  This chapter provides an overview of the methods and results for quantum
thermodynamic experiments with single-electron devices. The experiments with a
single-electron box on Jarzynski equality and Crooks relation, two-temperature
fluctuation relations, and Maxwell's demon performed over the past few years
are reviewed here. We further review the first experimental realization of an
autonomous Maxwell's demon using a single-electron box as the demon.


  We study the robustness and fragility of entanglement of open quantum systems
in some exactly solvable models in which the decoherence is caused by a pure
dephasing process. In particular, for the toy models presented in this paper,
we identify two different time scales, one is responsible for local dephasing,
while the other is for entanglement decay. For a class of fragile entangled
states defined in this paper, we find that the entanglement of two qubits, as
measured by concurrence, decays faster asymptotically than the quantum
dephasing of an individual qubit.


  Placing is a necessary skill for a personal robot to have in order to perform
tasks such as arranging objects in a disorganized room. The object placements
should not only be stable but also be in their semantically preferred placing
areas and orientations. This is challenging because an environment can have a
large variety of objects and placing areas that may not have been seen by the
robot before.
  In this paper, we propose a learning approach for placing multiple objects in
different placing areas in a scene. Given point-clouds of the objects and the
scene, we design appropriate features and use a graphical model to encode
various properties, such as the stacking of objects, stability, object-area
relationship and common placing constraints. The inference in our model is an
integer linear program, which we solve efficiently via an LP relaxation. We
extensively evaluate our approach on 98 objects from 16 categories being placed
into 40 areas. Our robotic experiments show a success rate of 98% in placing
known objects and 82% in placing new objects stably. We use our method on our
robots for performing tasks such as loading several dish-racks, a bookshelf and
a fridge with multiple items.


  Since the recent advent of regulations for data protection (e.g., the General
Data Protection Regulation), there has been increasing demand in deleting
information learned from sensitive data in pre-trained models without
retraining from scratch. The inherent vulnerability of neural networks towards
adversarial attacks and unfairness also calls for a robust method to remove or
correct information in an instance-wise fashion, while retaining the predictive
performance across remaining data. To this end, we consider instance-wise
unlearning, of which the goal is to delete information on a set of instances
from a pre-trained model, by either misclassifying each instance away from its
original prediction or relabeling the instance to a different label. We also
propose two methods that reduce forgetting on the remaining data: 1) utilizing
adversarial examples to overcome forgetting at the representation-level and 2)
leveraging weight importance metrics to pinpoint network parameters guilty of
propagating unwanted information. Both methods only require the pre-trained
model and data instances to forget, allowing painless application to real-life
settings where the entire training set is unavailable. Through extensive
experimentation on various image classification benchmarks, we show that our
approach effectively preserves knowledge of remaining data while unlearning
given instances in both single-task and continual unlearning scenarios.


  Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.


  Tensor decomposition methods are widely used for model compression and fast
inference in convolutional neural networks (CNNs). Although many decompositions
are conceivable, only CP decomposition and a few others have been applied in
practice, and no extensive comparisons have been made between available
methods. Previous studies have not determined how many decompositions are
available, nor which of them is optimal. In this study, we first characterize a
decomposition class specific to CNNs by adopting a flexible graphical notation.
The class includes such well-known CNN modules as depthwise separable
convolution layers and bottleneck layers, but also previously unknown modules
with nonlinear activations. We also experimentally compare the tradeoff between
prediction accuracy and time/space complexity for modules found by enumerating
all possible decompositions, or by using a neural architecture search. We find
some nonlinear decompositions outperform existing ones.


  Two-dimensional (2D) materials exhibit a number of improved mechanical,
optical, electronic properties compared to their bulk counterparts. The absence
of dangling bonds in the cleaved surfaces of these materials allows combining
different 2D materials into van der Waals heterostructures to fabricate p-n
junctions, photodetectors, 2D-2D ohmic contacts that show unexpected
performances. These intriguing results are regularly summarized in
comprehensive reviews. A strategy to tailor their properties even further and
to observe novel quantum phenomena consists in the fabrication of superlattices
whose unit cell is formed either by two dissimilar 2D materials or by a 2D
material subjected to a periodical perturbation, each component contributing
with different characteristics. Furthermore, in a 2D materials-based
superlattice, the interlayer interaction between the layers mediated by van der
Waals forces constitutes a key parameter to tune the global properties of the
superlattice. The above-mentioned factors reflect the potential to devise
countless combinations of van der Waals 2D materials based superlattices. In
the present feature article, we explain in detail the state-of-the-art of 2D
materials-based superlattices and we describe the different methods to
fabricate them, classified as vertical stacking, intercalation with atoms or
molecules, moir\'e patterning, strain engineering and lithographic design. We
also aim to highlight some of the specific applications for each type of
superlattices.


  In our previous works we found a power series expansion of a particular case
of Fox's $H$ function $H^{q,0}_{p,q}$ in a neighborhood of its positive
singularity. An inverse factorial series expansion of the integrand of
$H^{q,0}_{p,q}$ served as our main tool. However, a necessary restriction on
parameters is missing in those works. In this note we fill this gap and give a
simpler and shorter proof of the expansion around the positive singular point.
We further identify more precisely the abscissa of convergence of the
underlying inverse factorial series. Our new proof hinges on a slight
generalization of a particular case of Braaksma's theorem about analytic
continuation of Fox's $H$ function.


  We present a new method to bound the cardinality of triple product sets in
groups and give three applications. A new and unexpectedly short proof of the
Plunnecke-Ruzsa sumset inequalities for Abelian groups. A new proof of a
theorem of Tao on triple products, which generalises these inequalities when no
assumption on commutativity is made. A further generalisation of the
Plunnecke-Ruzsa inequalities in general groups.


  This paper argues for the following three theses: (1) There is a clear reason
to prefer physical theories with deterministic dynamical equations: such
theories are maximally rich in information and usually also maximally simple.
(2) There is a clear way how to introduce probabilities in a deterministic
physical theory, namely as answer to the question of what evolution of a
specific system we can reasonably expect under ignorance of its exact initial
conditions. This procedure works in the same manner for both classical and
quantum physics. (3) There is no cogent reason to take the parameters that
enter into the (deterministic) dynamical equations of physics to refer to
properties of the physical systems. Granting an ontological status to
parameters such as mass, charge, wave functions and the like does not lead to a
gain in explanation, but only to artificial problems. Against this background,
I argue that there is no conflict between determinism in physics and free will
(on whatever conception of free will), and, in general, point out the limits of
science when it comes to the central metaphysical issues.


  Determining the spin of new particles is critical in identifying the true
theory among various extensions of the Standard Model at the next generation of
colliders. Quantum interference between different helicity amplitudes was shown
to be effective when the final state is fully reconstructible. However, many
interesting new physics processes allow only for partial reconstruction. In
this paper, we show how the interference effect can be unambiguously extracted
even in processes that have two-fold ambiguity, by considering the correlation
between two decay planes in e+ e- collisions.


  Predicitions made by neural networks can be fraudulently altered by so-called
poisoning attacks. A special case are backdoor poisoning attacks. We study
suitable detection methods and introduce a new method called Heatmap
Clustering. There, we apply a $k$-means clustering algorithm on heatmaps
produced by the state-of-the-art explainable AI method Layer-wise relevance
propagation. The goal is to separate poisoned from un-poisoned data in the
dataset. We compare this method with a similar method, called Activation
Clustering, which also uses $k$-means clustering but applies it on the
activation of certain hidden layers of the neural network as input. We test the
performance of both approaches for standard backdoor poisoning attacks,
label-consistent poisoning attacks and label-consistent poisoning attacks with
reduced amplitude stickers. We show that Heatmap Clustering consistently
performs better than Activation Clustering. However, when considering
label-consistent poisoning attacks, the latter method also yields good
detection performance.


  We show that the limiting distributions of the coefficients of the
$q$-Catalan numbers and the generalized $q$-Catalan numbers are normal. Despite
the fact that these coefficients are not unimodal for small $n$, we conjecture
that for sufficiently large $n$, the coefficients are unimodal and even
log-concave except for a few terms of the head and tail.


  We derive the free energy for fermions and bosons from fragmentation data.
Inspired by the symmetry and pairing energy of the Weizsacker mass formula we
obtain the free energy of fermions (nucleons) and bosons (alphas and deuterons)
using Landau's free energy approach. We confirm previously obtained results for
fermions and show that the free energy for alpha particles is negative and very
close to the free energy for ideal Bose gases. Deuterons behave more similarly
to fermions (positive free energy) rather than bosons. This is due to their low
binding energy, which makes them very 'fragile', i.e., easily formed and
destroyed. We show that the {\alpha}-particle fraction is dominant at all
temperatures and densities explored in this work. This is consistent with their
negative free energy, which favors clusterization of nuclear matter into
{\alpha}-particles at subsaturation densities and finite temperatures. The role
of finite open systems and Coulomb repulsion is addressed.


  Segmentation of anatomical regions of interest such as vessels or small
lesions in medical images is still a difficult problem that is often tackled
with manual input by an expert. One of the major challenges for this task is
that the appearance of foreground (positive) regions can be similar to
background (negative) regions. As a result, many automatic segmentation
algorithms tend to exhibit asymmetric errors, typically producing more false
positives than false negatives. In this paper, we aim to leverage this
asymmetry and train a diverse ensemble of models with very high recall, while
sacrificing their precision. Our core idea is straightforward: A diverse
ensemble of low precision and high recall models are likely to make different
false positive errors (classifying background as foreground in different parts
of the image), but the true positives will tend to be consistent. Thus, in
aggregate the false positive errors will cancel out, yielding high performance
for the ensemble. Our strategy is general and can be applied with any
segmentation model. In three different applications (carotid artery
segmentation in a neck CT angiography, myocardium segmentation in a
cardiovascular MRI and multiple sclerosis lesion segmentation in a brain MRI),
we show how the proposed approach can significantly boost the performance of a
baseline segmentation method.


  If $g$ is a map from a space $X$ into $\mathbb R^m$ and $z\not\in g(X)$, let
$P_{2,1,m}(g,z)$ be the set of all lines $\Pi^1\subset\mathbb R^m$ containing
$z$ such that $|g^{-1}(\Pi^1)|\geq 2$. We prove that for any $n$-dimensional
metric compactum $X$ the functions $g\colon X\to\mathbb R^m$, where $m\geq
2n+1$, with $\dim P_{2,1,m}(g,z)\leq 0$ for all $z\not\in g(X)$ form a dense
$G_\delta$-subset of the function space $C(X,\mathbb R^m)$. A parametric
version of the above theorem is also provided.


  Holographic studies of the entanglement entropy of field theories dual to
charged and neutral black holes in asymptotically global AdS4 spacetimes are
presented. The goal is to elucidate various properties of the quantity that are
peculiar to working in finite volume, and to gain access to the behaviour of
the entanglement entropy in the rich thermodynamic phase structure that is
present at finite volume and large N. The entropy is followed through various
first order phase transitions, and also a novel second order phase transition.
Properties of a specific heat in the neighbourhood of the critical point are
computed. Behaviour is found that contrasts interestingly with an earlier
holographic study of a second order phase transition dual to an holographic
superconductor.


  We introduce a new model for testing graph properties which we call the
\emph{rejection sampling model}. We show that testing bipartiteness of
$n$-nodes graphs using rejection sampling queries requires complexity
$\widetilde{\Omega}(n^2)$. Via reductions from the rejection sampling model, we
give three new lower bounds for tolerant testing of Boolean functions of the
form $f\colon\{0,1\}^n\to \{0,1\}$:
  $\bullet$Tolerant $k$-junta testing with \emph{non-adaptive} queries requires
$\widetilde{\Omega}(k^2)$ queries.
  $\bullet$Tolerant unateness testing requires $\widetilde{\Omega}(n)$ queries.
  $\bullet$Tolerant unateness testing with \emph{non-adaptive} queries requires
$\widetilde{\Omega}(n^{3/2})$ queries.
  Given the $\widetilde{O}(k^{3/2})$-query non-adaptive junta tester of Blais
\cite{B08}, we conclude that non-adaptive tolerant junta testing requires more
queries than non-tolerant junta testing. In addition, given the
$\widetilde{O}(n^{3/4})$-query unateness tester of Chen, Waingarten, and Xie
\cite{CWX17b} and the $\widetilde{O}(n)$-query non-adaptive unateness tester of
Baleshzar, Chakrabarty, Pallavoor, Raskhodnikova, and Seshadhri \cite{BCPRS17},
we conclude that tolerant unateness testing requires more queries than
non-tolerant unateness testing, in both adaptive and non-adaptive settings.
These lower bounds provide the first separation between tolerant and
non-tolerant testing for a natural property of Boolean functions.


  We investigate the impact of gravity portal to properties of white dwarfs,
such as equation-of-state as well as cooling time. We find that the interaction
between dark matter spin-zero bosons and electrons in the mean field
approximation softens the equation-of-state, and the object coolers down slower
compared to the usual case.


  The exponential growth in numbers of parameters of neural networks over the
past years has been accompanied by an increase in performance across several
fields. However, due to their sheer size, the networks not only became
difficult to interpret but also problematic to train and use in real-world
applications, since hardware requirements increased accordingly. Tackling both
issues, we present a novel approach that either drops a neural network's
initial weights or inverts their respective sign. Put simply, a network is
trained by weight selection and inversion without changing their absolute
values. Our contribution extends previous work on masking by additionally
sign-inverting the initial weights and follows the findings of the Lottery
Ticket Hypothesis. Through this extension and adaptations of initialization
methods, we achieve a pruning rate of up to 99%, while still matching or
exceeding the performance of various baseline and previous models. Our approach
has two main advantages. First, and most notable, signed Supermask models
drastically simplify a model's structure, while still performing well on given
tasks. Second, by reducing the neural network to its very foundation, we gain
insights into which weights matter for performance. The code is available on
GitHub.


  Deception detection is an important task that has been a hot research topic
due to its potential applications. It can be applied in many areas, from
national security (e.g., airport security, jurisprudence, and law enforcement)
to real-life applications (e.g., business and computer vision). However, some
critical problems still exist and are worth more investigation. One of the
significant challenges in the deception detection tasks is the data scarcity
problem. Until now, only one multi-modal benchmark open dataset for human
deception detection has been released, which contains 121 video clips for
deception detection (i.e., 61 for deceptive class and 60 for truthful class).
Such an amount of data is hard to drive deep neural network-based methods.
Hence, those existing models often suffer from overfitting problems and low
generalization ability. Moreover, the ground truth data contains some unusable
frames for many factors. However, most of the literature did not pay attention
to these problems. Therefore, in this paper, we design a series of data
preprocessing methods to deal with the aforementioned problem first. Then, we
propose a multi-modal deception detection framework to construct our novel
emotional state-based feature and use the open toolkit openSMILE to extract the
features from the audio modality. We also design a voting scheme to combine the
emotional states information obtained from visual and audio modalities.
Finally, we can determine the novel emotion state transformation feature with
our self-designed algorithms. In the experiment, we conduct the critical
analysis and comparison of the proposed methods with the state-of-the-art
multi-modal deception detection methods. The experimental results show that the
overall performance of multi-modal deception detection has a significant
improvement in the accuracy from 87.77% to 92.78% and the ROC-AUC from 0.9221
to 0.9265.


  The trailing edge of tube drift-chamber signals for charged particles is
expected to provide information concerning the particle passage time. This
information may be useful for separating meaningful signals from overlapping
garbage at high-rate experiments, such as the future LHC experiments. We
carried out a cosmic-ray test using a small tube chamber in order to
investigate the feasibility of this idea. We achieved a trailing-edge time
resolution of 12 ns in rms by applying simple pulse shaping to eliminate a
signal tail. A comparison with a Monte Carlo simulation indicates the
importance of well-optimized signal shaping to achieve good resolution. The
resolution may be further improved with better shaping.


  Brain-Computer interfaces (BCIs) play a significant role in easing
neuromuscular patients on controlling computers and prosthetics. Due to their
high signal-to-noise ratio, steady-state visually evoked potentials (SSVEPs)
has been widely used to build BCIs. However, currently developed algorithms do
not predict the modulation of SSVEP amplitude, which is known to change as a
function of stimulus luminance contrast. In this study, we aim to develop an
integrated approach to simultaneously estimate the frequency and
contrast-related amplitude modulations of the SSVEP signal. To achieve that, we
developed a behavioral task in which human participants focused on a visual
flicking target which the luminance contrast can change through time in several
ways. SSVEP signals from 16 subjects were then recorded from electrodes placed
at the central occipital site using a low-cost, consumer-grade EEG. Our results
demonstrate that the filter bank canonical correlation analysis (FBCCA)
performed well in SSVEP frequency recognition, while the support vector
regression (SVR) outperformed the other supervised machine learning algorithms
in predicting the contrast-dependent amplitude modulations of the SSVEPs. These
findings indicate the applicability and strong performance of our integrated
method at simultaneously predicting both frequency and amplitude of visually
evoked signals, and have proven to be useful for advancing SSVEP-based
applications.


  We calculate the optical properties of atomic-sized core-shell graphene -
boron nitride nanoflakes with triangular shaped crossection using the density
functional theory. The optical properties can be tuned by using different sizes
and proportions of the core-shell materials. Anisotropic effects manifested in
the absorption of unpolarized light with different orientations of the optical
vector are pointed out.


  Finite mixture such as the Gaussian mixture is a flexible and powerful
probabilistic modeling tool for representing the multimodal distribution widely
involved in many estimation and learning problems. The core of it is
representing the target distribution by the arithmetic average (AA) of a finite
number of sub-distributions which constitute a mixture. While the mixture has
been widely used for single sensor filter design, it is only recent that the AA
fusion demonstrates compelling performance for multi-sensor filter design. In
this paper, some statistic and information-theoretic results are given on the
covariance consistency, mean square error, mode-preservation capacity, and the
information divergence of the AA fusion approach. In particular, based on the
concept of conservative fusion, the relationship of the AA fusion with the
existing conservative fusion approaches such as covariance union and covariance
intersection is exposed. A suboptimal weighting approach has been proposed,
which jointly with the best mixture-fit property of the AA fusion leads to a
max-min optimization problem. Linear Gaussian models are considered for
algorithm illustration and simulation comparison, resulting in the first-ever
AA fusion-based multi-sensor Kalman filter.


  The notions of minimum geometrical length and minimum length scale are
discussed with reference to correlation functions obtained from in-in and
in-out amplitudes in quantum field theory. Whereas the in-in propagator for
metric perturbations does not admit the former, the in-out Feynman propagator
shows the emergence of the latter. A connection between the Feynman propagator
of quantum field theories of gravity and the deformation parameter $\delta_0$
of the generalised uncertainty principle (GUP) is then exhibited, which allows
to determine an exact expression for $\delta_0$ in terms of the residues of the
causal propagator. A correspondence between the non-renormalisability of (some)
theories (of gravity) and the existence of a minimum length scale is then
conjectured to support the idea that non-renormalisable theories are
self-complete and finite. The role played by the sign of the deformation
parameter is further discussed by considering an implementation of the GUP on
the lattice.


  In this paper we demonstrate how the Legendre transform connects the
statements of Noether's theorem in Hamiltonian and Lagrangian mechanics. We
give precise definitions of symmetries and conserved quantities in both the
Hamiltonian and Lagrangian frameworks and discuss why these notions in the
Hamiltonian framework are somewhat less rigid. We explore conditions which,
when put on these definitions, allow the Legendre transform to set up a
one-to-one correspondence between them. We also discuss how to preserve this
correspondence when the definitions of symmetries and conserved quantities are
less restrictive.


  The inverse mapping of GANs'(Generative Adversarial Nets) generator has a
great potential value.Hence, some works have been developed to construct the
inverse function of generator by directly learning or adversarial
learning.While the results are encouraging, the problem is highly challenging
and the existing ways of training inverse models of GANs have many
disadvantages, such as hard to train or poor performance.Due to these reasons,
we propose a new approach based on using inverse generator ($IG$) model as
encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to
train the $IG$ model. In the proposed model, the difference between the input
and output, which are both the generated image of pre-trained GAN's generator,
of AutoEncoder is directly minimized. The optimizing method can overcome the
difficulty in training and inverse model of an non one-to-one function.We also
applied the inverse model of GANs' generators to image searching and
translation.The experimental results prove that the proposed approach works
better than the traditional approaches in image searching.


  We carry out a photometric search for new cataclysmic variable stars (CVs),
with the goal of identification for candidates of AR~Scorpii-type binary
systems. We select GAIA sources that are likely associated with unidentified
X-ray sources, and analyze the light curves taken by the Zwicky Transient
Facility, Transiting Exoplanet Survey Satellite, and Lulin One-meter Telescope
in Taiwan. We investigate eight sources as candidates for CVs, among which six
sources are new identifications. Another two sources have been recognized as
CVs in previous studies, but no detailed investigations have been done. We
identify two eclipsing systems that are associated with an unidentified
XMM-Newton or Swift source, and one promising candidate for polar associated
with an unidentified ASKA source. Two polar candidates may locate in the
so-called period gap of a CV, and the other six candidates have an orbital
period shorter than that of the period gap. Although we do not identify a
promising candidate for AR~Scorpii-type binary systems, our study suggests that
CV systems that have X-ray emission and do not show frequent outbursts may have
been missed in previous surveys.


  Among existing privacy attacks on the gradient of neural networks, \emph{data
reconstruction attack}, which reverse engineers the training batch from the
gradient, poses a severe threat on the private training data. Despite its
empirical success on large architectures and small training batches, unstable
reconstruction accuracy is also observed when a smaller architecture or a
larger batch is under attack. Due to the weak interpretability of existing
learning-based attacks, there is little known on why, when and how data
reconstruction attack is feasible.
  In our work, we perform the first analytic study on the security boundary of
data reconstruction from gradient via a microcosmic view on neural networks
with rectified linear units (ReLUs), the most popular activation function in
practice. For the first time, we characterize the insecure/secure boundary of
data reconstruction attack in terms of the \emph{neuron exclusivity state} of a
training batch, indexed by the number of \emph{\textbf{Ex}clusively
\textbf{A}ctivated \textbf{N}eurons} (ExANs, i.e., a ReLU activated by only one
sample in a batch). Intuitively, we show a training batch with more ExANs are
more vulnerable to data reconstruction attack and vice versa. On the one hand,
we construct a novel deterministic attack algorithm which substantially
outperforms previous attacks for reconstructing training batches lying in the
insecure boundary of a neural network. Meanwhile, for training batches lying in
the secure boundary, we prove the impossibility of unique reconstruction, based
on which an exclusivity reduction strategy is devised to enlarge the secure
boundary for mitigation purposes.


  In this note we present algorithms for computing Euclidean minima of cubic
number fields; in particular, we were able to find all norm-Euclidean cubic
number fields with discriminants -999 < d < 10000.


  In this paper, an efficient control strategy for physiological interaction
based anaesthetic drug infusion model is explored using the fractional order
(FO) proportional integral derivative (PID) controllers. The dynamic model is
composed of several human organs by considering the brain response to the
anaesthetic drug as output and the drug infusion rate as the control input.
Particle Swarm Optimisation (PSO) is employed to obtain the optimal set of
parameters for PID/FOPID controller structures. With the proposed FOPID control
scheme much less amount of drug-infusion system can be designed to attain a
specific anaesthetic target and also shows high robustness for +/-50%
parametric uncertainty in the patient's brain model.


  We describe an electronic guidebook, Sotto Voce, that enables visitors to
share audio information by eavesdropping on each other's guidebook activity. We
have conducted three studies of visitors using electronic guidebooks in a
historic house: one study with open air audio played through speakers and two
studies with eavesdropped audio. An analysis of visitor interaction in these
studies suggests that eavesdropped audio provides more social and interactive
learning resources than open air audio played through speakers.


  We consider the relativistic generalization of the problem of the "least
uncomfortable" linear trajectory from point A to point B. The traditional
problem minimizes the time-integrated squared acceleration (termed the
"discomfort"), and there is a universal solution for all distances and
durations. This universality fails when the maximum speed of the trajectory
becomes relativistic, and we consider the more general case of minimizing the
squared proper acceleration over a proper time. The least uncomfortable
relativistic trajectory has a rapidity that evolves like the motion of a
particle in a hyperbolic sine potential, agreeing with the classical solution
at low velocities. We consider the special case of a hypothetical trip to Alpha
Centauri, and compare the minimal-discomfort trajectory to one with uniform
Earth-like acceleration.


  Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.


  We have performed magnetotransport measurements on a multi-layer graphene
flake. At the crossing magnetic field Bc, an approximately
temperature-independent point in the measured longitudinal resistivity, which
is ascribed to the direct insulator-quantum Hall (I-QH) transition, is
observed. By analyzing the amplitudes of the magnetoresistivity oscillations,
we are able to measure the quantum mobility of our device. It is found that at
the direct I-QH transition, the product of the quantum mobility and is about
0.37 which is considerably smaller than 1. In contrast, at Bc, the longitudinal
resistivity is close to the Hall resistivity, i.e., the product of the
classical mobility and the crossing field is about 1. Therefore our results
suggest that different mobilities need to be introduced for the direct I-QH
transition observed in multi-layered graphene. Combined with existing
experimental results obtained in various material systems, our data obtained on
graphene suggest that the direct I-QH transition is a universal effect in 2D.


  Voice based applications are ruling over the era of automation because speech
has a lot of factors that determine a speakers information as well as speech.
Modern Automatic Speech Recognition (ASR) is a blessing in the field of
Human-Computer Interaction (HCI) for efficient communication among humans and
devices using Artificial Intelligence technology. Speech is one of the easiest
mediums of communication because it has a lot of identical features for
different speakers. Nowadays it is possible to determine speakers and their
identity using their speech in terms of speaker recognition. In this paper, we
presented a method that will provide a speakers geographical identity in a
certain region using continuous Bengali speech. We consider eight different
divisions of Bangladesh as the geographical region. We applied the Mel
Frequency Cepstral Coefficient (MFCC) and Delta features on an Artificial
Neural Network to classify speakers division. We performed some preprocessing
tasks like noise reduction and 8-10 second segmentation of raw audio before
feature extraction. We used our dataset of more than 45 hours of audio data
from 633 individual male and female speakers. We recorded the highest accuracy
of 85.44%.


  The effect of hydrodynamic interactions on the free-solution electrophoresis
of polyelectrolytes is investigated with coarse-grained molecular dynamics
simulations. By comparing the results to simulations with switched-off
hydrodynamic interactions, we demonstrate their importance in modelling the
experimentally observed behaviour. In order to quantify the hydrodynamic
interactions between the polyelectrolyte and the solution, we present a novel
way to estimate its effective charge. We obtain an effective friction that is
different from the hydrodynamic friction obtained from diffusion measurements.
This effective friction is used to explain the constant electrophoretic
mobility for longer chains. To further emphasize the importance of hydrodynamic
interactions, we apply the model to end-labeled free-solution electrophoresis.


  Particle detectors based on liquid argon are now recognised as an attractive
technology for dark matter direct detection and coherent elastic
neutrino-nucleus scattering measurement. A program using a dual-phase liquid
argon detector with a fiducial mass of 200~kg to detect coherent elastic
neutrino-nucleus scattering at Taishan Nuclear Power Plant has been proposed.
SiPMs will be used as the photon sensor because of their high radio-purity and
high photon detection efficiency. S13370-6050CN SiPM, made by Hamamatsu, is a
candidate for the detector. In this paper, the characterisation of
S13370-6050CN SiPM, including the cross talk and after pulse probabilities at
liquid argon temperature and the temperature dependence of break down voltage,
dark counting rate and relative quantum efficiency were presented.


  The moduli space of stable quotients introduced by Marian-Oprea-Pandharipande
provides a natural compactification of the space of morphisms from nonsingular
curves to a nonsingular projective variety and carries a natural virtual class.
We show that the analogue of Givental's J-function for the resulting twisted
projective invariants is described by the same mirror hypergeometric series as
the corresponding Gromov-Witten invariants (which arise from the moduli space
of stable maps), but without the mirror transform (in the Calabi-Yau case).
This implies that the stable quotients and Gromov-Witten twisted invariants
agree if there is enough "positivity", but not in all cases. As a corollary of
the proof, we show that certain twisted Hurwitz numbers arising in the stable
quotients theory are also described by a fundamental object associated with
this hypergeometric series. We thus completely answer some of the questions
posed by Marian-Oprea-Pandharipande concerning their invariants. Our results
suggest a deep connection between the stable quotients invariants of complete
intersections and the geometry of the mirror families. As in Gromov-Witten
theory, computing Givental's J-function (essentially a generating function for
genus 0 invariants with 1 marked point) is key to computing stable quotients
invariants of higher genus and with more marked points; we exploit this in
forthcoming papers.


  Given $m, n, q\in \mathbb{N}$ such that $q$ is a prime power and $m\geq 3$,
$a\in \mathbb{F}_q$, we establish a sufficient condition for the existence of
primitive pair $(\alpha, f(\alpha))$ in $\mathbb{F}_{q^m}$ such that $\alpha$
is normal over $\mathbb{F}_q$ and
$\text{Tr}_{\mathbb{F}_{q^m}/\mathbb{F}_q}(\alpha^{-1})=a$, where $f(x)\in
\mathbb{F}_{q^m}(x)$ is a rational function of degree sum $n$. Further, when
$n=2$ and $q=5^k$ for some $k\in \mathbb{N}$, such a pair definitely exists for
all $(q, m)$ apart from at most $20$ choices.


  Automatic surgical instruction generation is a prerequisite towards
intra-operative context-aware surgical assistance. However, generating
instructions from surgical scenes is challenging, as it requires jointly
understanding the surgical activity of current view and modelling relationships
between visual information and textual description. Inspired by the neural
machine translation and imaging captioning tasks in open domain, we introduce a
transformer-backboned encoder-decoder network with self-critical reinforcement
learning to generate instructions from surgical images. We evaluate the
effectiveness of our method on DAISI dataset, which includes 290 procedures
from various medical disciplines. Our approach outperforms the existing
baseline over all caption evaluation metrics. The results demonstrate the
benefits of the encoder-decoder structure backboned by transformer in handling
multimodal context.


  In this note, we prove that there is no number with the Lehmer property in
the sequences of Jaconsthsl or Jacobsthal-Lucas numbers.


  Spatial data is ubiquitous. Massive amounts of data are generated every day
from billions of GPS-enabled devices such as cell phones, cars, sensors, and
various consumer-based applications such as Uber, Tinder, location-tagged posts
in Facebook, Twitter, Instagram, etc. This exponential growth in spatial data
has led the research community to focus on building systems and applications
that can process spatial data efficiently. In the meantime, recent research has
introduced learned index structures. In this work, we use techniques proposed
from a state-of-the art learned multi-dimensional index structure (namely,
Flood) and apply them to five classical multi-dimensional indexes to be able to
answer spatial range queries. By tuning each partitioning technique for optimal
performance, we show that (i) machine learned search within a partition is
faster by 11.79\% to 39.51\% than binary search when using filtering on one
dimension, (ii) the bottleneck for tree structures is index lookup, which could
potentially be improved by linearizing the indexed partitions (iii) filtering
on one dimension and refining using machine learned indexes is 1.23x to 1.83x
times faster than closest competitor which filters on two dimensions, and (iv)
learned indexes can have a significant impact on the performance of low
selectivity queries while being less effective under higher selectivities.


  We derive for the first time the growth index of matter perturbations of the
FLRW flat cosmological models in which the vacuum energy depends on redshift. A
particularly well motivated model of this type is the so-called quantum field
vacuum, in which apart from a leading constant term $\Lambda_0$ there is also a
$H^{2}$-dependence in the functional form of vacuum, namely
$\Lambda(H)=\Lambda_{0}+3\nu (H^{2}-H^{2}_{0})$. Since $|\nu|\ll1$ this form
endows the vacuum energy of a mild dynamics which affects the evolution of the
main cosmological observables at the background and perturbation levels.
Specifically, at the perturbation level we find that the growth index of the
running vacuum cosmological model is $\gamma_{\Lambda_{H}} \approx
\frac{6+3\nu}{11-12\nu}$ and thus it nicely extends analytically the result of
the $\Lambda$CDM model, $\gamma_{\Lambda}\approx 6/11$.


  A directed edge polytope $\mathcal{A}_G$ is a lattice polytope arising from
root system $A_n$ and a finite directed graph $G$. If every directed edge of
$G$ belongs to a directed cycle in $G$, then $\mathcal{A}_G$ is terminal and
reflexive, that is, one can associate this polytope to a Gorenstein toric Fano
variety $X_G$ with terminal singularities. It is shown by Totaro that a toric
Fano variety which is smooth in codimension $2$ and $\mathbb{Q}$-factorial in
codimension $3$ is rigid. In the present paper, we classify all directed graphs
$G$ such that $X_G$ is a toric Fano variety which is smooth in codimension $2$
and $\mathbb{Q}$-factorial in codimension $3$.


  We examine the contribution of general Z*ZH and gamma*ZH three-point
interactions arising from new physics to the Higgs production process e+e- ->
HZ. From Lorentz covariance, each of these vertices may be written in terms of
three (complex) form factors, whose real and imaginary parts together make six
independent couplings. We take into account possible longitudinal or transverse
beam polarization likely to be available at a linear collider. We show how
partial cross sections and angular asymmetries in suitable combinations with
appropriate beam polarizations, can be used to disentangle various couplings
from one another. A striking result is that using transverse polarization, one
of the gammaZH couplings, not otherwise accessible, can be determined
independently of all other couplings. Transverse polarization also helps in the
independent determination of a combination of two other couplings, in contrast
to a combination of four accessible with unpolarized or longitudinally
polarized beams. We also obtain the sensitivity of the various observables in
constraining the new-physics interactions at a linear collider operating at a
centre-of-mass energy of 500 GeV with longitudinal or transverse polarization.


  This paper addresses the controversy between Mayers, Lo and Chau on one side,
and Yuen on the opposite side, on whether there exist or not unconditionally
secure protocols. For such purpose, a complete classification of all possible
bit commitment protocols is given, including all possible cheating attacks. For
the simplest class of protocols (non-aborting and with complete and perfect
verification), it is shown how naturally a game-theoretical situation arises.
For these protocols, bounds for the cheating probabilities are derived, which
turn out to be different from those given in the impossibility proof. The whole
classification and analysis has been carried out using a "finite open system"
approach. The discrepancy with the impossibility proof is explained on the
basis of the implicit adoption of a "closed system approach"--equivalent to
modeling the commitment as performed by two fixed machines interacting
unitarily in a overall "closed system". However, it is shown that the closed
system approach for the classification of commitment protocols unavoidably
leads to infinite dimensions, which then invalidate the continuity argument at
the basis of the impossibility proof.


  This paper reports the occurrence of several chimera patterns and the
associated transitions among them in a network of coupled oscillators, which
are connected by a long range interaction that obeys a distance-dependent power
law. This type of interaction is common in physics and biology and constitutes
a general form of coupling scheme, where by tuning the power-law exponent of
the long range interaction the coupling topology can be varied from local via
nonlocal to global coupling. To explore the effect of the power-law coupling on
collective dynamics, we consider a network consisting of a realistic ecological
model of oscillating populations, namely the Rosenzweig--MacArthur model, and
show that the variation of the power-law exponent mediates transitions between
spatial synchrony and various chimera patterns. We map the possible
spatiotemporal states and their scenarios that arise due to the interplay
between the coupling strength and the power-law exponent.


  Bitvector filtering is an important query processing technique that can
significantly reduce the cost of execution, especially for complex decision
support queries with multiple joins. Despite its wide application, however, its
implication to query optimization is not well understood.
  In this work, we study how bitvector filters impact query optimization. We
show that incorporating bitvector filters into query optimization
straightforwardly can increase the plan space complexity by an exponential
factor in the number of relations in the query. We analyze the plans with
bitvector filters for star and snowflake queries in the plan space of right
deep trees without cross products. Surprisingly, with some simplifying
assumptions, we prove that, the plan of the minimal cost with bitvector filters
can be found from a linear number of plans in the number of relations in the
query. This greatly reduces the plan space complexity for such queries from
exponential to linear.
  Motivated by our analysis, we propose an algorithm that accounts for the
impact of bitvector filters in query optimization. Our algorithm optimizes the
join order for an arbitrary decision support query by choosing from a linear
number of candidate plans in the number of relations in the query. We implement
our algorithm in Microsoft SQL Server as a transformation rule. Our evaluation
on both industry standard benchmarks and customer workload shows that, compared
with the original Microsoft SQL Server, our technique reduces the total CPU
execution time by 22%-64% for the workloads, with up to two orders of magnitude
reduction in CPU execution time for individual queries.


  Carrollian holography is supposed to describe gravity in four-dimensional
asymptotically flat space-time by the three-dimensional Carrollian CFT living
at null infinity. We transform superstring scattering amplitudes into the
correlation functions of primary fields of Carrollian CFT depending on the
three-dimensional coordinates of the celestial sphere and a retarded time
coordinate. The power series in the inverse string tension is converted to a
whole tower of both UV and IR finite descendants of the underlying
field-theoretical Carrollian amplitude. We focus on four-point amplitudes
involving gauge bosons and gravitons in type I open superstring theory and in
closed heterotic superstring theory at the tree-level. We also discuss the
limit of infinite retarded time coordinates, where the string world-sheet
becomes celestial.


  Critical thinking and skepticism are fundamental mechanisms that one may use
to prevent the spreading of rumors, fake-news and misinformation. We consider a
simple model in which agents without previous contact with the rumor, being
skeptically oriented, may convince spreaders to stop their activity or, once
exposed to the rumor, decide not to propagate it as a consequence, for example,
of fact-checking. We extend a previous, mean-field analysis of the combined
effect of these two mechanisms, active and passive skepticism, to include
spatial correlations. This can be done either analytically, through the pair
approximation, or simulating an agent-based version on diverse networks. Our
results show that while in mean-field there is no coexistence between spreaders
and susceptibles (although, depending on the parameters, there may be
bistability depending on the initial conditions), when spatial correlations are
included, because of the protective effect of the isolation provided by removed
agents, coexistence is possible.


  This paper relates the well-known Linear Temporal Logic with the logic of
propositional schemata introduced by the authors. We prove that LTL is
equivalent to a class of schemata in the sense that polynomial-time reductions
exist from one logic to the other. Some consequences about complexity are
given. We report about first experiments and the consequences about possible
improvements in existing implementations are analyzed.


  We report observations of a transient source \fermi\ from radio to \grs.
\fermi\ was discovered by the {\it Fermi-LAT} in May 2017. Follow-up {\it
Swift-XRT} observations revealed three flaring episodes through March 2018, and
the peak X-ray flux is about $10^3$ higher than the {\it ROSAT all-sky survey
(RASS)} flux upper limit. Optical spectral measurements taken by the {\it
Magellan 6.5-m telescope} and the {\it Lick-Shane telescope} both show a
largely featureless spectrum, strengthening the BL Lac interpretation first
proposed by \citet{Bruni18}. The optical and mid-infrared (MIR) emission goes
to a higher state in 2018, when the flux in high energies goes down to a lower
level. Our {\it RATAN-600m} measurements at 4.8~GHz and 8.2~GHz do not indicate
any significant radio flux variation over the monitoring seasons in 2017 and
2018, nor deviate from the archival {\it NVSS} flux level. During GeV flaring
times, the spectrum is very hard ($\Gamma_\gamma\sim$1.7) in the GeV band and
at times also very hard (($\Gamma_{\rm X}\lesssim2$) in the X-rays, similar to
a high-synchrotron-peak (or even an extreme) BL Lac object, making \fermi\ a
good target for ground-based {\it Cherenkov telescopes}.


  We extend the the definition of Kumjian-Pask algebras to include algebras
associated to finitely aligned higher-rank graphs. We show that these
Kumjian-Pask algebras are universally defined and have a graded uniqueness
theorem. We also prove the Cuntz-Kreiger uniqueness theorem; to do this, we use
a groupoid approach. As a consequence of the graded uniqueness theorem, we show
that every Kumjian-Pask algebra is isomorphic to the Steinberg algebra
associated to its boundary path groupoid. We then use Steinberg algebra results
to prove the Cuntz-Kreiger uniqueness theorem and also to characterize
simplicity and basic simplicity.


  The stability of the Cauchy horizon in spherically symmetric self-similar
collapse is studied by determining the flux of scalar radiation impinging on
the horizon. This flux is found to be finite.


  We demonstrate a new type of non-Hermitian phase transition in open systems
far from thermal equilibrium, which takes place in coupled systems interacting
with reservoirs at different temperatures. The frequency of the maximum in the
spectrum of energy flow through the system plays the role of the order
parameter, and is determined by an analog of the -potential. The phase
transition is exhibited in the frequency splitting of the spectrum at a
critical point, the value of which is determined by the relaxation rates and
the coupling strengths. Near the critical point, fluctuations of the order
parameter diverge according to a power law. We show that the critical exponent
depends only on the ratio of reservoir temperatures. This dependence indicates
the non-equilibrium nature of the phase transition at the critical point. This
new non-Hermitian phase transition can take place in systems without
exceptional points.


  We extend the calculus of multiplicative vector fields and differential forms
and their intrinsic derivatives from Lie groups to Lie groupoids; this
generalization turns out to include also the classical process of complete
lifting from arbitrary manifolds to tangent and cotangent bundles. Using this
calculus we give a new description of the Lie bialgebroid structure associated
with a Poisson groupoid.


  In their $1996$ paper Anantharam and Verd\'u showed that feedback does not
increase the capacity of a queue when the service time is exponentially
distributed. Whether this conclusion holds for general service times has
remained an open question which this paper addresses.
  Two main results are established for both the discrete-time and the
continuous-time models. First, a sufficient condition on the service
distribution for feedback to increase capacity under FIFO service policy.
Underlying this condition is a notion of weak feedback wherein instead of the
queue departure times the transmitter is informed about the instants when
packets start to be served. Second, a condition in terms of output entropy rate
under which feedback does not increase capacity. This condition is general in
that it depends on the output entropy rate of the queue but explicitly depends
neither on the queue policy nor on the service time distribution. This
condition is satisfied, for instance, by queues with LCFS service policies and
bounded service times.


  Discovering what other people think has always been a key aspect of our
information-gathering strategy. People can now actively utilize information
technology to seek out and comprehend the ideas of others, thanks to the
increased availability and popularity of opinion-rich resources such as online
review sites and personal blogs. Because of its crucial function in
understanding people's opinions, sentiment analysis (SA) is a crucial task.
Existing research, on the other hand, is primarily focused on the English
language, with just a small amount of study devoted to low-resource languages.
For sentiment analysis, this work presented a new multi-class Urdu dataset
based on user evaluations. The tweeter website was used to get Urdu dataset.
Our proposed dataset includes 10,000 reviews that have been carefully
classified into two categories by human experts: positive, negative. The
primary purpose of this research is to construct a manually annotated dataset
for Urdu sentiment analysis and to establish the baseline result. Five
different lexicon- and rule-based algorithms including Naivebayes, Stanza,
Textblob, Vader, and Flair are employed and the experimental results show that
Flair with an accuracy of 70% outperforms other tested algorithms.


  Sharding distributed ledgers is a promising on-chain solution for scaling
blockchains but lacks formal grounds, nurturing skepticism on whether such
complex systems can scale blockchains securely. We fill this gap by introducing
the first formal framework as well as a roadmap to robust sharding. In
particular, we first define the properties sharded distributed ledgers should
fulfill. We build upon and extend the Bitcoin backbone protocol by defining
consistency and scalability. Consistency encompasses the need for atomic
execution of cross-shard transactions to preserve safety, whereas scalability
encapsulates the speedup a sharded system can gain in comparison to a
non-sharded system.
  Using our model, we explore the limitations of sharding. We show that a
sharded ledger with $n$ participants cannot scale under a fully adaptive
adversary, but it can scale up to $m$ shards where $n=c'm\log m$, under an
epoch-adaptive adversary; the constant $c'$ encompasses the trade-off between
security and scalability. This is possible only if the sharded ledgers create
succinct proofs of the valid state updates at every epoch. We leverage our
results to identify the sufficient components for robust sharding, which we
incorporate in a protocol abstraction termed Divide & Scale. To demonstrate the
power of our framework, we analyze the most prominent sharded blockchains
(Elastico, Monoxide, OmniLedger, RapidChain) and pinpoint where they fail to
meet the desired properties.


  Rapid progress in electrically-controlled plasmonics in solids poses a
question about effects of electronic reservoirs on the properties of plasmons.
We find that plasmons in electronically open systems [i.e. in (semi)conductors
connected to leads] are prone to an additional damping due to charge carrier
penetration into contacts and subsequent thermalization. We develop a theory of
such lead-induced damping based on kinetic equation with self-consistent
electric field, supplemented by microscopic carrier transport at the
interfaces. The lifetime of plasmon in electronically open ballistic system
appears to be finite, order of conductor length divided by carrier Fermi
(thermal) velocity. The reflection loss of plasmon incident on the contact of
semi-conductor and perfectly conducting metal also appears to be finite, order
of Fermi velocity divided by wave phase velocity. Recent experiments on
plasmon-assisted photodetection are discussed in light of the proposed
lead-induced damping phenomenon.


  Learning from demonstration is an effective method for human users to
instruct desired robot behaviour. However, for most non-trivial tasks of
practical interest, efficient learning from demonstration depends crucially on
inductive bias in the chosen structure for rewards/costs and policies. We
address the case where this inductive bias comes from an exchange with a human
user. We propose a method in which a learning agent utilizes the information
bottleneck layer of a high-parameter variational neural model, with auxiliary
loss terms, in order to ground abstract concepts such as spatial relations. The
concepts are referred to in natural language instructions and are manifested in
the high-dimensional sensory input stream the agent receives from the world. We
evaluate the properties of the latent space of the learned model in a
photorealistic synthetic environment and particularly focus on examining its
usability for downstream tasks. Additionally, through a series of controlled
table-top manipulation experiments, we demonstrate that the learned manifold
can be used to ground demonstrations as symbolic plans, which can then be
executed on a PR2 robot.


  Although iceberg models have been used for decades, they have received far
more widespread attention in recent years, in part due to efforts to explicitly
represent icebergs in climate models. This calls for increased scrutiny of all
aspects of typical iceberg models. An important component of iceberg models is
the representation of iceberg capsizing, or rolling. Rolling occurs
spontaneously when the ratio of iceberg width to height falls below a critical
threshold. Here we examine previously proposed representations of this
threshold, and we find that there have been crucial errors in the
representation of rolling in many modeling studies to date. We correct these
errors and identify an accurate model representation of iceberg rolling. Next,
we assess how iceberg rolling influences simulation results in a hierarchy of
models. Rolling is found to substantially prolong the lifespan of individual
icebergs and allow them to drift farther offshore, although it is found to have
relatively small impacts on the large-scale freshwater distribution in
comprehensive model simulations. The results suggest that accurate
representations of iceberg rolling may be of particular importance for
operational forecast models of iceberg drift, as well as for regional changes
in high-resolution climate model simulations.


  We separate the $AF$-algebras (correspondingly action of the countable groups
on Cantor sets) onto two classes ---- "completely smooth" for which the set of
all indecomposable traces (correspondingly list of all invariant ergodic
measures) has nice parametrization, and the second class --- "completely
non-smooth" for which the set of all traces (correspondingly set of all
invariant measures) is Poulsen simplex and therefore there is no suitable
parametrization of indecomposable traces, (ergodic measures) e.g.Choquet
boundary.
  Important example of the first type of $AF$-algebra is group algebra of
infinite symmetric group, and of the second type
  --- group algebra of some locally finite solvable group. The questions of
recognition of those cases are closely related to the orbit theory of dynamical
systems and theory of filtrations of sigma-fields in measure spaces and Borel
spaces.


  We describe the current content moderation strategy employed by Meta to
remove policy-violating content from its platforms. Meta relies on both
handcrafted and learned risk models to flag potentially violating content for
human review. Our approach aggregates these risk models into a single ranking
score, calibrating them to prioritize more reliable risk models. A key
challenge is that violation trends change over time, affecting which risk
models are most reliable. Our system additionally handles production challenges
such as changing risk models and novel risk models. We use a contextual bandit
to update the calibration in response to such trends. Our approach increases
Meta's top-line metric for measuring the effectiveness of its content
moderation strategy by 13%.


  We generalize the rejection-free event-chain Monte Carlo algorithm from many
particle systems with pairwise interactions to systems with arbitrary three- or
many-particle interactions. We introduce generalized lifting probabilities
between particles and obtain a general set of equations for lifting
probabilities, the solution of which guarantees maximal global balance. We
validate the resulting three-particle event-chain Monte Carlo algorithms on
three different systems by comparison with conventional local Monte Carlo
simulations: (i) a test system of three particles with a three-particle
interaction that depends on the enclosed triangle area; (ii) a hard-needle
system in two dimensions, where needle interactions constitute three-particle
interactions of the needle end points; (iii) a semiflexible polymer chain with
a bending energy, which constitutes a three-particle interaction of neighboring
chain beads. The examples demonstrate that the generalization to many-particle
interactions broadens the applicability of event-chain algorithms considerably.


  A status report on the topic Compton scattering and polarizabilities is
presented with emphasis on the scalar t-channel as entering into dispersion
theory. Precise values for the polarizabilities are obtained leading to
$\alpha_p = 12.0\pm 0.6$ $(12.0)$, $\beta_p=1.9\mp 0.6$ $(1.9)$, $\alpha_n=
12.5\pm 1.7$ $(13.4)$, $\beta_n= 2.7 \mp 1.8$ $(1.8)$ in units of $10^{-4}$
fm$^3$ and $\gamma^{(p)}_\pi = -36.4 \pm 1.5$ $(-36.6)$, $\gamma^{(n)}_\pi =
58.6 \pm 4.0$ $(58.3)$, $(\gamma^{(p)}_0= -0.58\pm 0.20)$, $(\gamma^{(n)}_0 =
+0.38\pm 0.22)$ in units of $10^{-4}$ fm$^4$, for the proton (p) and neutron
(n), respectively. The data given with an error are {\it recommended}
experimental values with updates compared to [1] where necessary, the data in
parentheses are predicted values. These predicted values are not contained in
[1], but are the result of a newly developed analysis which is the main topic
of the present paper. The most important recent discovery is that the largest
part of the electric polarizability and the total diamagnetic polarizability of
the nucleon are properties of the $\sigma$ meson as part of the
constituent-quark structure, as expected from the mechanism of chiral symmetry
breaking. This view is supported by an experiment on Compton scattering by the
proton carried out in the second resonance region, where a large contribution
from the $\sigma$ meson enters into the scattering amplitudes. This experiment
led to a determination of the mass of the $\sigma$ meson of $m_\sigma = 600 \pm
70$ MeV. From the experimental $\alpha_p$ and predicted differences $(\alpha_n
- \alpha_p)$ neutron polarizabilities in the range $\alpha_n= 12.0 - 13.4$ are
predicted, where the uncertainties are related to the $f_0(980)$ and $a_0(980)$
scalar mesons.


  The redshift distribution of all 46,400 quasars in the Sloan Digital Sky
Survey (SDSS) Quasar Catalog III, Third Data Release, is examined. Six Peaks
that fall within the redshift window below z = 4, are visible. Their positions
agree with the preferred redshift values predicted by the decreasing intrinsic
redshift (DIR) model, even though this model was derived using completely
independent evidence. A power spectrum analysis of the full dataset confirms
the presence of a single, significant power peak at the expected redshift
period. Power peaks with the predicted period are also obtained when the upper
and lower halves of the redshift distribution are examined separately. The
periodicity detected is in linear z, as opposed to log(1+z). Because the peaks
in the SDSS quasar redshift distribution agree well with the preferred
redshifts predicted by the intrinsic redshift relation, we conclude that this
relation, and the peaks in the redshift distribution, likely both have the same
origin, and this may be intrinsic redshifts, or a common selection effect.
However, because of the way the intrinsic redshift relation was determined it
seems unlikely that one selection effect could have been responsible for both.


  This article addresses the U.S. Supreme Court's central purpose formulation
in Reporters Committee v. Department of Justice under the federal Freedom of
Information Act. By examining all lower federal court opinions interpreting
Reporters Committee and by analyzing the effects of the Court's opinion on the
implementation of the FOIA, the paper finds that the Court's opinion has
greatly narrowed the scope of the FOIA and limited the power of EFOIA to
democratize electronic information. To assist in remedying the damage to the
public interest in freedom of information, the author urges judicial
consideration of a Privacy Act case, Tobey v. NRB, that more subtly treats
information collected by government about individuals. The paper concludes that
the Privacy Act demonstrates clearly that information, particularly
computerized databases, can not be treated categorically for purposes of
access.


  We introduce the notion of scattering space $S_b^r$ for $N$-body quantum
mechanical systems, where $b$ is a cluster decomposition with $2\le |b|\le N$
and $r$ is a real number $0\le r\le 1$. Utilizing these spaces, we give a
decomposition of continuous spectral subspace by $S_b^1$ for $N$-body quantum
systems with long-range pair potentials
$V_\alpha^L(x_\alpha)=O(|x_\al|^{-\ep})$. This is extended to a decomposition
by $S_b^r$ with $0\le r\le 1$ for some long-range case. We also prove a
characterization of ranges of wave operators by $S_b^0$.


  The recent surge in women reporting sexual assault and harassment (e.g.,
#metoo campaign) has highlighted a longstanding societal crisis. This injustice
is partly due to a culture of discrediting women who report such crimes and
also, rape myths (e.g., 'women lie about rape'). Social web can facilitate the
further proliferation of deceptive beliefs and culture of rape myths through
intentional messaging by malicious actors. This multidisciplinary study
investigates Twitter posts related to sexual assaults and rape myths for
characterizing the types of malicious intent, which leads to the beliefs on
discrediting women and rape myths. Specifically, we first propose a novel
malicious intent typology for social media using the guidance of social
construction theory from policy literature that includes Accusational,
Validational, or Sensational intent categories. We then present and evaluate a
malicious intent classification model for a Twitter post using semantic
features of the intent senses learned with the help of convolutional neural
networks. Lastly, we analyze a Twitter dataset of four months using the intent
classification model to study narrative contexts in which malicious intents are
expressed and discuss their implications for gender violence policy design.


  We point out a generic inconsistency of the coupling of ordinary gravity as
described by general relativity with matter invariant under the subgroup of
unimodular diffeomorphisms (TDiffs), and some previously studied exceptions are
pointed out. The most general Lagrangian invariant under TDiff up to dimension
five operators is determined, and restrictions on parameters coming from
existing observations are studied in some cases.


  The neutron yields observed in inertial confinement fusion experiments for
higher convergence ratios are about two orders of magnitude smaller than the
neutron yields predicted by one-dimensional models, the discrepancy being
attributed to the development of instabilities. We consider the possibility
that ignition and a moderate gain could be achieved with existing laser
facilities if the laser driver energy is used to produce only the radial
compression of the fuel capsule to high densities but relatively low
temperatures, while the ignition of the fusion reactions in the compressed fuel
capsule will be effected by a synchronized hypervelocity impact. A
positively-charged incident projectile can be accelerated to velocities of 3.5
x 10^6 m/s, resulting in ignition temperatures of about 4 keV, by a
conventional low-beta linac having a length of 13 km if deuterium-tritium
densities of 570 g/cm^3 could be obtained by laser-driven compression.


  We consider properties of a two-dimensional electron system in a random
magnetic field. It is assumed that the magnetic field not only influences
orbital electron motion but also acts on the electron spin. For calculations,
we suggest a new trick replacing the initial Hamiltonian by a Dirac
Hamiltonian. This allows us to do easily a perturbation theory and derive a
supermatrix sigma model, which takes a form of the conventional sigma model
with the unitary symmetry. Using this sigma model we calculate several
correlation functions including a spin-spin correlation function. As compared
to the model without spin, we get different expressions for the single-particle
lifetime and the transport time. The diffusion constant turns out to be 2 times
smaller than the one for spinless particles.


  We present a method to construct explicitly L-infinity algebras governing
simultaneous deformations of various kinds of algebraic structures and of their
morphisms. It is an alternative to the heavy use of the operad machinery of the
existing approaches. Our method relies on Voronov's derived bracket
construction.


  Given an invertible sheaf, does it come from a Cartier divisor? This might
fail in presence of embedded components. I give some examples and characterize
those invertible sheaves that allow a Cartier divisor.


  We study the ratchet effect in a narrow pinning-free superconductive ring
based on time-dependent Ginzburg-Landau (TDGL) equations. Voltage responses to
external dc an ac currents at various magnetic fields are studied. Due to
asymmetric barriers for flux penetration and flux exit in the ring-shaped
superconductor, the critical current above which the flux-flow state is
reached, as well as the critical current for the transition to the normal
state, are different for the two directions of applied current. These effects
cooperatively cause ratchet signal reversal at high magnetic fields, which has
not been reported to date in a pinning-free system. The ratchet signal found
here is larger than those induced by asymmetric pinning potentials. Our results
also demonstrate the feasibility of using mesoscopic superconductors to employ
superconducting diode effect in versatile superconducting devices.


  By using modular functions on the upper complex half-plane, we study a class
of strain energies for crystalline materials whose global invariance originates
from the full symmetry group of the underlying lattice. This follows Ericksen's
suggestion which aimed at extending the Landau-type theories to encompass the
behavior of crystals undergoing structural phase transformation, with twinning,
microstructure formation, and possibly associated plasticity effects. Here we
investigate such Ericksen-Landau strain energies for the modelling of
reconstructive transformations, focusing on the prototypical case of the
square-hexagonal phase change in 2D crystals. We study the bifurcation and
valley-floor network of these potentials, and use one in the simulation of a
quasi-static shearing test. We observe typical effects associated with the
micro-mechanics of phase transformation in crystals, in particular, the bursty
progression of the structural phase change, characterized by intermittent
stress-relaxation through microstructure formation, mediated, in this
reconstructive case, by defect nucleation and movement in the lattice.


  The purpose of this paper is to study metrics suitable for assessing
uncertainty of power spectra when these are based on finite second-order
statistics. The family of power spectra which is consistent with a given range
of values for the estimated statistics represents the uncertainty set about the
"true" power spectrum. Our aim is to quantify the size of this uncertainty set
using suitable notions of distance, and in particular, to compute the diameter
of the set since this represents an upper bound on the distance between any
choice of a nominal element in the set and the "true" power spectrum. Since the
uncertainty set may contain power spectra with lines and discontinuities, it is
natural to quantify distances in the weak topology---the topology defined by
continuity of moments. We provide examples of such weakly-continuous metrics
and focus on particular metrics for which we can explicitly quantify spectral
uncertainty. We then consider certain high resolution techniques which utilize
filter-banks for pre-processing, and compute worst-case a priori uncertainty
bounds solely on the basis of the filter dynamics. This allows the a priori
tuning of the filter-banks for improved resolution over selected frequency
bands.


  Big Data-driven transportation engineering has the potential to improve
utilization of road infrastructure, decrease traffic fatalities, improve fuel
consumption, decrease construction worker injuries, among others. Despite these
benefits, research on Big Data-driven transportation engineering is difficult
today due to the computational expertise required to get started. This work
proposes BoaT, a transportation-specific programming language, and it's Big
Data infrastructure that is aimed at decreasing this barrier to entry. Our
evaluation that uses over two dozen research questions from six categories show
that research is easier to realize as a BoaT computer program, an order of
magnitude faster when this program is run, and exhibits 12-14x decrease in
storage requirements.


  In an ongoing effort to identify and study high-mass protostellar candidates
we have observed in various tracers a sample of 235 sources selected from the
IRAS Point Source Catalog, mostly with dec < -30 deg, with the SEST antenna at
millimeter wavelengths. The sample contains 142 Low sources and 93 High, which
are believed to be in different evolutionary stages. Both sub-samples have been
studied in detail by comparing their physical properties and morphologies.
Massive dust clumps have been detected in all but 8 regions, with usually more
than one clump per region. The dust emission shows a variety of complex
morphologies, sometimes with multiple clumps forming filaments or clusters. The
mean clump has a linear size of ~0.5 pc, a mass of ~320 Msolar for a dust
temperature Td=30 K, an H_2 density of 9.5E5 cm-3, and a surface density of 0.4
g cm-2. The median values are 0.4 pc, 102 Msolar, 4E4 cm-3, and 0.14 g cm-2,
respectively. The mean value of the luminosity-to-mass ratio, L/M ~99
Lsolar/Msolar, suggests that the sources are in a young, pre-ultracompact HII
phase. We have compared the millimeter continuum maps with images of the mid-IR
MSX emission, and have discovered 95 massive millimeter clumps non-MSX
emitters, either diffuse or point-like, that are potential prestellar or
precluster cores. The physical properties of these clumps are similar to those
of the others, apart from the mass that is ~3 times lower than for clumps with
MSX counterpart. Such a difference could be due to the potential prestellar
clumps having a lower dust temperature. The mass spectrum of the clumps with
masses above M ~100 Msolar is best fitted with a power-law dN/dM proportional
to M-alpha with alpha=2.1, consistent with the Salpeter (1955) stellar IMF,
with alpha=2.35.


  The performance of modern lithium-sulfur (Li/S) battery systems critically
depends on the electrolyte and solvent compositions. For fundamental molecular
insights and rational guidance of experimental developments, efficient and
sufficiently accurate molecular simulations are thus in urgent need. Here, we
construct a molecular dynamics (MD) computer simulation model of representative
state-of-the art electrolyte-solvent systems for Li/S batteries constituted by
lithium-bis(trifluoromethane)sulfonimide (LiTFSI) and LiNO3 electrolytes in
mixtures of the organic solvents 1,2-dimethoxyethane (DME) and 1,3-dioxolane
(DOL). We benchmark and verify our simulations by comparing structural and
dynamic features with various available experimental reference systems and
demonstrate their applicability for a wide range of electrolyte-solvent
compositions. For the state-of-the-art battery solvent, we finally calculate
and discuss the detailed composition of the first lithium solvation shell, the
temperature dependence of lithium diffusion, as well as the electrolyte
conductivities and lithium transference numbers. Our model will serve as a
basis for efficient future predictions of electrolyte structure and transport
in complex electrode confinements for the optimization of modern Li/S batteries
(and related devices).


  In physics, entanglement 'reduces' the entropy of an entity, because the (von
Neumann) entropy of, e.g., a composite bipartite entity in a pure entangled
state is systematically lower than the entropy of the component sub-entities.
We show here that this 'genuinely non-classical reduction of entropy as a
result of composition' also holds whenever two concepts combine in human
cognition and, more generally, it is valid in human culture. We exploit these
results and make a 'new hypothesis' on the nature of entanglement, namely, the
production of entanglement in the preparation of a composite entity can be seen
as a 'dynamical process of collaboration between its sub-entities to reduce
uncertainty', because the composite entity is in a pure state while its
sub-entities are in a non-pure, or density, state, as a result of the
preparation. We identify within the nature of this entanglement a mechanism of
contextual updating and illustrate the mechanism in the example we analyze. Our
hypothesis naturally explains the 'non-classical nature' of some quantum
logical connectives, as due to Bell-type correlations.


  We prove higher order asymptotic formulas for determinants and traces of
finite block Toeplitz matrices generated by matrix functions belonging to
generalized H\"older spaces with characteristic functions from the
Bari-Stechkin class. We follow the approach of B\"ottcher and Silbermann and
generalize their results for symbols in standard H\"older spaces.


  The colossal magnetoresistance in manganites AMnO_3 is studied from the
viewpoint of multicritical phenomena. To understand the complicated interplay
of various phases, we study the Ginzburg-Landau theory in terms of both the
mean-field approximation and the renormalization-group analysis to compare with
the observed phase diagram. Several novel features, such as the first-order
ferromagnetic transition, and the dip in the transition temperature near the
multicritical point, can be understood as driven by enhanced fluctuations near
the multicritical point. Furthermore, we obtain a universal scaling relation
for the H/M-M^2 plot (Arrott plot), which fits rather well with the
experimental data, providing the further evidence for the enhanced fluctuation.


  Let $f$ be an ordinary polynomial in $\mathbb{C}[z_1,..., z_n]$ with no
negative exponents and with no factor of the form $z_1^{\alpha_1}...
z_n^{\alpha_n}$ where $\alpha_i$ are non zero natural integer. If we assume in
addicting that $f$ is maximally sparse polynomial (that its support is equal to
the set of vertices of its Newton polytope), then a complement component of the
amoeba $\mathscr{A}_f$ in $\mathbb{R}^n$ of the algebraic hypersurface
$V_f\subset (\mathbb{C}^*)^n$ defined by $f$, has order lying in the support of
$f$, which means that $\mathscr{A}_f$ is solid. This gives an affirmative
answer to Passare and Rullg\aa rd question in [PR2-01].


  We present a brief overview of the different domain and space decomposition
techniques that enter in developing and analyzing solvers for discontinuous
Galerkin methods. Emphasis is given to the novel and distinct features that
arise when considering DG discretizations over conforming methods. Connections
and differences with the conforming approaches are emphasized.


  Inflationary models predicting a scale-dependent large amplification of the
density perturbations have recently attracted a lot of attention because the
amplified perturbations can seed a sizable amount of primordial black holes
(PBHs) and stochastic background of gravitational waves (GWs). While the power
spectra in these models are computed based on the linear equation of motion, it
is not obvious whether loop corrections are negligible when such a large
amplification occurs during inflation. In this paper, as a first step to
discuss the loop corrections in such models, we use the in-in formalism and
calculate the one-loop scalar power spectrum numerically and analytically in an
illustrative model where the density perturbations are resonantly amplified due
to oscillatory features in the inflaton potential. Our calculation is
technically new in that the amplified perturbations are numerically taken into
account in the in-in formalism for the first time. In arriving at our
analytical estimates, we highlight the role that the Wronskian condition of
perturbations, automatically satisfied in our model, plays in obtaining the
correct estimates. We also discuss the necessary conditions for subdominant
loop corrections in this model. We find that, for the typical parameter space
leading to the $\mathcal O(10^7)$ amplification of the power spectrum required
for a sufficient PBH production, the one-loop power spectrum dominates over the
tree-level one, indicating the breakdown of the perturbation theory.


  We use numerical simulation to investigate the triggering of starbursts in
merging disk galaxies. The properties of the merger-driven starbursts are
sensitive to the structure of the progenitor galaxies; specifically, to the
amount of material in a dense central bulge. Galaxies without bulges develop
bars shortly after their first close passage, driving significant gas inflow
and subsequent starbursts in the centers of the galaxies. These starbursts
significantly deplete the star-forming gas, so that only relatively weak
starbursts arise during the final merger. By contrast, models of galaxies with
central bulges show that a bulge acts to stabilize the galaxies against inflow
and starbursts until the galaxies actually merge. At this time, strong
dissipation leads to the formation of a massive central gas mass and an ensuing
star formation rate two orders of magnitude greater than that in our isolated
disk models. These starbursts are very short in duration, typically $\sim$ 50
Myr, suggesting that the rarity of ultraluminous infrared galaxies is a result
of their being in a very short evolutionary phase, rather than special and rare
formation conditions. The fact that these mergers display many of the
properties of ultraluminous infrared galaxies -- tidal features, double nuclei,
massive compact gas concentrations, and extreme levels of starburst activity --
suggests that merger-driven starbursts can explain the emission from many
ultraluminous infrared galaxies without an active nucleus.


  Polarized fusion has long ago be proposed as a method to strongly increase
the efficiency of fusion reactors. However, the required nuclear
spin-polarization conservation in fusion plasmas has never been proven
experimentally. Here we report on first experimental data suggesting an
increased ion flux from a polarized $^3\mkern-1mu$He target heated by a PW
laser pulse as well as evidence for an almost complete persistence of their
nuclear-polarization after acceleration to MeV energies. These findings also
validate the concept of using pre-polarized targets for plasma acceleration of
polarized beams.


  Most often in chemical physics, long range van der Waals surface interactions
are approximated by the exact asymptotic result at vanishing distance, the well
known additive approximation of London dispersion forces due to Hamaker.
However, the description of retardation effects that is known since the time of
Casimir is completely neglected for lack of a tractable expression. Here we
show that it is possible to describe surface van der Waals forces at arbitrary
distances in one single simple equation. The result captures the long sought
crossover from non-retarded (London) to retarded (Casimir) interactions, the
effect of polarization in condensed media and the full suppression of retarded
interactions at large distance. This is achieved with similar accuracy and the
same material properties that are used to approximate the Hamaker constant in
conventional applications. The results show that at ambient temperature,
retardation effects significantly change the power law exponent of the
conventional Hamaker result for distances of just a few nanometers.


  The challenge in object-based visual reasoning lies in generating descriptive
yet distinct concept representations. Moreover, doing this in an unsupervised
fashion requires human users to understand a model's learned concepts and
potentially revise false concepts. In addressing this challenge, we introduce
the Neural Concept Binder, a new framework for deriving discrete concept
representations resulting in what we term "concept-slot encodings". These
encodings leverage both "soft binding" via object-centric block-slot encodings
and "hard binding" via retrieval-based inference. The Neural Concept Binder
facilitates straightforward concept inspection and direct integration of
external knowledge, such as human input or insights from other AI models like
GPT-4. Additionally, we demonstrate that incorporating the hard binding
mechanism does not compromise performance; instead, it enables seamless
integration into both neural and symbolic modules for intricate reasoning
tasks, as evidenced by evaluations on our newly introduced CLEVR-Sudoku
dataset.


  Maintaining the stability of renewable-dominant power systems requires the
procurement of virtual inertia services from non-synchronous resources (e.g.,
batteries, wind turbines) in addition to inertia traditionally provided by
synchronous resources (e.g., thermal generators). However, the pricing of
inertia provision has not been studied in a stochastic electricity market,
where the uncertainty characteristics of renewable energy sources (RES) are
considered. To fill in this research gap, this paper formulates a
chance-constrained stochastic unit commitment model with inertia requirements
and computes equilibrium energy, reserve and inertia prices using convex
duality. Numerical experiments on an illustrative system and a modified IEEE
118-bus system show the performance of the proposed pricing mechanism. By
allowing new virtual inertia providers to contribute to system inertia
requirements, the total operating cost reduces. Moreover, the proposed
stochastic electricity market internalizes RES uncertainty, which yields
additional cost reductions by co-optimizing energy, reserve and inertia
procurement.


  The anisotropic quantum Heisenberg model with Curie-Weiss-type interactions
is studied analytically in several variants of the microcanonical ensemble.
(Non)equivalence of microcanonical and canonical ensembles is investigated by
studying the concavity properties of entropies. The microcanonical entropy
s(e,m) is obtained as a function of the energy e and the magnetization vector m
in the thermodynamic limit. Since, for this model, e is uniquely determined by
m, the same information can be encoded either in s(m) or s(e,m1,m2). Although
these two entropies correspond to the same physical setting of fixed e and m,
their concavity properties differ. The entropy s_h(u), describing the model at
fixed total energy u and in a homogeneous external magnetic field h of
arbitrary direction, is obtained by reduction from the nonconcave entropy
s(e,m1,m2). In doing so, concavity, and therefore equivalence of ensembles, is
restored. s_h(u) has nonanalyticities on surfaces of co-dimension 1 in the
(u,h)-space. Projecting these surfaces into lower-dimensional phase diagrams,
we observe that the resulting phase transition lines are situated in the
positive-temperature region for some parameter values, and in the
negative-temperature region for others. In the canonical setting of a system
coupled to a heat bath of positive temperatures, the nonanalyticities in the
microcanonical negative-temperature region cannot be observed, and this leads
to a situation of effective nonequivalence even when formal equivalence holds.


  Poor sitting habits have been identified as a risk factor to musculoskeletal
disorders and lower back pain especially on the elderly, disabled people, and
office workers. In the current computerized world, even while involved in
leisure or work activity, people tend to spend most of their days sitting at
computer desks. This can result in spinal pain and related problems. Therefore,
a means to remind people about their sitting habits and provide recommendations
to counterbalance, such as physical exercise, is important. Posture recognition
for seated postures have not received enough attention as most works focus on
standing postures. Wearable sensors, pressure or force sensors, videos and
images were used for posture recognition in the literature. The aim of this
study is to build Machine Learning models for classifying sitting posture of a
person by analyzing data collected from a chair platted with two 32 by 32
pressure sensors at its seat and backrest. Models were built using five
algorithms: Random Forest (RF), Gaussian Na\"ive Bayes, Logistic Regression,
Support Vector Machine and Deep Neural Network (DNN). All the models are
evaluated using KFold cross-validation technique. This paper presents
experiments conducted using the two separate datasets, controlled and
realistic, and discusses results achieved at classifying six sitting postures.
Average classification accuracies of 98% and 97% were achieved on the
controlled and realistic datasets, respectively.


  In industrial systems, certain process variables that need to be monitored
for detecting faults are often difficult or impossible to measure. Soft sensor
techniques are widely used to estimate such difficult-to-measure process
variables from easy-to-measure ones. Soft sensor modeling requires training
datasets including the information of various states such as operation modes,
but the fault dataset with the target variable is insufficient as the training
dataset. This paper describes a semi-supervised approach to soft sensor
modeling to incorporate an incomplete dataset without the target variable in
the training dataset. To incorporate the incomplete dataset, we consider the
properties of processes at transition points between operation modes in the
system. The regression coefficients of the operation modes are estimated under
constraint conditions obtained from the information on the mode transitions. In
a case study, this constrained soft sensor modeling was used to predict
refrigerant leaks in air-conditioning systems with heating and cooling
operation modes. The results show that this modeling method is promising for
soft sensors in a system with multiple operation modes.


  The structure of odd-$A$ $^{(115,117)}$Rh and $^{(115,117)}$Pd isotopes is
studied by means of the neutron-proton Interacting Boson-Fermion Model
(IBFM-2). $J^P = \frac{1}{2}^+$ quantum number assignment for the
$^{(115,117)}$Pd ground-states is critically discussed and the predicted energy
levels are compared to the existing experimental data. The resulting nuclear
wave functions are used to compute the $\beta$-decay $ft$ values of the
transitions from $^{(115,117)}$Rh to $^{(115,117)}$Pd in the microscopic IBFM-2
and the results compared with the data.


  We study semilinear problems in bounded $C^{1,1}$ domains for non-local
operators with a boundary condition. The operators cover and extend the case of
the spectral fractional Laplacian. We also study harmonic functions with
respect to the non-local operator and boundary behaviour of Green and Poisson
potentials.


  Ion channels form pores across the lipid bilayer, selectively allowing
inorganic ions to cross the membrane down their electrochemical gradient. While
the study of ion desolvation free-energies have attracted much attention, the
role of water inside the pore is less clear. Here, molecular dynamics
simulations of a reduced model of the KcsA selectivity filter indicate that the
equilibrium position of Na+, but not of K+, is strongly influenced by confined
water. The latter forms a stable complex with Na+, moving the equilibrium
position of the ion to the plane of the backbone carbonyls. Almost at the
centre of the binding site, the water molecule is trapped by favorable
electrostatic interactions and backbone hydrogen-bonds. In the absence of
confined water the equilibrium position of both Na+ and K+ is identical. Our
observations strongly suggest a previously unnoticed active role of confined
water in the selectivity mechanism of ion channels.


  Let F be a local field of characteristic zero. Let D be a quaternion algebra
over F. Let E be a quadratic field extension of F. Let {\mu} be a character of
GL(1,E). We study the distinction problem for the pair (GL(n,D), GL(n,E)) and
we prove that any bi-(GL(n,E), {\mu})-equivariant tempered generalized function
on GL(n,D) is invariant with respect to an anti-involution. Then it implies
that dimHom({\pi},{\mu}) is at most 1 by the generalized Gelfand-Kazhdan
criterion. Thus we give a new proof to the fact that (GL(2n,F),GL(n,E)) is a
Gelfand pair when {\mu} is trivial and D splits.


  We carry out extended symmetry analysis of the (1+2)-dimensional
Boiti-Leon-Pempinelli system, which corrects, enhances and generalizes many
results existing in the literature. The point-symmetry pseudogroup of this
system is computed using an original megaideal-based version of the algebraic
method. A number of meticulously selected differential constraints allow us to
construct families of exact solutions of this system, which are significantly
larger than all known ones. After classifying one- and two-dimensional
subalgebras of the entire (infinite-dimensional) maximal Lie invariance algebra
of this system, we study only its essential Lie reductions, which give
solutions beyond the above solution families. Among reductions of the
Boiti-Leon-Pempinelli system using differential constraints or Lie symmetries,
we identify a number of famous partial and ordinary differential equations. We
also show how all the constructed solution families can significantly be
extended by Laplace and Darboux transformations.


  We put forward the Axionic String Ansatz (ASA), which provides a unified
description for the worldsheet dynamics of confining strings in pure
Yang--Mills theory both in $D=3$ and $D=4$ space-time dimensions. The ASA is
motivated by the excitation spectrum of long confining strings, as measured on
a lattice, and by recently constructed integrable axionic non-critical string
models. According to the ASA, pure gluodynamics in 3D is described by a
non-critical bosonic string theory without any extra local worldsheet degrees
of freedom. We argue that this assumption fixes the set of quantum numbers
(spins, $P$- and $C$-parities) of almost all glueball states. We confront the
resulting predictions with the properties of approximately $1^2+2^2+3^2+5^2=39$
lightest glueball states measured on a lattice and find a good agreement. On
the other hand, the spectrum of low lying glueballs in 4D gluodynamics suggests
the presence of a massive pseudoscalar mode on the string worldsheet, in
agreement with the ASA and lattice data for long strings.


  We obtain the exact large deviation functions of the density profile and of
the current, in the non-equilibrium steady state of a one dimensional symmetric
simple exclusion process coupled to boundary reservoirs with slow rates.
Compared to earlier results, where rates at the boundaries are comparable to
the bulk ones, we show how macroscopic fluctuations are modified when the
boundary rates are slower by an order of inverse of the system length.


  An AC electric field applied to a junction comprising two spin-orbit coupled
weak links connecting a quantum dot to two electronic terminals is proposed to
induce a DC current and to generate a voltage drop over the junction if it is a
part of an open circuit. This photovoltaic effect requires a junction in which
mirror reflection-symmetry is broken. Its origin lies in the different fashion
inelastic processes modify the reflection of electrons from the junction back
into the two terminals, which leads to uncompensated DC transport. The effect
can be detected by measuring the voltage drop that is built up due to that DC
current. This voltage is an even function of the frequency of the AC electric
field and is {\em not} related to quantum pumping.


  We derive, in 2+1 dimensions, classical solutions for metric and motion of
two or more spinning particles, in the conformal Coulomb gauge introduced
previously. The solutions are exact in the $N$-body static case, and are
perturbative in the particles' velocities in the dynamic two-body case. A
natural boundary for the existence of our gauge choice is provided by some
``CTC horizons'' encircling the particles, within which closed timelike curves
occur.