  This paper proposes a soft sensor embedded in a soft ring actuator with five
fingers as a soft hand to identify the bifurcation of manipulated objects
during the in-hand manipulation process. The manipulation is performed by
breaking the symmetry method with an underactuated control system by
bifurcating the object to clockwise or counter-clockwise rotations. Two soft
sensors are embedded in parallel over a single soft finger, and the difference
in the resistance measurements is compared when the finger is displaced or bent
in a particular direction, which can identify the bifurcation direction and aid
in the break of symmetry approach without the need of external tracking
devices. The sensors performance is also characterised by extending and bending
the finger without an object interaction. During an experiment that performs a
break of symmetry, manipulated objects turn clockwise and counter-clockwise
depending on the perturbation and actuation frequency, sensors can track the
direction of rotation. The embedded sensors provide a self-sensing capability
for implementing a closed-loop control in future work. The soft ring actuator
performance presents a self-organisation behaviour with soft fingers rotating
an object without a required control for rotating the object. Therefore, the
soft fingers are an underactuated system with complex behaviour when
interacting with objects that serve in-hand manipulation field.


  Estimating direct and indirect causal effects from observational data is
crucial to understanding the causal mechanisms and predicting the behaviour
under different interventions. Causal mediation analysis is a method that is
often used to reveal direct and indirect effects. Deep learning shows promise
in mediation analysis, but the current methods only assume latent confounders
that affect treatment, mediator and outcome simultaneously, and fail to
identify different types of latent confounders (e.g., confounders that only
affect the mediator or outcome). Furthermore, current methods are based on the
sequential ignorability assumption, which is not feasible for dealing with
multiple types of latent confounders. This work aims to circumvent the
sequential ignorability assumption and applies the piecemeal deconfounding
assumption as an alternative. We propose the Disentangled Mediation Analysis
Variational AutoEncoder (DMAVAE), which disentangles the representations of
latent confounders into three types to accurately estimate the natural direct
effect, natural indirect effect and total effect. Experimental results show
that the proposed method outperforms existing methods and has strong
generalisation ability. We further apply the method to a real-world dataset to
show its potential application.


  Using tools of nonequilibirum mechanics, we study a model of self-propelled
hard rods on a substrate in two dimensions to quantify the interplay of
self-propulsion and excluded-volume effects. We derive of a Smoluchowski
equation for the configurational probability density of self-propelled rods
that contains several modifications as compared to the familiar Smoluchowski
equation for thermal rods. As a side-product of our work, we also present a
purely dynamical derivation of the Onsager form of the mean field excluded
volume interaction among thermal hard rods.


  Gravitationally-bound clusters that survive gas removal represent an unusual
mode of star formation in the Milky Way and similar spiral galaxies. While
forming, they can be distinguished observationally from unbound star formation
by their high densities, virialised velocity structures, and star formation
histories that accelerate toward the present, but extend multiple free-fall
times into the past. In this paper we examine several proposed scenarios for
how such structures might form and evolve, and carry out a Bayesian analysis to
test these models against observed distributions of protostellar age, counts of
young stellar objects relative to gas, and the overall star formation rate of
the Milky Way. We show that models in which the acceleration of star formation
is due either to a large-scale collapse or a time-dependent increase in star
formation efficiency are unable to satisfy the combined set of observational
constraints. In contrast, models in which clusters form in a "conveyor belt"
mode where gas accretion and star formation occur simultaneously, but the star
formation rate per free-fall time is low, can match the observations.


  In recent years, there has been a significant focus on advancing the next
generation of power systems. Despite these efforts, persistent challenges
revolve around addressing the operational impact of uncertainty on predicted
data, especially concerning economic dispatch and optimal power flow. To tackle
these challenges, we introduce a stochastic day-ahead scheduling approach for a
community. This method involves iterative improvements in economic dispatch and
optimal power flow, aiming to minimize operational costs by incorporating
quantile forecasting. Then, we present a real-time market and payment problem
to handle optimization in real-time decision-making and payment calculation. We
assess the effectiveness of our proposed method against benchmark results and
conduct a test using data from 50 real households to demonstrate its
practicality. Furthermore, we compare our method with existing studies in the
field across two different seasons of the year. In the summer season, our
method decreases optimality gap by 60% compared to the baseline, and in the
winter season, it reduces optimality gap by 67%. Moreover, our proposed method
mitigates the congestion of distribution network by 16.7\% within a day caused
by uncertain energy, which is a crucial aspect for implementing energy markets
in the real world.


  Quantum interference effects and decoherence mechanisms in single-molecule
junctions are analyzed employing a nonequilibrium Green's function approach.
Electrons tunneling through quasi-degenerate states of a nanoscale molecular
junction exhibit interference effects. We show that electronic-vibrational
coupling, inherent to any molecular junction, strongly quenches such
interference effects. As a result, the electrical current can be significantly
larger than without electronic-vibrational coupling. The analysis reveals that
the quenching of quantum interference is particularly pronounced if the
junction is vibrationally highly excited, e.g. due to current-induced
nonequilibrium effects in the resonant transport regime.


  Auditory-verbal training is essential for children with hearing challenges,
and the gamification approach has become a promising direction for improving
the rehabilitation experience and effect. However, the specific influence of
the gamified training approach on participants at different rehabilitation
stages has not been empirically studied. This paper is thusly intended to
investigate the research questions: Do the training performances of children at
advanced rehabilitation stage differ before and after using the gamified
training system? Do the training performances of children at intermediate
rehabilitation stage differ before and after using the gamified training
system? Do children enjoy the gamified training approach? For the purpose, a
digital gamified auditory-verbal training system was originally developed, and
a series of user experiments were organized. Particularly, 31
hearing-challenged children aging between three-six years old at an
auditory-verbal rehabilitation center were recruited to take the training, and
six professional therapists were also invited to assist with the experiments
and attend the interviews. Based on the training performance observation and
interviews with participants, their parents and the therapists, it can be found
that generally the gamified training approach can effectively facilitate the
training experience, and help with the basic auditory memory and expression
capabilities. Regarding the specific influence, the gamified way can better
improve the basic auditory-verbal performance of children at the intermediate
stage, since they focus more on the ease of learning and adaption to the
training system. These findings and conclusions can provide insights for the
further exploration and application of the gamification approach in children's
auditory-verbal rehabilitation.


  Non-interacting topological states of matter can be realized in band
insulators with intrinsic spin-orbital couplings as a result of the nontrivial
band topology. In recent years, the possibility of realizing novel
interaction-driven topological phase has attracted a lot of research
activities, which may significantly extend the classes of topological states of
matter. Here, we report a new finding of an interaction-driven spontaneous
quantum Hall effect (QHE) (Chern insulator) emerging in an extended
fermion-Hubbard model on kagome lattice. By means of the state-of-the-art
density-matrix renormalization group, we expose universal properties of the QHE
including time-reversal symmetry spontaneous breaking and quantized Hall
conductance. By accessing the ground state in large systems, we demonstrate the
robustness of the QHE against finite-size effects. Moreover, we map out a phase
diagram and identify two competing charge density wave phases by varying
interactions, where transitions to the QHE phase are determined to be of the
first order. Our study provides a "proof-of-the-principle" demonstration of
interaction-driven QHE without requirement of external magnetic field or
magnetic doping.


  We develop a theoretical scheme to perform a read-out of the properties of a
quasi-periodic system by coupling it to one or two qubits. We show that the
decoherence dynamics of a single qubit coupled via a pure dephasing type term
to a 1D quasi-periodic system with a potential given by the
Andr\'e-Aubry-Harper (AAH) model and its generalized versions (GAAH model) is
sensitive to the nature of the single particle eigenstates (SPEs). More
specifically, we can use the non-markovianity of the qubit dynamics as
quantified by the backflow of information to clearly distinguish the localized,
delocalized, and mixed regimes with a mobility edge of the AAH and GAAH model
and evidence the transition between them. By attaching two qubits at distinct
sites of the system, we demonstrate that the transport property of the
quasi-periodic system is encoded in the scaling of the threshold time to
develop correlations between the qubits with the distance between the qubits.
This scaling can also be used to distinguish and infer different regimes of
transport such as ballistic, diffusive and no transport engendered by SPEs that
are delocalized, critical and localized respectively. When there is a mobility
edge allowing the coexistence of different kinds of SPEs in the spectrum, such
as the coexistence of localized and delocalized states in the GAAH models, we
find that the transport behaviour and the scaling of the threshold time with
qubit separation is governed by the fastest spreading states.


  Test measurements on the silicon pixel detector for the beam trajectory
monitor at the free electron laser of the TESLA test facility are presented. To
determine the electronic noise of detector and read-out and to calibrate the
signal amplitude of different pixels the 6 keV photons of the manganese K line
are used. Two different methods determine the spatial accuracy of the detector:
In one setup a laser beam is focused to a straight line and moved across the
pixel structure. In the other the detector is scanned using a low-intensity
electron beam of an electron microscope. Both methods show that the symmetry
axis of the detector defines a straight line within 0.4 microns. The
sensitivity of the detector to low energy X-rays is measured using a vacuum
ultraviolet beam at the synchrotron light source HASYLAB. Additionally, the
electron microscope is used to study the radiation hardness of the detector.


  We introduce a Prony-like method to recover a continuous domain 2-D piecewise
smooth image from few of its Fourier samples. Assuming the discontinuity set of
the image is localized to the zero level-set of a trigonometric polynomial, we
show the Fourier transform coefficients of partial derivatives of the signal
satisfy an annihilation relation. We present necessary and sufficient
conditions for unique recovery of piecewise constant images using the above
annihilation relation. We pose the recovery of the Fourier coefficients of the
signal from the measurements as a convex matrix completion algorithm, which
relies on the lifting of the Fourier data to a structured low-rank matrix; this
approach jointly estimates the signal and the annihilating filter. Finally, we
demonstrate our algorithm on the recovery of MRI phantoms from few
low-resolution Fourier samples.


  It is tempting to evaluate F2(x,1) and similar univariate specializations of
Appell's functions by evaluating the apparent power series at x=0 straight away
using the Gauss formula for 2F1(1). But this kind of naive evaluation can lead
to errors as the 2F1(1) coefficients might eventually diverge; then the actual
power series at x=0 might involve branching terms. This paper demonstrates
these complications on concrete examples.


  Recent years have seen significant developments in the field of License Plate
Recognition (LPR) through the integration of deep learning techniques and the
increasing availability of training data. Nevertheless, reconstructing license
plates (LPs) from low-resolution (LR) surveillance footage remains challenging.
To address this issue, we introduce a Single-Image Super-Resolution (SISR)
approach that integrates attention and transformer modules to enhance the
detection of structural and textural features in LR images. Our approach
incorporates sub-pixel convolution layers (also known as PixelShuffle) and a
loss function that uses an Optical Character Recognition (OCR) model for
feature extraction. We trained the proposed architecture on synthetic images
created by applying heavy Gaussian noise to high-resolution LP images from two
public datasets, followed by bicubic downsampling. As a result, the generated
images have a Structural Similarity Index Measure (SSIM) of less than 0.10. Our
results show that our approach for reconstructing these low-resolution
synthesized images outperforms existing ones in both quantitative and
qualitative measures. Our code is publicly available at
https://github.com/valfride/lpr-rsr-ext/


  We make use of a recently developed method to, not only obtain the exactly
known eigenstates and eigenvalues of a number of quasi-exactly solvable
Hamiltonians, but also construct a convergent approximation scheme for locating
those levels, not amenable to analytical treatments. The fact that, the above
method yields an expansion of the wave functions in terms of corresponding
energies, enables one to treat energy as a variational parameter, which can be
effectively used for the identification of the eigenstates. It is particularly
useful for the quasi-exactly solvable systems, where the ground state is known
and a number of eigenstates are bounded, both below and above. The efficacy of
the procedure is illustrated by obtaining, the low-lying excited states of a
prototypical double-well potential, where the conventional techniques are not
very reliable. Our approach yields the approximate eigenfunctions and
eigenvalues, whose accuracy can be improved to any desired level, in a
controlled manner. Comparing the present results with those of an independent
numerical method, it was found that, the first few terms in our approximate
solutions are enough to yield the excited state eigenvalues, accurate upto the
third place of the decimal.


  Let $\gamma_n $ denote the length of the $n$-th zone of instability of the
Hill operator $Ly= -y^{\prime \prime} - [4t\alpha \cos2x + 2 \alpha^2 \cos 4x ]
y,$ where $\alpha \neq 0, $ and either both $\alpha, t $ are real, or both are
pure imaginary numbers. For even $n$ we prove: if $t, n $ are fixed, then, for
$ \alpha \to 0, $
  $$ \gamma_n = | \frac{8\alpha^n}{2^n [(n-1)!]^2} \prod_{k=1}^{n/2} (t^2 -
(2k-1)^2) | (1 + O(\alpha)), $$
  and if $ \alpha, t $ are fixed, then, for $ n \to \infty, $
  $$ \gamma_n = \frac{8 |\alpha/2|^n}{[2 \cdot 4 ... (n-2)]^2} | \cos
(\frac{\pi}{2} t) | [ 1 + O (\frac{\log n}{n}) ]. $$
  Similar formulae (see Theorems \ref{thm2} and \ref{thm4}) hold for odd $n.$
The asymptotics for $\alpha \to 0 $ imply interesting identities for squares of
integers.


  Testability is the probability whether tests will detect a fault, given that
a fault in the program exists. How efficiently the faults will be uncovered
depends upon the testability of the software. Various researchers have proposed
qualitative and quantitative techniques to improve and measure the testability
of software. In literature, a plethora of reliability growth models have been
used to assess and measure the quantitative quality assessment of software
during testing and operational phase. The knowledge about failure distribution
and their complexity can improve the testability of software. Testing effort
allocation can be made easy by knowing the failure distribution and complexity
of faults, and this will ease the process of revealing faults from the
software. As a result, the testability of the software will be improved. The
parameters of the model along with the proportion of faults of different
complexity to be removed from the software have been presented in the paper .We
have used failure data of two object oriented software developed under open
source environment namely MySQL for python and Squirrel SQL Client for
estimation purpose


  A causal input-output system may be described by a function space for inputs,
a function space for outputs, and a causal operator mapping the input space
into the output space. A particular representation of the state of such a
system at any instant has been defined as an operator from the space of
possible future inputs to that of future outputs. This representation is called
the natural state. The purpose of this report is to investigate additional
properties of the natural state in two areas. The first area has to do with the
possibility of determining the input-output system from its natural state set.
A counterexample where this is not possible is given. Sufficient conditions for
identifying the system from its natural state set are given. The results in
this area are mostly for time-invariant systems. There are also some
preliminary observations on reachability. The second area deals with
differentiability properties involving the natural state inherited from the
input-output system, including differentiability of the natural state and
natural state trajectories. A differential equation representation is given.
The results presented in this report may be considered as aids in modeling
physical systems because system identification from state set holds in many
models and is only tacitly assumed; also, differentiability is a useful
property for many systems.


  This paper provides lower bounds on the convergence rate of Derivative Free
Optimization (DFO) with noisy function evaluations, exposing a fundamental and
unavoidable gap between the performance of algorithms with access to gradients
and those with access to only function evaluations. However, there are
situations in which DFO is unavoidable, and for such situations we propose a
new DFO algorithm that is proved to be near optimal for the class of strongly
convex objective functions. A distinctive feature of the algorithm is that it
uses only Boolean-valued function comparisons, rather than function
evaluations. This makes the algorithm useful in an even wider range of
applications, such as optimization based on paired comparisons from human
subjects, for example. We also show that regardless of whether DFO is based on
noisy function evaluations or Boolean-valued function comparisons, the
convergence rate is the same.


  In this paper, we establish a Kantorovich duality for weak optimal total
variation transport problems. As consequences, we recover a version of duality
formula for partial optimal transports established by Caffarelli and McCann;
and we also get another proof of Kantorovich-Rubinstein Theorem for generalized
Wasserstein distance $\widetilde{W}_1^{a,b}$ proved before by Piccoli and
Rossi. Then we apply our duality formula to study generalized Wasserstein
barycenters. We show the existence of these barycenters for measures with
compact supports. Finally, we prove the consistency of our barycenters.


  Let $X$ be an unbounded metric space, $B(x,r) = \{y\in X: d(x,y) \leqslant
r\}$ for all $x\in X$ and $r\geqslant 0$. We endow $X$ with the discrete
topology and identify the Stone-\v{C}ech compactification $\beta X$ of $X$ with
the set of all ultrafilters on $X$. Our aim is to reveal some features of
algebra in $\beta X$ similar to the algebra in the Stone-\v{C}ech
compactification of a discrete semigroup \cite{b6}.
  We denote $X^# = \{p\in \beta X: \mbox{each}P\in p\mbox{is unbounded in}X\}$
and, for $p,q \in X^#$, write $p\parallel q$ if and only if there is $r
\geqslant 0$ such that $B(Q,r)\in p$ for each $Q\in q$, where $B(Q,
r)=\cup_{x\in Q}B(x,r)$. A subset $S\subseteq X^#$ is called invariant if $p\in
S$ and $q\parallel p$ imply $q\in S$. We characterize the minimal closed
invariant subsets of $X$, the closure of the set $K(X^#) = \bigcup\{M :
M\mbox{is a minimal closed invariant subset of}X^#\}$, and find the number of
all minimal closed invariant subsets of $X^#$.
  For a subset $Y\subseteq X$ and $p\in X^#$, we denote $\bigtriangleup_p(Y) =
Y^# \cap \{q\in X^#: p \parallel q\}$ and say that a subset $S\subseteq X^#$ is
an ultracompanion of $Y$ if $S = \bigtriangleup_p(Y)$ for some $p\in X^#$. We
characterize large, thick, prethick, small, thin and asymptotically scattered
spaces in terms of their ultracompanions.


  J.-B. Meilhan and the second author showed that any Milnor
$\bar{\mu}$-invariant of length between 3 and $2k+1$ can be represented as a
combination of HOMFLYPT polynomial of knots obtained by certain band sum of the
link components, if all $\bar{\mu}$-invariants of length $\leq k$ vanish. They
also showed that their formula does not hold for length $2k+2$. In this paper,
we improve their formula to give the $\bar{\mu}$-invariants of length $2k+2$ by
adding correction terms. The correction terms can be given by a combination of
HOMFLYPT polynomial of knots determined by $\bar{\mu}$-invariants of length
$k+1$. In particular, for any 4-component link the $\bar{\mu}$-invariants of
length 4 are given by our formula, since all $\bar{\mu}$-invariants of length 1
vanish.


  We examine the black hole bomb model which consists of a rotating black hole
of five-dimenensional minimal ungauged supergravity and a reflecting mirror
around it. For low-frequency scalar perturbations, we find solutions to the
Klein-Gordon equation in the near-horizon and far regions of the black hole
spacetime. To avoid solutions with logarithmic terms, we assume that the
orbital quantum number $ l $ takes on nearly, but not exactly, integer values
and perform the matching of these solutions in an intermediate region. This
allows us to calculate analytically the frequency spectrum of quasinormal
modes, taking the limits as $ l $ approaches even or odd integers separately.
We find that all $ l $ modes of scalar perturbations undergo negative damping
in the regime of superradiance, resulting in exponential growth of their
amplitudes. Thus, the model under consideration would exhibit the superradiant
instability, eventually behaving as a black hole bomb in five dimensions.


  The objective of this work is to report on the modifications in air shower
development due to muon bremsstrahlung and muonic pair production. In order to
do that we have implemented new muon bremsstrahlung and muonic pair production
procedures in the AIRES air shower simulation system, and have used it to
simulate ultra high energy showers in different conditions.
  The influence of the mentioned processes in the global development of the air
shower is important for primary particles of large zenith angles, while they do
not introduce significant changes in the position of the shower maximum.


  They are weak, they are small, and they are often overlooked, but they are
numerous and an ubiquitous sign of accreting black holes: compact radio cores
and jets in low-power AGN. Here I summarize our work concerning these radio
cores and jets in recent years, specifically focusing on the large population
of low-luminosity AGN. Special attention is also given to Sgr A*, the
supermassive black hole candidate at the Galactic Center, whose radio
properties are reviewed in more detail. This source exhibits a submm-bump,
possibly from an ultra-compact region around the black hole which should allow
imaging of the event horizon of the black hole in the not too distant future. A
jet model is proposed which explains the basic feature of Sgr A*: its slightly
inverted radio spectrum, the submm-bump, the lack of extended emission, and the
X-ray emission. This model also works for famous sources like M81*, NGC4258, or
GRS1915+105 based on the argument that radio cores are jets whose emission can
be scaled with the accretion power over many orders of magnitude. This scaling
is corroborated by the detection of many Sgr A*-like radio cores in nearby
Low-Luminosity AGN (LLAGN), some of which show jet structures on the VLBI (Very
Long Baseline Interferometry) scale. These cores confirm an AGN origin of about
half of the known low-luminosity AGN classified as LINERs and dwarf-Seyferts.
It is argued that in fact most of the compact radio emission at centimeter
waves in LLAGN is produced by a compact radio jet and not an Advection
Dominated Accretion Flow (ADAF). In general one can say that compact radio
cores are a genuine feature of AGN, allowing one to precisely pinpoint black
holes in many galaxies - not only in luminous quasars.


  This article is a rough, quirky overview of both the history and present
state of the art of density functional theory. The field is so huge that no
attempt to be comprehensive is made. We focus on the underlying exact theory,
the origin of approximations, and the tension between empirical and
non-empirical approaches. Many ideas are illustrated on the exchange energy and
hole. Features unique to this article include how approximations can be
systematically derived in a non-empirical fashion and a survey of warm dense
matter.


  We obtained new VLT/ISAAC H-band observations for five MACHO LMC source stars
and adjacent LMC field regions. After combining our near-IR photometry with
HST/PC BVRI optical photometry, we compared the MACHO objects to the adjacent
field stars in a variety of color-magnitude and color-color diagrams. These
diagnostic diagrams were chosen to be sensitive to our hypothesis that at least
some of the MACHO lenses were foreground Galactic disk or thick disk M dwarfs.
For the five lensed objects we studied, our hypothesis could be ruled out for
main sequence lens masses >= 0.1 Mo for distances out to 4 kpc. On the other
hand, the fact that LMC-MACHO-5, an object not in our study, has been recently
found to have just such a foreground lens, highlights that the remainder of the
LMC MACHO objects should be searched for the signature of their lenses using
our photometric technique, or via near-IR spectroscopy. We also constructed
diagnostic color-color diagrams sensitive to determining reddening for the
individual MACHO source stars and found that these five objects did not show
evidence for significant additional reddening. At least these five MACHO
objects are thus also inconsistent with the LMC self-lensing hypothesis.


  In this paper, we generalize the embedded homology groups of hypergraphs
initially given in [S. Bressan, J. Li, S. Ren, and J. Wu, The embedded homology
of hypergraphs and applications, Asian J. Math. 23(3)(2019) 479-500] and study
the relative embedded homology groups of hypergraph pairs. We prove some long
exact sequences as well as a Mayer-Vietoris sequence for the relative embedded
homology groups of hypergraph pairs. Moreover, we briefly discuss the
two-dimensional persistence for the relative embedded homology groups of
hypergraph pairs.


  In this paper, we investigate the action of pseudogroup of all point
transformations on the natural bundle of equations
$y''=a^3(x,y)(y')^3+a^2(x,y)(y')^2+a^1(x,y)y'+a^0(x,y)$. We construct
differential invariants of this action and solve the equivalence problem for
some classes of these equations in particular for generic equations.


  This paper concentrates on a priori error estimates of two fully discrete
coupled schemes for Biot's consolidation model based on the three-field
formulation introduced by Oyarzua et al. (SIAM Journal on Numerical Analysis,
2016). The spatial discretizations are based on the Taylor-Hood finite elements
combined with Lagrange elements for the three primary variables. For time
discretization, we consider two methods. One uses the backward Euler method,
and the other applies a combination of the backward Euler and Crank-Nicolson
methods. A priori error estimates show that the two schemes are unconditionally
convergent with optimal error orders. Detailed numerical experiments are
presented to validate the theoretical analysis.


  We prove two bounds for discrete moments of Weyl sums. The first one can be
obtained using a standard approach. The second one involves an observation how
this method can be improved, which leads to a sharper bound in certain ranges.
The proofs both build on the recently proved main conjecture for Vinogradov's
mean value theorem.
  We present two selected applications: First, we prove a new $k$- th
derivative test for the number of integer points close to a curve by an
exponential sum approach. This yields a stronger bound than existing results
obtained via geometric methods, but it is only applicable for specific
functions. As second application we prove a new improvement of the polynomial
large sieve inequality for one-variable polynomials of degree $k \geq 4$.


  We calculate the $\beta$ spectrum in the decay of $^6$He using Quantum Monte
Carlo methods with nuclear interactions derived from chiral Effective Field
Theory and consistent weak vector and axial currents. We work at second order
in the multipole expansion, retaining terms suppressed by $\mathcal
O(q^2/m_\pi^2)$, where $q$ denotes low-energy scales such as the reaction's
$\mathcal Q$-value or the electron energy, and $m_\pi$ the pion mass. We go
beyond the impulse approximation by including the effects of two-body vector
and axial currents. We estimate the theoretical error on the spectrum by using
four potential models in the Norfolk family of local two- and three-nucleon
interactions, which have different cut-off, fit two-nucleon data up to
different energies and use different observables to determine the couplings in
the three-body force. We find the theoretical uncertainty on the $\beta$
spectrum, normalized by the total rate, to be well below the permille level,
and to receive contributions of comparable size from first and second order
corrections in the multipole expansion. We consider corrections to the $\beta$
decay spectrum induced by beyond-the-Standard Model charged-current
interactions in the Standard Model Effective Field Theory, with and without
sterile neutrinos, and discuss the sensitivity of the next generation of
experiments to these interactions.


  A systematic search for different viable models of the dark energy universe,
all of which give rise to finite-time, future singularities, is undertaken,
with the purpose to try to find a solution to this common problem. After some
work, a universal procedure to cure all future singularities is developed and
carefully tested with the help of explicit examples corresponding to each one
of the four different types of possible singularities, as classified in the
literature. The cases of a fluid with an equation of state which depends on
some parameter, of modified gravity non-minimally coupled to a matter
Lagrangian, of non-local gravity, and of isotropic turbulence in a dark fluid
universe theory are investigated in detail.


  For normalised analytic functions $f$ defined on the open unit disc
$\mathbb{D}$ satisfying the condition $\sup_{z\in \mathbb{D}}(1-|z^2|)
|f'(z)|\leq 1$, known as Bloch functions, we determine various starlikeness
radii.


  This paper presents our solution for the Real Robot Challenge (RRC) III, a
competition featured in the NeurIPS 2022 Competition Track, aimed at addressing
dexterous robotic manipulation tasks through learning from pre-collected
offline data. Participants were provided with two types of datasets for each
task: expert and mixed datasets with varying skill levels. While the simplest
offline policy learning algorithm, Behavioral Cloning (BC), performed
remarkably well when trained on expert datasets, it outperformed even the most
advanced offline reinforcement learning (RL) algorithms. However, BC's
performance deteriorated when applied to mixed datasets, and the performance of
offline RL algorithms was also unsatisfactory. Upon examining the mixed
datasets, we observed that they contained a significant amount of expert data,
although this data was unlabeled. To address this issue, we proposed a
semi-supervised learning-based classifier to identify the underlying expert
behavior within mixed datasets, effectively isolating the expert data. To
further enhance BC's performance, we leveraged the geometric symmetry of the
RRC arena to augment the training dataset through mathematical transformations.
In the end, our submission surpassed that of all other participants, even those
who employed complex offline RL algorithms and intricate data processing and
feature engineering techniques.


  We report a decreased surface wettability when polymer films on a glass
substrate are treated by ultra-fast laser pulses in a back-illumination
geometry. We propose that back-illumination through the substrate confines
chemical changes beneath the surface of polymer films, leaving the surface
blistered but chemically intact. To confirm this hypothesis, we measure the
phase contrast of the polymer when observed with a focused ion beam. We observe
a void at the polymer-quartz interface that results from the expansion of an
ultrafast laser-induced plasma. A modified polymer layer surrounds the void,
but otherwise the film seems unmodified. We also use X-ray photoelectron
spectroscopy to confirm that there is no chemical change to the surface. When
patterned with partially overlapping blisters, our polymer surface shows
increased hydrophobicity. The increased hydrophobicity of back-illuminated
surfaces can only result from the morphological change. This contrasts with the
combined chemical and morphological changes of the polymer surface caused by a
front-illumination geometry.


  We consider recent work of Haber and Ruthotto 2017 and Chang et al. 2018,
where deep learning neural networks have been interpreted as discretisations of
an optimal control problem subject to an ordinary differential equation
constraint. We review the first order conditions for optimality, and the
conditions ensuring optimality after discretisation. This leads to a class of
algorithms for solving the discrete optimal control problem which guarantee
that the corresponding discrete necessary conditions for optimality are
fulfilled. The differential equation setting lends itself to learning
additional parameters such as the time discretisation. We explore this
extension alongside natural constraints (e.g. time steps lie in a simplex). We
compare these deep learning algorithms numerically in terms of induced flow and
generalisation ability.


  Consider the power pseudorandom-number generator in a finite field ${\mathbb
F}_q$. That is, for some integer $e\ge2$, one considers the sequence
$u,u^e,u^{e^2},\dots$ in ${\mathbb F}_q$ for a given seed $u\in {\mathbb
F}_q^\times$. This sequence is eventually periodic. One can consider the number
of cycles that exist as the seed $u$ varies over ${\mathbb F}_q^\times$. This
is the same as the number of cycles in the functional graph of the map
$x\mapsto x^e$ in ${\mathbb F}_q^\times$. We prove some estimates for the
maximal and average number of cycles in the case of prime finite fields.


  Cosmological Gravitational Waves (GWs) are usually associated with the
transverse-traceless part of the metric perturbations in the context of the
theory of cosmological perturbations. These modes are just the usual
polarizations `+' and `x' which appear in the general relativity theory.
However, in the majority of the alternative theories of gravity, GWs can
present more than these two polarization states. In this context, the
Newman-Penrose formalism is particularly suitable for evaluating the number of
non-null GW modes. In the present work we intend to take into account these
extra polarization states for cosmological GWs in alternative theories of
gravity. As an application, we derive the dynamical equations for cosmological
GWs for two specific theories, namely, a general scalar-tensor theory which
presents four polarization states and a massive bimetric theory which is in the
most general case with six polarization states for GWs. The mathematical tool
presented here is quite general, so it can be used to study cosmological
perturbations in all metric theories of gravity.


  We investigate spin dynamics in antiferromagnetic (AF) molecular rings at
finite temperature in the presence of spin-phonon (s-p) interaction. We derive
a general expression for the spin susceptibility in the weak s-p coupling limit
and then we focus on the low-frequency behavior, in order to discuss a possible
microscopic mechanism for nuclear relaxation in this class of magnetic
materials. To lowest order in a perturbative expansion, we find that the
susceptibility takes a Lorentzian profile and all spin operators ($S^x$, $S^y,
S^z$) contribute to spin dynamics at wave vectors $q\ne 0$. Spin anisotropies
and local s-p coupling play a key role in the proposed mechanism. Our results
prove that small changes in the spatial symmetry of the ring induce qualitative
changes in the spin dynamics at the nuclear frequency, providing a novel
mechanism for nuclear relaxation. Possible experiments are proposed.


  LiDAR-camera extrinsic calibration (LCEC) is crucial for data fusion in
intelligent vehicles. Offline, target-based approaches have long been the
preferred choice in this field. However, they often demonstrate poor
adaptability to real-world environments. This is largely because extrinsic
parameters may change significantly due to moderate shocks or during extended
operations in environments with vibrations. In contrast, online, target-free
approaches provide greater adaptability yet typically lack robustness,
primarily due to the challenges in cross-modal feature matching. Therefore, in
this article, we unleash the full potential of large vision models (LVMs),
which are emerging as a significant trend in the fields of computer vision and
robotics, especially for embodied artificial intelligence, to achieve robust
and accurate online, target-free LCEC across a variety of challenging
scenarios. Our main contributions are threefold: we introduce a novel framework
known as MIAS-LCEC, provide an open-source versatile calibration toolbox with
an interactive visualization interface, and publish three real-world datasets
captured from various indoor and outdoor environments. The cornerstone of our
framework and toolbox is the cross-modal mask matching (C3M) algorithm,
developed based on a state-of-the-art (SoTA) LVM and capable of generating
sufficient and reliable matches. Extensive experiments conducted on these
real-world datasets demonstrate the robustness of our approach and its superior
performance compared to SoTA methods, particularly for the solid-state LiDARs
with super-wide fields of view.


  A class of systems is considered, where immobile species associated to
distinct patches, the nodes of a network, interact both locally and at a
long-range, as specified by an (interaction) adjacency matrix. Non local
interactions are treated in a mean-field setting which enables the system to
reach a homogeneous consensus state, either constant or time dependent. We
provide analytical evidence that such homogeneous solution can turn unstable
under externally imposed disturbances, following a symmetry breaking mechanism
which anticipates the subsequent outbreak of the patterns. The onset of the
instability can be traced back, via a linear stability analysis, to a
dispersion relation that is shaped by the spectrum of an unconventional
reactive Laplacian. The proposed mechanism prescinds from the classical Local
Activation and Lateral Inhibition scheme, which sits at the core of the Turing
recipe for diffusion driven instabilities. Examples of systems displaying a
fixed-point or a limit cycle, in their uncoupled versions, are discussed. Taken
together, our results pave the way for alternative mechanisms of pattern
formation, opening new possibilities for modeling ecological, chemical and
physical interacting systems.


  It is well known that (possibly non-unique) suitable field dynamics can be
prescribed in spacetimes with timelike boundaries by means of appropriate
boundary conditions. In Ref. [J. Math. Phys. {\bf 21}, 2802 (1980)], Wald
derived a conserved energy functional for each prescribed dynamics. This
conserved energy is related to the positive self-adjoint extensions of the
spatial part $A$ of the wave equation $\partial^2\Phi/\partial t^2=-A\Phi$ ($A$
may not be, in principle, essentially self-adjoint). This is quite surprising
since the canonical energy is not conserved in these cases. In this paper, we
rederive this energy functional from an action principle (with appropriate
boundary terms) following Ref. [Phys. Rev. D, {\bf 69}, 085005, (2004)] and
consider field dynamics arising from non-positive self-adjoint extensions of
$A$. The spectrum of the resulting theory fails to be positive and unstable
mode solutions for classical fields come to light. By studying fields in
half-Minkowski spacetime, we illustrate that these unstable classical solutions
come as a consequence of an inverted parabolic potential governing their
dynamics. From the quantum mechanical point of view, this leads to an effective
inverted harmonic oscillator at the boundary. We then explore these unstable
modes behavior, as well as their instabilities, at the quantum level.


  We demonstrate a new mode of operation for the Time Stretched A/D converter.
The technique is superior to the sampling scope in that it permits the capture
of non periodic events such as clustered noise.


  A computer code or simulator is a mathematical representation of a physical
system, for example a set of differential equations. Running the code with
given values of the vector of inputs, x, leads to an output y(x) or several
such outputs. For instance, one application we use for illustration simulates
the average tidal power, y, generated as a function of the turbine location, x
= (x1, x2), in the Bay of Fundy, Nova Scotia, Canada (Ranjan et al. 2011).
Performing scientific or engineering experiments via such a computer code is
often more time and cost effective than running a physical experiment.
  Choosing new runs sequentially for optimization, moving y to a target, etc.
has been formalized using the concept of expected improvement (Jones et al.
1998). The next experimental run is made where the expected improvement in the
function of interest is largest. This expectation is with respect to the
predictive distribution of y from a statistical model relating y to x. By
considering a set of possible inputs x for the new run, we can choose that
which gives the largest expectation.


  An overview of the most recent measurements of inclusive top quark pair
production cross section is presented. The results are obtained using data
collected with the CMS experiment in proton-proton collisions at centre-of-mass
energies of 5.02, 7, 8 and 13 TeV. The data are compared with the predictions
from perturbative QCD calculations at full NNLO+NNLL accuracy.


  The efficiency of acoustooptic (AO) diffraction in a-BaB2O4 and Li2B4O7
crystals is studied experimentally. The crystals are shown to be quite good AO
materials. The efficiency of AO diffraction in a-BaB2O4 reaches h=30% at the
electric signal power of P=0.7W for the transverse acoustic wave and 15% at the
power of P=0.56W for the longitudinal wave. The same parameter for Li2B4O7
reaches h=21% at P=0,81W for the longitudinal acoustic wave.


  We report on transport features in heavily boron doped nanocrystalline
diamond (BNCD) films which are not seen in conventional (s-wave) granular
superconductors. Observations include an anomalous resistance peak near to the
superconducting transition temperature as well as a strong zero bias
conductance peak in the current-voltage spectra. The effect of finite bias
current on the evolution of the resistance peak is systematically investigated
in this system. The shape of the resistance-temperature curves near the
critical temperature is seen to be strongly influenced by both magnetic field
and bias current. As the bias current is lowered the resistance peak becomes
more pronounced whereas when the magnetic field is varied the peak shifts
towards lower temperatures, the resistance upturn shows a quadratic temperature
dependence as expected for a Kondo transition. We find that a number of
transport features such as resistance peak height, zero bias conduction peak
height and width as well as magnetoresistance peaks scale according to a power
law dependence. We interpret these features as a result of a charge-Kondo
effect where hole dopants act as degenerate Kondo impurities by opening
additional pseudo-spin scattering channels.


  We review some theoretical aspects connected with the interplay of strong
electron correlations and vibron phenomena in negative fullerene ions and in
solid fulleride conductors, superconductors and insulators. The first part
covers molecular ions, their intra-molecular Jahn-Teller effect, Coulomb
(Hund's rule) exchange interactions, molecular vibrons and multiplet
splittings. The second part addresses electron propagation in molecular
fullerides, with special emphasis given to trivalent cases such as K3C60 and
NH3 K3C60, where metallic, superconducting and Mott insulating phases are at
play. Dynamical mean field theory approaches to a simplified Hamiltonian for
this system are discussed in the light of some of the observed phenomenology.
It is argued in particular that NH3 K3C60 is a Mott-Jahn-Teller insulator,
which under pressure turns into a strongly correlated superconductor, thus
sharing some important elements with the high-Tc cuprates.


  We suggest an adaptive control scheme for the control of zero-lag and cluster
synchronization in delay-coupled networks. Based on the speed-gradient method,
our scheme adapts the topology of a network such that the target state is
realized. It is robust towards different initial condition as well as changes
in the coupling parameters. The emerging topology is characterized by a
delicate interplay of excitatory and inhibitory links leading to the
stabilization of the desired cluster state. As a crucial parameter determining
this interplay we identify the delay time. Furthermore, we show how to
construct networks such that they exhibit not only a given cluster state but
also with a given oscillation frequency. We apply our method to coupled
Stuart-Landau oscillators, a paradigmatic normal form that naturally arises in
an expansion of systems close to a Hopf bifurcation. The successful and robust
control of this generic model opens up possible applications in a wide range of
systems in physics, chemistry, technology, and life science.


  Collaborative filtering is widely used in modern recommender systems. Recent
research shows that variational autoencoders (VAEs) yield state-of-the-art
performance by integrating flexible representations from deep neural networks
into latent variable models, mitigating limitations of traditional linear
factor models. VAEs are typically trained by maximizing the likelihood (MLE) of
users interacting with ground-truth items. While simple and often effective,
MLE-based training does not directly maximize the recommendation-quality
metrics one typically cares about, such as top-N ranking. In this paper we
investigate new methods for training collaborative filtering models based on
actor-critic reinforcement learning, to directly optimize the
non-differentiable quality metrics of interest. Specifically, we train a critic
network to approximate ranking-based metrics, and then update the actor network
(represented here by a VAE) to directly optimize against the learned metrics.
In contrast to traditional learning-to-rank methods that require to re-run the
optimization procedure for new lists, our critic-based method amortizes the
scoring process with a neural network, and can directly provide the
(approximate) ranking scores for new lists. Empirically, we show that the
proposed methods outperform several state-of-the-art baselines, including
recently-proposed deep learning approaches, on three large-scale real-world
datasets. The code to reproduce the experimental results and figure plots is on
Github: https://github.com/samlobel/RaCT_CF


  This paper studies the statistical models of the noise-robust normalized
subband adaptive filter (NR-NSAF) algorithm in the mean and mean square
deviation senses involving transient-state and steady-state behavior by
resorting to the method of the vectorization operation and the Kronecker
product. The analysis method does not require the Gaussian input signal.
Moreover, the proposed analysis removes the paraunitary assumption imposed on
the analysis filter banks as in the existing analyses of subband adaptive
algorithms. Simulation results in various conditions demonstrate the
effectiveness of our theoretical analysis. For a special form of the algorithm,
the proposed steady-state expression is also better accurate than the previous
analysis.


  This paper investigates mobility management strategies from the point of view
of their need of signalling and processing resources on the backbone network
and load on the air interface. A method is proposed to model the serving
network and mobile node mobility in order to be able to compare the different
types of mobility management algorithms. To obtain a good description of the
network we calculate descriptive parameters from given topologies. Most
mobility approaches derived from existing protocols are analyzed and their
performances are numerically compared in various network and mobility
scenarios. We developed a mobility management framework that is able to give
general designing guidelines for the next generation mobility managements on
given network, technology and mobility properties. With our model an operator
can design the network and tune the parameters to obtain the optimal
implementation of course revising existing systems is also possible. We present
a vertical handover decision method as a special application of our model
framework.


  Recent years have witnessed a boom of connected medical devices, which brings
security issues in the meantime. Medical imaging devices, an essential part of
medical cyber-physical systems, play a vital role in modern hospitals and are
often life-critical. However, security and privacy issues in these medical
cyber-physical systems are sometimes ignored.
  In this paper, we perform an empirical study on imaging devices to analyse
the security of medical cyber-physical systems. To be precise, we design a
threat model and propose prospective attack techniques for medical imaging
devices. To tackle potential cyber threats, we introduce protection mechanisms,
evaluate the effectiveness and efficiency of protection mechanisms as well as
its interplay with attack techniques. To scoring security, we design a
hierarchical system that provides actionable suggestions for imaging devices in
different scenarios. We investigate 15 devices from 9 manufacturers to
demonstrate empirical comprehension and real-world security issues.


  We report on the investigation of strong radiative shocks generated with the
high energy, sub-nanosecond iodine laser at PALS. These shock waves are
characterized by a developed radiative precursor and their dynamics is analyzed
over long time scales (~50 ns), approaching a quasi-stationary limit. We
present the first preliminary results on the rear side XUV spectroscopy. These
studies are relevant to the understanding of the spectroscopic signatures of
accretion shocks in Classical T Tauri Stars.


  The class of linearly ordered sets with one order preserving unary operation
has the Strong Amalgamation Property (SAP). The class of linearly ordered sets
with one strict order preserving unary operation has AP but not SAP. The class
of linearly ordered sets with two order preserving unary operations has not AP.
For every set $F$, the class of linearly ordered sets with an $F$-indexed
family of automorphisms has SAP. Corresponding results are proved in the case
of order reversing operations. Various subclasses of the above classes are
considered and some model-theoretical consequences are presented.


  We consider a coupled Polya's urn scheme for social dynamics on networks.
Agents hold continuum-valued opinions on a two-state issue and randomly
converse with their neighbors on a graph, agreeing on one of the two states.
The probability of agreeing on a given state is a simple function of both of
agents' opinions, with higher importance given to agents who have participated
in more conversations. Opinions are then updated based on the results of the
conversation. We show that this system is governed by a discrete version of the
stochastic heat equation, and prove that the system reaches a consensus of
opinion.


  In this article we present the motivation and the core thesis towards the
implementation of a Quantum Knowledge Seeking Agent (QKSA). QKSA is a general
reinforcement learning agent that can be used to model classical and quantum
dynamics. It merges ideas from universal artificial general intelligence,
constructor theory and genetic programming to build a robust and general
framework for testing the capabilities of the agent in a variety of
environments. It takes the artificial life (or, animat) path to artificial
general intelligence where a population of intelligent agents are instantiated
to explore valid ways of modelling the perceptions. The multiplicity and
survivability of the agents are defined by the fitness, with respect to the
explainability and predictability, of a resource-bounded computational model of
the environment. This general learning approach is then employed to model the
physics of an environment based on subjective observer states of the agents. A
specific case of quantum process tomography as a general modelling principle is
presented. The various background ideas and a baseline formalism are discussed
in this article which sets the groundwork for the implementations of the QKSA
that are currently in active development.


  This paper investigates the dependence of existing state-of-the-art person
re-identification models on the presence and visibility of human faces. We
apply a face detection and blurring algorithm to create anonymized versions of
several popular person re-identification datasets including Market1501,
DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing
state-of-the-art models that range in accuracy and computational efficiency, we
evaluate the effect of this anonymization on re-identification performance
using standard metrics. Perhaps surprisingly, the effect on mAP is very small,
and accuracy is recovered by simply training on the anonymized versions of the
data rather than the original data. These findings are consistent across
multiple models and datasets. These results indicate that datasets can be
safely anonymized by blurring faces without significantly impacting the
performance of person reidentification systems, and may allow for the release
of new richer re-identification datasets where previously there were privacy or
data protection concerns.


  During the lifecycle of many single-stranded RNA viruses, including many
human pathogens, a protein shell called the capsid spontaneously assembles
around the viral genome. Understanding the mechanisms by which capsid proteins
selectively assemble around the viral RNA amidst diverse host RNAs is a key
question in virology. In one proposed mechanism, sequence elements (packaging
sites) within the genomic RNA promote rapid and efficient assembly through
specific interactions with the capsid proteins. In this work we develop a
coarse-grained particle-based computational model for capsid proteins and RNA
which represents protein-RNA interactions arising both from non-specific
electrostatics and specific packaging sites interactions. Using Brownian
dynamics simulations, we explore how the efficiency and specificity of assembly
depend on solution conditions (which control protein-protein and nonspecific
protein-RNA interactions) as well as the strength and number of packaging
sites. We identify distinct regions in parameter space in which packaging sites
lead to highly specific assembly via different mechanisms, and others in which
packaging sites lead to kinetic traps. We relate these computational
predictions to in vitro assays for specificity in which cognate viral RNAs are
compete against non-cognate RNAs for assembly by capsid proteins.


  The University of Hawaii Wide-Field Imager (UHWFI) is a focal compressor
system designed to project the full half-degree field of the UH 2.2 m telescope
onto the refurbished UH 8Kx8K CCD camera. The optics use Ohara glasses and are
mounted in an oil-filled cell to minimize light losses and ghost images from
the large number of internal lens surfaces. The UHWFI is equipped with a
six-position filter wheel and a rotating sector blade shutter,both driven by
stepper motors. The instrument saw first light in 2004 in an engineering mode.
After filling the lens cell with index matching oil, integration of all
software components into the user interface, tuning of the CCD performance, and
the purchase of the final filter set, UHWFI is now fully commissioned at the UH
2.2 m telescope.


  This paper aims for high-performance offline LiDAR-based 3D object detection.
We first observe that experienced human annotators annotate objects from a
track-centric perspective. They first label the objects with clear shapes in a
track, and then leverage the temporal coherence to infer the annotations of
obscure objects. Drawing inspiration from this, we propose a high-performance
offline detector in a track-centric perspective instead of the conventional
object-centric perspective. Our method features a bidirectional tracking module
and a track-centric learning module. Such a design allows our detector to infer
and refine a complete track once the object is detected at a certain moment. We
refer to this characteristic as "onCe detecTed, neveR Lost" and name the
proposed system CTRL. Extensive experiments demonstrate the remarkable
performance of our method, surpassing the human-level annotating accuracy and
the previous state-of-the-art methods in the highly competitive Waymo Open
Dataset without model ensemble. The code will be made publicly available at
https://github.com/tusen-ai/SST.


  In this paper we show explicit examples of several families of immersions
with constant mean curvature and non constant principal curvatures, in
semi-riemannian manifolds with constant sectional curvature. In particular, we
prove that every h in [-1,-2 sqrt{n-1}/n) can be realized as the constant
curvature of a complete immersion of S_1^{n-1} x R in the (n+1)-dimensional de
Sitter space S_1^{n+1}. We provide 3 types of immersions with cmc in the
Minkowski space, 5 types of immersion with cmc in the de Sitter space and 5
types of immersion with cmc in the anti de Sitter space. At the end of the
paper we analyze the families of examples that can be extended to closed
hypersurfaces.


  Real food web data available in the literature presents us with the relations
between various species, sizes of these species, metabolic types of the species
and other useful information, which allows us to define parameters for the
mathematical dynamical models of these food webs, and perform theoretical
studies of these models. Unfortunately, the researches often face the problem
of the extinction of the species in such situations, which could be an
important limiting factor. In this paper, we propose a simple algorithm of
parameterisation that leads to the existence of positive steady state and
improves persistence of the species in multi-species models of real food webs.


  I review briefly some dynamical models of structures in the outer parts of
disc galaxies, including models of polar rings, tidal tails and bridges. I then
discuss the density distribution in the outer parts of discs. For this, I
compare observations to results of a model in which the disc galaxy is in fact
the remnant of a major merger, and find good agreement. This comparison
includes radial profiles of the projected surface density and of stellar age,
as well as time evolution of the break radius and of the inner and outer disc
scale lengths. I also compare the radial projected surface density profiles of
dynamically motivated mono-age populations and find that, compared to older
populations, younger ones have flatter density profiles in the inner region and
steeper in the outer one. The break radius, however, does not vary with stellar
age, again in good agreement with observations.


  In recent years, deep discriminative models have achieved extraordinary
performance on supervised learning tasks, significantly outperforming their
generative counterparts. However, their success relies on the presence of a
large amount of labeled data. How can one use the same discriminative models
for learning useful features in the absence of labels? We address this question
in this paper, by jointly modeling the distribution of data and latent features
in a manner that explicitly assigns zero probability to unobserved data. Rather
than maximizing the marginal probability of observed data, we maximize the
joint probability of the data and the latent features using a two step EM-like
procedure. To prevent the model from overfitting to our initial selection of
latent features, we use adversarial regularization. Depending on the task, we
allow the latent features to be one-hot or real-valued vectors and define a
suitable prior on the features. For instance, one-hot features correspond to
class labels and are directly used for the unsupervised and semi-supervised
classification task, whereas real-valued feature vectors are fed as input to
simple classifiers for auxiliary supervised discrimination tasks. The proposed
model, which we dub discriminative encoder (or DisCoder), is flexible in the
type of latent features that it can capture. The proposed model achieves
state-of-the-art performance on several challenging tasks.


  Modeling cameras for the simulation of autonomous robotics is critical for
generating synthetic images with appropriate realism to effectively evaluate a
perception algorithm in simulation. In many cases though, simulated images are
produced by traditional rendering techniques that exclude or superficially
handle processing steps and aspects encountered in the actual camera pipeline.
The purpose of this contribution is to quantify the degree to which the
exclusion from the camera model of various image generation steps or aspects
affect the sim-to-real gap in robotics. We investigate what happens if one
ignores aspects tied to processes from within the physical camera, e.g., lens
distortion, noise, and signal processing; scene effects, e.g., lighting and
reflection; and rendering quality. The results of the study demonstrate,
quantitatively, that large-scale changes to color, scene, and location have far
greater impact than model aspects concerned with local, feature-level
artifacts. Moreover, we show that these scene-level aspects can stem from lens
distortion and signal processing, particularly when considering white-balance
and auto-exposure modeling.


  To assess the classification accuracy of a continuous diagnostic result, the
receiver operating characteristic (ROC) curve is commonly used in applications.
The partial area under the ROC curve (pAUC) is one of widely accepted summary
measures due to its generality and ease of probability interpretation. In the
field of life science, a direct extension of the pAUC into the time-to-event
setting can be used to measure the usefulness of a biomarker for disease
detection over time. Without using a trapezoidal rule, we propose nonparametric
estimators, which are easily computed and have closed-form expressions, for the
time-dependent pAUC. The asymptotic Gaussian processes of the estimators are
established and the estimated variance-covariance functions are provided, which
are essential in the construction of confidence intervals. The finite sample
performance of the proposed inference procedures are investigated through a
series of simulations. Our method is further applied to evaluate the
classification ability of CD4 cell counts on patient's survival time in the
AIDS Clinical Trials Group (ACTG) 175 study. In addition, the inferences can be
generalized to compare the time-dependent pAUCs between patients received the
prior antiretroviral therapy and those without it.


  Consider the problem, usually called the P\'olya-Chebotarev problem, of
finding a continuum in the complex plane including some given points such that
the logarithmic capacity of this continuum is minimal. We prove that each
connected inverse image $\T_n^{-1}([-1,1])$ of a polynomial $\T_n$ is always
the solution of a certain P\'olya-Chebotarev problem. By solving a nonlinear
system of equations for the zeros of $\T_n^2-1$, we are able to construct
polynomials $\T_n$ with a connected inverse image.


  We propose a novel keypoint voting scheme based on intersecting spheres, that
is more accurate than existing schemes and allows for fewer, more disperse
keypoints. The scheme is based upon the distance between points, which as a 1D
quantity can be regressed more accurately than the 2D and 3D vector and offset
quantities regressed in previous work, yielding more accurate keypoint
localization. The scheme forms the basis of the proposed RCVPose method for 6
DoF pose estimation of 3D objects in RGB-D data, which is particularly
effective at handling occlusions. A CNN is trained to estimate the distance
between the 3D point corresponding to the depth mode of each RGB pixel, and a
set of 3 disperse keypoints defined in the object frame. At inference, a sphere
centered at each 3D point is generated, of radius equal to this estimated
distance. The surfaces of these spheres vote to increment a 3D accumulator
space, the peaks of which indicate keypoint locations. The proposed radial
voting scheme is more accurate than previous vector or offset schemes, and is
robust to disperse keypoints. Experiments demonstrate RCVPose to be highly
accurate and competitive, achieving state-of-the-art results on the LINEMOD
99.7% and YCB-Video 97.2% datasets, notably scoring +4.9% higher 71.1% than
previous methods on the challenging Occlusion LINEMOD dataset, and on average
outperforming all other published results from the BOP benchmark for these 3
datasets. Our code is available at http://www.github.com/aaronwool/rcvpose.


  The extreme computational costs of calculating the sign of the Wilson matrix
within the overlap operator have so far prevented four dimensional dynamical
overlap simulations on realistic lattice sizes, because the computational power
required to invert the overlap operator, the time consuming part of the Hybrid
Monte Carlo algorithm, is too high. In this series of papers we introduced the
optimal approximation of the sign function and have been developing
preconditioning and relaxation techniques which reduce the time needed for the
inversion of the overlap operator by over a factor of four, bringing the
simulation of dynamical overlap fermions on medium-size lattices within the
range of Teraflop-computers.
  In this paper we adapt the HMC algorithm to overlap fermions. We approximate
the matrix sign function using the Zolotarev rational approximation, treating
the smallest eigenvalues of the Wilson operator exactly within the fermionic
force. We then derive the fermionic force for the overlap operator, elaborating
on the problem of Dirac delta-function terms from zero crossings of eigenvalues
of the Wilson operator. The crossing scheme proposed shows energy violations
which are better than O($\Delta\tau^2$) and thus are comparable with the
violations of the standard leapfrog algorithm over the course of a trajectory.
We explicitly prove that our algorithm satisfies reversibility and area
conservation. Finally, we test our algorithm on small $4^4$, $6^4$, and $8^4$
lattices at large masses.


  The main result is that for a connected hyperbolic complete K\"ahler manifold
with bounded geometry of order two and exactly one end, either the first
compactly supported cohomology with values in the structure sheaf vanishes or
the manifold admits a proper holomorphic mapping onto a Riemann surface.


  Analytic functions defined on a tube domain $T^{C}\subset \mathbb{C}^{n}$ and
taking values in a Banach space $X$ which are known to have $X$-valued
distributional boundary values are shown to be in the Hardy space
$H^{p}(T^{C},X)$ if the boundary value is in the vector valued Lebesgue space
$L^{p}(\mathbb{R}^{n},X)$, where $1\leq p \leq \infty$ and $C$ is a regular
open convex cone. Poisson integral transform representations of elements of
$H^{p}(T^{C}, X)$ are also obtained for certain classes of Banach spaces,
including reflexive Banach spaces.


  This paper is meant to be a short review and summary of recent results on the
structure of finite and affine classical W-algebras, and the application of the
latter to the theory of generalized Drinfeld-Sokolov hierarchies.


  Doping is an effective way to tune the property of metal oxides1-5, for
achieving functional oxide electronics6-8. Previously we developed a
controllable hydrogen doping technology at ambient conditions by use of
electron-proton synergistic doping strategy, which enables one to get rid of
high-temperature/pressure treatments required by traditional technologies9.
Here, based on this facile doping route, we achieve a visual and reversible
insulator-metal transition (MIT) for tungsten trioxide (WO3) film. Its
outstanding spatial selection is comparable to standard UV lithography, which
shows the potential of becoming a viable way for rewritable WO3 grating device
fabrication. Furthermore, the period of the obtained WO3 structural grating can
also be easily changed for requirement by doping area selection. This advanced
doping technology opens up alternative approaches for developing not only
optical devices, but also rewritable ions devices and integrated circuits for
various oxide electronics.


  We present a study of eegamma and mumugamma events using 1109 (1009) pb-1 of
data in the electron (muon) channel, respectively. These data were collected
with the D0 detector at the Fermilab Tevatron ppbar Collider at sqrt(s) = 1.96
TeV. Having observed 453 (515) candidates in the eegamma (mumugamma) final
state, we measure the Zgamma production cross section for a photon with
transverse energy ET > 7 GeV, separation between the photon and leptons Delta
R(lgamma) > 0.7, and invariant mass of the di-lepton pair M(ll) > 30 GeV/c^2,
to be 4.96 +/- 0.30(stat. + syst.) +/- 0.30(lumi.) pb, in agreement with the
standard model prediction of 4.74 +/- 0.22 pb. This is the most precise Zgamma
cross section measurement at a hadron collider. We set limits on anomalous
trilinear Zgammagamma and ZZgamma gauge boson couplings of -0.085 <
h(30)^(gamma) < 0.084, -0.0053 < h(40)^(gamma) < 0.0054 and -0.083 < h(30)^(Z)
< 0.082, -0.0053 < h(40)^(Z) < 0.0054 at the 95% C.L. for the form-factor scale
Lambda = 1.2 TeV.


  Let $K$ be an algebraically closed field of characteristic $0$ and let $G$ be
a finite cyclic group of order $n$. In this note we prove, using induction on
the number of prime divisors of $n$, that $R_K(G)/I \cong \mathbb{Z}[X]/\langle
\Phi_n(X) \rangle$ where $R_K(G)$ denotes the ring of $K$-representations of
$G$ and $I$ is the sum of ideals $\mathrm{Ind}_H^G(R_K(H))$ of $R_K(G)$ as $H$
varies over all proper subgroups of $G$. This gives us an idea of how many
representations of $G$ are not induced from representations of a proper
subgroup.


  Using a simple analytic approach, we study the universal properties of
second-order phase transition in holographic superconductor models. We explore
a general model in arbitrary dimensions in which the condensation occurs via
the St\"uckelberg spontaneous symmetry breaking mechanism. All the possible
second-order phase transitions and their universal characteristics can be
identified analytically. The relationship between the critical temperature and
charge density is generic, and the critical exponents can be greater than the
typical mean field value 1/2. In addition, the related numerical factors can
also be computed qualitatively.


  We present BeppoSAX X-ray spectra of the remnant of the supernova of AD 1006,
which cover a broad spectral range of 0.1-10 keV. In our analysis we
concentrate on the thermal emission from the central region of the remnant. For
this purpose we fitted the spectra of the whole remnant using a new version of
the spectral program SPEX, which takes into account the interdependence of
spectra of different regions as the result of the point spread function of the
instruments. This is in particular important for separating the synchrotron
emission, coming from the bright rims of the remnant, from the thermal emission
from the central regions.
  The thermal emission appears best fitted with a combination of two spectral
components with electron temperatures of about 0.7 keV and more than 3 keV. The
plasma in SN 1006 appears to be very far out of ionization equilibrium (nt = 2
10^15 m^-3 s), which means that the L-shell emission of magnesium, silicon and
sulphur contribute significantly to the flux below 0.5 keV. We also note the
presence of Fe K line emission at 6.3+/-0.2 keV, in agreement with an origin
from inner shell ionizations and excitations. We confirm that the abundances in
SN 1006 are clearly non-solar. In particular silicon is very abundant. We did
not find substantial variations in plasma properties over the face of the
remnant, although the hotter component seems more dominant in the Northern half
of the remnant.


  This paper considers a linear panel model with interactive fixed effects and
unobserved individual and time heterogeneities that are captured by some latent
group structures and an unknown structural break, respectively. To enhance
realism the model may have different numbers of groups and/or different group
memberships before and after the break. With the preliminary
nuclear-norm-regularized estimation followed by row- and column-wise linear
regressions, we estimate the break point based on the idea of binary
segmentation and the latent group structures together with the number of groups
before and after the break by sequential testing K-means algorithm
simultaneously. It is shown that the break point, the number of groups and the
group memberships can each be estimated correctly with probability approaching
one. Asymptotic distributions of the estimators of the slope coefficients are
established. Monte Carlo simulations demonstrate excellent finite sample
performance for the proposed estimation algorithm. An empirical application to
real house price data across 377 Metropolitan Statistical Areas in the US from
1975 to 2014 suggests the presence both of structural breaks and of changes in
group membership.


  We calculate the radio frequency (RF) spectrum of fermionic atoms near a
narrow Feshbach resonance, explaining observations made in ultracold samples of
$^6\rm{Li}$ [E. L. Hazlett {\it et al.}, Phys. Rev. Lett. {\bf 108}, 045304
(2012)]. We use a two channel resonance model to show that the RF spectrum
contains two peaks. In the wide-resonance limit, nearly all spectral weight
lies in one of these peaks, and typically the second peak is very broad. We
find strong temperature dependence, which can be traced to the energy
dependence of the two-particle scattering. In addition to microscopic
calculations, we use sum rule arguments to find generic features of the
spectrum which are model independent.


  We study mixed-integer programming formulations for the piecewise linear
lower and upper bounds (in other words, piecewise linear relaxations) of
nonlinear functions that can be modeled by a new class of combinatorial
disjunctive constraints (CDCs), generalized $n$D-ordered CDCs. We first
introduce a general formulation technique to model piecewise linear lower and
upper bounds of univariate nonlinear functions concurrently so that it uses
fewer binary variables than modeling bounds separately. Next, we propose
logarithmically sized ideal non-extended formulations to model the piecewise
linear relaxations of univariate and higher-dimensional nonlinear functions
under the CDC and independent branching frameworks. We also perform
computational experiments for the approaches modeling the piecewise linear
relaxations of univariate nonlinear functions and show significant speed-ups of
our proposed formulations. Furthermore, we demonstrate that piecewise linear
relaxations can provide strong dual bounds of the original problems with less
computational time in order of magnitude.


  This work introduces a new higher-order accurate super compact (HOSC) finite
difference scheme for solving complex unsteady three-dimensional (3D)
non-Newtonian fluid flow problems. As per the author's knowledge, the proposed
scheme is the first ever developed finite difference scheme to solve
three-dimensional non-Newtonian flow problem. Not only that, the proposed
method is fourth-order accurate in space variables and second-order accurate in
time. Also, the proposed scheme utilizes only seven directly adjacent grid
points, at the $(n+1)^{th}$ time level, around which the finite difference
discretization is made. The governing equations are solved using a
time-marching methodology, and pressure is calculated using a
pressure-correction strategy based on the modified artificial compressibility
method. Using the power-law viscosity model, we tackle the benchmark problem of
a 3D lid-driven cavity, systematically analyzing the varied rheological
behavior of shear-thinning ($n=0.5$), shear-thickening $(n=1.5)$, and Newtonian
$(n=1.0)$ fluids across different Reynolds numbers $(Re= 1, 50, 100, 200)$.
Both Newtonian and non-Newtonian results are carefully investigated in terms of
streamlines, velocity variation, pressure distributions, and viscosity
contours, and the computed results are validated with the existing benchmark
results. The findings demonstrate excellent agreement with the existing
results. This extensive analysis, using the new HOSC scheme, not only increases
our understanding of non-Newtonian fluid behavior but also provides a robust
foundation for future research and practical applications.


  We extend the proof from arXiv:1012.3137, which interprets the AGT relation
as the Hubbard-Stratonovich duality relation to the case of 5d gauge theories.
This involves an additional q-deformation. Not surprisingly, the extension
turns out to be trivial: it is enough to substitute all relevant numbers by
q-numbers in all the formulas, Dotsenko-Fateev integrals by the Jackson sums
and the Jack polynomials by the MacDonald ones. The problem with extra poles in
individual Nekrasov functions continues to exist, therefore, such a proof works
only for \beta = 1, i.e. for q=t in MacDonald's notation. For \beta\ne 1 the
conformal blocks are related in this way to a non-Nekrasov decomposition of the
LMNS partition function into a double sum over Young diagrams.


  A high-throughput screening based on first-principles calculations was
performed to search for new ternary inorganic electrides. From the available
materials database, we identified three new thermodynamically stable materials
(Li$_{12}$Mg$_3$Si$_4$, NaBa$_2$O and Ca$_5$Ga$_2$N$_4$) as potential
electrides made by main group elements, in addition to the well known mayenite
based electride (C12A7:$e^-$). Different from those conventional inorganic
electrides in which the excess electrons play only the role of anions, the
three new materials, resembling the electrides found in simple metals under
high pressure, possess mixed ionic and metallic bonding. The interplay between
two competing mechanisms, together with the different crystal packing motifs,
gives rise to a variety of geometries in anionic electrons, and rich physical
phenomena such as ferromagnetism, superconductivity and metal-insulator
transition. Our finding here bridges the gap between electrides found at
ambient and high pressure conditions.


  We describe the kernel of the canonical map from the Floyd boundary of a
relatively hyperbolic group to its Bowditch boundary.
  Using our methods we then prove that a finitely generated group $H$ admitting
a quasi-isometric map $\phi$ into a relatively hyperbolic group $G$ is
relatively hyperbolic with respect to a system of subgroups whose image under
$\phi$ is situated in a uniformly bounded distance from the parabolic subgroups
of $G$.


  We show that various functionals related to the supremum of a real function
defined on an arbitrary set or a measure space are Hadamard directionally
differentiable. We specifically consider the supremum norm, the supremum, the
infimum, and the amplitude of a function. The (usually non-linear) derivatives
of these maps adopt simple expressions under suitable assumptions on the
underlying space. As an application, we improve and extend to the
multidimensional case the results in \cite{Raghavachari} regarding the limiting
distributions of Kolmogorov-Smirnov type statistics under the alternative
hypothesis. Similar results are obtained for analogous statistics associated
with copulas. We additionally solve an open problem about the Berk-Jones
statistic proposed by \cite{Jager-Wellner-2004}. Finally, the asymptotic
distribution of maximum mean discrepancies over Donsker classes of functions is
derived.


  For each nonzero $h\in \mathbb{F}[x]$, where $\mathbb{F}$ is a field, let
$\mathsf{A}_h$ be the unital associative algebra generated by elements $x,y$,
satisfying the relation $yx-xy = h$. This gives a parametric family of
subalgebras of the Weyl algebra $\mathsf{A}_1$, containing many well-known
algebras which have previously been studied independently. In this paper, we
give a full description the Hochschild cohomology
$\mathsf{HH}^\bullet(\mathsf{A}_h)$ over a field of arbitrary characteristic.
In case $\mathbb{F}$ has positive characteristic, the center of $\mathsf{A}_h$
is nontrivial and we describe $\mathsf{HH}^\bullet(\mathsf{A}_h)$ as a module
over its center. The most interesting results occur when $\mathbb{F}$ has
characteristic $0$. In this case, we describe
$\mathsf{HH}^\bullet(\mathsf{A}_h)$ as a module over the Lie algebra
$\mathsf{HH}^1(\mathsf{A}_h)$ and find that this action is closely related to
the intermediate series modules over the Virasoro algebra. We also determine
when $\mathsf{HH}^\bullet(\mathsf{A}_h)$ is a semisimple
$\mathsf{HH}^1(\mathsf{A})$-module.


  Let G and K be groupoids. We present the notion of a
(G_{\alpha},K_{\beta})-set and we prove a duality theorem in this context,
which extends the duality theorem for graded algebras by groups. For A a unital
G-graded algebra and X a finite split G-set, we show that there is an
isomorphism between the category of the left A-modules X-graded and the
category of the left A \#_{\alpha}^{G}X-modules. As an application of this
isomorphism, we construct a Morita context.


  One-way quantum computing is an important and novel approach to quantum
computation. By exploiting the existing particle-particle interactions, we
report the first experimental realization of the complete process of
deterministic one-way quantum Deutsch-Josza algorithm in NMR, including graph
state preparation, single-qubit measurements and feed-forward corrections. The
findings in our experiment may shed light on the future scalable one-way
quantum computation.


  The Multiple Drone-Delivery Scheduling Problem (MDSP) is a scheduling problem
that optimizes the maximum reward earned by a set of $m$ drones executing a
sequence of deliveries on a truck delivery route. The current best-known
approximation algorithm for the problem is a $\frac{1}{4}$-approximation
algorithm developed by Jana and Mandal (2022). In this paper, we propose exact
and approximation algorithms for the general MDSP, as well as a unit-cost
variant. We first propose a greedy algorithm which we show to be a
$\frac{1}{3}$-approximation algorithm for the general MDSP problem formulation,
provided the number of conflicting intervals is less than the number of drones.
We then introduce a unit-cost variant of MDSP and we devise an exact dynamic
programming algorithm that runs in polynomial time when the number of drones
$m$ can be assumed to be a constant.


  Recall assistance methods are among the key aspects that improve the accuracy
of online dietary assessment surveys. These methods still mainly rely on
experience of trained interviewers with nutritional background, but data driven
approaches could improve cost-efficiency and scalability of automated dietary
assessment. We evaluated the effectiveness of a recommender algorithm developed
for an online dietary assessment system called Intake24, that automates the
multiple-pass 24-hour recall method. The recommender builds a model of eating
behavior from recalls collected in past surveys. Based on foods they have
already selected, the model is used to remind respondents of associated foods
that they may have omitted to report. The performance of prompts generated by
the model was compared to that of prompts hand-coded by nutritionists in two
dietary studies. The results of our studies demonstrate that the recommender
system is able to capture a higher number of foods omitted by respondents of
online dietary surveys than prompts hand-coded by nutritionists. However, the
considerably lower precision of generated prompts indicates an opportunity for
further improvement of the system.


  Language-based environment manipulation requires agents to manipulate the
environment following natural language instructions, which is challenging due
to the huge space of the environments. To address this challenge, various
approaches have been proposed in recent work. Although these approaches work
well for their intended environments, they are difficult to generalize across
environments. In this work, we propose LEMON, a general framework for
language-based environment manipulation tasks. Specifically, we first specify a
task-agnostic approach for language-based environment manipulation tasks, which
can deal with various environments using the same generative language model.
Then we propose an execution-guided pre-training strategy to inject prior
knowledge of environments to the language model with a pure synthetic
pre-training corpus. Experimental results on tasks including Alchemy, Scene,
Tangrams, ProPara and Recipes demonstrate the effectiveness of LEMON: it
achieves new state-of-the-art results on four of the tasks, and the
execution-guided pre-training strategy brings remarkable improvements on all
experimental tasks.


  We consider several basic questions on distributed routing in directed graphs
with multiple additive costs, or metrics, and multiple constraints. Distributed
routing in this sense is used in several protocols, such as IS-IS and OSPF. A
practical approach to the multi-constraint routing problem is to, first,
combine the metrics into a single `composite' metric, and then apply one-to-all
shortest path algorithms, e.g. Dijkstra, in order to find shortest path trees.
We show that, in general, even if a feasible path exists and is known for every
source and destination pair, it is impossible to guarantee a distributed
routing under several constraints. We also study the question of choosing the
optimal `composite' metric. We show that under certain mathematical assumptions
we can efficiently find a convex combination of several metrics that maximizes
the number of discovered feasible paths. Sometimes it can be done analytically,
and is in general possible using what we call a 'smart iterative approach'. We
illustrate these findings by extensive experiments on several typical network
topologies.


  We present a systematic investigation of the star formation rate (hereafter
SFR) in interacting disk galaxies. We determine the dependence of the overall
SFR on different spatial alignments and impact parameters of more than 50
different configurations in combined N-body/hydrodynamic simulations. We also
show mass profiles of the baryonic components. We find that galaxy-galaxy
interactions can enrich the surrounding intergalatic medium with metals very
efficiently up to distances of several 100 kpc. This enrichment can be
explained in terms of indirect processes like thermal driven galactic winds or
direct processes like 'kinetic' spreading of baryonic matter. In the case of
equal mass mergers the direct -kinetic- redistribution of gaseous matter (after
5 Gyr) is less efficient than the environmental enrichment of the same isolated
galaxies by a galactic wind. In the case of non-equal mass mergers however, the
direct -kinetic- process dominates the redistribution of gaseous matter.
Compared to the isolated systems, the integrated star formation rates (ISFRs)
($\int_{t = 0 Gyr}^{t = 5 Gyr}\textnormal{SFR(t)}dt$) in the modelled
interacting galaxies are in extreme cases a factor of 5 higher and on average a
factor of 2 higher in interacting galaxies. Co-rotating and counter-rotating
interactions do not show a common trend for the enhancement of the ISFRs
depending on the interaction being edge-on or face-on. The latter case shows an
increase of the ISFRs for the counter-rotating system of about 100%, whereas
the edge-on counter-rotating case results in a lower increase ($\sim$ 10%).


  This paper proposes a thought experiment to search for efficient bounded
algorithms of NPC problems by machine enumeration. The key contributions are:
  -- On Universal Turing Machines, a program's time complexity should be
characterized as: execution time(n) = loading time(n) + running time(n).
  -- Introduces the concept of bounded algorithms; proposes a comparison based
criterion to decide if a bounded algorithm is inefficient; and establishes the
length upper bound of efficient bounded programs.
  -- Introduces the growth rate characteristic function to evaluate program
complexity, which is more easily machine checkable based on observations.
  -- Raises the theoretical question: if there exists any bounded algorithm
with polynomial execution time for NPC problems.


  We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented
dialogues grounded in multiple documents. Most previous works treat
document-grounded dialogue modeling as a machine reading comprehension task
based on a single given document or passage. In this work, we aim to address
more realistic scenarios where a goal-oriented information-seeking conversation
involves multiple topics, and hence is grounded on different documents. To
facilitate such a task, we introduce a new dataset that contains dialogues
grounded in multiple documents from four different domains. We also explore
modeling the dialogue-based and document-based context in the dataset. We
present strong baseline approaches and various experimental results, aiming to
support further research efforts on such a task.


  We study a class of close-packed dimer models on the square lattice, in the
presence of small but extensive perturbations that make them non-determinantal.
Examples include the 6-vertex model close to the free-fermion point, and the
dimer model with plaquette interaction previously analyzed in
\cite{A,AL,GMT17a,GMT17b}. By tuning the edge weights, we can impose a non-zero
average tilt for the height function, so that the considered models are in
general not symmetric under discrete rotations and reflections. In the
determinantal case, height fluctuations in the massless (or `liquid') phase
scale to a Gaussian log-correlated field and their amplitude is a universal
constant, independent of the tilt. When the perturbation strength $\lambda$ is
sufficiently small we prove, by fermionic constructive Renormalization Group
methods, that log-correlations survive, with amplitude $A$ that, generically,
depends non-trivially and non-universally on $\lambda$ and on the tilt. On the
other hand, $A$ satisfies a universal scaling relation (`Haldane' or `Kadanoff'
relation), saying that it equals the anomalous exponent of the dimer-dimer
correlation.


  The goal of coordinated multi-robot exploration tasks is to employ a team of
autonomous robots to explore an unknown environment as quickly as possible.
Compared with human-designed methods, which began with heuristic and rule-based
approaches, learning-based methods enable individual robots to learn
sophisticated and hard-to-design cooperation strategies through deep
reinforcement learning technologies. However, in decentralized multi-robot
exploration tasks, learning-based algorithms are still far from being
universally applicable to the continuous space due to the difficulties
associated with area calculation and reward function designing; moreover,
existing learning-based methods encounter problems when attempting to balance
the historical trajectory issue and target area conflict problem. Furthermore,
the scalability of these methods to a large number of agents is poor because of
the exponential explosion problem of state space. Accordingly, this paper
proposes a novel approach - Multi-head Attention-based Multi-robot Exploration
in Continuous Space (MAMECS) - aimed at reducing the state space and
automatically learning the cooperation strategies required for decentralized
multi-robot exploration tasks in continuous space. Computational geometry
knowledge is applied to describe the environment in continuous space and to
design an improved reward function to ensure a superior exploration rate.
Moreover, the multi-head attention mechanism employed helps to solve the
historical trajectory issue in the decentralized multi-robot exploration task,
as well as to reduce the quadratic increase of action space.


  I measure the physical properties of 38 transiting extrasolar planetary
systems, bringing the total number studied within the Homogeneous Studies
project to 82. Transit light curves are modelled using JKTEBOP, with careful
attention paid to limb darkening, eccentricity and third light. The physical
properties of each system are obtained from the photometric parameters,
published spectroscopic measurements and five sets of theoretical stellar model
predictions. Statistical errors are assessed using Monte Carlo and
residual-permutation algorithms and propagated via a perturbation algorithm.
Systematic errors are estimated from the interagreement between results
calculated using five theoretical stellar models. The headline result is a
major upward revision of the radius of the planet in the OGLE-TR-56 system,
from 1.23-1.38 Rjup to 1.734 +/- 0.051 +/- 0.029 Rjup (statistical and
systematic errors, respectively). Its density is three times lower than
previously thought. This change comes from the first complete analysis of
published high-quality photometry. Significantly larger planetary radii are
also found for Kepler-15, KOI-428, WASP-13, WASP-14 and WASP-21 compared to
previous work. I present the first results based on Kepler short-cadence data
for Kepler-14, Kepler-15 and KOI-135. More extensive long-cadence data s used
to improve the measured properties of KOI-196, KOI-204, KOI-254, KOI-423 and
KOI-428. Detailed analyses are given for HAT-P-3, HAT-P-6, HAT-P-9, HAT-P-14
and WASP-12, based on more extensive datasets than considered in previous
studies. I revisit the correlations between orbital period and surface gravity,
and orbital period and mass of the transiting planets, finding both to be
significant at the 4 sigma level. I conclude by discussing the opportunities
for follow-up observations, the sky positions and the discovery rate of the
known transiting planets.


  Planning trajectories for nonholonomic systems is difficult and
computationally expensive. When facing unexpected events, it may therefore be
preferable to deform in some way the initially planned trajectory rather than
to re-plan entirely a new one. We suggest here a method based on affine
transformations to make such deformations. This method is exact and fast: the
deformations and the resulting trajectories can be computed algebraically, in
one step, and without any trajectory re-integration. To demonstrate the
possibilities offered by this new method, we use it to derive position and
orientation correction algorithms for the general class of planar wheeled
robots and for a tridimensional underwater vehicle. These algorithms allow in
turn achieving more complex applications, including obstacle avoidance,
feedback control or gap filling for sampling-based kinodynamic planners.


  In this short note, we prove that there is a well behaved notion of normal
hull for smooth algebraic group schemes over a field and that the commutator
group $(G,H)$ is well defined for $H\subset G$ smooth, even when both of them
are not connected.


  We develop the inverse scattering transform for the KdV equation with real
singular initial data $q(x)$ of the form $q(x) = r'(x) + r(x)^2$, where $r\in
L^2_{\textrm{loc}}$ and $r=0$ on $\mathbb R_+$. As a consequence we show that
the solution $q(x,t)$ is a meromorphic function with no real poles for any
$t>0$.


  We propose two robust schemes to generate controllable (deterministic) atomic
W-states of three three-level atoms interacting with an optical cavity and a
laser beam. Losses due to atomic spontaneous emissions and to cavity decay are
efficiently suppressed by employing adiabatic passage technique and
appropriately designed atom-field couplings. In these schemes the three atoms
traverse the cavity-mode and the laser beam and become entangled in the free
space outside the cavity.