placed in the 3D printed cavity. This inflatable
structure is made of materials, which can also be at some point reproduced on
Mars through ISRU. Depending on location the habitat and the production system
are powered by a system combining solar and kite power. The ultimate goal is to
develop an autarkic D2RP system for building subsurface autarkic habitats on
Mars from locally obtained materials.


Abstract:
  Multimodal MRI provides complementary and clinically relevant information to
probe tissue condition and to characterize various diseases. However, it is
often difficult to acquire sufficiently many modalities from the same subject
due to limitations in study plans, while quantitative analysis is still
demanded. In this work, we propose a unified conditional disentanglement
framework to synthesize any arbitrary modality from an input modality. Our
framework hinges on a cycle-constrained conditional adversarial training
approach, where it can extract a modality-invariant anatomical feature with a
modality-agnostic encoder and generate a target modality with a conditioned
decoder. We validate our framework on four MRI modalities, including
T1-weighted, T1 contrast enhanced, T2-weighted, and FLAIR MRI, from the
BraTS'18 database, showing superior performance on synthesis quality over the
comparison methods. In addition, we report results from experiments on a tumor
segmentation task carried out with synthesized data.


Abstract:
  A method based on the Fast Fourier Transform is proposed to obtain the
dispersion relation of acoustic waves in heterogeneous periodic media with
arbitrary microstructures. The microstructure is explicitly considered using a
voxelized Representative Volume Element (RVE). The dispersion diagram is
obtained solving an eigenvalue problem for Bloch waves in Fourier space. To
this aim, two linear operators representing stiffness and mass are defined
through the use of differential operators in Fourier space. The smallest
eigenvalues are obtained using the implicitly restarted Lanczos and the
subspace iteration methods, and the required inverse of the stiffness operator
is done using the conjugate gradient with a preconditioner. The method is used
to study the propagation of acoustic waves in elastic polycrystals, showing the
strong effect of crystal anistropy and polycrystaline texture on the
propagation. It is shown that the method combines the simplicity of classical
Fourier series analysis with the versatility of Finite Elements to account for
complex geometries proving an efficient and general approach which allows the
use of large RVEs in 3D.


Abstract:
  With the increasing use of the internet and social media, it is obvious that
cyberbullying has become a major problem. The most basic way for protection
against the dangerous consequences of cyberbullying is to actively detect and
control the contents containing cyberbullying. When we look at today's internet
and social media statistics, it is impossible to detect cyberbullying contents
only by human power. Effective cyberbullying detection methods are necessary in
order to make social media a safe communication space. Current research efforts
focus on using machine learning for detecting and eliminating cyberbullying.
Although most of the studies have been conducted on English texts for the
detection of cyberbullying, there are few studies in Turkish. Limited methods
and algorithms were also used in studies conducted on the Turkish language. In
addition, the scope and performance of the algorithms used to classify the
texts containing cyberbullying is different, and this reveals the importance of
using an appropriate algorithm. The aim of this study is to compare the
performance of different machine learning algorithms in detecting Turkish
messages containing cyberbullying. In this study, nineteen different
classification algorithms were used to identify texts containing cyberbullying
using Turkish natural language processing techniques. Precision, recall,
accuracy and F1 score values were used to evaluate the performance of
classifiers. It was determined that the Light Gradient Boosting Model (LGBM)
algorithm showed the best performance with 90.788% accuracy and 90.949% F1
Score value.


Abstract:
  In this paper we introduce Neural Network Coding(NNC), a data-driven approach
to joint source and network coding. In NNC, the encoders at each source and
intermediate node, as well as the decoder at each destination node, are neural
networks which are all trained jointly for the task of communicating correlated
sources through a network of noisy point-to-point links. The NNC scheme is
application-specific and makes use of a training set of data, instead of making
assumptions on the source statistics. In addition, it can adapt to any
arbitrary network topology and power constraint. We show empirically that, for
the task of transmitting MNIST images over a network, the NNC scheme shows
improvement over baseline schemes, especially in the low-SNR regime.


Abstract:
  We derive a thermodynamic uncertainty relation for first passage processes in
quantum Markov chains. We consider first passage processes stopping after a
fixed number of jumps, which contrasts with typical quantum Markov chains which
end at a fixed time. We bound observables of first passage processes in quantum
Markov chains by the Loschmidt echo, which quantifies the extent of
irreversibility in quantum many-body systems. Considering a particular case of
the bound, we show that the lower bound corresponds to the quantum Fisher
information, which has played a fundamental role in uncertainty relations in
quantum systems. Moreover, our bound reduces to a thermodynamic uncertainty
relation for classical first passage processes when considering classical
dynamics.


Abstract:
  In this study, we investigated the motion of active SiO2-Pt Janus particles
in the 2D bath of smaller silica tracers dispersed in varying areal densities.
The effect on the organization of the tracer particles around the active JPs
was also explored. Our experiments indicate that the interaction between the
tracers and the active JPs mainly depend on the nature of collision marked by
the duration of contact. For all the concentration regimes, we have shown that
the short time collisions do not have significant impact on the motion of
active JPs, however, during moderate/long-time collisions tracer(s) can lead to
a significant change in active JPs motion and even cause them to rotate. In the
concentrated regime, our experiments reveal the emergence of a novel
organizational behavior of the passive tracers on the trailing Pt and the
leading SiO2 with a strong dependence on the nature of collision.


Abstract:
  We study scalar, fermionic and gauge fields coupled nonminimally to gravity
in the Einstein-Cartan formulation. We construct a wide class of models with
nondynamical torsion whose gravitational spectra comprise only the massless
graviton. Eliminating non-propagating degrees of freedom, we derive an
equivalent theory in the metric formulation of gravity. It features contact
interactions of a certain form between and among the matter and gauge currents.
We also discuss briefly the inclusion of curvature-squared terms.


Abstract:
  Social comparison-based features are widely used in social computing apps.
However, most existing apps are not grounded in social comparison theories and
do not consider individual differences in social comparison preferences and
reactions. This paper is among the first to automatically personalize social
comparison targets. In the context of an m-health app for physical activity, we
use artificial intelligence (AI) techniques of multi-armed bandits. Results
from our user study (n=53) indicate that there is some evidence that motivation
can be increased using the AI-based personalization of social comparison. The
detected effects achieved small-to-moderate effect sizes, illustrating the
real-world implications of the intervention for enhancing motivation and
physical activity. In addition to design implications for social comparison
features in social apps, this paper identified the personalization paradox, the
conflict between user modeling and adaptation, as a key design challenge of
personalized applications for behavior change. Additionally, we propose
research directions to mitigate this Personalization Paradox.


Abstract:
  Weeds are a significant threat to the agricultural productivity and the
environment. The increasing demand for sustainable agriculture has driven
innovations in accurate weed control technologies aimed at reducing the
reliance on herbicides. With the great success of deep learning in various
vision tasks, many promising image-based weed detection algorithms have been
developed. This paper reviews recent developments of deep learning techniques
in the field of image-based weed detection. The review begins with an
introduction to the fundamentals of deep learning related to weed detection.
Next, recent progresses on deep weed detection are reviewed with the discussion
of the research materials including public weed datasets. Finally, the
challenges of developing practically deployable weed detection methods are
summarized, together with the discussions of the opportunities for future
research.We hope that this review will provide a timely survey of the field and
attract more researchers to address this inter-disciplinary research problem.


Abstract:
  The axion mass receives a large correction from small instantons if the QCD
gets strongly coupled at high energies. We discuss the size of the new CP
violating phases caused by the fact that the small instantons are sensitive to
the UV physics. We also discuss the effects of the mass correction on the axion
abundance of the Universe. Taking the small-instanton contributions into
account, we propose a natural scenario of axion dark matter where the axion
decay constant is as large as $10^{15\text{-}16}$GeV. The scenario works in the
high-scale inflation models.


Abstract:
  Cloud service providers are distributing data centers geographically to
minimize energy costs through intelligent workload distribution. With
increasing data volumes in emerging cloud workloads, it is critical to factor
in the network costs for transferring workloads across data centers. For
geo-distributed data centers, many researchers have been exploring strategies
for energy cost minimization and intelligent inter-data-center workload
distribution separately. However, prior work does not comprehensively and
simultaneously consider data center energy costs, data transfer costs, and data
center queueing delay. In this paper, we propose a novel game theory-based
workload management framework that takes a holistic approach to the cloud
operating cost minimization problem by making intelligent scheduling decisions
aware of data transfer costs and the data center queueing delay. Our framework
performs intelligent workload management that considers heterogeneity in data
center compute capability, cooling power, interference effects from task
co-location in servers, time-of-use electricity pricing, renewable energy, net
metering, peak demand pricing distribution, and network pricing. Our
simulations show that the proposed game-theoretic technique can minimize the
cloud operating cost more effectively than existing approaches.


Abstract:
  We describe the full group of isometries of absolutely simple, compact,
connected real Lie groups, of SO(4) and of U(n), endowed with suitable
bi-invariant Riemannian metrics.


Abstract:
  The objective of this work is to annotate sign instances across a broad
vocabulary in continuous sign language. We train a Transformer model to ingest
a continuous signing stream and output a sequence of written tokens on a
large-scale collection of signing footage with weakly-aligned subtitles. We
show that through this training it acquires the ability to attend to a large
vocabulary of sign instances in the input sequence, enabling their
localisation. Our contributions are as follows: (1) we demonstrate the ability
to leverage large quantities of continuous signing videos with weakly-aligned
subtitles to localise signs in continuous sign language; (2) we employ the
learned attention to automatically generate hundreds of thousands of
annotations for a large sign vocabulary; (3) we collect a set of 37K manually
verified sign instances across a vocabulary of 950 sign classes to support our
study of sign language recognition; (4) by training on the newly annotated data
from our method, we outperform the prior state of the art on the BSL-1K sign
language recognition benchmark.


Abstract:
  In this letter, we note that the observed in the LIGO / Virgo experiment
ratio of the detection rate of black holes to the rate of detection of binary
neutron stars requires the assumption of a "conservative" collapse of massive
stars into a black hole: almost all the mass of the collapsing star goes under
the horizon. This is consistent with the large masses of black holes detected
by LIGO/Virgo. On the other hand, the assumption of a small loss of matter
during the collapse into a black hole is in good agreement with the small
eccentricity of Single-lined Binaries. At the same time, the absence of X-rays
from most black holes in binary systems with blue stars is explained. We argue
that three sets of LIGO / Virgo observations and data on the Single-lined
Binary with a Candidate Black Hole Component confirm the scenario of the
evolution of massive field binaries.


Abstract:
  We explore the phenomenology of a model of monopolium based on an
electromagnetic dual formulation of Zwanziger and lattice gauge theory. The
monopole is assumed to have a finite-sized inner structure based on a 't
Hooft-Polyakov like solution, with the magnetic charge uniformly distributed on
the surface of a sphere. The monopole and anti-monopole potential becomes
linear plus Coulomb outside the sphere, analogous to the Cornell potential
utilised in the study of quarkonium states. Discovery of a resonance feature in
the diphoton channel as well as in a higher multiplicity photon channel would
be a smoking gun for the existence of monopoles within this monopolium
construction, with the mass and bound state properties extractable. Utilising
the current LHC results in the diphoton channel, constraints on the monopole
mass are determined for a wide range of model parameters. These are compared to
the most recent MoEDAL results and found to be significantly more stringent in
certain parameter regions, providing strong motivation for exploring higher
multiplicity photon final state searches.


Abstract:
  This paper presents an exact formula for calculating the fourth-moment tensor
from the second-moment tensor for the three dimensional Jeffery's equation.
Although this approach falls within the category of a moment tensor closure, it
does not rely upon an approximation, either analytic or curve fit, of the
fourth-moment tensor as do previous closures. This closure is orthotropic in
the sense of \cite{cintra:95}, or equivalently, a natural closure in the sense
of \cite{verleye:93}. The existence of these explicit formulae has been
asserted previously, but as far as the authors know, the explicit forms have
yet to be published. The formulae involve elliptic integrals, and are valid
whenever fiber orientation was isotropic at some point in time. Finally, this
paper presents the Fast Exact Closure (FEC), a fast and in principle exact
method for solving Jeffery's equation, which does not require approximate
closures, nor the elliptic integral computation.


Abstract:
  In this paper, we present a linear-time approximation scheme for $k$-means
clustering of \emph{incomplete} data points in $d$-dimensional Euclidean space.
An \emph{incomplete} data point with $\Delta>0$ unspecified entries is
represented as an axis-parallel affine subspaces of dimension $\Delta$. The
distance between two incomplete data points is defined as the Euclidean
distance between two closest points in the axis-parallel affine subspaces
corresponding to the data points. We present an algorithm for $k$-means
clustering of axis-parallel affine subspaces of dimension $\Delta$ that yields
an $(1+\epsilon)$-approximate solution in $O(nd)$ time. The constants hidden
behind $O(\cdot)$ depend only on $\Delta, \epsilon$ and $k$. This improves the
$O(n^2 d)$-time algorithm by Eiben et al.[SODA'21] by a factor of $n$.


Abstract:
  Context. Requirements engineering remains a discipline that is faced with a
large number of challenges, including the implementation of a requirements
elicitation process in industry. Although several proposals have been suggested
by researchers and academics, little is known of the practices that are
actually followed in industry. Objective. We investigate the SoTA with respect
to requirements elicitation, examining practitioners' practices. We focus on
the techniques, the roles involved, and the challenges associated to the
process. Method. We conducted an interview-based survey study involving 24
practitioners from 12 different Swedish IT companies. Results. We found that
group interaction techniques, including meetings and workshops, are the most
popular type of elicitation techniques that are employed by the practitioners,
except in the case of small projects. We noted that customers are frequently
involved in the elicitation process, except in the case of market-driven
organizations. Technical staff (for example, developers and architects) are
more frequently involved in the elicitation process compared to the involvement
of business- or strategic staff. Finally, we identified a number of challenges
with respect to stakeholders. These challenges include difficulties in
understanding and prioritizing their needs. Further, it was noted that
requirements instability (i.e., caused by changing needs or priorities) was a
predominant challenge. These observations need to be interpreted in the context
of the study. Conclusion. The relevant observations regarding the survey
participants' experiences should be of interest to the industry; experiences
that should be analyzed in the practitioners' context. Researchers may find
evidence for the use of academic results in practice, thereby inspiring future
theoretical work, as well as further empirical studies in the same area.


Abstract:
  Measurements at particle collider experiments, even if primarily aimed at
understanding Standard Model processes, can have a high degree of model
independence, and implicitly contain information about potential contributions
from physics beyond the Standard Model. The Contur package allows users to
benefit from the hundreds of measurements preserved in the Rivet library to
test new models against the bank of LHC measurements to date. This method has
proven to be very effective in several recent publications from the Contur
team, but ultimately, for this approach to be successful, the authors believe
that the Contur tool needs to be accessible to the wider high energy physics
community. As such, this manual accompanies the first user-facing version:
Contur v2. It describes the design choices that have been made, as well as
detailing pitfalls and common issues to avoid. The authors hope that with the
help of this documentation, external groups will be able to run their own
Contur studies, for example when proposing a new model, or pitching a new
search.


Abstract:
  The combination of data from multiple sensors, also known as sensor fusion or
data fusion, is a key aspect in the design of autonomous robots. In particular,
algorithms able to accommodate sensor fusion techniques enable increased
accuracy, and are more resilient against the malfunction of individual sensors.
The development of algorithms for autonomous navigation, mapping and
localization have seen big advancements over the past two decades. Nonetheless,
challenges remain in developing robust solutions for accurate localization in
dense urban environments, where the so called last-mile delivery occurs. In
these scenarios, local motion estimation is combined with the matching of
real-time data with a detailed pre-built map. In this paper, we utilize data
gathered with an autonomous delivery robot to compare different sensor fusion
techniques and evaluate which are the algorithms providing the highest accuracy
depending on the environment. The techniques we analyze and propose in this
paper utilize 3D lidar data, inertial data, GNSS data and wheel encoder
readings. We show how lidar scan matching combined with other sensor data can
be used to increase the accuracy of the robot localization and, in consequence,
its navigation. Moreover, we propose a strategy to reduce the impact on
navigation performance when a change in the environment renders map data
invalid or part of the available map is corrupted.


Abstract:
  We obtain local numerators satisfying the BCJ color-kinematics duality at one
loop for super-Yang-Mills theory in ten dimensions. This is done explicitly for
six points via the field-theory limit of the genus-one open superstring
correlators for different color orderings, in an analogous manner to an earlier
derivation of local BCJ-satisfying numerators at tree level from disk
correlators. These results solve an outstanding puzzle from a previous analysis
where the six-point numerators did not satisfy the color-kinematics duality.


Abstract:
  In most of computer vision applications, motion blur is regarded as an
undesirable artifact. However, it has been shown that motion blur in an image
may have practical interests in fundamental computer vision problems. In this
work, we propose a novel framework to estimate optical flow from a single
motion-blurred image in an end-to-end manner. We design our network with
transformer networks to learn globally and locally varying motions from encoded
features of a motion-blurred input, and decode left and right frame features
without explicit frame supervision. A flow estimator network is then used to
estimate optical flow from the decoded features in a coarse-to-fine manner. We
qualitatively and quantitatively evaluate our model through a large set of
experiments on synthetic and real motion-blur datasets. We also provide
in-depth analysis of our model in connection with related approaches to
highlight the effectiveness and favorability of our approach. Furthermore, we
showcase the applicability of the flow estimated by our method on deblurring
and moving object segmentation tasks.


Abstract:
  Over the last two decades, a large population of close-in planets has been
detected around a wide variety of host stars. Such exoplanets are likely to
undergo planetary migration through magnetic and tidal interactions. We aim to
follow the orbital evolution of a planet along the structural and rotational
evolution of its host star by taking into account simultaneously tidal and
magnetic torques, in order to explain some properties of the distribution of
observed close-in planets. We rely on a numerical model of a coplanar circular
star-planet system taking into account stellar structural changes, wind braking
and star-planet interactions, called ESPEM. We browse the parameter space of
star-planet systems' configurations and assess the relative influence of
magnetic and tidal torques on secular evolution. We then synthesize star-planet
populations and confront their distribution in orbital and stellar rotation
periods to Kepler satellite data. First, we find that after the dissipation of
the protoplanetary disk, both types of interactions can dominate secular
evolution depending on the initial configuration of the system and the
evolutionary phase considered. Moreover, different populations of star-planet
systems emerge from the combined action of both kinds of interactions,
according to the evolutionary phase during which the planet migrates
significantly. This may affect significantly the detectability of star-planet
systems as well as the validity of gyrochonology. All in all, star-planet
interactions significantly impact the global distribution in orbital periods
during the main sequence, while the global distribution in stellar rotation
periods is marginally affected. More precisely, star-planet magnetic
interactions significantly affect the distribution of super-Earths around
slowly rotating stars, while tidal effects are found to shape the distribution
of giant planets.


Abstract:
  BatchNorm is a critical building block in modern convolutional neural
networks. Its unique property of operating on "batches" instead of individual
samples introduces significantly different behaviors from most other operations
in deep learning. As a result, it leads to many hidden caveats that can
negatively impact model's performance in subtle ways. This paper thoroughly
reviews such problems in visual recognition tasks, and shows that a key to
address them is to rethink different choices in the concept of "batch" in
BatchNorm. By presenting these caveats and their mitigations, we hope this
review can help researchers use BatchNorm more effectively.


Abstract:
  We study the Hilbert scheme $\mathcal{H}^\mathcal{L}_{d,g,r}$ parametrizing
smooth, irreducible, non-degenerate and linearly normal curves of degree $d$
and genus $g$ in $\mathbb{P}^r$ whose complete and very ample hyperplane linear
series $\mathcal{D}$ have relatively small index of speciality
$i(\mathcal{D})=g-d+r$. In particular we show the existence (and non-existence
as well in some sporadic cases) of every Hilbert scheme of linearly normal
curves with $i(\mathcal{D})=4$. We also determine the irreducibility of
$\mathcal{H}^\mathcal{L}_{2r+4,r+8,r}$ for $3\le r\le 8$, which are rather
peculiar families in a certain sense.


Abstract:
  Semantic information of a sentence is crucial for improving the
expressiveness of a text-to-speech (TTS) system, but can not be well learned
from the limited training TTS dataset just by virtue of the nowadays encoder
structures. As large scale pre-trained text representation develops,
bidirectional encoder representations from transformers (BERT) has been proven
to embody text-context semantic information and applied to TTS as additional
input. However BERT can not explicitly associate semantic tokens from point of
dependency relations in a sentence. In this paper, to enhance expressiveness,
we propose a semantic representation learning method based on graph neural
network, considering dependency relations of a sentence. Dependency graph of
input text is composed of edges from dependency tree structure considering both
the forward and the reverse directions. Semantic representations are then
extracted at word level by the relational gated graph network (RGGN) fed with
features from BERT as nodes input. Upsampled semantic representations and
character-level embeddings are concatenated to serve as the encoder input of
Tacotron-2. Experimental results show that our proposed method outperforms the
baseline using vanilla BERT features both in LJSpeech and Blizzard Challenge
2013 datasets, and semantic representations learned from the reverse direction
are more effective for enhancing expressiveness.


Abstract:
  A recently developed high-order implicit shock tracking (HOIST) framework for
resolving discontinuous solutions of inviscid, steady conservation laws [41,
43] is extended to the unsteady case. Central to the framework is an
optimization problem which simultaneously computes a discontinuity-aligned mesh
and the corresponding high-order approximation to the flow, which provides
nonlinear stabilization and a high-order approximation to the solution. This
work extends the implicit shock tracking framework to the case of unsteady
conservation laws using a method of lines discretization via a diagonally
implicit Runge-Kutta method by "solving a steady problem at each timestep". We
formulate and solve an optimization problem that produces a feature-aligned
mesh and solution at each Runge-Kutta stage of each timestep, and advance this
solution in time by standard Runge-Kutta update formulas. A Rankine-Hugoniot
based prediction of the shock location together with a high-order, untangling
mesh smoothing procedure provides a high-quality initial guess for the
optimization problem at each time, which results in Newton-like convergence of
the sequential quadratic programing (SQP) optimization solver. This method is
shown to deliver highly accurate solutions on coarse, high-order
discretizations without nonlinear stabilization and recover the design accuracy
of the Runge-Kutta scheme. We demonstrate this framework on a series of
inviscid, unsteady conservation laws in both one- and two- dimensions. We also
verify that our method is able to recover the design order of accuracy of our
time integrator in the presence of a strong discontinuity.


Abstract:
  Frequency domain Unsteady Lifting-Line Theory (ULLT) provides a means by
which the aerodynamics of oscillating wings may be studied at low computational
cost without neglecting the interacting effects of aspect ratio and oscillation
frequency. Renewed interest in the method has drawn attention to several
uncertainties however. Firstly, to what extent is ULLT practically useful for
rectangular wings, despite theoretical limitations? And secondly, to what
extent is a complicated wake model needed in the outer solution for good
accuracy? This paper aims to answer these questions by presenting a complete
ULLT based on the work of Sclavounos, along with a novel ULLT that considers
only the streamwise vorticity and a Prandtl-like pseudosteady ULLT. These are
compared to high fidelity Euler CFD for cases of rectangular wings at multiple
aspect ratios and oscillation frequencies. The results of this work establish
ULLT as a low computational cost model capable of accounting for interacting
finite-wing and oscillation frequency effects, and identify the aspect ratio
and frequency regimes where the three ULLTs are most accurate. This research
paves the way towards the construction of time-domain or numerical ULLTs which
may be augmented to account for non-linearities such as flow separation.


Abstract:
  Nonlinear quantum graphs are metric graphs equipped with a nonlinear
Schr{\"o}dinger equation. Whereas in the last ten years they have known
considerable developments on the theoretical side, their study from the
numerical point of view remains in its early stages. The goal of this paper is
to present the Grafidi library, a Python library which has been developed with
the numerical simulation of nonlinear Schr{\"o}dinger equations on graphs in
mind. We will show how, with the help of the Grafidi library, one can implement
the popular normalized gradient flow and nonlinear conjugate gradient flow
methods to compute ground states of a nonlinear quantum graph. We will also
simulate the dynamics of the nonlinear Schr{\"o}dinger equation with a
Crank-Nicolson relaxation scheme and a Strang splitting scheme. Finally, in a
series of numerical experiments on various types of graphs, we will compare the
outcome of our numerical calculations for ground states with the existing
theoretical results, thereby illustrating the versatility and efficiency of our
implementations in the framework of the Grafidi library.


Abstract:
  This paper proposes a new simultaneous terahertz (THz) information and power
transfer (STIPT) system, which is assisted by reconfigurable intelligent
surface (RIS) for both the information data and power transmission. We aim to
maximize the information users' (IUs') data rate while guaranteeing the energy
users' (EUs') and RIS's power harvesting requirements. To solve the formulated
non-convex problem, the block coordinate descent (BCD) based algorithm is
adopted to alternately optimize the transmit precoding of IUs, RIS's reflecting
coefficients, and RIS's coordinate. The Penalty Constrained Convex
Approximation (PCCA) Algorithm is proposed to solve the intractable
optimization problem of the RIS's coordinate, where the solution's feasibility
is guaranteed by the introduced penalties. Simulation results confirm that the
proposed BCD algorithm can significantly enhance the performance of STIPT by
employing RIS.


Abstract:
  Resonant optical excitation of certain molecular vibrations in
$\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Br has been shown to induce transient
superconducting-like optical properties at temperatures far above equilibrium
$T_c$. Here, we report experiments across the bandwidth-tuned phase diagram of
this class of materials, and study the Mott insulator
$\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Cl and the metallic compound
$\kappa$-(BEDT-TTF)$_2$Cu(NCS)$_2$. We find non-equilibrium photoinduced
superconductivity only in $\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Br, indicating
that the proximity to the Mott insulating phase and possibly the presence of
preexisting superconducting fluctuations are pre-requisites for this effect.


Abstract:
  In this paper, we consider the problem of training neural network (NN)
controllers for nonlinear dynamical systems that are guaranteed to satisfy
safety and liveness (e.g., reach-avoid) properties. Our approach is to combine
model-based design methodologies for dynamical systems with data-driven
approaches to achieve this target. We confine our attention to NNs with
Rectifier Linear Unit (ReLU) nonlinearity which are known to represent
Continuous Piece-Wise Affine (CPWA) functions. Given a mathematical model of
the dynamical system, we compute a finite-state abstract model that captures
the closed-loop behavior under all possible CPWA controllers. Using this
finite-state abstract model, our framework identifies a family of CPWA
functions guaranteed to satisfy the safety requirements. We augment the
learning algorithm with a NN weight projection operator during training that
enforces the resulting NN to represent a CPWA function from the provably safe
family of CPWA functions. Moreover, the proposed framework uses the
finite-state abstract model to identify candidate CPWA functions that may
satisfy the liveness properties. Using such candidate CPWA functions, the
proposed framework biases the NN training to achieve the liveness
specification. We show the efficacy of the proposed framework both in
simulation and on an actual robotic vehicle.


Abstract:
  We present TICaM, a Time-of-flight In-car Cabin Monitoring dataset for
vehicle interior monitoring using a single wide-angle depth camera. Our dataset
addresses the deficiencies of currently available in-car cabin datasets in
terms of the ambit of labeled classes, recorded scenarios and provided
annotations; all at the same time. We record an exhaustive list of actions
performed while driving and provide for them multi-modal labeled images (depth,
RGB and IR), with complete annotations for 2D and 3D object detection, instance
and semantic segmentation as well as activity annotations for RGB frames.
Additional to real recordings, we provide a synthetic dataset of in-car cabin
images with same multi-modality of images and annotations, providing a unique
and extremely beneficial combination of synthetic and real data for effectively
training cabin monitoring systems and evaluating domain adaptation approaches.
The dataset is available at https://vizta-tof.kl.dfki.de/.


Abstract:
  Many neutron star (NS) properties, such as the proton fraction within a NS,
reflect the symmetry energy contributions to the Equation of State that
dominate when neutron and proton densities differ strongly. To constrain these
contributions at supra-saturation densities, we measure the spectra of charged
pions produced by colliding rare isotope tin (Sn) beams with isotopically
enriched Sn targets. Using ratios of the charged pion spectra measured at high
transverse momenta, we deduce the slope of the symmetry energy to be $42 < L <
117$ MeV. This value is slightly lower but consistent with the $L$ values
deduced from a recent measurement of the neutron skin thickness of $^{208}$Pb.


Abstract:
  The problem of online prediction with sequential side information under
logarithmic loss is studied, and general upper and lower bounds on the minimax
regret incurred by the predictor is established. The upper bounds on the
minimax regret are obtained by providing and analyzing a probability assignment
inspired by mixture probability assignments in universal compression, and the
lower bounds are obtained by way of a redundancy-capacity theorem. The tight
characterization of the regret is provided in some special settings.