  The control of neuronal networks, whether biological or neuromorphic, relies
on tools for estimating parameters in the presence of model uncertainty. In
this work, we explore the robustness of adaptive observers for neuronal
estimation. Inspired by biology, we show that decentralization and redundancy
help recover the performance of a centralized recursive mean square algorithm
in the presence of uncertainty and mismatch on the internal dynamics of the
model.


  We compare the dispersion of the charges in a central rapidity box according
to the Dual Parton Model with the predictions of statistical models.
Significant deviations are found in heavy ion collisions at RHIC and LHC
energies. Hence the charged particle fluctuations should provide a clear signal
of the dynamics of heavy ion processes. They should allow to directly measure
the degree of thermalization in a quantitative way.


  This paper describes an approach to detect idiomaticity only from the
contextualized representation of a MWE over multilingual pretrained language
models. Our experiments find that larger models are usually more effective in
idiomaticity detection. However, using a higher layer of the model may not
guarantee a better performance. In multilingual scenarios, the convergence of
different languages are not consistent and rich-resource languages have big
advantages over other languages.


  We prove Davis and Garsia Inequalities for dyadic perturbations of Hardy
Martingales. We apply those to estimate the $L^1 $ distance of a dyadic
martingale to the class of Hardy martingales. We revisit Bourgains embedding of
$L^1$ into the quotient space $ L^1 / H^1 . $


  The future of work does not require a choice between human and robot. Aside
from explicit human-robot collaboration, robotics can play an increasingly
important role in helping train workers as well as the tools they may use,
especially in complex tasks that may be difficult to automate or effectively
roboticize. This paper introduces a form of smart tool for use by human workers
and shows how training the tool for task recognition, one of the key
requirements, can be accomplished. Machine learning (ML) with purely
human-based data can be extremely laborious and time-consuming. First, we show
how data synthetically-generated by a robot can be leveraged in the ML training
process. Later, we demonstrate how fine-tuning ML models for individual
physical tasks and workers can significantly scale up the benefits of using ML
to provide this feedback. Experimental results show the effectiveness and
scalability of our approach, as we test data size versus accuracy. Smart hand
tools of the type introduced here can provide insights and real-time analytics
on efficient and safe tool usage and operation, thereby enhancing human
participation and skill in a wide range of work environments. Using robotic
platforms to help train smart tools will be essential, particularly given the
diverse types of applications for which smart hand tools are envisioned for
human use.


  This paper presents a practical approach towards implementing pathfinding
algorithms on real-world and low-cost non- commercial hardware platforms. While
using robotics simulation platforms as a test-bed for our algorithms we easily
overlook real- world exogenous problems that are developed by external factors.
Such problems involve robot wheel slips, asynchronous motors, abnormal sensory
data or unstable power sources. The real-world dynamics tend to be very painful
even for executing simple algorithms like a Wavefront planner or A-star search.
This paper addresses designing techniques that tend to be robust as well as
reusable for any hardware platforms; covering problems like controlling
asynchronous drives, odometry offset issues and handling abnormal sensory
feedback. The algorithm implementation medium and hardware design tools have
been kept general in order to present our work as a serving platform for future
researchers and robotics enthusiast working in the field of path planning
robotics.


  Foundation models have exhibited remarkable success in various applications,
such as disease diagnosis and text report generation. To date, a foundation
model for endoscopic video analysis is still lacking. In this paper, we propose
Endo-FM, a foundation model specifically developed using massive endoscopic
video data. First, we build a video transformer, which captures both local and
global long-range dependencies across spatial and temporal dimensions. Second,
we pre-train our transformer model using global and local views via a
self-supervised manner, aiming to make it robust to spatial-temporal variations
and discriminative across different scenes. To develop the foundation model, we
construct a large-scale endoscopy video dataset by combining 9 publicly
available datasets and a privately collected dataset from Baoshan Branch of
Renji Hospital in Shanghai, China. Our dataset overall consists of over 33K
video clips with up to 5 million frames, encompassing various protocols, target
organs, and disease types. Our pre-trained Endo-FM can be easily adopted for a
given downstream task via fine-tuning by serving as the backbone. With
experiments on 3 different types of downstream tasks, including classification,
segmentation, and detection, our Endo-FM surpasses the current state-of-the-art
(SOTA) self-supervised pre-training and adapter-based transfer learning methods
by a significant margin, such as VCL (3.1% F1, 4.8% Dice, and 5.5% F1 for
classification, segmentation, and detection) and ST-Adapter (5.9% F1, 9.6%
Dice, and 9.9% F1 for classification, segmentation, and detection). Code,
datasets, and models are released at https://github.com/med-air/Endo-FM.


  Entanglement constitutes one of the key concepts in quantum mechanics and
serves as an indispensable tool in the understanding of quantum many-body
systems. In this work, we perform extensive numerical investigations of
extensive entanglement properties of coupled quantum spin chains. This setup
has proven useful for e.g. extending the Lieb-Schultz-Mattis theorem to open
systems, and contrasts the majority of previous research where the entanglement
cut has one lower dimension than the system. We focus on the cases where the
entanglement Hamiltonian is either gapless or exhibits spontaneous symmetry
breaking behavior. We further employ conformal field theoretical formulae to
identify the universal behavior in the former case. The results in our work can
serve as a paradigmatic starting point for more systematic exploration of the
largely uncharted physics of extensive entanglement, both analytical and
numerical.


  We investigate Anderson localization in a three dimensional (3d) kicked
rotor. By a finite size scaling analysis we have identified a mobility edge for
a certain value of the kicking strength $k = k_c$. For $k > k_c$ dynamical
localization does not occur, all eigenstates are delocalized and the spectral
correlations are well described by Wigner-Dyson statistics. This can be
understood by mapping the kicked rotor problem onto a 3d Anderson model (AM)
where a band of metallic states exists for sufficiently weak disorder. Around
the critical region $k \approx k_c$ we have carried out a detailed study of the
level statistics and quantum diffusion. In agreement with the predictions of
the one parameter scaling theory (OPT) and with previous numerical simulations
of a 3d AM at the transition, the number variance is linear, level repulsion is
still observed and quantum diffusion is anomalous with $<p^2 > \propto
t^{2/3}$. We note that in the 3d kicked rotor the dynamics is not random but
deterministic. In order to estimate the differences between these two
situations we have studied a 3d kicked rotor in which the kinetic term of the
associated evolution matrix is random. A detailed numerical comparison shows
that the differences between the two cases are relatively small. However in the
deterministic case only a small set of irrational periods was used. A
qualitative analysis of a much larger set suggests that the deviations between
the random and the deterministic kicked rotor can be important for certain
choices of periods. Contrary to intuition correlations in the deterministic
case can either suppress or enhance Anderson localization effects.


  We review the current state of the art concerning the characterization of
traces of the spaces $W^{1, p} (\mathbb{B}^{m-1}\times (0,1), \mathcal{N})$ of
Sobolev mappings with values into a compact manifold $\mathcal{N}$. In
particular, we exhibit a new analytical obstruction to the extension, which
occurs when $p < m$ is an integer and the homotopy group $\pi_p (\mathcal{N})$
is non trivial. On the positive side, we prove the surjectivity of the trace
operator when the fundamental group $\pi_1 (\mathcal{N})$ is finite and $\pi_2
(\mathcal{N}) \simeq \dotsb \simeq \pi_{\lfloor p - 1 \rfloor} (\mathcal{N})
\simeq \{0\}$. We present several open problems connected to the extension
problem.


  We found, using density-functional theory calculations within the generalized
gradient approximation, that Cs$_2$AgF$_4$ is stabilized in the insulating
orthorhombic phase rather than in the metallic tetragonal phase. The lattice
distortion present in the orthorhombic phase corresponds to the
$x^2-z^2$/$y^2-z^2$ hole-orbital ordering of the Ag$^{2+}$ $4d^9$ ions, and
this orbital ordering leads to the observed ferromagnetism, as confirmed by the
present total-energy calculations. This picture holds in the presence of
moderate 4d-electron correlation. The results are compared with the picture of
ferromagnetism based on the metallic tetragonal phase.


  We describe the design, data analysis, and basic results of the Giant
Metrewave Radio Telescope Cold-HI AT $z\approx1$ (GMRT-CAT$z$1) survey, a
510-hour upgraded GMRT HI 21 cm emission survey of galaxies at $z=0.74-1.45$ in
the DEEP2 survey fields. The GMRT-CAT$z$1 survey is aimed at characterising HI
in galaxies during and just after the epoch of peak star-formation activity in
the Universe, a key epoch in galaxy evolution. We obtained high-quality HI 21
cm spectra for 11,419 blue star-forming galaxies at $z=0.74-1.45$, in seven
pointings on the DEEP2 subfields. We detect the stacked HI 21 cm emission
signal of the 11,419 star-forming galaxies, which have an average stellar mass
of $M_* \approx 10^{10} M_\odot$, at $7.1\sigma$ statistical significance,
obtaining an average HI mass of $\langle M_{HI}\rangle=(13.7\pm1.9)\times10^{9}
M_\odot$. This is significantly higher than the average HI mass of $\langle
M_{HI} \rangle=(3.96 \pm 0.17)\times10^{9} M_\odot$ in star-forming galaxies at
$z \approx 0$ with an identical stellar-mass distribution. We stack the
rest-frame 1.4 GHz continuum emission of our 11,419 galaxies to infer an
average star-formation rate (SFR) of $8.07\pm0.82 M_\odot yr^{-1}$. Combining
our average HI mass and average SFR estimates yields an HI depletion timescale
of $1.70\pm0.29$ Gyr, for star-forming galaxies at $z\approx1$, $\approx3$
times lower than that of local galaxies. We thus find that, although
main-sequence galaxies at $z\approx1$ have a high HI mass, their short HI
depletion timescale is likely to cause quenching of their star-formation
activity in the absence of rapid gas accretion from the circumgalactic medium.


  Let (R,m) be a complete Noetherian local ring and let M be a finite R--module
of positive Krull dimension n. It is shown that any subset T of Assh_R(M) can
be expressed as the set of attached primes of the top local cohomology module
H^n_a(M) for some ideal a of R. Moreover if a is an ideal of R such that the
set of attached primes of H^n_a(M) is a non--empty proper subset of Assh_R(M),
then H^n_a(M)=H^n_b(M) for some ideal b of R with dim_R (R/b)=1.


  In real teaching scenarios, an excellent teacher always teaches what he (or
she) is good at but the student is not. This gives the student the best
assistance in making up for his (or her) weaknesses and becoming a good one
overall. Enlightened by this, we introduce the "Teaching what you Should Teach"
strategy into a knowledge distillation framework, and propose a data-based
distillation method named "TST" that searches for desirable augmented samples
to assist in distilling more efficiently and rationally. To be specific, we
design a neural network-based data augmentation module with priori bias, which
assists in finding what meets the teacher's strengths but the student's
weaknesses, by learning magnitudes and probabilities to generate suitable data
samples. By training the data augmentation module and the generalized
distillation paradigm in turn, a student model is learned with excellent
generalization ability. To verify the effectiveness of our method, we conducted
extensive comparative experiments on object recognition, detection, and
segmentation tasks. The results on the CIFAR-10, ImageNet-1k, MS-COCO, and
Cityscapes datasets demonstrate that our method achieves state-of-the-art
performance on almost all teacher-student pairs. Furthermore, we conduct
visualization studies to explore what magnitudes and probabilities are needed
for the distillation process.


  In this article we study the properties of preprojective algebras of
representation finite species. To understand the structure of a preprojective
algebra, one often studies its Nakayama automorphism. A complete description of
the Nakayama automorphism is given by Brenner, Butler and King when the algebra
is given by a path algebra. We generalize this result to the species case.
  We show that the preprojective algebra of a representation finite species is
an almost Koszul algebra. With this we know that almost Koszul complexes exist.
It turns out that the almost Koszul complex for a representation finite species
is given by a mapping cone of a certain chain map. We also study a higher
dimensional analogue of representation finite hereditary algebras called
$d$-representation finite algebras. One source of $d$-representation finite
algebras comes from taking tensor products. By introducing a functor called the
Segre product, we manage to give a complete description of the almost Koszul
complex of the preprojective algebra of a tensor product of two species with
relations with certain properties, in terms of the knowledge of the given
species with relations. This allows us to compute the almost Koszul complex
explicitly for certain species with relations more easily.


  We consider a nonlinear Klein Gordon equation (NLKG) with short range
potential with eigenvalues and show that in the contest of complex valued
solutions the small standing waves are attractors for small solutions of the
NLKG. This extends the results already known for the nonlinear Schr\"odinger
equation and for the nonlinear Dirac equation. In addition, this extends a
result of Bambusi and Cuccagna (which in turn was an extension of a result by
Soffer and Weinstein) which considered only real valued solutions of the NLKG.


  Synchronization phenomena, frequency shift and phase noise are often limiting
key factors in the performances of oscillators. The perturbation projection
method allows to characterize how the oscillator's output is modified by these
disturbances. In this brief we discuss the appropriate decomposition of
perturbations for synchronization and phase noise analysis of planar nonlinear
oscillators. We derive analytical formulas for the vectors spanning the
directions along which the perturbations have to be projected. We also discuss
the implications of this decomposition in control theory and to what extent a
simple orthogonal projection is correct.


  Vocal wow is a slow 0.2 - 3 Hz modulation of the voice that may be
distinguished from the 4 - 7 Hz modulation of tremor or vibrato. We use a
simple model of laryngeal muscle activation, mediated by time-delayed auditory
feedback, to show that wow may arise due to Andronov-Hopf bifurcation. The
model demonstrates a differential effect of feedback gain and delay on
modulation depth and frequency, respectively. Parametric formulas for
recovering feedback parameters from the acoustic signal are presented.
Interactions between reflex and auditory parameters are also assessed in a full
model that includes a neuromuscular reflex loop. Model predictions are tested
in two subjects


  Recently, there has been a lot of interest in the integration of Description
Logics and rules on the Semantic Web.We define guarded hybrid knowledge bases
(or g-hybrid knowledge bases) as knowledge bases that consist of a Description
Logic knowledge base and a guarded logic program, similar to the DL+log
knowledge bases from (Rosati 2006). G-hybrid knowledge bases enable an
integration of Description Logics and Logic Programming where, unlike in other
approaches, variables in the rules of a guarded program do not need to appear
in positive non-DL atoms of the body, i.e. DL atoms can act as guards as well.
Decidability of satisfiability checking of g-hybrid knowledge bases is shown
for the particular DL DLRO, which is close to OWL DL, by a reduction to guarded
programs under the open answer set semantics. Moreover, we show
2-EXPTIME-completeness for satisfiability checking of such g-hybrid knowledge
bases. Finally, we discuss advantages and disadvantages of our approach
compared with DL+log knowledge bases.


  To apply the latest computer vision techniques that require a large
computational cost in real industrial applications, knowledge distillation
methods (KDs) are essential. Existing logit-based KDs apply the constant
temperature scaling to all samples in dataset, limiting the utilization of
knowledge inherent in each sample individually. In our approach, we classify
the dataset into two categories (i.e., low energy and high energy samples)
based on their energy score. Through experiments, we have confirmed that low
energy samples exhibit high confidence scores, indicating certain predictions,
while high energy samples yield low confidence scores, meaning uncertain
predictions. To distill optimal knowledge by adjusting non-target class
predictions, we apply a higher temperature to low energy samples to create
smoother distributions and a lower temperature to high energy samples to
achieve sharper distributions. When compared to previous logit-based and
feature-based methods, our energy-based KD (Energy KD) achieves better
performance on various datasets. Especially, Energy KD shows significant
improvements on CIFAR-100-LT and ImageNet datasets, which contain many
challenging samples. Furthermore, we propose high energy-based data
augmentation (HE-DA) for further improving the performance. We demonstrate that
meaningful performance improvement could be achieved by augmenting only 20-50%
of dataset, suggesting that it can be employed on resource-limited devices. To
the best of our knowledge, this paper represents the first attempt to make use
of energy function in knowledge distillation and data augmentation, and we
believe it will greatly contribute to future research.


  Recently, end-to-end known-interference cancellation (E2E-KIC) has been
proposed as a promising technique for wireless networks. It sequentially
cancels out the known interferences at each node so that wireless multi-hop
transmission can achieve a similar throughput as single-hop transmission.
Existing work on E2E-KIC assumed that the interference of a transmitter to
those nodes outside the transmitter's communication range is negligible. In
practice, however, this assumption is not always valid. There are many cases
where a transmitter causes notable interference to nodes beyond its
communication distance. From a wireless networking perspective, such
interference is caused by a node to other nodes that are multiple hops away,
thus we call it multi-hop interference. The presence of multi-hop interference
poses fundamental challenges to E2E-KIC, where known procedures cannot be
directly applied. In this paper, we propose an E2E-KIC approach which allows
the existence of multi-hop interference. In particular, we present a method
that iteratively cancels unknown interferences transmitted across multiple
hops. We analyze mathematically why and when this approach works and the
performance of it. The analysis is further supported by numerical results.


  The problem of column subset selection has recently attracted a large body of
research, with feature selection serving as one obvious and important
application. Among the techniques that have been applied to solve this problem,
the greedy algorithm has been shown to be quite effective in practice. However,
theoretical guarantees on its performance have not been explored thoroughly,
especially in a distributed setting. In this paper, we study the greedy
algorithm for the column subset selection problem from a theoretical and
empirical perspective and show its effectiveness in a distributed setting. In
particular, we provide an improved approximation guarantee for the greedy
algorithm which we show is tight up to a constant factor, and present the first
distributed implementation with provable approximation factors. We use the idea
of randomized composable core-sets, developed recently in the context of
submodular maximization. Finally, we validate the effectiveness of this
distributed algorithm via an empirical study.


  Credit cards play an exploding role in modern economies. Its popularity and
ubiquity have created a fertile ground for fraud, assisted by the cross boarder
reach and instantaneous confirmation. While transactions are growing, the fraud
percentages are also on the rise as well as the true cost of a dollar fraud.
Volume of transactions, uniqueness of frauds and ingenuity of the fraudster are
main challenges in detecting frauds. The advent of machine learning, artificial
intelligence and big data has opened up new tools in the fight against frauds.
Given past transactions, a machine learning algorithm has the ability to
'learn' infinitely complex characteristics in order to identify frauds in
real-time, surpassing the best human investigators. However, the developments
in fraud detection algorithms has been challenging and slow due the massively
unbalanced nature of fraud data, absence of benchmarks and standard evaluation
metrics to identify better performing classifiers, lack of sharing and
disclosure of research findings and the difficulties in getting access to
confidential transaction data for research. This work investigates the
properties of typical massively imbalanced fraud data sets, their availability,
suitability for research use while exploring the widely varying nature of fraud
distributions. Furthermore, we show how human annotation errors compound with
machine classification errors. We also carry out experiments to determine the
effect of PCA obfuscation (as a means of disseminating sensitive transaction
data for research and machine learning) on algorithmic performance of
classifiers and show that while PCA does not significantly degrade performance,
care should be taken to use the appropriate principle component size
(dimensions) to avoid overfitting.


  Laser driven plasma accelerators promise much shorter particle accelerators
but their development requires detailed simulations that challenge or exceed
current capabilities. We report the first direct simulations of stages up to 1
TeV from simulations using a Lorentz boosted calculation frame resulting in a
million times speedup, thanks to a frame boost as high as gamma=1300. Effects
of the hyperbolic rotation in Minkowski space resulting from the frame boost on
the laser propagation in the plasma is shown to be key in the mitigation of a
numerical instability that was limiting previous attempts.


  We study several fundamental properties of a class of stochastic processes
called spatial Lambda-coalescents. In these models, a number of particles
perform independent random walks on some underlying graph G. In addition,
particles on the same vertex merge randomly according to a given coalescing
mechanism. A remarkable property of mean-field coalescent processes is that
they may come down from infinity, meaning that, starting with an infinite
number of particles, only a finite number remains after any positive amount of
time, almost surely. We show here however that, in the spatial setting, on any
infinite and bounded-degree graph, the total number of particles will always
remain infinite at all times, almost surely. Moreover, if G=Z^d, and the
coalescing mechanism is Kingman's coalescent, then starting with N particles at
the origin, the total number of particles remaining is of order (log* N)^d at
any fixed positive time (where log* is the inverse tower function). At
sufficiently large times the total number of particles is of order (log*
N)^{d-2}, when d>2. We provide parallel results in the recurrent case d=2. The
spatial Beta-coalescents behave similarly, where log log N is replacing log* N.


  We study 95 split gates of different size on a single chip using a
multiplexing technique. Each split gate defines a one-dimensional channel on a
modulation-doped GaAs/AlGaAs heterostructure, through which the conductance is
quantized. The yield of devices showing good quantization decreases rapidly as
the length of the split gates increases. However, for the subset of devices
showing good quantization, there is no correlation between the electrostatic
length of the one dimensional channel (estimated using a saddle point model),
and the gate length. The variation in electrostatic length and the
one-dimensional subband spacing for devices of the same gate length exceeds the
variation in the average values between devices of different length. There is a
clear correlation between the curvature of the potential barrier in the
transport direction and the strength of the "0.7 anomaly": the conductance
value of the 0.7 anomaly reduces as the barrier curvature becomes shallower.
These results highlight the key role of the electrostatic environment in
one-dimensional systems. Even in devices with clean conductance plateaus,
random fluctuations in the background potential are crucial in determining the
potential landscape in the active device area such that nominally identical
gate structures have different characteristics.


  When a droplet containing a non-volatile component is dried on a substrate,
it leaves a ringlike deposit on the substrate. We propose a theory which
predicts the deposit distribution based on a model of fluid flow and contact
line motion of the droplet. It is shown that the deposition pattern changes
continuously from coffee-ring to volcano-like and to mountain-like depending on
the mobility of the contact line and the evaporation rate. Analytical
expression is given for the peak position of the distribution of deposit left
on the substrate.


  We introduce a phenomenological theory of dislocation motion appropriate for
two dimensional lattices. A coarse grained description is proposed that
involves as primitive variables local lattice rotation and Burgers vector
densities along distinguished slip systems of the lattice. We then use symmetry
considerations to propose phenomenological equations for both defect energies
and their dissipative motion. As a consequence, the model includes explicit
dependences on the local state of lattice orientation, and allows for
differential defect mobilities along distinguished directions. Defect densities
and lattice rotation need to determined self consistently and we show specific
results for both square and hexagonal lattices. Within linear response,
dissipative equations of motion for the defect densities are derived which
contain defect mobilities that depend nonlocally on defect distribution.


  The complementary characteristics of active and passive depth sensing
techniques motivate the fusion of the Li-DAR sensor and stereo camera for
improved depth perception. Instead of directly fusing estimated depths across
LiDAR and stereo modalities, we take advantages of the stereo matching network
with two enhanced techniques: Input Fusion and Conditional Cost Volume
Normalization (CCVNorm) on the LiDAR information. The proposed framework is
generic and closely integrated with the cost volume component that is commonly
utilized in stereo matching neural networks. We experimentally verify the
efficacy and robustness of our method on the KITTI Stereo and Depth Completion
datasets, obtaining favorable performance against various fusion strategies.
Moreover, we demonstrate that, with a hierarchical extension of CCVNorm, the
proposed method brings only slight overhead to the stereo matching network in
terms of computation time and model size. For project page, see
https://zswang666.github.io/Stereo-LiDAR-CCVNorm-Project-Page/


  In this paper, we present a new deep learning disruption prediction algorithm
based on important findings from explorative data analysis which effectively
allows knowledge transfer from existing devices to new ones, thereby predicting
disruptions using very limited disruptive data from the new devices. The
explorative data analysis conducted via unsupervised clustering techniques
confirms that time-sequence data are much better separators of disruptive and
non-disruptive behavior than the instantaneous plasma state data with further
advantageous implications for a sequence-based predictor. Based on such
important findings, we have designed a new algorithm for multi-machine
disruption prediction that achieves high predictive accuracy on the C-Mod
(AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973) tokamaks with limited
hyperparameter tuning. Through numerical experiments, we show that boosted
accuracy (AUC=0.959) is achieved on EAST predictions by including in the
training only 20 disruptive discharges, thousands of non-disruptive discharges
from EAST, and combining this with more than a thousand discharges from DIII-D
and C-Mod. The improvement of predictive ability obtained by combining
disruptive data from other devices is found to be true for all permutations of
the three devices. Furthermore, by comparing the predictive performance of each
individual numerical experiment, we find that non-disruptive data are
machine-specific while disruptive data from multiple devices contain
device-independent knowledge that can be used to inform predictions for
disruptions occurring on a new device.


  In this paper we work on (bi)simulation semantics of processes that exhibit
both nondeterministic and probabilistic behaviour. We propose a probabilistic
extension of the modal mu-calculus and show how to derive characteristic
formulae for various simulation-like preorders over finite-state processes
without divergence. In addition, we show that even without the fixpoint
operators this probabilistic mu-calculus can be used to characterise these
behavioural relations in the sense that two states are equivalent if and only
if they satisfy the same set of formulae.


  The analysis of causal effects when the outcome of interest is possibly
truncated by death has a long history in statistics and causal inference. The
survivor average causal effect is commonly identified with more assumptions
than those guaranteed by the design of a randomized clinical trial or using
sensitivity analysis. This paper demonstrates that individual level causal
effects in the `always survivor' principal stratum can be identified with no
stronger identification assumptions than randomization. We illustrate the
practical utility of our methods using data from a clinical trial on patients
with prostate cancer. Our methodology is the first and, as of yet, only
proposed procedure that enables detecting causal effects in the presence of
truncation by death using only the assumptions that are guaranteed by design of
the clinical trial. This methodology is applicable to all types of outcomes.


  We construct a family of holographic duals to anisotropic states in a
strongly coupled gauge theory. On the field theory side the anisotropy is
generated by giving a vacuum expectation value to a dimension three operator.
We obtain our gravity duals by considering the geometry corresponding to the
intersection of D3- and D5- branes along 2+1 dimensions. Our backgrounds are
supersymmetric and solve the fully backreacted equations of motion of
ten-dimensional supergravity with smeared D5-brane sources. In all cases the
geometry flows to $AdS_{5}\times {\mathbb S}^5$ in the UV, signaling an
isotropic UV fixed point of the dual field theory. In the IR, depending on the
parameters of the solution, we find two possible behaviors: an isotropic fixed
point or a geometry with anisotropic Lifshitz-like scaling symmetry. We study
several properties of the solutions, including the entanglement entropy of
strips. We show that any natural extension of existing $c$-functions will
display non-monotonic behavior, conforming with the presence of new degrees of
freedom only at intermediate energy scales.


  Combinatorial $t$-designs have been an interesting topic in combinatorics for
decades. It is a basic fact that the codewords of a fixed weight in a code may
hold a $t$-design. Till now only a small amount of work on constructing
$t$-designs from codes has been done. In this paper, we determine the weight
distributions of two classes of cyclic codes: one related to the triple-error
correcting binary BCH codes, and the other related to the cyclic codes with
parameters satisfying the generalized Kasami case, respectively. We then obtain
infinite families of $2$-designs from these codes by proving that they are both
affine-invariant codes, and explicitly determine their parameters. In
particular, the codes derived from the dual of binary BCH codes hold five
$3$-designs when $m=4$.


  This paper presents a search for standard model Higgs boson production in
association with a $W$ boson using events recorded by the CDF experiment in a
dataset corresponding to an integrated luminosity of 5.6 fb-1. The search is
performed using a matrix element technique in which the signal and background
hypotheses are used to create a powerful discriminator. The discriminant output
distributions for signal and background are fit to the observed events using a
binned likelihood approach to search for the Higgs boson signal. We find no
evidence for a Higgs boson, and 95% confidence level (C.L.) upper limits are
set on the Higgs boson production rate. The observed limits range from 3.5 to
37.6 relative to the standard model expectation for Higgs boson masses between
100 and 150 GeV. The 95% C.L. expected limit is estimated from the median of an
ensemble of simulated experiments and varies between 2.9 and 32.7 relative to
the production rate predicted by the standard model over the Higgs boson mass
range studied.


  Soft robotics has emerged as the standard solution for grasping deformable
objects, and has proven invaluable for mobile robotic exploration in extreme
environments. However, despite this growth, there are no widely adopted
computational design tools that produce quality, manufacturable designs. To
advance beyond the diminishing returns of heuristic bio-inspiration, the field
needs efficient tools to explore the complex, non-linear design spaces present
in soft robotics, and find novel high-performing designs. In this work, we
investigate a hierarchical design optimization methodology which combines the
strengths of topology optimization and quality diversity optimization to
generate diverse and high-performance soft robots by evolving the design
domain. The method embeds variably sized void regions within the design domain
and evolves their size and position, to facilitating a richer exploration of
the design space and find a diverse set of high-performing soft robots. We
demonstrate its efficacy on both benchmark topology optimization problems and
soft robotic design problems, and show the method enhances grasp performance
when applied to soft grippers. Our method provides a new framework to design
parts in complex design domains, both soft and rigid.


  Many poker systems, whether created with heuristics or machine learning, rely
on the probability of winning as a key input. However calculating the precise
probability using combinatorics is an intractable problem, so instead we
approximate it. Monte Carlo simulation is an effective technique that can be
used to approximate the probability that a player will win and/or tie a hand.
However, without the use of a memory-intensive lookup table or a supercomputer,
it becomes infeasible to run millions of times when training an agent with
self-play. To combat the space-time tradeoff, we use deep learning to
approximate the probabilities obtained from the Monte Carlo simulation with
high accuracy. The learned model proves to be a lightweight alternative to
Monte Carlo simulation, which ultimately allows us to use the probabilities as
inputs during self-play efficiently. The source code and optimized neural
network can be found at
https://github.com/brandinho/Poker-Probability-Approximation


  Artificial neural networks (NN) are instrumental in realizing
highly-automated driving functionality. An overarching challenge is to identify
best safety engineering practices for NN and other learning-enabled components.
In particular, there is an urgent need for an adequate set of metrics for
measuring all-important NN dependability attributes. We address this challenge
by proposing a number of NN-specific and efficiently computable metrics for
measuring NN dependability attributes including robustness, interpretability,
completeness, and correctness.


  During COVID-19, misinformation on social media affects the adoption of
appropriate prevention behaviors. It is urgent to suppress the misinformation
to prevent negative public health consequences. Although an array of studies
has proposed misinformation suppression strategies, few have investigated the
role of predominant credible information during crises. None has examined its
effect quantitatively using longitudinal social media data. Therefore, this
research investigates the temporal correlations between credible information
and misinformation, and whether predominant credible information can suppress
misinformation for two prevention measures (i.e. topics), i.e. wearing masks
and social distancing using tweets collected from February 15 to June 30, 2020.
We trained Support Vector Machine classifiers to retrieve relevant tweets and
classify tweets containing credible information and misinformation for each
topic. Based on cross-correlation analyses of credible and misinformation time
series for both topics, we find that the previously predominant credible
information can lead to the decrease of misinformation (i.e. suppression) with
a time lag. The research findings provide empirical evidence for suppressing
misinformation with credible information in complex online environments and
suggest practical strategies for future information management during crises
and emergencies.


  We clarify the pairing mechanism of high-$T_c$ superconductivity in bilayer
La$_3$Ni$_2$O$_7$ under high pressure by employing the static auxiliary field
Monte Carlo approach to simulate a minimal effective model that contains local
$d_{z^2}$ interlayer spin singlets and metallic $d_{x^2-y^2}$ bands.
Superconductivity is induced when the local spin singlet pairs are mobilized
and attain long-distance phase coherence by hybridization with the metallic
bands. When projected onto realistic Fermi surfaces, it yields a nodeless
$s$-wave gap on the $\gamma$ Fermi surface, and extended $s$-wave gaps of the
same (opposite) sign on the $\alpha$ ($\beta$) Fermi surface due to its bonding
(antibonding) character, with nodes or gap minima along the diagonal direction
of the two-dimensional Brillouin zone. We find a dual role of the hybridization
that not only induces global phase coherence but also competes with the spin
singlet formation. This lead to a tentative phase diagram where $T_c$ varies
nonmonotonically with the hybridization, in good correspondence with
experimental observations. A roughly linear relation is obtained for realistic
hopping and hybridization parameters: $T_c\approx 0.04-0.05 J$, where $J$ is
the interlayer superexchange interaction. We emphasize the peculiar tunability
of the bilayer structure and propose that $T_c$ may be further enhanced by hole
doping or applying uniaxial pressure along the $c$ axis on superconducting
La$_3$Ni$_2$O$_7$. Our work provides reliable numerical evidence for the
pairing mechanism of high-$T_c$ superconductivity in La$_3$Ni$_2$O$_7$ and
points out a potential route to achieve even higher $T_c$.


  Quantum Machine Learning (QML) algorithms to solve classifications problems
have been made available thanks to recent advancements in quantum computation.
While the number of qubits are still relatively small, they have been used for
"quantum enhancement" of machine learning. An important question is related to
the efficacy of such protocols. We evaluate this efficacy using common baseline
data sets, in addition to recent coronavirus spread data as well as the quantum
metal-insulator transition in three dimensions. For the computation, we used
the 16 qubit IBM quantum computer. We find that the "quantum enhancement" is
not generic and fails for more complex machine learning tasks.


  We briefly discuss energy, normal modes and dynamics of a vortex in a trapped
Bose-Einstein condensate. Theoretical results are compared with current
experiments.


  A linear output feedback control scheme is developed for a coupled map
lattice system. H-infinity control theory is used to make the scheme local:
both the collection of information and the feedback are implemented through an
array of locally coupled control sites. Robustness properties of the control
scheme are discussed.


  It was proved by Raychaudhuri in 1987 that if a graph power $G^{k-1}$ is an
interval graph, then so is the next power $G^k$. This result was extended to
$m$-trapezoid graphs by Flotow in 1995. We extend the statement for interval
graphs by showing that any interval representation of $G^{k-1}$ can be extended
to an interval representation of $G^k$ that induces the same left endpoint and
right endpoint orders. The same holds for unit interval graphs. We also show
that a similar fact does not hold for trapezoid graphs.


  A rich variety of brane cosmologies is obtained once one allows for energy
exchange between the brane and the bulk, depending on the precise form of
energy transfer, on the equation of state of matter on the brane and on the
spatial topology. This is demonstrated in the context of a non-factorizable
background geometry with zero effective cosmological constant on the brane. An
accelerating era is generically a feature of these solutions. In the case of
low-density flat universe more dark matter than in the conventional FRW picture
is predicted, while spatially compact solutions are found to delay their
re-collapse. In addition to the above, which the interested reader will find in
greater detail in [1], a first attempt towards a complete description of the
full dynamics of both the bulk and the brane is reported.


  We revisit the notion of parametrization invariance while introducing certain
weakened notions of invariance in the calculus of variations. In this work, we
employ a straightforward approach in the classical setting and mostly restrict
attention to functionals on one-dimensional domains. We establish a connection
between parametrization invariant functionals and functionals embodying a
weaker notion of invariance of their Lagrangian; we term this notion as
T-Lagrangian analogous to the well-known idea of null Lagrangian. However, the
Euler-Lagrange operator of a T-Lagrangian vanishes only along the tangential
direction in the configuration space. On one-dimensional domain and for first-
and second-order theories, we show that functional described by such a
Lagrangian is necessarily a parametrization invariant functional modulo null
Lagrangian. Keeping the motivation for partial differential equations, we also
introduce and explore the notion of N-Lagrangian, with an invariance
complementary to the case of T-Lagrangian, whose Euler-Lagrange operator
vanishes along normal directions. We find that in a one-dimensional setting,
every N-Lagrangian is simply a null Lagrangian.


  A first-principles derived approach is combined with the inverse Monte Carlo
technique to determine the atomic orderings leading to prefixed properties in
Pb(Sc_{0.5}Nb_{0.5})O_{3} perovskite alloy. We find that some arrangements
between Sc and Nb atoms result in drastic changes with respect to the
disordered material, including ground states of new symmetries, large
enhancement of electromechanical responses, and considerable shift of the Curie
temperature. We discuss the microscopic mechanisms responsible for these
unusual effects.


  We predict the force-free scalar Aharonov-Bohm effect of a Cooper pair box in
an electric field at a distance without forming a closed path of the
interfering charges. The superposition of different charge states plays a major
role in eliminating the closed loop, which is distinct from the original
topological Aharonov-Bohm effect. The phase shift is determined by the
charge-state-dependent local field interaction energy. In addition, our
proposed setup does not require a pulse experiment for fast switching of a
potential, which eliminates the major experimental obstacle for observing the
ideal electric Aharonov-Bohm effect.


  This paper studies the classification problem on electroencephalogram (EEG)
data of mental tasks, using standard architecture of three-layer CNN, stacked
LSTM, stacked GRU. We further propose a novel classifier - a mixed LSTM model
with a CNN decoder. A hyperparameter optimization on CNN shows validation
accuracy of 72% and testing accuracy of 62%. The stacked LSTM and GRU models
with FFT preprocessing and downsampling on data achieve 55% and 51% testing
accuracy respectively. As for the mixed LSTM model with CNN decoder, validation
accuracy of 75% and testing accuracy of 70% are obtained. We believe the mixed
model is more robust and accurate than both CNN and LSTM individually, by using
the CNN layer as a decoder for following LSTM layers. The code is completed in
the framework of Pytorch and Keras. Results and code can be found at
https://github.com/theyou21/BigProject.


  This work addresses the problem of solving the Cahn-Hilliard equation
numerically. For that we introduce an abstract formulation for Cahn-Hilliard
type equations with dynamic boundary conditions, we conduct the spatial
semidiscretization via finite elements and prove error bounds based on the
technique of energy estimates. The variational formulation for
Cahn-Hilliard/Cahn-Hilliard coupling, will apply to a larger abstract class of
problems and is similar to the usual weak formulation of parabolic problems. In
contrast to problems with non dynamic boundary conditions, the Hilbert spaces
$L^2(\Omega)$ and $H^1(\Omega)$ are exchanged with the spaces
$L^2(\Omega)\times L^2(\Gamma)$ and $\lbrace v\in H^1(\Omega): \gamma v \in
H^1(\Gamma)\rbrace$, respectively. Because we are considering a fourth-order
differential equation, which will be described by a system of two second-order
differential equations, the variational formulation also consists of a system
of two equations.


  We consider a finite set $E$ of points in the $n$-dimensional affine space
and two sets of objects that are generated by the set $E$: the system $\Sigma$
of $n$-dimensional simplices with vertices in $E$ and the system $\Gamma$ of
chambers. The incidence matrix $A= \parallel a_{\sigma, \gamma}\parallel$,
$\sigma \in \Sigma$, $\gamma \in \Gamma$, induces the notion of linear
independence among simplices (and among chambers). We present an algorithm of
construction of bases of simplices (and bases of chambers). For the case $n=2$
such an algorithm was described in the author's paper {\em Combinatorial bases
in systems of simplices and chambers} (Discrete Mathematics 157 (1996) 15--37).
However, the case of $n$-dimensional space required a different technique. It
is also proved that the constructed bases of simplices are geometrical.


  We study the effect of Coulomb potential on high-order harmonic generation
(HHG) numerically and analytically. We focus on the influence of Coulomb
potential on emission times of HHG associated with specific electron
trajectories. By using a numerical procedure based on numerical solution of
time-dependent Schr\"{o}dinger equation (TDSE) in three dimensions, we extract
the HHG emission times both for long and short electron trajectories. We
compare TDSE predictions with those of a Coulomb-modified model arising from
strong-field approximation (SFA). We show that the Coulomb effect induces
earlier HHG emission times than those predicted by the general SFA model
without considering the Coulomb potential. In particular, this effect
influences differently on long and short electron trajectories and is more
remarkable for low-energy harmonics than high ones. It also changes the HHG
amplitudes for long and short electron trajectories. We validate our
discussions with diverse laser parameters and forms of Coulomb potential. Our
results strongly support a four-step model of HHG.


  ABC (approximate Bayesian computation) is a general approach for dealing with
models with an intractable likelihood. In this work, we derive ABC algorithms
based on QMC (quasi- Monte Carlo) sequences. We show that the resulting ABC
estimates have a lower variance than their Monte Carlo counter-parts. We also
develop QMC variants of sequential ABC algorithms, which progressively adapt
the proposal distribution and the acceptance threshold. We illustrate our QMC
approach through several examples taken from the ABC literature. Keywords:
Approximate Bayesian computation, Likelihood-free inference, Quasi Monte Carlo,
Randomized Quasi-Monte Carlo, Adaptive importance sampling


  We study the performance of Local Causal Discovery (LCD), a simple and
efficient constraint-based method for causal discovery, in predicting causal
effects in large-scale gene expression data. We construct practical estimators
specific to the high-dimensional regime. Inspired by the ICP algorithm, we use
an optional preselection method and two different statistical tests.
Empirically, the resulting LCD estimator is seen to closely approach the
accuracy of ICP, the state-of-the-art method, while it is algorithmically
simpler and computationally more efficient.


  We present preliminary results of the PACS-CS project which simulates 2+1
flavor lattice QCD toward the physical point with the nonperturbatively
O(a)-improved Wilson quark action and the Iwasaki gauge action. Calculations
are carried out at beta=1.9 on a 32^3x64 lattice with the use of the
domain-decomposed HMC algorithm to reduce the up-down quark mass. The resulting
pseudoscalar meson masses range from 730 MeV down to 210 MeV. We discuss the
physical results including the chiral analysis in the pseudoscalar meson sector
and the hadron spectrum. Some algorithmic issues are also discussed.


  We present new sets of next-to-leading order fragmentation functions
describing the production of charged pions, kaons and protons from the gluon
and from each of the quarks, obtained by fitting to all relevant data sets from
e+ e- annihilation. The individual light quark flavour fragmentation functions
are obtained phenomenologically for the first time by including in the data the
light quark tagging probabilities obtained by the OPAL Collaboration.


  The asymptotic behavior of the scalar field and its physical meaning are
discussed for T=0 and T\neq 0 for the large enough coupling parameter \omega.
The special character of the Brans-Dicke theory is also discussed for local and
cosmological problems in comparison with general relativity and the selection
rules are introduced respectivley. The scalar field by locally-distributed
matter should exhibit the asymptotic behavior \phi = <\phi> +O(1/\omega)
because of the presence of cosmological matter. The scalar field of a proper
cosmological solution should have the asymptotic form \phi =O(\rho /\omega) and
should converge to zero in the continuous limit \rho /\omega \to 0.


  Recent observation shows that general relativity (GR) is not valid in the
strong regime. $\mathit{f(R)}$ gravity where $\mathit{R}$ is the Ricci scalar,
is regarded to be one of good candidates able to cure the anomalies appeared in
the conventional general relativity. In this realm, we apply the equation of
motions of $\mathit{f(R)}$ gravity to a spherically symmetric spacetime with
two unknown functions and derive original black hole (BH) solutions without any
constrains on the Ricci scalar as well as on the form of $\mathit{f(R)}$
gravity. Those solutions depend on a convolution function and are deviating
from the Schwarzschild solution of the Einstein GR. These solutions are
characterized by the gravitational mass of the system and the convolution
function that in the asymptotic form gives extra terms that are responsible to
make such BHs different from GR. Also, we show that these extra terms make the
singularities of the invariants much weaker than those of the GR BH. We analyze
such BHs using the trend of thermodynamics and show their consistency with the
well known quantities in thermodynamics like the Hawking radiation, entropy and
quasi-local energy. We also show that our BH solutions satisfy the first law of
thermodynamics. Moreover, we study the stability analysis using the odd-type
mode and shows that all the derived BHs are stable and have radial speed equal
to one. Finally, using the geodesic deviations we derive the stability
conditions of these BHs.


  Proportional ranking rules aggregate approval-style preferences of agents
into a collective ranking such that groups of agents with similar preferences
are adequately represented. Motivated by the application of live Q&A platforms,
where submitted questions need to be ranked based on the interests of the
audience, we study a dynamic extension of the proportional rankings setting. In
our setting, the goal is to maintain the proportionality of a ranking when
alternatives (i.e., questions) -- not necessarily from the top of the ranking
-- get selected sequentially. We propose generalizations of well-known
aggregation rules to this setting and study their monotonicity and
proportionality properties. We also evaluate the performance of these rules
experimentally, using realistic probabilistic assumptions on the selection
procedure.


  We present a transparent conducting electrode composed of a periodic
two-dimensional network of silver nanowires. Networks of Ag nanowires are made
with wire diameters of 45-110 nm and pitch of 500, 700 and 1000 nm. Anomalous
optical transmission is observed, with an averaged transmission up to 91% for
the best transmitting network and sheet resistances as low as 6.5 {\Omega}/sq
for the best conducting network. Our most dilute networks show lower sheet
resistance and higher optical transmittance than an 80 nm thick layer of ITO
sputtered on glass. By comparing measurements and simulations we identify four
distinct physical phenomena that govern the transmission of light through the
networks: all related to the excitation of localized surface plasmons and
surface plasmon polaritons on the wires. The insights given in this paper
provide the key guidelines for designing high-transmittance and low-resistance
nanowire electrodes for optoelectronic devices, including thin-film solar
cells. For these latter, we discuss the general design principles to use the
nanowire electrodes also as a light trapping scheme.


  A wide-bandwidth and low-noise amplification chain in the microwave regime is
crucial for the efficient read-out of quantum systems based on superconducting
detectors, such as Microwave Kinetic Inductance Detectors (MKIDs), Transition
Edge Sensors (TESs), Magnetic Microcalorimeters (MMCs), and RF cavities, as
well as qubits. Kinetic Inductance Travelling Wave Parametric Amplifiers
(KI-TWPAs) operated in a three-wave mixing fashion have demonstrated
exceptional dynamic range and low-noise performance, approaching the quantum
limit. These amplifiers can be fabricated using a single layer of a high
kinetic inductance film as weakly dispersive artificial transmission lines,
with the ability to control the phase-matched bandwidth through dispersion
engineering. In this study, we present the optimisation of the rf
sputter-deposition process of NbTiN films using a Nb80%T20 target, with the
goal of achieving precise control over film characteristics, resulting in high
kinetic inductance while maintaining a high transition temperature. The
parameter landscape related to the different sputtering conditions, such as
pressure, power, and nitrogen flow, has been explored and the film thickness
has been used as a fine-tuning parameter to adjust the properties of the final
NbTiN films used for the fabrication of KI-TWPAs. As a final result, we have
obtained a NbTiN film with a kinetic inductance of 8.5 pH/sq which we have
exploited to fabricate KI-TWPA prototype devices, showing promising
amplification performance.


  Given a linear group G over a field k, we define a notion of index and
residue of an element g of G(k((t)). This provides an alternative proof of
Gabber's theorem stating that G has no subgroups isomorphic to the additive or
the commutative group iff G(k[[t]])= G(k((t))). In the case of a reductive
group, we offer an explicit connection with the theory of affine grassmannians.


  The nuclear symmetry energy, especially at suprasaturation densities, plays
crucial roles in many astrophysical studies. However, nowadays the high-density
behavior of the symmetry energy is still very controversial in nuclear
community. To constrain the high-density behavior of the symmetry energy,
neutron-rich nuclei collisions at medium energies are considered to be one of
the most effective methods. While probing the high-density symmetry energy by
using heavy-ion collisions, blind spots may exist. In the framework of the
Isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model, the blind
spots of probing the high-density symmetry energy by the n/p ratio in the
central Au+Au reaction at 300 MeV/nucleon are demonstrated. It is found that
the nucleon observable neutron to proton ratio n/p in heavy-ion collisions
cannot effectively probe the high-density symmetry energy when the high-density
symmetry energy is less density-dependent.


  Given a surface with boundary and some points on its boundary, a polygon
diagram is a way to connect those points as vertices of non-overlapping
polygons on the surface. Such polygon diagrams represent non-crossing
permutations on a surface with any genus and number of boundary components. If
only bigons are allowed, then it becomes an arc diagram. The count of arc
diagrams is known to have a rich structure. We show that the count of polygon
diagrams exhibits the same interesting behaviours, in particular it is almost
polynomial in the number of points on the boundary components, and the leading
coefficients of those polynomials are the intersection numbers on the
compactified moduli space of curves.


  The AERQS is a project aimed at the construction of an all-sky statistically
well-defined sample of local (z < 0.3) and bright (R < 15.5) QSOs. Present
uncertainties on the theoretical modeling of the QSO evolution are to be
ascribed to the relatively small number of objects observed in this magnitude
and redshift domain. We are filling this gap with a sample of 392 AGN covering
all the sky at high Galactic latitude: we present here the Clustering analysis
and the Luminosity Function of AGN at low redshift. The AERQS sets an important
zero point about the properties of the local QSO population and the
evolutionary pattern of QSOs between the present epoch and the highest
redshift.


  Motivation: Smart grids design requires energy distribution operations to be
adaptable to abnormality. This requirement entails distribution system
operators (DSOs) to optimize restoration to normal operational states
dynamically. However, these design challenges demand collaborative research
efforts on sophisticated modeling and simulation approaches. Approach: In the
ESOSEG research project, analyzing the smart grid domain as a
software-intensive system, we employed a dynamic architecture approach,
particularly the FOCUS theory, to model and assure the domains' self-healing
requirements. Although some works specify various self-healing systems, to the
best of our knowledge, the use of the approach in smart grids is the first work
to enable a formal specification and verification of self-healing properties in
smart grids. Results: As a result, to support the modeling and verification
process, we developed tool support with Eclipse Modeling Framework (EMF),
Xtext, and other languages in the EMF ecosystem. The tool includes a grammar or
a meta-model of the DSL, an interface to enable textual and graphical modeling
of architectural patterns and code transformer engine for verification.
Furthermore, we evaluated the modeling and verification features of the tool
support with an e-Car charging scenario for modeling adaptive self-healing
properties. Futureworks: As an outlook, future works could include
investigation of comprehensive case studies. These, for instance, could be
further particular adaptability scenarios addressing challenges in DSOs.
Another interesting aspect could be the evaluation of the modeling approach by
investigating its use with engineers involved in a smart grid design. Next, the
evaluation could be followed with abstractions of the verification process to
make it useable by system architects with no knowledge of the proof language,
Isabelle/HOL.


  We present a simple systematic construction and analysis of solutions of the
two-dimensional parabolic wave equation that exhibit far-field localisation
near a given algebraic plane curve. Our solutions are complex contour integral
superpositions of elementary plane wave solutions with polynomial phase, the
desired localisation being associated with the coalescence of saddle points.
Our solutions provide a unified framework in which to describe some classical
phenomena in two-dimensional high frequency wave propagation, including smooth
and cusped caustics, whispering gallery and creeping waves, and tangent ray
diffraction by a smooth boundary. We also study a subclass of solutions
exhibiting localisation near a cubic parabola, and discuss their possible
relevance to the study of the canonical inflection point problem governing the
transition from whispering gallery waves to creeping waves.


  The governance of data used for biomedical research and clinical trials is an
important requirement for generating accurate results. To improve the
visibility of data quality and analysis, we developed TrialChain, a
blockchain-based platform that can be used to validate data integrity from
large, biomedical research studies. We implemented a private blockchain using
the MultiChain platform and integrated it with a data science platform deployed
within a large research center. An administrative web application was built
with Python to manage the platform, which was built with a microservice
architecture using Docker. The TrialChain platform was integrated during data
acquisition into our existing data science platform. Using NiFi, data were
hashed and logged within the local blockchain infrastructure. To provide public
validation, the local blockchain state was periodically synchronized to the
public Ethereum network. The use of a combined private/public blockchain
platform allows for both public validation of results while maintaining
additional security and lower cost for blockchain transactions. Original data
and modifications due to downstream analysis can be logged within TrialChain
and data assets or results can be rapidly validated when needed using API calls
to the platform. The TrialChain platform provides a data governance solution to
audit the acquisition and analysis of biomedical research data. The platform
provides cryptographic assurance of data authenticity and can also be used to
document data analysis.


  With recent advances in radio-frequency identification (RFID), wireless
sensor networks, and Web services, physical things are becoming an integral
part of the emerging ubiquitous Web. Finding correlations of ubiquitous things
is a crucial prerequisite for many important applications such as things
search, discovery, classification, recommendation, and composition. This
article presents DisCor-T, a novel graph-based method for discovering
underlying connections of things via mining the rich content embodied in
human-thing interactions in terms of user, temporal and spatial information. We
model these various information using two graphs, namely spatio-temporal graph
and social graph. Then, random walk with restart (RWR) is applied to find
proximities among things, and a relational graph of things (RGT) indicating
implicit correlations of things is learned. The correlation analysis lays a
solid foundation contributing to improved effectiveness in things management.
To demonstrate the utility, we develop a flexible feature-based classification
framework on top of RGT and perform a systematic case study. Our evaluation
exhibits the strength and feasibility of the proposed approach.


  In the age of social media, disasters and epidemics usher not only a
devastation and affliction in the physical world, but also prompt a deluge of
information, opinions, prognoses and advice to billions of internet users. The
coronavirus epidemic of 2019-2020, or COVID-19, is no exception, with the World
Health Organization warning of a possible "infodemic" of fake news. In this
study, we examine the alternative narratives around the coronavirus outbreak
through advertisements promoted on Facebook, the largest social media platform
in the US. Using the new Facebook Ads Library, we discover advertisers from
public health and non-profit sectors, alongside those from news media,
politics, and business, incorporating coronavirus into their messaging and
agenda. We find the virus used in political attacks, donation solicitations,
business promotion, stock market advice, and animal rights campaigning. Among
these, we find several instances of possible misinformation, ranging from
bioweapons conspiracy theories to unverifiable claims by politicians. As we
make the dataset available to the community, we hope the advertising domain
will become an important part of quality control for public health
communication and public discourse in general.


  Multi-state models are frequently applied for representing processes evolving
through a discrete set of state. Important classes of multi-state models arise
when transitions between states may depend on the time since entry into the
current state or on the time elapsed from the starting of the process. The
former models are called semi-Markov while the latter are known as
inhomogeneous Markov models. Inference for both the models presents
computational difficulties when the process is only observed at discrete time
points with no additional information about the state transitions. Indeed, in
both the cases, the likelihood function is not available in closed form. In
order to obtain Bayesian inference under these two classes of models we
reconstruct the whole unobserved trajectories conditioned on the observed
points via a Metropolis-Hastings algorithm. As proposal density we use that
given by the nested Markov models whose conditioned trajectories can be easily
drawn by the uniformization technique. The resulting inference is illustrated
via simulation studies and the analysis of two benchmark data sets for multi
state models.


  We show that the points of a global function field, whose classes are
2-divisible in the Picard group, form a connected graph, with the incidence
relation generalizing the well known quadratic reciprocity law. We prove that
for every global function field the dimension of this graph is precisely 2. In
addition we develop an analog of global square theorem that concerns points
2-divisible in the Picard group.


  The Cover Suffix Tree (CST) of a string $T$ is the suffix tree of $T$ with
additional explicit nodes corresponding to halves of square substrings of $T$.
In the CST an explicit node corresponding to a substring $C$ of $T$ is
annotated with two numbers: the number of non-overlapping consecutive
occurrences of $C$ and the total number of positions in $T$ that are covered by
occurrences of $C$ in $T$. Kociumaka et al. (Algorithmica, 2015) have shown how
to compute the CST of a length-$n$ string in $O(n \log n)$ time. We show how to
compute the CST in $O(n)$ time assuming that $T$ is over an integer alphabet.
  Kociumaka et al. (Algorithmica, 2015; Theor. Comput. Sci., 2018) have shown
that knowing the CST of a length-$n$ string $T$, one can compute a linear-sized
representation of all seeds of $T$ as well as all shortest $\alpha$-partial
covers and seeds in $T$ for a given $\alpha$ in $O(n)$ time. Thus our result
implies linear-time algorithms computing these notions of quasiperiodicity. The
resulting algorithm computing seeds is substantially different from the
previous one (Kociumaka et al., SODA 2012, ACM Trans. Algorithms, 2020).
Kociumaka et al. (Algorithmica, 2015) proposed an $O(n \log n)$-time algorithm
for computing a shortest $\alpha$-partial cover for each $\alpha=1,\ldots,n$;
we improve this complexity to $O(n)$.
  Our results are based on a new characterization of consecutive overlapping
occurrences of a substring $S$ of $T$ in terms of the set of runs (see Kolpakov
and Kucherov, FOCS 1999) in $T$. This new insight also leads to an $O(n)$-sized
index for reporting overlapping consecutive occurrences of a given pattern $P$
of length $m$ in $O(m+output)$ time, where $output$ is the number of
occurrences reported. In comparison, a general index for reporting bounded-gap
consecutive occurrences of Navarro and Thankachan (Theor. Comput. Sci., 2016)
uses $O(n \log n)$ space.


  We consider the reinforcement learning problem for the constrained Markov
decision process (CMDP), which plays a central role in satisfying safety or
resource constraints in sequential learning and decision-making. In this
problem, we are given finite resources and a MDP with unknown transition
probabilities. At each stage, we take an action, collecting a reward and
consuming some resources, all assumed to be unknown and need to be learned over
time. In this work, we take the first step towards deriving optimal
problem-dependent guarantees for the CMDP problems. We derive a logarithmic
regret bound, which translates into a
$O(\frac{1}{\Delta\cdot\eps}\cdot\log^2(1/\eps))$ sample complexity bound, with
$\Delta$ being a problem-dependent parameter, yet independent of $\eps$. Our
sample complexity bound improves upon the state-of-art $O(1/\eps^2)$ sample
complexity for CMDP problems established in the previous literature, in terms
of the dependency on $\eps$. To achieve this advance, we develop a new
framework for analyzing CMDP problems. To be specific, our algorithm operates
in the primal space and we resolve the primal LP for the CMDP problem at each
period in an online manner, with \textit{adaptive} remaining resource
capacities. The key elements of our algorithm are: i) a characterization of the
instance hardness via LP basis, ii) an eliminating procedure that identifies
one optimal basis of the primal LP, and; iii) a resolving procedure that is
adaptive to the remaining resources and sticks to the characterized optimal
basis.


  This paper describes the use of fuzzy logic controller for efficiency
optimization control of a drive while keeping good dynamic response. At
steady-state light-load condition, the fuzzy controller adaptively adjusts the
excitation current with respect to the torque current to give the minimum total
copper and iron loss. The measured input power such that, for a given load
torque and speed, the drive settles down to the minimum input power, i.e.,
operates at maximum efficiency. The low-frequency pulsating torque due to
decrementation of flux is compensated in a feed forward manner. If the load
torque or speed commands changes, the efficiency search algorithm is abandoned
and the rated flux is established to get the best dynamic response. The drive
system with the proposed efficiency optimization controller has been simulated
with lossy models of converter and machine, and its performance has been
thoroughly investigated.


  To understand the structure of an algebraic variety we often embed it in
various projective spaces. This develops the notion of projective geometry
which has been an invaluable tool in algebraic geometry. We develop a
perfectoid analog of projective geometry, and explore how equipping a
perfectoid space with a map to a certain analog of projective space can be a
powerful tool to understand its geometric and arithmetic structure. In
particular, we show that maps from a perfectoid space $X$ to the perfectoid
analog of projective space correspond to line bundles on $X$ together with some
extra data, reflecting the classical theory. Along the way we give a complete
classification of vector bundles on the perfectoid unit disk, and compute the
Picard group of the perfectoid analog of projective space.


  We consider the (Renyi) mutual information, $I^{(n)}(A,B) =
S^{(n)}_A+S^{(n)}_{B} - S^{(n)}_{A \cup B}$, of distant compact spatial regions
A and B in the vacuum state of a free scalar field. The distance r between A
and B is much greater than their sizes $R_{A,B}$. It is known that
$I^{(n)}(A,B) \sim C^{(n)}_{AB} \left<0| \phi(r)\phi(0) |0\right>^2$ . We
obtain the direct expression of $C^{(n)}_{AB}$ for arbitrary regions A and B.
We perform the analytical continuation of $n$ and obtain the mutual
information. The direct expression is useful for the numerical computation. By
using the direct expression, we can compute directly $I(A,B)$ without computing
$S_A, S_B$ and $S_{A \cup B}$ respectively, so it reduces significantly the
amount of computation.


  The localization of sound sources by the human brain is computationally
simulated from a neurobiological perspective. The simulation includes the
neural representation of temporal differences in acoustic signals between the
ipsilateral and contralateral ears for constant sound intensities (angular
localization), and of volumetric differences in acoustic signals for constant
azimuthal angles (radial localization). The transmission of the original
acoustic signal from the environment, through each significant stage of
intermediate neurons, to the primary auditory cortex, is also simulated. The
errors that human brains make in attempting to localize sounds in
evolutionarily uncommon environments (such as when one ear is in water and one
ear is in air) are then mathematically predicted. A basic overview of the
physiology behind sound localization in the brain is also provided.


  We report here the first equation of state measurements of Fe$_2$O$_3$
obtained with laser-driven shock compression. The data are in excellent
agreement with previous dynamic and static compression measurements at low
pressure, and extend the known Hugoniot up to 700 GPa. We observe a large
volume drop of $\sim$10% at 86 GPa, which could be associated, according to
static compression observations, with the iron spin transition. Our
measurements also suggest a change of the Hugoniot curve between 150 and 250
GPa. Above 250 GPa and within our error bars, we do not observe significant
modifications up to the maximum pressure of 700 GPa reached in our experiment.


  Vector-like top partner plays a central role in many new physics models which
attempt to address the hierarchy problem. The top partner is conventionally
assumed to decay to a quark and a SM boson. However, it is also possible that
the top partner decays to a non-SM scalar and a top quark. Such an exotic decay
channel can be the main decay channel of top partner, and thus provides new
windows to search for top partner at the LHC. In this paper, with classical
machine learning method Boosted Decision Tree (BDT), we perform a model
independent study of the discovery potential of this new signature at the LHC.
In order to suppress the main QCD background, we consider subdominant but clean
decay channel $a\to \gamma\gamma$. For completeness, the single production
process and pair production process of top partner are all taken into account.
We find that, for both single and pair production, the future High-Luminosity
LHC can exclude the top partner mass up to TeV scale through channel $T\to t a$
($a\to \gamma\gamma$), even if $BR(a\to \gamma\gamma)$ is as small as
$\mathcal{O}$(0.1\%). Besides, our result shows that single production can
overmatch pair production at 14 TeV LHC, provided that top partner is heavier
than $800\sim 900$ GeV.


  We prove that a simplicial 2-sphere satisfying a certain condition is the
underlying simplicial complex of a 3-dimensional non-singular complete fan. In
particular, this implies that any simplicial 2-sphere with $\leq 18$ vertices
is the underlying simplicial complex of such a fan.


  In this paper we comment the Post inversion formula for Laplace transform,
and its possible application to the branch of Analytic Number theory
(Arithmetical functions, RH and PNT), involving a condition in the form of
iterated limit to calculate the Riemann Hypothesis.


  We propose Shift R-CNN, a hybrid model for monocular 3D object detection,
which combines deep learning with the power of geometry. We adapt a Faster
R-CNN network for regressing initial 2D and 3D object properties and combine it
with a least squares solution for the inverse 2D to 3D geometric mapping
problem, using the camera projection matrix. The closed-form solution of the
mathematical system, along with the initial output of the adapted Faster R-CNN
are then passed through a final ShiftNet network that refines the result using
our newly proposed Volume Displacement Loss. Our novel, geometrically
constrained deep learning approach to monocular 3D object detection obtains top
results on KITTI 3D Object Detection Benchmark, being the best among all
monocular methods that do not use any pre-trained network for depth estimation.


  With the experimental data from STAR and PHENIX on the centrality dependence
of the $p_T$ spectra of protons and anti-protons produced at mid-rapidity in
Au+Au collisions at 200 GeV, we show that for protons and anti-protons there
exists a scaling distribution independent of the colliding centrality. The
scaling functions can also describe data from BRAHMS for both proton and
anti-proton spectra at $y=2.2$ and 3.2. The scaling behaviors are shown to be
incompatible with the usual string fragmentation scenario for particle
production.


  Let p be an odd prime, and A_n the alternating group of degree n. We
determine which ordinary irreducible representations of A_n remain irreducible
in characteristic p, verifying the author's conjecture from [Represent. Theory
14, 601-626]. Given the preparatory work done in [op. cit.], our task is to
determine which self-conjugate partitions label Specht modules for the
symmetric group in characteristic p having exactly two composition factors.
This is accomplished through the use of the Robinson-Brundan-Kleshchev
'i-restriction' functors, together with known results on decomposition numbers
for the symmetric group and additional results on the Mullineux map and
homomorphisms between Specht modules.


  Extremal quasimodular forms have been introduced by M.~Kaneko and M.Koike as
as quasimodular forms which have maximal possible order of vanishing at
$i\infty$. We show an asymptotic formula for the Fourier coefficients of such
forms. This formula is then used to show that all but finitely many Fourier
coefficients of such forms of depth $\leq4$ are positive, which partially
solves a conjecture stated by M.~Kaneko and M.Koike. Numerical experiments
based on constructive estimates confirm the conjecture for weights $\leq200$
and depths between $1$ and $4$.


  Multimodal named entity recognition (MNER) is a critical step in information
extraction, which aims to detect entity spans and classify them to
corresponding entity types given a sentence-image pair. Existing methods either
(1) obtain named entities with coarse-grained visual clues from attention
mechanisms, or (2) first detect fine-grained visual regions with toolkits and
then recognize named entities. However, they suffer from improper alignment
between entity types and visual regions or error propagation in the two-stage
manner, which finally imports irrelevant visual information into texts. In this
paper, we propose a novel end-to-end framework named MNER-QG that can
simultaneously perform MRC-based multimodal named entity recognition and query
grounding. Specifically, with the assistance of queries, MNER-QG can provide
prior knowledge of entity types and visual regions, and further enhance
representations of both texts and images. To conduct the query grounding task,
we provide manual annotations and weak supervisions that are obtained via
training a highly flexible visual grounding model with transfer learning. We
conduct extensive experiments on two public MNER datasets, Twitter2015 and
Twitter2017. Experimental results show that MNER-QG outperforms the current
state-of-the-art models on the MNER task, and also improves the query grounding
performance.


  We provide sufficient conditions for a set $E\subset\mathbb{R}^n$ to be a
non-universal differentiability set, i.e. to be contained in the set of points
of non-differentiability of a real-valued Lipschitz function. These conditions
are motivated by a description of the ideal generated by sets of
non-differentiability of Lipschitz self-maps of $\mathbb{R}^n$ given by
Alberti, Cs\"ornyei and Preiss, which eventually led to the result of Jones and
Cs\"ornyei that for every Lebesgue null set $E$ in $\mathbb{R}^n$ there is a
Lipschitz map $f:\mathbb{R}^n\to\mathbb{R}^n$ not differentiable at any point
of $E$, even though for $n>1$ and for Lipschitz functions from $\mathbb{R}^n$
to $\mathbb{R}$ there exist Lebesgue null universal differentiability sets.


  In machine learning it is common to interpret each data point as a vector in
Euclidean space. However the data may actually be functional i.e.\ each data
point is a function of some variable such as time and the function is
discretely sampled. The naive treatment of functional data as traditional
multivariate data can lead to poor performance since the algorithms are
ignoring the correlation in the curvature of each function. In this paper we
propose a method to analyse subspace structure of the functional data by using
the state of the art Low-Rank Representation (LRR). Experimental evaluation on
synthetic and real data reveals that this method massively outperforms
conventional LRR in tasks concerning functional data.


  Atomistic computations of the Peierls stress in fcc metals are relatively
scarce. By way of contrast, there are many more atomistic computations for bcc
metals, as well as mixed discrete-continuum computations of the Peierls-Nabarro
type for fcc metals. One of the reasons for this is the low Peierls stresses in
fcc metals. Because atomistic computations of the Peierls stress take place in
finite simulation cells, image forces caused by boundaries must either be
relaxed or corrected for if system size independent results are to be obtained.
One of the approaches that has been developed for treating such boundary forces
is by computing them directly and subsequently subtracting their effects, as
developed by V. B. Shenoy and R. Phillips [Phil. Mag. A, 76 (1997) 367]. That
work was primarily analytic, and limited to screw dislocations and special
symmetric geometries. We extend that work to edge and mixed dislocations, and
to arbitrary two-dimensional geometries, through a numerical finite element
computation. We also describe a method for estimating the boundary forces
directly on the basis of atomistic calculations. We apply these methods to the
numerical measurement of the Peierls stress and lattice resistance curves for a
model aluminum (fcc) system using an embedded-atom potential.


  We construct the 4D N=2 SCFTs of class-S, which stem from the $E_8$ (2,0)
theory. There are 49,836 isolated SCFTs which arise as 3-punctured spheres. Of
these, 149 are "mixed" (contain free hypermultiplets accompanying the
interacting SCFT) and 775 have enhanced global symmetries (beyond the manifest
global symmetry associated to the punctures). Among the 49,836 3-punctured
spheres we find (after removing any free hypermultiplets which may be present)
29 that are product SCFTs. Turning to 4-punctured spheres, we find 1,025,438 4D
SCFTs arising as a gauging (with a simple gauge group) of a pair of 3-punctured
spheres. We discuss a number of applications, including recovering several
known 4D SCFTs. Our full set of results can be accessed on the Web at
https://golem.ph.utexas.edu/class-S/E8/ .


  Asteroids and other small celestial bodies have markedly prolate shapes, and
the perturbative triaxial torques which are applied during pericenter passages
in highly eccentric orbits trigger and sustain a state of chaotic rotation.
Because the prograde spin rate around the principal axis of inertia is not
bounded from above, it can accidentally reach the threshold value corresponding
to rotational break-up. Previous investigations of this process were limited to
integrations of $\sim 10^3$ orbits because of the stiff equation of motion. We
present here a fast 1D simulation method to compute the evolution of this spin
rate over $\sim 10^9$ orbits. We apply the method to the most eccentric solar
system asteroid known, 2006 HY51 (with $e = 0.9684$), and find that for any
reasonably expected shape parameters, it can never be accelerated to break-up
speed. However, primordial solar system asteroids on more eccentric orbits may
have already broken up from this type of rotational fission. The method also
represents a promising opportunity to investigate the long-term evolution of
extremely eccentric triaxial exo-asteroids ($e > 0.99$), which are thought to
be common in white dwarf planetary systems


  The discovery of quantum oscillations in the normal-state electrical
resistivity of YBa2Cu3O6.5 provides the first evidence for the existence of
Fermi surface (FS) pockets in an underdoped cuprate. However, the pockets'
electron vs. hole character, and the very interpretation in terms of closed FS
contours, are the subject of considerable debate. Angle-resolved photoemission
spectroscopy (ARPES), with its ability to probe electronic dispersion as well
as the FS, is ideally suited to address this issue. Unfortunately, the ARPES
study of YBa2C3O7-d (YBCO) has been hampered by the technique's surface
sensitivity. Here we show that this stems from the polarity and corresponding
self-doping of the YBCO surface. By in-situ deposition of potassium atoms on
the cleaved surface, we are able to continuously tune the doping of a single
sample from the heavily overdoped to the underdoped regime. This reveals the
progressive collapse of the normal-metal-like FS into four disconnected nodal
FS arcs, or perhaps into hole but not electron pockets, in underdoped YBCO6.5.


  In this work, we demonstrate that the Bochner integral representation of the
Algebraic Riccati Equations (ARE) are well-posed without any compactness
assumptions on the coefficient and semigroup operators. From this result, we
then are able to determine that, under some assumptions, the solution to the
Galerkin approximations to these equations are convergent to the infinite
dimensional solution. Going further, we apply this general result to
demonstrate that the finite element approximation to the ARE are optimal for
weakly damped wave semigroup processes in the $H^1(\Omega) \times L^2(\Omega)$
norm. Optimal convergence rates of the functional gain for a weakly damped wave
optimal control system in both the $H^1(\Omega) \times L^2(\Omega)$ and
$L^2(\Omega)\times L^2(\Omega)$ norms are demonstrated in the numerical
examples.


  Given a smooth plane curve $\overline{C}$ of genus $g\geq 3$ over an
algebraically closed field $\overline{k}$, a field $L\subseteq\overline{k}$ is
said to be a \emph{plane model-field of definition for $\overline{C}$} if $L$
is a field of definition for $\overline{C}$, i.e. $\exists$ a smooth curve $C'$
defined over $L$ where $C'\times_L\overline{k}\cong \overline{C}$, and such
that $C'$ is $L$-isomorphic to a non-singular plane model $F(X,Y,Z)=0$ in
$\mathbb{P}^2_{L}$.
  {In this short note, we construct a smooth plane curve $\overline{C}$ over
$\overline{\mathbb{Q}}$, such that the field of moduli of $\overline{C}$ is not
a field of definition for $\overline{C}$, and also fields of definition do not
coincide with plane model-fields of definition for $\overline{C}$.} As far as
we know, this is the first example in the literature with the above property,
since this phenomenon does not occur for hyperelliptic curves, replacing plane
model-fields of definition with the so-called hyperelliptic model-fields of
definition.


  We outline the general features of the conventional mean-field theory for the
description of Bose-Einstein condensates at near zero temperatures. This
approach, based on a phenomenological model, appears to give excellent
agreement with experimental data. We argue, however, that such an approach is
not rigorous and cannot contain the full effect of collisional dynamics due to
the presence of the mean-field. We thus discuss an alternative microscopic
approach and explain, within our new formalism, the physical origin of these
effects. Furthermore, we discuss the potential formulation of a consistent
finite-temperature mean-field theory, which we claim necessiates an analysis
beyond the conventional treatment.


  Hydrogen in metals is a significant research area with far-reaching
implications, encompassing diverse fields such as hydrogen storage,
metal-insulator transitions, and the recently emerging phenomenon of
room-temperature ($\textit{$T_C$}$) superconductivity under high pressure.
Hydrogen atoms pose challenges in experiments as they are nearly invisible, and
they are considered within ideal crystalline structures in theoretical
predictions, which hampers research on the formation of meta-stable hydrides.
Here, we propose pressure-induced hydrogen migration from tetrahedral site
($\textit{T}$) to octahedral site ($\textit{O}$),forming $LaH_x^OH_{2-x}^{T}$
in cubic $LaH^2$.Under decompression, it retains $H_x^O$ occupancy, and is
dynamically stable even at ambient pressure, enabling a synthesis route of
metastable dihydrides via compression-decompression process. We predict that
the electron phonon coupling strength of $LaH_x^OH_{2-x}^{T}$ is enhanced with
increasing $\textit{x}$, and the associated $\textit{$T_C$}$ reaches up to 10.8
$\textit{K}$ at ambient pressure. Furthermore, we calculated stoichiometric
hydrogen migration threshold pressure ($\textit{$P_C$}$) for various
lanthanides dihydrides ($\textit{R}$$H_2$, where $\textit{R}$=Y, Sc, Nd, and
Lu), and found an inversely linear relation between $\textit{$P_C$}$ and ionic
radii of $\textit{R}$. We propose that the highest $\textit{$T_C$}$ in the
face-centered-cubic dihydride system can be realized by optimizing the
$\textit{O}$/$\textit{T}$-site occupancies.


  In relational event networks, the tendency for actors to interact with each
other depends greatly on the past interactions between the actors in a social
network. Both the quantity of past interactions and the time that elapsed since
the past interactions occurred affect the actors' decision-making to interact
with other actors in the network. Recently occurred events generally have a
stronger influence on current interaction behavior than past events that
occurred a long time ago--a phenomenon known as "memory decay". Previous
studies either predefined a short-run and long-run memory or fixed a parametric
exponential memory using a predefined half-life period. In real-life relational
event networks however it is generally unknown how the memory of actors about
the past events fades as time goes by. For this reason it is not recommendable
to fix this in an ad hoc manner, but instead we should learn the shape of
memory decay from the observed data. In this paper, a novel semi-parametric
approach based on Bayesian Model Averaging is proposed for learning the shape
of the memory decay without requiring any parametric assumptions. The method is
applied to relational event history data among socio-political actors in India.


  The VQE algorithm has turned out to be quite expensive to run given the way
we currently access quantum processors (i.e. over the cloud). In order to
alleviate this issue, we introduce Quantum Sampling Regression (QSR), an
alternative hybrid quantum-classical algorithm, and analyze some of its use
cases based on time complexity in the low qubit number regime. In exchange for
some extra classical resources, this novel strategy is proved to be optimal in
terms of the number of samples it requires from the quantum processor. We
develop a simple analytical model to evaluate when this algorithm is more
efficient than VQE, and, from the same theoretical considerations, establish a
threshold above which quantum advantage can occur. Finally, we demonstrate the
efficacy of our algorithm for a benchmark problem.


  In open Hamiltonian systems, the escape from a bounded region of phase space
according to an exponential decay law is frequently associated with the
existence of hyperbolic dynamics in such a region. Furthermore, exponential
decay laws based on the ergodic hypothesis are used to describe escapes in
these systems. However, we uncover that the presence of the set that governs
the hyperbolic dynamics, commonly known as the chaotic saddle, invalidates the
assumption of ergodicity. For the paradigmatic H\'enon-Heiles system, we use
both theoretical and numerical arguments to show that the escaping dynamics is
non-ergodic independently of the existence of KAM tori, since the chaotic
saddle, in whose vicinity trajectories are more likely to spend a finite amount
of time evolving before escaping forever, is not utterly spread over the energy
shell. Taking this into consideration, we provide a clarifying discussion about
ergodicity in open Hamiltonian systems and explore the limitations of ergodic
decay laws when describing escapes in this kind of systems. Finally, we
generalize our claims by deriving a new decay law in the relativistic regime
for an inertial and a non-inertial reference frames under the assumption of
ergodicity, and suggest another approach to the description of escape laws in
open Hamiltonian systems.


  We consider lithological tomography in which the posterior distribution of
(hydro)geological parameters of interest is inferred from geophysical data by
treating the intermediate geophysical properties as latent variables. In such a
latent variable model, one needs to estimate the intractable likelihood of the
(hydro)geological parameters given the geophysical data. The pseudo-marginal
method is an adaptation of the Metropolis-Hastings algorithm in which an
unbiased approximation of this likelihood is obtained by Monte Carlo averaging
over samples from, in this setting, the noisy petrophysical relationship
linking (hydro)geological and geophysical properties. To make the method
practical in data-rich geophysical settings with low noise levels, we
demonstrate that the Monte Carlo sampling must rely on importance sampling
distributions that well approximate the posterior distribution of petrophysical
scatter around the sampled (hydro)geological parameter field. To achieve a
suitable acceptance rate, we rely both on (1) the correlated pseudo-marginal
method, which correlates the samples used in the proposed and current states of
the Markov chain, and (2) a model proposal scheme that preserves the prior
distribution. As a synthetic test example, we infer porosity fields using
crosshole ground-penetrating radar (GPR) first-arrival travel times. We use a
(50x50)-dimensional pixel-based parameterization of the multi-Gaussian porosity
field with known statistical parameters, resulting in a parameter space of high
dimension. We demonstrate that the correlated pseudo-marginal method with our
proposed importance sampling and prior-preserving proposal scheme outperforms
current state-of-the-art methods in both linear and non-linear settings by
greatly enhancing the posterior exploration.


  In this paper,we show the existence of a class of 6-body central
configurations with two isosceles triangles; which are congruent to each other
and keep some distance.We also study the necessary conditions about masses for
the bodies which can form a central configuration.


  We prove that there is no quantum speed-up when using quantum Monte-Carlo to
estimate the mean (and other moments) of analytically-defined log-concave
probability distributions prepared as quantum states using the Grover-Rudolph
method.


  We prove integral curvature bounds in terms of the Betti numbers for compact
submanifolds of the Euclidean space with low codimension. As an application, we
obtain topological obstructions for $\delta$-pinched immersions. Furthermore,
we obtain intrinsic obstructions for minimal submanifolds in spheres with
pinched second fundamental form.


  Here we study the structure of a highly ionizing shock wave in a gas of high
atmospheric pressure. We take into account the gas ionization when the gas
temperature reaches few orders of an ionization potential. It is shown that
after gasdynamic temperature-raising shock and formation of a highly-ionized
nonisothermal plasma $T_e>>T_i$ only the solitary ion-sound wave (soliton) can
propagate in this plasma. In such a wave the charge separation occurs:
electrons and ions form the double electric layer with the electric field. The
shock wave form, its amplitude and front width are obtained.


  Chaitin [G. J. Chaitin, J. Assoc. Comput. Mach., vol.22, pp.329-340, 1975]
introduced \Omega number as a concrete example of random real. The real \Omega
is defined as the probability that an optimal computer halts, where the optimal
computer is a universal decoding algorithm used to define the notion of
program-size complexity. Chaitin showed \Omega to be random by discovering the
property that the first n bits of the base-two expansion of \Omega solve the
halting problem of the optimal computer for all binary inputs of length at most
n. In the present paper we investigate this property from various aspects. We
consider the relative computational power between the base-two expansion of
\Omega and the halting problem by imposing the restriction to finite size on
both the problems. It is known that the base-two expansion of \Omega and the
halting problem are Turing equivalent. We thus consider an elaboration of the
Turing equivalence in a certain manner.